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Socially assistive robots (SARs) are becoming more prevalent in everyday life, emphasizing the need to make them socially acceptable and aligned with users' expectations. Robots' appearance impacts users' behaviors and attitudes towards them. Therefore, product designers choose visual qualities to give the robot a character and to imply its functionality and personality. In this work, we sought to investigate the effect of cultural differences on Israeli and German designers' perceptions of SARs' roles and appearance in four different contexts: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. The key insight is that although Israeli and German designers share similar perceptions of visual qualities for most of the robotics roles, we found differences in the perception of the COVID-19 officer robot's role and, by that, its most suitable visual design. This work indicates that context and culture play a role in users' perceptions and expectations; therefore, they should be taken into account when designing new SARs for diverse contexts.
This report examines exporters’ challenges and possible solutions for public intervention to promote foreign trade. Based on fieldwork conducted in Georgia, we explore which policy approaches can help to stimulate Georgian exports further. Our outcomes show that exporters face substantial barriers such as navigating complex trade regulations, lack of knowledge about target markets, trade finance gaps, as well as new export promotion programs (EPPs) in competitor countries. Other upper-middle-income countries can learn from our results that exporters can significantly benefit from a comprehensive export promotion strategy combined with an ecosystem-based “team” approach. EPPs related to awareness and capacity building in Georgia should be part of this strategy, focusing on challenges such as a lack of knowledge about trade practices and international business skills. Other EPPs must help to mitigate related market failures, as information gathering is costly, and firms have no incentive to share this information with competitors. Furthermore, targeted marketing support and customer matchmaking can answer Georgian exporters’ challenges, such as lack of market access and low sector visibility. Our results also show that public intervention through financial support and risk mitigation is essential for firms with an international orientation. The high-quality, rich outcomes provide significant value for other upper-middle-income countries by exploring the example of Georgia’s contemporary circumstances in an in-depth manner based on extensive interviews and document analysis. Limitations include that our work primarily relies on qualitative data and further research could involve a quantitative study with a diverse range of sectors.
This paper presents a streaming-based E-Learning environment where closer integration between learning and work is achieved by integrating multimedia services into manufacturing processes. It contains a comprehensive and detailed explanation of the proposed E-Learning streaming framework, especially the adaption of streaming services to mobile environments. We first analyze several scenarios where E-Learning streaming services can be integrated into manufacturing processes. To allow systematic and tailor-made integration, we develop a model and a specification language for E-Learning streaming services and apply the model using practical scenarios from real manufacturing processes. Adaption of multimedia streaming services to mobile devices is discussed based on Synchronized Multimedia Integration Language (SMIL). Last, we comment on the benefits of using E-Learning streaming services as part of manufacturing processes and analyze the acceptance of the developed system. The key components of our E-Learning environment are 1) an xml based streaming service specification language, 2) adaption of multimedia E-Learning services to mobile environments, and 3) Web Services for searching, registration, and creation of E-Learning streaming services.
There is an ongoing debate about the use and scope of Clayton M. Christensen´s idea of disruptive innovation, including the question of whether it is a management buzz phrase or a valuable theory. This discussion considers the general question of how innovation in the field of management theories and concepts finds its way to the different target groups. This conceptual paper combines the different concepts of the creation and dissemination of management trends in a basic framework based on a short review of models for the dissemination of management ideas. This framework allows an analysis of the character of new management ideas like disruptive innovation. By measuring the impact of the theory on the academic sphere using a bibliometric statistic of the number of academic publications on Google scholar and Scopus and a meta-analysis of research papers, we show the significant influence of disruptive innovation beyond pure management fads.
We revisit the quantitative analysis of the ultrafast magnetoacoustic experiment in a freestanding nickel thin film by Kim and Bigot [J.-W. Kim and J.-Y. Bigot, Phys. Rev. B 95, 144422 (2017)] by applying our recently proposed approach of magnetic and acoustic eigenmode decomposition. We show that the application of our modeling to the analysis of time-resolved reflectivity measurements allows for the determination of amplitudes and lifetimes of standing perpendicular acoustic phonon resonances with unprecedented accuracy. The acoustic damping is found to scale as ∝ω2 for frequencies up to 80 GHz, and the peak amplitudes reach 10−3. The experimentally measured magnetization dynamics for different orientations of an external magnetic field agrees well with numerical solutions of magnetoelastically driven magnon harmonic oscillators. Symmetry-based selection rules for magnon-phonon interactions predicted by our modeling approach allow for the unambiguous discrimination between spatially uniform and nonuniform modes, as confirmed by comparing the resonantly enhanced magnetoelastic dynamics simultaneously measured on opposite sides of the film. Moreover, the separation of timescales for (early) rising and (late) decreasing precession amplitudes provide access to magnetic (Gilbert) and acoustic damping parameters in a single measurement.
The technique of laser ultrasonics perfectly meets the need for noncontact, noninvasive, nondestructive mechanical probing of nanometer- to millimeter-size samples. However, this technique is limited to the excitation of low-amplitude strains, below the threshold for optical damage of the sample. In the context of strain engineering of materials, alternative optical techniques enabling the excitation of high-amplitude strains in a nondestructive optical regime are needed. We introduce here a nondestructive method for laser-shock wave generation based on additive superposition of multiple laser-excited strain waves. This technique enables strain generation up to mechanical failure of a sample at pump laser fluences below optical ablation or melting thresholds. We demonstrate the ability to generate nonlinear surface acoustic waves (SAWs) in Nb-SrTiO3 substrates, with associated strains in the percent range and pressures up to 3 GPa at 1 kHz repetition rate and close to 10 GPa for several hundred shocks. This study paves the way for the investigation of a host of high-strain SAW-induced phenomena, including phase transitions in conventional and quantum materials, plasticity and a myriad of material failure modes, chemistry and other effects in bulk samples, thin layers, and two-dimensional materials.
The utilisation of artificial intelligence (AI) is progressively emerging as a significant mechanism for innovation in human resource management (HRM). The capacity to facilitate the transformation of employee performance across numerous responsibilities. AI development, there remains a dearth of comprehensive exploration into the potential opportunities it presents for enhancing workplace performance among employees. To bridge this gap in knowledge, the present work carried out a survey with 300 participants, utilises a fuzzy set-theoretic method that is grounded on the conceptualisation of AI, KS, and HRM. The findings of our study indicate that the exclusive adoption of AI technologies does not adequately enhance HRM engagements. In contrast, the integration of AI and KS offers a more viable HRM approach for achieving optimal performance in a dynamic digital society. This approach has the potential to enhance employees’ proficiency in executing their responsibilities and cultivate a culture of creativity inside the firm.
Purpose
Although start-ups have gained increasing scholarly attention, we lack sufficient understanding of their entrepreneurial strategic posture (ESP) in emerging economies. The purpose of this study is to examine the processes of ESP of new technology venture start-ups (NTVs) in an emerging market context.
Design/methodology/approach
In line with grounded theory guidelines and the inductive research traditions, the authors adopted a qualitative approach involving 42 in-depth semi-structured interviews with Ghanaian NTV entrepreneurs to gain a comprehensive analysis at the micro-level on the entrepreneurs' strategic posturing. A systematic procedure for data analysis was adopted.
Findings
From the authors' analysis of Ghanaian NTVs, the authors derived a three-stage model to elucidate the nature and process of ESP Phase 1 spotting and exploiting market opportunities, Phase II identifying initial advantages and Phase III ascertaining and responding to change.
Originality/value
The study contributes to advancing research on ESP by explicating the process through which informal ties and networks are utilised by NTVs and NTVs' founders to overcome extreme resource constraints and information vacuums in contexts of institutional voids. The authors depart from past studies in demonstrating how such ties can be harnessed in spotting and exploiting market opportunities by NTVs. On this basis, the paper makes original contributions to ESP theory and practice.
Purpose
Although recent literature has examined diverse measures adopted by SMEs to navigate the COVID-19 turbulence, there is a shortage of evidence on how crisis-time strategy creation behaviour and digitalization activities increase (1) sales and (2) cash flow. Thus, predicated on a novel strategy creation perspective, this inquiry aims to investigate the crisis behaviour, sales and cash flow performance of 528 SMEs in Morocco.
Design/methodology/approach
Novel links between (1) aggregate wage cuts, (2) variable operating hours, (3) deferred payment to suppliers, (4) deferred payment to tax authorities and (5) sales performance are developed and tested. A further link between sales performance and cash flow is also examined and the analysis is conducted using a non-linear structural equation modelling technique.
Findings
While there is a significant association between strategy creation behaviours and sales performance, only variable operating hours have a positive effect. Also, sales performance increases cash flow and this relationship is substantially strengthened by e-commerce digitalization and innovation.
Originality/value
Theoretically, to the best of the authors’ knowledge, this is one of the first inquiries to espouse the strategy creation view to explain SMEs' crisis-time behaviour and digitalization. For practical purposes, to supplement Moroccan SMEs' propensity to seek tax deferrals, it is argued that debt and equity support measures are also needed to boost sales performance and cash flow.
Analysing and predicting the advance rate of a tunnel boring machine (TBM) in hard rock is integral to tunnelling project planning and execution. It has been applied in the industry for several decades with varying success. Most prediction models are based on or designed for large-diameter TBMs, and much research has been conducted on related tunnelling projects. However, only a few models incorporate information from projects with an outer diameter smaller than 5 m and no penetration prediction model for pipe jacking machines exists to date. In contrast to large TBMs, small-diameter TBMs and their projects have been considered little in research. In general, they are characterised by distinctive features, including insufficient geotechnical information, sometimes rather short drive lengths, special machine designs and partially concurring lining methods like pipe jacking and segment lining. A database which covers most of the parameters mentioned above has been compiled to investigate the performance of small-diameter TBMs in hard rock. In order to provide sufficient geological and technical variance, this database contains 37 projects with 70 geotechnically homogeneous areas. Besides the technical parameters, important geotechnical data like lithological information, unconfined compressive strength, tensile strength and point load index is included and evaluated. The analysis shows that segment lining TBMs have considerably higher penetration rates in similar geological and technical settings mostly due to their design parameters. Different methodologies for predicting TBM penetration, including state-of-the-art models from the literature as well as newly derived regression and machine learning models, are discussed and deployed for backward modelling of the projects contained in the database. New ranges of application for small-diameter tunnelling in several industry-standard penetration models are presented, and new approaches for the penetration prediction of pipe jacking machines in hard rock are proposed.
In this study, circular economy (CE) relevance in Germany will be discussed based on LinkedIn readily available data. LinkedIn company profiles located in Germany with ‘circular economy’ in their description or any other field were selected and used as a data source to analyze their CE relation. Overall, 514 German companies were analyzed in reference to the 15 German regions they belong. Most companies are located in the federal state of Berlin (126), followed by North Rhine-Westphalia (96) and Bavaria (77). In terms of the industry sector, they are self-classified to environmental services (64), management consulting (50), renewables & environment (33), research (31), and computer software (18) etc. Regarding their employees with LinkedIn profiles, 22,621 people are affiliated with these companies, ranging from one to 7,877. All examined companies have a total of 819,632 followers on LinkedIn, ranging from none to 88,167. An increase in CE-related companies was recorded in 13 of the 16 federal states of Germany over a one-year period. This work provides essential insights into the increasing relevance and trends of the circular economy in German enterprises and will help conduct further national studies with readily available data from LinkedIn.
Human interaction frequently includes decision-making processes during which interactants call on verbal and non-verbal resources to manage the flow of interaction. In 2017, Stevanovic et al. carried out pioneering work, analyzing the unfolding of moment-by-moment dynamics by investigating the behavioral matching during search and decision-making phases. By studying the similarities in the participant's body sway during a conversation task in Finnish, the authors showed higher behavioral matching during decision phases than during search phases. The purpose of this research was to investigate the whole-body sway and its coordination during joint search and decision-making phases as a replication of the study by Stevanovic et al. (2017) but based on a German population. Overall, 12 dyads participated in this study and were asked to decide on 8 adjectives, starting with a pre-defined letter, to describe a fictional character. During this joint-decision task (duration: 206.46 ± 116.08 s), body sway of both interactants was measured using a 3D motion capture system and center of mass (COM) accelerations were computed. Matching of body sway was calculated using a windowed cross correlation (WCC) of the COM accelerations. A total of 101 search and 101 decision phases were identified for the 12 dyads. Significant higher COM accelerations (5.4*10−3 vs. 3.7*10−3 mm/s2, p < 0.001) and WCC coefficients (0.47 vs. 0.45, p = 0.043) were found during decision-making phases than during search phases. The results suggest that body sway is one of the resources humans use to communicate the arrival at a joint decision. These findings contribute to a better understanding of interpersonal coordination from a human movement science perspective.
Femtosecond (fs) time-resolved magneto-optics is applied to investigate laser-excited ultrafast dynamics of one-dimensional nickel gratings on fused silica and silicon substrates for a wide range of periodicities Λ = 400–1500 nm. Multiple surface acoustic modes with frequencies up to a few tens of GHz are generated. Nanoscale acoustic wavelengths Λ/n have been identified as nth-spatial harmonics of Rayleigh surface acoustic wave (SAW) and surface skimming longitudinal wave (SSLW), with acoustic frequencies and lifetimes being in agreement with theoretical calculations. Resonant magnetoelastic excitation of the ferromagnetic resonance (FMR) by SAW’s third spatial harmonic, and, most interestingly fingerprints of the parametric resonance at 1/2 SAW frequency have been observed. Numerical solutions of Landau–Lifshitz–Gilbert (LLG) equation magnetoelastically driven by complex polychromatic acoustic fields quantitatively reproduce all resonances at once. Thus, our results provide a solid experimental and theoretical base for a quantitative understanding of ultrafast fs-laser-driven magnetoacoustics and tailoring the magnetic-grating-based metasurfaces at the nanoscale.
Precisely synchronized communication is a major precondition for many industrial applications. At the same time, hardware cost and power consumption need to be kept as low as possible in the Internet of Things (IoT) paradigm. While many wired solutions on the market achieve these requirements, wireless alternatives are an interesting field for research and development. This article presents a novel IEEE802.11n/ac wireless solution, exhibiting several advantages over state-of-the-art competitors. It is based on a market-available wireless System on a Chip with modified low-level communication firmware combined with a low-cost field-programmable gate array. By achieving submicrosecond synchronization accuracy, our solution outperforms the precision of low-cost products by almost four orders of magnitude. Based on inexpensive hardware, the presented wireless module is up to 20 times cheaper than software-defined-radio solutions with comparable timing accuracy. Moreover, it consumes three to five times less power. To back up our claims, we report data that we collected with a high sampling rate (2000 samples per second) during an extended measurement campaign of more than 120 h, which makes our experimental results far more representative than others reported in the literature. Additional support is provided by the size of the testbed we used during the experiments, composed of a hybrid network with nine nodes divided into two independent wireless segments connected by a wired backbone. In conclusion, we believe that our novel Industrial IoT module architecture will have a significant impact on the future technological development of high-precision time-synchronized communication for the cost-sensitive industrial IoT market.
Optimization of energetic refurbishment roadmaps for multi-family buildings utilizing heat pumps
(2023)
A novel methodology for calculating optimized refurbishment roadmaps is developed in this paper. The aim of the roadmaps is to determine when and how should which component of the building envelope and heat generation system be refurbished to achieve the lowest net present value. The integrated optimization approach couples a particle swarm optimization algorithm with a dynamic building simulation of the building envelope and the heat supply system. Due to a free selection of implementation times and refurbishment depth, the optimization method achieves the lowest net present value and high CO2 reduction and is therefore an important contribution to achieve climate neutrality in the building stock.
The method is exemplarily applied to a multi-family house built in 1970. In comparison to a standard refurbishment roadmap, cost savings of 6–16 % and CO2 savings of 6–59 % are possible. The sensitivity of the refurbishment roadmap measures is analyzed on the basis of a parametric analysis. Robust optimization results are obtained with a mean refurbishment level of approx. 50 kWh/m2/a of the building envelope. The preferred heat generation system is a bivalent brine-heat pump system with a share of 70 % of the heat load being covered by the electric heat pump.
Heat pumps play a central role in decarbonizing the heat supply of buildings. However, in this article, implementing heat pumps in existing buildings, a significant challenge is still presented due to high temperature requirements. In this article, a systematic analysis of the effects of heat source temperatures, maximum heat pump condenser temperatures, and system temperatures on the seasonal performance of heat pump (HP) systems is presented. The quantitative performance analysis encompasses over 50 heat pumps installed in residential buildings, revealing correlations between the building characteristics, observed temperatures, and heat pump type. The performance of an HP system retrofitted to a 30-dwelling multifamily building is presented in more detail. The bivalent HP system combines air and ground as heat sources and achieves a seasonal performance factor of 3.25 with a share of the gas boiler of 27% in its first year of operation. In these findings, the technical feasibility of retrofitting heat pumps is demonstrated in existing buildings and insights are provided into overcoming the challenges associated with high temperature requirements.
Physical unclonable functions (PUFs) are increasingly generating attention in the field of hardware-based security for the Internet of Things (IoT). A PUF, as its name implies, is a physical element with a special and unique inherent characteristic and can act as the security anchor for authentication and cryptographic applications. Keeping in mind that the PUF outputs are prone to change in the presence of noise and environmental variations, it is critical to derive reliable keys from the PUF and to use the maximum entropy at the same time. In this work, the PUF output positioning (POP) method is proposed, which is a novel method for grouping the PUF outputs in order to maximize the extracted entropy. To achieve this, an offset data is introduced as helper data, which is used to relax the constraints considered for the grouping of PUF outputs, and deriving more entropy, while reducing the secret key error bits. To implement the method, the key enrollment and key generation algorithms are presented. Based on a theoretical analysis of the achieved entropy, it is proven that POP can maximize the achieved entropy, while respecting the constraints induced to guarantee the reliability of the secret key. Moreover, a detailed security analysis is presented, which shows the resilience of the method against cyber-security attacks. The findings of this work are evaluated by applying the method on a hybrid printed PUF, where it can be practically shown that the proposed method outperforms other existing group-based PUF key generation methods.
In this work the nonlinear behavior of layered surface acoustic wave (SAW) resonators is studied with the help of finite element (FE) computations. The full calculations depend strongly on the availability of accurate tensor data. While there are accurate material data for linear computations, the complete sets of higher-order material constants, needed for nonlinear simulations, are still not available for relevant materials. To overcome this problem, scaling factors were used for each available nonlinear tensor. The approach here considers piezoelectricity, dielectricity, electrostriction, and elasticity constants up to the fourth order. These factors act as a phenomenological estimate for incomplete tensor data. Since no set of fourth-order material constants for LiTaO3 is available, an isotropic approximation for the fourth-order elastic constants was applied. As a result, it was found that the fourth-order elastic tensor is dominated by one-fourth order Lamé constant. With the help of the FE model, derived in two different, but equivalent ways, we investigate the nonlinear behavior of a SAW resonator with a layered material stack. The focus was set to third-order nonlinearity. Accordingly, the modeling approach is validated using measurements of third-order effects in test resonators. In addition, the acoustic field distribution is analyzed.
Neural networks tend to overfit the training distribution and perform poorly on out-ofdistribution data. A conceptually simple solution lies in adversarial training, which introduces worst-case perturbations into the training data and thus improves model generalization to some extent. However, it is only one ingredient towards generally more robust models and requires knowledge about the potential attacks or inference time data corruptions during model training. This paper focuses on the native robustness of models that can learn robust behavior directly from conventional training data without out-of-distribution examples. To this end, we study the frequencies in learned convolution filters. Clean-trained models often prioritize high-frequency information, whereas adversarial training enforces models to shift the focus to low-frequency details during training. By mimicking this behavior through frequency regularization in learned convolution weights, we achieve improved native robustness to adversarial attacks, common corruptions, and other out-of-distribution tests. Additionally, this method leads to more favorable shifts in decision-making towards low-frequency information, such as shapes, which inherently aligns more closely with human vision.
Purpose
This study aims to investigate a systematic approach to the production and use of additively manufactured injection mould inserts in product development (PD) processes. For this purpose, an evaluation of the additive tooling design method (ATDM) is performed.
Design/methodology/approach
The evaluation of the ATDM is conducted within student workshops, where students develop products and validate them using AT-prototypes. The evaluation process includes the analysis of work results as well as the use of questionnaires and participant observation.
Findings
This study shows that the ATDM can be successfully used to assist in producing and using AT mould inserts to produce valid AT prototypes. As a reference for the implementation of AT in industrial PD, extracts from the work of the student project groups and suitable process parameters for prototype production are presented.
Originality/value
This paper presents the application and evaluation of a method to support AT in PD that has not yet been scientifically evaluated.
Background
Internal tibial loading is influenced by modifiable factors with implications for the risk of stress injury. Runners encounter varied surface steepness (gradients) when running outdoors and may adapt their speed according to the gradient. This study aimed to quantify tibial bending moments and stress at the anterior and posterior peripheries when running at different speeds on surfaces of different gradients.
Methods
Twenty recreational runners ran on a treadmill at 3 different speeds (2.5 m/s, 3.0 m/s, and 3.5 m/s) and gradients (level: 0%; uphill: +5%, +10%, and +15%; downhill: –5%, –10%, and –15%). Force and marker data were collected synchronously throughout. Bending moments were estimated at the distal third centroid of the tibia about the medial–lateral axis by ensuring static equilibrium at each 1% of stance. Stress was derived from bending moments at the anterior and posterior peripheries by modeling the tibia as a hollow ellipse. Two-way repeated-measures analysis of variance were conducted using both functional and discrete statistical analyses.
Results
There were significant main effects for running speed and gradient on peak bending moments and peak anterior and posterior stress. Higher running speeds resulted in greater tibial loading. Running uphill at +10% and +15% resulted in greater tibial loading than level running. Running downhill at –10% and –15% resulted in reduced tibial loading compared to level running. There was no difference between +5% or –5% and level running.
Conclusion
Running at faster speeds and uphill on gradients ≥+10% increased internal tibial loading, whereas slower running and downhill running on gradients ≥–10% reduced internal loading. Adapting running speed according to the gradient could be a protective mechanism, providing runners with a strategy to minimize the risk of tibial stress injuries.
Footwear plays a critical role in our daily lives, affecting our performance, health and overall well-being. Well-designed footwear can provide protection, comfort and improved foot functionality, while poorly designed footwear can lead to mobility problems and declines in physical activity. The overall goal of footwear research is to provide a scientific basis for professionals in the field to provide an optimal footwear solution for a given person, for a given task, in a given environment, while using sustainable manufacturing processes. This article suggests potential directions for future research with a focus on athletic footwear biomechanics. Directions include the evidence-based individualisation of footwear, the interaction between design and prolonged use, and improving the sustainability of footwear. The authors also provide a speculative outlook on methodological developments that may provide greater insight into these areas. These developments may include: (1) the use of larger scale, real-world and representative data, (2) the use of 3D printing to create experimental footwear, (3) the advancement of in silico research methods, and (4) furthering multidisciplinary collaboration. If successfully applied in the future, footwear research will contribute to active and healthy lifestyles across the lifespan.
Injury prevention is essential in running due to the risk of overuse injury development. Tailoring running shoes to individual needs may be a promising strategy to reduce this risk. Novel manufacturing processes allow the production of individualised running shoes that incorporate features that meet individual biomechanical and experiential needs. However, specific ways to individualise footwear to reduce injury risk are poorly understood. Therefore, this scoping review provides an overview of (1) footwear design features that have the potential for individualisation; and (2) the literature on the differential responses to footwear design features between selected groups of individuals. These purposes focus exclusively on reducing the risk of overuse injuries. We included studies in the English language on adults that analysed: (1) potential interaction effects between footwear design features and subgroups of runners or covariates (e.g., age, sex) for running-related biomechanical risk factors or injury incidences; (2) footwear comfort perception for a systematically modified footwear design feature. Most of the included articles (n = 107) analysed male runners. Female runners may be more susceptible to footwear-induced changes and overuse injury development; future research should target more heterogonous sampling. Several footwear design features (e.g., midsole characteristics, upper, outsole profile) show potential for individualisation. However, the literature addressing individualised footwear solutions and the potential to reduce biomechanical risk factors is limited. Future studies should leverage more extensive data collections considering relevant covariates and subgroups while systematically modifying isolated footwear design features to inform footwear individualisation.
Background
To assess the in-field walking mechanics during downhill hiking of patients with total knee arthroplasty five to 14 months after surgery and an age-matched healthy control group and relate them to the knee flexor and extensor muscle strength.
Methods
Participants walked on a predetermined hiking trail at a self-selected, comfortable pace wearing an inertial sensor system for recording the whole-body 3D kinematics. Sagittal plane hip, knee, and ankle joint angles were evaluated over the gait cycle at level walking and two different negative slopes. The concentric and eccentric lower extremity muscle strength of the knee flexors and extensors isokinetically at 50 and 120°/s were measured.
Findings
Less knee flexion angles during stance have been measured in patients in the operated limb compared to healthy controls in all conditions (level walking, moderate downhill, steep downhill). The differences increased with steepness. Muscle strength was lower in patients for both muscle groups and all measured conditions. The functional hamstrings to quadriceps ratio at 120°/sec correlated with knee angle during level and downhill walking at the moderate slope in patients, showing higher ratios with lower peak knee flexion angles.
Interpretation
The study shows that even if rehabilitation has been completed successfully and complication-free, five to 14 months after surgery, the muscular condition was still insufficient to display a normal gait pattern during downhill hiking. The muscle balance between quadriceps and hamstring muscles seems related to the persistence of a stiff knee gait pattern after knee arthroplasty. LoE: III.
Energy efficiency and hygrothermal performance of hemp clay walls for Moroccan residential buildings
(2023)
Hemp-based building envelopes have gained significant popularity in developed countries, and now the trend of constructing houses with hemp-clay blocks is spreading to developing countries like Morocco. Investigating the hygrothermal behavior of such structures under actual climate conditions is essential for advancing and promoting this sustainable practice. This paper presents an in-depth experimental characterization of a commercial hemp-clay brick that has been exposed to the outdoor environment for four years, in addition to field measurements on a building scale demonstration prototype. Additionally, the study simulates 17 representative cities to assess the hygrothermal performance and energy-saving potential in each of Morocco's six existing climate zones, using the EnergyPlus engine. The experimental campaign's findings demonstrate excellent indoor air temperature and relative humidity regulation within the hemp-clay wall building, leading to satisfactory levels of thermal comfort within hemp-clay wall buildings. This is attributed to the material's good thermal conductivity and excellent moisture buffering capacity (found to be 0.31 W/mK and 2.25 g/m2%RH), respectively). The energy simulation findings also point to significant energy savings, with cooling and heating energy reductions ranging from 27.7% to 47.5% and 33.7% to 79.8%, respectively, as compared to traditional Moroccan buildings.
Given the looming threats of climate change and the rapid worldwide urbanization, it is a necessity to prioritize the transition towards a carbon-free built environment. This research study provides a holistic digital methodology for parametric design of urban residential buildings with regard to the Mediterranean semi-arid climate zone of Morocco in the early design phase. The morphological parameters of the urban residential buildings, namely the buildings’ typology, the distance between buildings, the urban grid’s orientation, and the window-towall ratio, are evaluated in order to identify the key combinations of passive and active solar design strategies that determine the high energy performing configurations, based on the introduced Energy Performance Index (EPI), which is the ratio between solar BIPV production to maximum available installed BIPV capacity and the normalized thermal energy needs. Through an automated processing of 2187 iterations via Grasshopper, we simulate daylight autonomy, indoor thermal comfort and solar rooftop photovoltaic and building integrated photovoltaic (BIPV) energy potential. Then, we analyze the conflicting objectives of energy efficiency measures, active solar design strategies, and indoor visual comfort in the decision-making process that supports our goal of getting closer to net zero urban residential buildings. The digital workflow showed interesting trends in reaching a balanced equilibrium between performance metrics influenced by the contrasting impact of solar exposure on indoor daylight autonomy and thermal energy demand. Furthermore, the study’s findings indicate that it is possible to achieve an annual load match exceeding 66,56 % while simultaneously ensuring an acceptable visual indoor comfort (sDA higher than 0.4). The findings also highlight the important role of the BIPV system in shifting towards the net zero energy goal, by contributing up to 30 % of the overall solar energy output and covering up to 20 % of the yearly self-consumption. Moreover, the energy balance evaluation on an hourly basis indicates that BIPV system notably enhances the daily load cover factor by up to 5.5 %, particularly in the case of slab SN typology, throughout the different seasons. Graphical representations of the yearly, monthly and hourly load matches and the hourly energy balance of the best performing configurations provide a thorough understanding of the potential evolution of the urban energy system over time as a result of the gradual integration of active solar electricity production.
International trade requires sufficient, reliable, and affordable sources of financing. Export credit agencies (ECAs) fill trade finance gaps by offering financing, insurance and guarantees to provide liquidity or mitigate risks. They help to create or secure jobs in the domestic economy. However, comprehensive government support is required to create significant impact. This includes ‘full faith and credit’ of the state. In the context of public foreign trade promotion, full faith and credit is defined as an explicit, direct or indirect, irrevocable, legal commitment to accept all liabilities of an ECA as unconditional obligations of the respective government. Our policy recommendations for countries with relatively young ECAs, for example in Ukraine, Armenia, and Malawi, are to establish a full guarantee in addition to an efficient legal set-up, sufficient capital, and sound risk management of the respective agency. Without full faith and credit, policy goals of fostering economic growth through foreign trade fall short.
Public export credits and trade insurance require a global framework of institutions, rules and regulations to avoid subsidies and a race to the bottom. The extensive modernisation of the Arrangement on Officially Supported Export Credits (Arrangement) of the Organisation for Economic Co-operation and Development intends to re-level the playing field. This Practitioner Commentary describes the demand for adequate government interventions, considers the need for the reform and discusses key aspects of the new Arrangement. We argue that there is a breakthrough in several important areas such as tenors, repayment terms and green finance. However, we also find that the modernisation falls short in areas such as the interplay between different rulebooks, pre-shipment instruments' regulations and climate action.
In this paper, the J-integral is derived for temperature-dependent elastic–plastic materials described by incremental plasticity. It is implemented using the equivalent domain integral method for assessment of three-dimensional cracks based on results of finite-element calculations. The J-integral considers contributions from inhomogeneous temperature fields and temperature-dependent elastic and plastic material properties as well as from gradients in the plastic strains and the hardening variables. Different energy densities are considered, the Helmholtz free energy and the stress-working density, providing a physical meaning of the J-integral as a fracture criteria for crack growth. Results obtained for a plate with two different crack configurations each loaded by a cool-down thermal shock show domain-independence of the incremental J-integral for different energy densities even for high temperature gradients and significant temperature-dependence of the yield stress and the hardening exponent in the presence of large scale yielding. Hence, the derived J-integral is an appropriate parameter for the assessment of cracks in thermomechanically loaded components.
High performance thin layer chromatography (HPTLC) is a frequently used separation technique which works well for quantification of caffeine and quinine in beverages. Competing separation techniques, e.g. high-performance liquid chromatography (HPLC) or gas chromatography (GC), are not suitable for sugar-containing samples, because these methods need special pretreatment by the analyst. In HPTLC, however, it is possible to separate ‘dirty’ samples without time-consuming pretreatment, because disposable HPTLC plates are used. A convenient method for quantification of caffeine and quinine in beverages, without sample pretreatment, is presented below. The basic theory of in-situ quantification in HPTLC by use of remitted light is introduced and discussed. Several linearization models are discussed.
A home-made diode-array scanner has been used for quantification; this, for the first time, enables simultaneous measurements at different wavelengths. The new scanner also enables fluorescence evaluation without further equipment. Simultaneous recording at different wavelengths improves the accuracy and reliability of HPTLC analysis. These aspects result in substantial improvement of in-situ quantitative densitometric analysis and enable quantification of compounds in beverages.
A new diode-array scanner in combination with a computer-controlled application system meets all the demands of modern HPTLC measurement. Automatic application, simultaneous measurements at different wavelengths, and different linearization models enable appropriate evaluation of all analytical questions. The theory of error propagation recommends quantification at reflectance values smaller than 0.8; this can be verified only by use of diode-array scanning. The same theory also recommends quantification by use of peak height data, because the theory predicts best precision only for peak height evaluation. Diode-array scanning with reflectance monitoring enables appropriate validation in TLC and HPTLC analysis. All these aspects result in substantial improvement of in-situ quantitative densitometric analysis, and simultaneous recording at different wavelengths opens the way for chemometric evaluation, e.g. peak purity monitoring, which improves the accuracy and reliability of HPTLC analysis.
Fluorescence Enhancement of Pyrene Measured by Thin-Layer Chromatography with Diode-Array Detection
(2003)
In-situ densitometry for qualitative or quantitative purposes is a key step in thin-layer chromatography. It offers a simple way of quantifying by measuring the optical density of the separated spots directly on the plate. A new TLC scanner has been developed which is able to measure TLC plates or HPTLC plates, at different wavelengths simultaneously, without destroying the plate surface. The system enables absorbance and fluorescence measurements in one run. Fluorescence measurements are possible without filters or other adjustments.
The measurement of fluorescence from a TLC plate is a versatile means of making TLC analysis more sensitive. Fluorescence measurements with the new scanner are possible without filters or special lamps. Improvement of the signal-to-noise ratio is achieved by wavelength bundling. During plate scanning the scattered light and the fluorescence are both emitted from the surface of the TLC plate and this emitted light provides the desired spectral information from substances on the TLC plate. The measurement of fluorescence spectra and absorbance spectra directly from a TLC plate is based on differential measurement of light emerging from sample-free and sample-containing zones.
The literature recommends dipping TLC plates in viscous liquids to enhance fluorescence. Measurement of the fluorescence and absorbance spectra of pyrene spots reveals the mechanism of enhancement of plate dipping in viscous liquids—blocked contact of the fluorescent molecules with the stationary phase or other sample molecules is responsible for the enhanced fluorescence at lower concentrations.
In conclusion, dipping in TLC analysis is no miracle. It is based on similar mechanisms observable in liquids. The measured TLC spectra are also very similar to liquid spectra and this makes TLC spec-troscopy an important tool in separation analysis.
A new formula is presented for transforming fluorescence measurements in accordance with Kubelka-Munk theory. The fluorescence signals, the absorption signals, and data from a selected reference are combined in one expression. Only diode-array techniques can measure all the required data simultaneously to linearize fluorescence data correctly. To prove the new theory HPTLC quantification of the analgesic flupirtine was performed over the mass range 300 to 5000 ng per spot. The fluorescence calibration curve was linear over the whole range. The transformation of fluorescence measurements into linear mass-dependent data extends the technique of in-situ fluorescence analysis to the high concentration range. It also extends Kubelka-Munk theory from absorption to fluorescence analysis. The results presented also emphasize the importance of Kubelka-Munk theory for in-situ measurements in scattering media, especially in planar chromatography.
Material flow simulation is a core technology of Industry 4.0. It can analyze and improve large-scale production systems through experimentation with digital simulation models. However, modeling in discrete event simulation is considered as an effortful and time-consuming activity and challenges especially small and medium-sized enterprises. Systematic experiments and what-if-analysis require a large number of models. Modeling and simulation becomes a repetitive activity and the ability to model and simulate instantly becomes crucial for industry, 4.0. However, model generation typically uses specific methods to build models with individual properties for specific physical systems. A general literature review cannot sufficiently describe the current state of model generation. This study aims to provide an analysis of model generation based on the modeling strategy, modeling view, and production system type, as well as model properties and limitations.
Bud type carbon nanohorns (CNHs) are composed of carbon and have a closed conical tip at one end protruding from an aggregate structure. By employing a simple oxidation process in CO2 atmosphere, it is possible to open the CNH tips which increases their specific surface area by four fold. These tip opened CNHs combine the microporous nature of activated carbons and the crystalline mesoporous character of carbon nanotubes. The results for the high pressure CO2 gas adsorption of tip opened CNHs are reported herein for the first time and are found to be superior to traditional CO2 adsorbents like zeolites. The modified CNHs are also found to be promising materials for lithium ion batteries and the performance is found to be on a par with carbon nanotubes and carbon nanofibers.
In this study the dynamics and stability of thin and electrically conductive aqueous films under the influence of a time-periodic electric field are explored. With the help of analytical linear stability analysis for long wavelength disturbances, the stability threshold of the system as a function of various electrochemical parameters and transport coefficients is presented. The contributions of parameters like surface tension, disjoining pressure, electric double layer (Debye length and interfacial zeta potential), and unsteady Maxwell and viscous stresses are highlighted with the help of appropriate dimensionless groups. The physical mechanisms affecting the stability of thin films are detailed with the above-mentioned forces and parametric dependence of stability trends is discussed.
Automatic Identification of Travel Locations in Rare Books - Object Oriented Information Management
(2017)
The digital content of the Internet is growing exponentially and mass digitization of printed media opens access to literature, in particular the genre of travel literature from the 18th and 19th century, which consists of diaries or travel books describing routes, observations or inspirations. The identification of described locations in the digital text is a long-standing challenge which requires information technology to supply dynamic links to sources by new forms of interaction and synthesis between humanistic texts and scientific observations.
Using object oriented information technology, a prototype of a software tool is developed which makes it possible to automatically identify geographic locations and travel routes mentioned in rare books. The information objects contain properties such as names and classification codes for populated places, streams, mountains and regions. Together, with the latitudes and longitudes of every single location, it is possible to geo-reference this information in order that all processed and filtered datasets can be displayed by a map application. This method has already been used in the Humboldt Digital Library to present Alexander von Humboldt’s maps and was tested in a case study to prove the correctness and reliability of the automatic identification of locations based on the work of Alexander von Humboldt and Johann Wolfgang von Goethe.
The results reveal numerous errors due to misspellings, change of location names, equality of terms and location names. But on the other hand it becomes very clear that results of the automatic object detection and recognition can be improved by error-free and comprehensive sources. As a result an increase in quality and usability of the service can be expected, accompanied by more options to detect unknown locations in the descriptions of rare books.
In the 19th century Alexander von Humboldt explored the nature and was conceived a new vision of nature that still influences the way we understand the new world. Humboldt believed in the importance of accurate measurements and precise description of observations. His vision of nature included not only facts but also emotions.
Nowadays smart solutions will be developed by using computer technology, which will influence our relationship to nature, our handling of the complexity and diversity of nature itself and the technological influences on the society. Could we avoid a new form of “Colonialism”, when a network of super computers will create a smarter world?
The bandwidth behavior of graded-index multimode fibers (GI-MMFs) for different launching conditions is investigated to understand and characterize the effect of differential mode delay. In order to reduce the launch-power distribution the near field of a single-mode fiber is used to produce a controlled restricted launch. The baseband response is measured by observing the broadening of a narrow input pulse (time-domain measurement). The paper verifies the degradation in bandwidth due to profile distortion by scanning the spot of the single-mode fiber with a transversal offset from the center of the test sample. In addition, the impact of the launch-power distribution tuned by different spot-size diameters is demonstrated. Measurements were taken on ‘older’ 50-μm and 62.5-μm GI-MMFs as well as on laser-performance-optimized fibers more recently developed.
BACKGROUND
Various neutral and alkaline peptidases are commercially available for use in protein hydrolysis under neutral to alkaline conditions. However, the hydrolysis of proteins under acidic conditions by applying fungal aspartic peptidases (FAPs) has not been investigated in depth so far. The aim of this study, thus, was to purify a FAP from the commercial enzyme preparation, ROHALASE® BXL, determine its biochemical characteristics, and investigate its application for the hydrolysis of food and animal feed proteins under acidic conditions.
RESULTS
A Trichoderma reesei derived FAP, with an apparent molecular mass of 45.8 kDa (sodium dodecyl sulfate–polyacrylamide gel electrophoresis; SDS-PAGE) was purified 13.8-fold with a yield of 37% from ROHALASE® BXL. The FAP was identified as an aspartate protease (UniProt ID: G0R8T0) by inhibition and nano-LC-ESI-MS/MS studies. The FAP showed the highest activity at 50°C and pH 4.0. Monovalent cations, organic solvents, and reducing agents were tolerated well by the FAP. The FAP underwent an apparent competitive product inhibition by soy protein hydrolysate and whey protein hydrolysate with apparent Ki-values of 1.75 and 30.2 mg*mL−1, respectively. The FAP showed promising results in food (soy protein isolate and whey protein isolate) and animal feed protein hydrolyses. For the latter, an increase in the soluble protein content of 109% was noted after 30 min.
CONCLUSION
Our results demonstrate the applicability of fungal aspartic endopeptidases in the food and animal feed industry. Efficient protein hydrolysis of industrially relevant substrates such as acidic whey or animal feed proteins could be conducted by applying fungal aspartic peptidases. © 2022 Society of Chemical Industry.
This review provides an overview on the production and analysis techniques of antioxidative peptides from food proteins. Regarding the production of antioxidative peptides, interlinked factors must be considered. Depending on the protein substrate, different peptidases or peptidase systems containing multiple enzymes as well as a specific production process must be chosen. The antioxidative peptides might be produced in a batch process including multiple pre- and post-treatments, besides the hydrolyses with peptidases itself. As an alternative, the potential of continuous production systems is discussed in this review. Furthermore, robust analyses tools are needed to gain control of the process and final product properties. With no standardized methodology available for antioxidative peptide evaluation, pros and cons of various strategies for peptide separation and antioxidative measurement are discussed in this review. Therefore, this review provides a roadmap for antioxidative peptide generation from various sources for research and development as well as for potential industrial use.
Inadequate mechanical compliance of orthopedic implants can result in excessive strain of the bone interface, and ultimately, aseptic loosening. It is hypothesized that a fiber-based biometal with adjustable anisotropic mechanical properties can reduce interface strain, facilitate continuous remodeling, and improve implant survival under complex loads. The biometal is based on strategically layered sintered titanium fibers. Six different topologies are manufactured. Specimens are tested under compression in three orthogonal axes under 3-point bending and torsion until failure. Biocompatibility testing involves murine osteoblasts. Osseointegration is investigated by micro-computed tomography and histomorphometry after implantation in a metaphyseal trepanation model in sheep. The material demonstrates compressive yield strengths of up to 50 MPa and anisotropy correlating closely with fiber layout. Samples with 75% porosity are both stronger and stiffer than those with 85% porosity. The highest bending modulus is found in samples with parallel fiber orientation, while the highest shear modulus is found in cross-ply layouts. Cell metabolism and morphology indicate uncompromised biocompatibility. Implants demonstrate robust circumferential osseointegration in vivo after 8 weeks. The biometal introduced in this study demonstrates anisotropic mechanical properties similar to bone, and excellent osteoconductivity and feasibility as an orthopedic implant material.
Acoustic waves are investigated which are guided at the edge (apex line) of a wedge-shaped elastic body or at the edge of an elastic plate. The edges contain a periodic sequence of modifications, consisting either of indentations or inclusions with a different elastic material which gives rise to high acoustic mismatch. Dispersion relations are computed with the help of the finite element method. They exhibit zero-group velocity points on the dispersion branches of edge-localized acoustic modes. These special points also occur at Bloch-Floquet wavenumbers away from the Brillouin zone boundary. Deep indentations lead to flat branches corresponding to largely non-interacting, Einstein-oscillator like vibrations of the tongues between the grooves of the periodic structure. Due to the nonlinearity of the elastic media, quantified by their third-order elastic constants, an acoustic mode localized at a periodically modified edge generates a second harmonic which partly consists of surface and plate modes propagating into the elastic medium in the direction vertical to the edge. This acoustic radiation at the second-harmonic frequency is investigated for an elastic plate and a truncated sharp-angle wedge with periodic inclusions at their edges. Unlike nonlinear bulk wave generation by surface acoustic waves in an interdigital structure, surface and plate mode radiation by edge-localized modes can be visualized directly in laser-ultrasound experiments.
Surface treatment intensity monitoring is still an open and challenging nondestructive testing problem. For the estimation of residual stress with ultrasonic measurements, local linear and nonlinear elastic constants are needed as input. In this paper, nonlinear elastic-wave interactions (also called wave mixing or scattering) — namely, the generation of secondary ultrasonic waves in a nonlinear medium — are considered as a prospective means for near-surface nonlinear elastic parameter evaluation. The allowed interactions between bulk and surface waves, as well as the dependence of the scattering efficiency on the frequency and angle between source waves, were investigated through an analytical model, then compared with FEM simulations and experimental results. Finally, possible future steps for the development of the applied methods for the determination of near-surface higher-order elastic constants are discussed. In addition, several problem-relevant data processing procedures are presented.
Research is often conducted to investigate footwear mechanical properties and their effects on running biomechanics, but little is known about their influence on runner satisfaction, or how well the shoe is perceived. A tool to predict runner satisfaction in a shoe from its mechanical properties would be advantageous for footwear companies. Data in this study were from a database (n = 615 subject-shoe pairings) of satisfaction ratings (gathered after participants ran on a treadmill), and mechanical testing data for 87 unique subjects across 61 unique shoes. Random forest and elastic net logistic regression models were built to test if footwear mechanical properties and subject characteristics could predict runner satisfaction in 3 ways: degree-of-satisfaction on a 7-point Likert scale, overall satisfaction on a 3-point Likert scale, and willingness-to-purchase the shoe (yes/no response). Data were divided into training and validation sets, using an 80–20 split, to build the models and test their accuracy, respectively. Model accuracies were compared against the no-information rate (i.e. proportion of data belonging to the largest class). The models were not able to predict degree-of-satisfaction or overall satisfaction from footwear mechanical properties but could predict runner’s willingness to purchase with 68–75% accuracy. Midsole Gmax at the heel and forefoot appeared in the top five of variable importance rankings across both willingness-to-purchase models, suggesting its role as a major factor in purchase decisions. The negative regression coefficient for both heel and forefoot Gmax indicated that softer midsoles increase the likelihood of a shoe purchase. Future models to predict satisfaction may improve accuracy with the addition of more subject-specific parameters, such as running goals or foot proportions.
Running footwear is continuously being modified and improved; however, running-related overuse injury rates remain high. Nevertheless, novel manufacturing processes enable the production of individualized running shoes that can fit the individual needs of runners, with the potential to reduce injury risk. For this reason, it is essential to investigate functional groups of runners, a collective of runners who respond similarly to a footwear intervention. Therefore, the objective of this study was to develop a framework to identify functional groups based on their individual footwear response regarding injury-specific running-related risk factors for Achilles tendinopathy, Tibial stress fractures, Medial tibial stress syndrome, and Patellofemoral pain syndrome. In this work, we quantified the footwear response patterns of 73 female and male participants when running in three different footwear conditions using unsupervised learning (k-means clustering). For each functional group, we identified the footwear conditions minimizing the injury-specific risk factors. We described differences in the functional groups regarding their running style, anthropometric, footwear perception, and demographics. The results implied that most functional groups showed a tendency for a single footwear condition to reduce most biomechanical risk factors for a specific overuse injury. Functional groups often differed in their hip and pelvis kinematics as well as their subjective rating of the footwear conditions. The footwear intervention only partially affected biomechanical risk factors attributed to more proximal joints. Due to its adaptive nature, the framework could be applied to other footwear interventions or performance-related biomechanical variables.
High-performance thin-layer chromatography (HPTLC), as the modern form of TLC (thin-layer chromatography), is suitable for detecting pharmaceutically active compounds over a wide polarity range using the gradient multiple development (GMD) technique. Diode-array detection (DAD) in conjunction with HPTLC can simultaneously acquire ultraviolet‒visible (UV‒VIS) and fluorescence spectra directly from the plate. Visualization as a contour plot helps to identify separated zones. An orange peel extract is used as an example to show how GMD‒DAD‒HPTLC in seven different developments with seven different solvents can provide an overview of the entire sample. More than 50 compounds in the extract can be separated on a 6-cm HPTLC plate. Such separations take place in the biologically inert stationary phase of HPTLC, making it a suitable method for effect-directed analysis (EDA). HPTLC‒EDA can even be performed with living organism, as confirmed by the use of Aliivibrio fischeri bacteria to detect bioluminescence as a measure of toxicity. The combining of gradient multiple development planar chromatography with diode-array detection and effect-directed analysis (GMD‒DAD‒HPTLC‒EDA) in conjunction with specific staining methods and time-of-flight mass spectrometry (TOF‒MS) will be the method of choice to find new chemical structures from plant extracts that can serve as the basic structure for new pharmaceutically active compounds.
Two solvent mixtures for high-performance thin-layer chromatographic (HPTLC) separation of some compounds showing estrogenic activity in the yeast estrogen screen (YES) assay are presented. The new method, planar yeast estrogen screen (pYES) combines the YES assay and a chromatographic separation on silica gel HPTLC plates with the performance of the YES assay. For separation, the analytes were applied bandwise to HPTLC plates (10 × 20 cm) with fluorescent dye (Merck, Germany). The plates were developed in a vertical developing chamber after 30 min of chamber saturation over a separation distance of 70 mm, using cyclohexane‒methyl-ethyl ketone (2:1, V/V) or cyclohexane‒CPME (3:2, V/V) as solvents. Both solvents allow separation of estriol, daidzein, genistein, 17β-estradiol, 17α-ethinyl estradiol, estrone, 4-nonylphenol and bis(2-ethylhexyl) phthalate.
This paper aims to draw attention to an urgent need for reform of the regulatory framework of the broader export credit system to ensure a new and comprehensive "safe haven" for officially supported export credits. The purpose is to analyse the complex debate on disciplines of the World Trade Organization (WTO) and the Organisation for Economic Co-operation and Development (OECD), creating a point of reference for future analysis of and debates around the "carve-out clause" of the Agreement on Subsidies and Countervailing Measures (ASCM) and a "safe haven" in a broader sense.
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail due to computational constraints. To represent key processes, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM (European Center for Medium-Range Weather Forecast-Hamburg-Hamburg) global climate aerosol model using the M7 microphysics, but high computational costs make it very expensive to run with finer resolution or for a longer time. We aim to use machine learning to emulate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input–output pairs to train a neural network (NN) on it. We are able to learn the variables’ tendencies achieving an average R² score of 77.1%. We further explore methods to inform and constrain the NN with physical knowledge to reduce mass violation and enforce mass positivity. On a Graphics processing unit (GPU), we achieve a speed-up of up to over 64 times faster when compared to the original model.
Many commonly well-performing convolutional neural network models have shown to be susceptible to input data perturbations, indicating a low model robustness. To reveal model weaknesses, adversarial attacks are specifically optimized to generate small, barely perceivable image perturbations that flip the model prediction. Robustness against attacks can be gained by using adversarial examples during training, which in most cases reduces the measurable model attackability. Unfortunately, this technique can lead to robust overfitting, which results in non-robust models. In this paper, we analyze adversarially trained, robust models in the context of a specific network operation, the downsampling layer, and provide evidence that robust models have learned to downsample more accurately and suffer significantly less from downsampling artifacts, aka. aliasing, than baseline models. In the case of robust overfitting, we observe a strong increase in aliasing and propose a novel early stopping approach based on the measurement of aliasing.
Linear acceleration is a key performance determinant and major training component of many sports. Although extensive research about lower limb kinetics and kinematics is available, consistent definitions of distinctive key body positions, the underlying mechanisms and their related movement strategies are lacking. The aim of this ‘Method and Theoretical Perspective’ article is to introduce a conceptual framework which classifies the sagittal plane ‘shin roll’ motion during accelerated sprinting. By emphasising the importance of the shin segment’s orientation in space, four distinctive key positions are presented (‘shin block’, ‘touchdown’, ‘heel lock’ and ‘propulsion pose’), which are linked by a progressive ‘shin roll’ motion during swing-stance transition. The shin’s downward tilt is driven by three different movement strategies (‘shin alignment’, ‘horizontal ankle rocker’ and ‘shin drop’). The tilt’s optimal amount and timing will contribute to a mechanically efficient acceleration via timely staggered proximal-to-distal power output. Empirical data obtained from athletes of different performance levels and sporting backgrounds are required to verify the feasibility of this concept. The framework presented here should facilitate future biomechanical analyses and may enable coaches and practitioners to develop specific training programs and feedback strategies to provide athletes with a more efficient acceleration technique.
In this paper, the influence of the material hardening behavior on plasticity-induced fatigue crack closure is investigated for strain-controlled loading and fully plastic, large-scale yielding conditions by means of the finite element method. The strain amplitude and the strain ratio are varied for given Ramberg–Osgood material properties representing materials with different hardening behavior. The results show a pronounced influence of the hardening behavior on crack closure, while no significant effect is found from the considered strain amplitude and strain ratio. The effect of the hardening behavior on the crack opening stress cannot be described by existing crack opening stress equations.
In this paper, the effect of the polycrystalline microstructure on crack-tip opening displacement and crack closure is investigated for microstructural short plane strain fatigue cracks using the finite-element method. To this end, cracks are introduced in synthetically generated microstructures and the grain properties are described using a single crystal plasticity model with kinematic hardening. Additionally, finite-element calculations without resolved microstructure and von Mises plasticity with kinematic hardening are performed. Fully-reversed strain-controlled cyclic loadings are considered under large-scale yielding conditions as typical for low-cycle fatigue problems. The crack opening stress and the cyclic crack-tip opening displacement are significantly influenced by the local grain structure. While the stabilized crack opening stresses obtained with the microstructure-based finite-element model are in good accordance with the von Mises plasticity results, the differences in the cyclic crack opening displacement are addressed to the asymmetric plastic strain fields in the plastic wake behind the crack-tip of the microstructure-based model. The asymmetric plastic strain fields result in discontinuous and premature contact of the crack flanks.
Virtual reality (VR) offers the opportunity to create virtual worlds that could replace real experiences. This research investigates the influence of user motivation, temporal distance and experience type on the satisfaction with the VR experience, and the degree of acceptance of a VR experience as a substitute for a real experience. The results suggest that the degree of acceptance of a VR experience as a substitute for a real experience is higher for passive VR experiences compared to active VR experiences. Furthermore, the results support the assumption that users are more satisfied with passive VR experiences.
Cloud computing is the emerging technology providing IT as a utility through internet. The benefits of cloud computing are but not limited to service based, scalable, elastic, shared pool of resources, metered by use. Due to mentioned benefits the concept of cloud computing fits very well with the concept of m-learning which differs from other forms of e-learning, covers a wide range of possibilities opened up by the convergence of new mobile technologies, wireless communication structure and distance learning development. The concept of cloud computing like any other concept has not only benefits but also introduces myriad of security issues, such as transparency between cloud user and provider, lack of standards, security concerns related to identity, Service Level Agreements (SLA) inadequacy etc. Providing secure, transparent, and reliable services in cloud computing environment is an important issue. This paper introduces a secured three layered architecture with an advance Intrusion Detection System (advIDS), which overcomes different vulnerabilities on cloud deployed applications. This proposed architecture can reduce the impact of different attacks by providing timely alerts, rejecting the unauthorized access over services, and recording the new threat profiles for future verification. The goal of this research is to provide more control over data and applications to the cloud user, which are now mainly controlled by Cloud Service Provider (CSP).
The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.
Heat generation that is coupled with electricity usage, like combined heat and power generators or heat pumps, can provide operational flexibility to the electricity sector. In order to make use of this in an optimized way, the flexibility that can be provided by such plants needs to be properly quantified. This paper proposes a method for quantifying the flexibility provided through a cluster of such heat generators. It takes into account minimum operational time and minimum down-time of heat generating units. Flexibility is defined here as the time period over which plant operation can be either delayed or forced into operation, thus providing upward or downward regulation to the power system on demand. Results for one case study show that a cluster of several smaller heat generation units does not provide much more delayed operation flexibility than one large unit with the same power, while it more than doubles the forced operation flexibility. Considering minimum operational time and minimum down-time of the units considerably limits the available forced and delayed operation flexibility, especially in the case of one large unit.
Lithium-ion batteries exhibit a well-known trade-off between energy and power, which is problematic for electric vehicles which require both high energy during discharge (high driving range) and high power during charge (fast-charge capability). We use two commercial lithium-ion cells (high-energy [HE] and high-power) to parameterize and validate physicochemical pseudo-two-dimensional models. In a systematic virtual design study, we vary electrode thicknesses, cell temperature, and the type of charging protocol. We are able to show that low anode potentials during charge, inducing lithium plating and cell aging, can be effectively avoided either by using high temperatures or by using a constant-current/constant-potential/constant-voltage charge protocol which includes a constant anode potential phase. We introduce and quantify a specific charging power as the ratio of discharged energy (at slow discharge) and required charging time (at a fast charge). This value is shown to exhibit a distinct optimum with respect to electrode thickness. At 35°C, the optimum was achieved using an HE electrode design, yielding 23.8 Wh/(min L) volumetric charging power at 15.2 min charging time (10% to 80% state of charge) and 517 Wh/L discharge energy density. By analyzing the various overpotential contributions, we were able to show that electrolyte transport losses are dominantly responsible for the insufficient charge and discharge performance of cells with very thick electrodes.
Jürgen Zierep passed away on July 29, 2021, at the age of 92. To him, science and education was not only a profession, but an affair of the heart. His impressive contributions in fluid mechanics comprise about 200 scientific publications in the fields of gas dynamics, similarity laws, flow instabilities, flows with energy transfer, and non-Newtonian fluids. In addition, he wrote eleven textbooks with great dedication. Those books by the “scientist who loves to teach” are nowadays available in different languages and regularly appear in new editions.
Significant progress in the development and commercialization of electrically conductive adhesives has been made. This makes shingling a very attractive approach for solar cell interconnection. In this study, we investigate the shading tolerance of two types of solar modules based on shingle interconnection: first, the already commercialized string approach, and second, the matrix technology where solar cells are intrinsically interconnected in parallel and in series. An experimentally validated LTspice model predicts major advantages for the power output of the matrix layout under partial shading. Diagonal as well as random shading of a 1.6-m2 solar module is examined. Power gains of up to 73.8 % for diagonal shading and up to 96.5 % for random shading are found for the matrix technology compared to the standard string approach. The key factor is an increased current extraction due to lateral current flows. Especially under minor shading, the matrix technology benefits from an increased fill factor as well. Under diagonal shading, we find the probability of parts of the matrix module being bypassed to be reduced by 40 % in comparison to the string module. In consequence, the overall risk of hotspot occurrence in matrix modules is decreased significantly.
A versatile liquid metal (LM) printing process enabling the fabrication of various fully printed devices such as intra- and interconnect wires, resistors, diodes, transistors, and basic circuit elements such as inverters which are process compatible with other digital printing and thin film structuring methods for integration is presented. For this, a glass capillary-based direct-write method for printing LMs such as eutectic gallium alloys, exploring the potential for fully printed LM-enabled devices is demonstrated. Examples for successful device fabrication include resistors, p–n diodes, and field effect transistors. The device functionality and easiness of one integrated fabrication flow shows that the potential of LM printing is far exceeding the use of interconnecting conventional electronic devices in printed electronics.
Objective: To quantify the effect of inhaled 5% carbon-dioxide/95% oxygen on EEG recordings from patients in non-convulsive status epilepticus (NCSE).
Methods: Five children of mixed aetiology in NCSE were given high flow of inhaled carbogen (5% carbon dioxide/95% oxygen) using a face mask for maximum 120s. EEG was recorded concurrently in all patients. The effects of inhaled carbogen on patient EEG recordings were investigated using band-power, functional connectivity and graph theory measures. Carbogen effect was quantified by measuring effect size (Cohen's d) between "before", "during" and "after" carbogen delivery states.
Results: Carbogen's apparent effect on EEG band-power and network metrics across all patients for "before-during" and "before-after" inhalation comparisons was inconsistent across the five patients.
Conclusion: The changes in different measures suggest a potentially non-homogeneous effect of carbogen on the patients' EEG. Different aetiology and duration of the inhalation may underlie these non-homogeneous effects. Tuning the carbogen parameters (such as ratio between CO2 and O2, duration of inhalation) on a personalised basis may improve seizure suppression in future.
Purpose: Participation and accessibility issues faced by gamers with multi-sensory disabilities are themes yet to be fully understood by accessible technology researchers. In this work, we examine the personal experiences and perceptions of individuals with deafblindness who play games despite their disability, as well as the reasons that lead some of them to stop playing games.
Materials and methods: We conducted 60 semi-structured interviews with individuals living with deafblindness in five European countries: United Kingdom, Germany, Netherlands, Greece and Sweden.
Results: Participants stated that reasons for playing games included them being a fun and entertaining hobby, for socialization and meeting others, or for occupying the mind. Reasons for stop playing games included essentially accessibility issues, followed by high cognitive demand, changes in gaming experience due their disability, financial reasons, or because the accessible version of a specific game was not considered as fun as the original one.
Conclusions: We identified that a considerable number of individuals with deafblindness enjoy playing casual mobile games such as Wordfeud and Sudoku as a pastime activity. Despite challenging accessibility issues, games provide meaningful social interactions to players with deafblindness. Finally, we introduce a set of user-driven recommendations for making digital games more accessible to players with a diverse combination of sensory abilities.
IMPLICATIONS FOR REHABILITATION
- Digital games were considered a fun and entertaining hobby by participants with deafblindness. Furthermore, participants play games for socialization and meeting others, or for occupying the mind.
- Digital games provide meaningful social interactions and past time to persons with deafblindness.
- On top of accessibility implications, our findings draw attention to the importance of the social element of gaming for persons with deafblindness.
- Based on interviews, we introduce a set of user-driven recommendations for making digital games more accessible to players with a diverse combination of sensory abilities.
The compliant nature of distal limb muscle-tendon units is traditionally considered suboptimal in explosive movements when positive joint work is required. However, during accelerative running, ankle joint net mechanical work is positive. Therefore, this study aims to investigate how plantar flexor muscle-tendon behavior is modulated during fast accelerations. Eleven female sprinters performed maximum sprint accelerations from starting blocks, while gastrocnemius muscle fascicle lengths were estimated using ultrasonography. We combined motion analysis and ground reaction force measurements to assess lower limb joint kinematics and kinetics, and to estimate gastrocnemius muscle-tendon unit length during the first two acceleration steps. Outcome variables were resampled to the stance phase and averaged across three to five trials. Relevant scalars were extracted and analyzed using one-sample and two-sample t-tests, and vector trajectories were compared using statistical parametric mapping. We found that an uncoupling of muscle fascicle behavior from muscle-tendon unit behavior is effectively used to produce net positive mechanical work at the joint during maximum sprint acceleration. Muscle fascicles shortened throughout the first and second steps, while shortening occurred earlier during the first step, where negative joint work was lower compared with the second step. Elastic strain energy may be stored during dorsiflexion after touchdown since fascicles did not lengthen at the same time to dissipate energy. Thus, net positive work generation is accommodated by the reuse of elastic strain energy along with positive gastrocnemius fascicle work. Our results show a mechanism of how muscles with high in-series compliance can contribute to net positive joint work.
Surface acoustic waves are propagated toward the edge of an anisotropic elastic medium (a silicon crystal), which supports leaky waves with a high degree of localization at the tip of the edge. At an angle of incidence corresponding to phase matching with this leaky wedge wave, a sharp peak in the reflection coefficient of the surface wave was found. This anomalous reflection is associated with efficient excitation of the leaky wedge wave. In laser ultrasound experiments, surface acoustic wave pulses were excited and their reflection from the edge of the sample and their partial conversion into leaky wedge wave pulses was observed by optical probe-beam deflection. The reflection scenario and the pulse shapes of the surface and wedge-localized guided waves, including the evolution of the acoustic pulse traveling along the edge, have been confirmed in detail by numerical simulations.
Properties of higher-order surface acoustic wave modes in Al(1-x)Sc(x)N / sapphire structures
(2021)
In this work, surface acoustic wave (SAW) modes and their dependence on propagation directions in epitaxial Al0.68Sc0.32N(0001) films on Al2O3(0001) substrates were studied using numerical and experimental methods. In order to find optimal propagation directions for higher-order SAW modes, phase velocity dispersion branches of Al0.68Sc0.32N on Al2O3 with Pt mass loading were computed for the propagation directions <11-20> and <1-100> with respect to the substrate. Experimental investigations of phase velocities and electromechanical coupling were performed for comparison with the numerical results. Simulations carried out with the finite element method (FEM) and with a Green function approach allowed identification of each wave type, including Rayleigh, Sezawa and shear horizontal wave modes. For the propagation direction <1-100>, significantly increased wave guidance of the Sezawa mode compared to other directions was observed, resulting in enhanced electromechanical coupling (k2eff = 1.6 %) and phase velocity (vphase = 6 km/s). We demonstrated, that selecting wave propagation in <1-100> with high mass density electrodes results in increased electromechanical coupling without significant reduction in phase velocities for the Sezawa wave mode. An improved combination of metallization, Sc concentration x, and SAW propagation direction is suggested which exhibits both high electromechanical coupling (k2eff > 6 %) and high velocity (vphase = 5.5 km/s) for the Sezawa mode.
It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.
In the last decade, deep learning models for condition monitoring of mechanical systems increasingly gained importance. Most of the previous works use data of the same domain (e.g., bearing type) or of a large amount of (labeled) samples. This approach is not valid for many real-world scenarios from industrial use-cases where only a small amount of data, often unlabeled, is available.
In this paper, we propose, evaluate, and compare a novel technique based on an intermediate domain, which creates a new representation of the features in the data and abstracts the defects of rotating elements such as bearings. The results based on an intermediate domain related to characteristic frequencies show an improved accuracy of up to 32 % on small labeled datasets compared to the current state-of-the-art in the time-frequency domain.
Furthermore, a Convolutional Neural Network (CNN) architecture is proposed for transfer learning. We also propose and evaluate a new approach for transfer learning, which we call Layered Maximum Mean Discrepancy (LMMD). This approach is based on the Maximum Mean Discrepancy (MMD) but extends it by considering the special characteristics of the proposed intermediate domain. The presented approach outperforms the traditional combination of Hilbert–Huang Transform (HHT) and S-Transform with MMD on all datasets for unsupervised as well as for semi-supervised learning. In most of our test cases, it also outperforms other state-of-the-art techniques.
This approach is capable of using different types of bearings in the source and target domain under a wide variation of the rotation speed.
Wood juice, a liquid produced during wood processing, is a harmful waste that requires utilization. To achieve a circular economy, biowastes should be recycled, reducing fossil carbon usage. Therefore, the objective of this work was to examine the potential of wood juice as a feedstock for bioplastic synthesis by Bacillus sp. G8_19. Polyhydroxyalkanoate (PHA) syntheses using wood juice from Douglas fir trees and that from a mixture of spruce/fir trees were compared. It was found that the PHA content was higher after using wood juice from spruce/fir trees than that from Douglas fir trees (18.0% vs 6.1% of cell dry mass). Gas chromatography analysis showed that, with both wood juices, Bacillus sp. G8_19 accumulated poly(3-hydroxybutyrate-co-3-hydroxyvalerate). The content of 3-hydroxyvalerate (3HV) monomers was higher when spruce/fir wood juice was used (10.7% vs 1.9%). The C/N ratio did not have a statistically significant effect on the copolymer content in biomass, but it did significantly influence the 3HV content. The proposed concept may serve as an approach to wood waste valorization via production of biodegradable materials.
Interpreting seismic data requires the characterization of a number of key elements such as the position of faults and main reflections, presence of structural bodies, and clustering of areas exhibiting a similar amplitude versus angle response. Manual interpretation of geophysical data is often a difficult and time-consuming task, complicated by lack of resolution and presence of noise. In recent years, approaches based on convolutional neural networks have shown remarkable results in automating certain interpretative tasks. However, these state-of-the-art systems usually need to be trained in a supervised manner, and they suffer from a generalization problem. Hence, it is highly challenging to train a model that can yield accurate results on new real data obtained with different acquisition, processing, and geology than the data used for training. In this work, we introduce a novel method that combines generative neural networks with a segmentation task in order to decrease the gap between annotated training data and uninterpreted target data. We validate our approach on two applications: the detection of diffraction events and the picking of faults. We show that when transitioning from synthetic training data to real validation data, our workflow yields superior results compared to its counterpart without the generative network.
The work focuses on predictive capabilities of fundamental cyclic plasticity and fatigue life models, which can be calibrated using limited amount of experiments as specific ones needed for more advanced models are often absent. The analyses are conducted for the synthetic case of exhaust manifold made from cast iron. The thermal boundary conditions from the forced convection were obtained from the computational fluid dynamics considered as a conjugate heat transfer problem. Two rate-independent and temperature-dependent material models were calibrated for structural analyses. Both were validated with experiments on isothermal and anisothermal levels. Sequential thermal–mechanical finite element simulations were performed. Two fatigue life models were employed. The first was a temperature-dependent strain-based fatigue life criterion calibrated from uniaxial data. The second was a temperature-independent energy-based fatigue life criterion resulting in twice lower life than the strain-based criterion, while none of the plasticity models made a significant difference in that prediction.
Drawing off the technical flexibility of building polygeneration systems to support a rapidly expanding renewable electricity grid requires the application of advanced controllers like model predictive control (MPC) that can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints amongst other features. In this original work, an economic-MPC-based optimal scheduling of a real-world building energy system is demonstrated and its performance is evaluated against a conventional controller. The demonstration includes the steps to integrate an optimisation-based supervisory controller into a standard building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms for solving complex nonlinear mixed integer optimal control problems. With the MPC, quantitative benefits in terms of 6–12% demand-cost savings and qualitative benefits in terms of better controller adaptability and hardware-friendly operation are identified. Further research potential for improving the MPC framework in terms of field-level stability, minimising constraint violations, and inter-system communication for its deployment in a prosumer-network is also identified.
The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI’s trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.
Due to higher combustion chamber temperatures and pressures in efficient combustion engines, both the high-cycle and thermomechanical fatigue loads on service life-critical components, such as the cylinder head, are increasing. Material comparisons and analysis of damage behavior are very expensive and time-consuming using component tests. This study therefore develops a test method for cylinder head materials that takes into account the combined loading conditions from the above-mentioned loads and allows realistic temperature transients and gradients on near-component samples. The near-component cylinder head sample represents the failure-critical exhaust valve crosspiece and is tested in a test rig specially designed with the aid of conjugate heat transfer simulations. In the test rig, the sample is subjected to thermal stress by a hot gas burner and to mechanical stress by a high-frequency pulsator. Optical crack detection allows permanent observation of fatigue crack growth and crack closure during the test. Fractographic and metallo-graphic examinations of the fracture areas as well as analyses of the damage patterns show that loads close to engine operation can be set in this way and their influences on the damage can be monitored.
Despite increasing budgets for social media activities and a wide variety of performance measurement possibilities, many companies do not measure the performance of their social media activities. Research shows that those companies that measure the performance of social media activities use incorrect, too few or inappropriate metrics. A central problem is that there is often an inadequate performance measurement process. This article presents a process that focuses on the objectives of social media activities. In phase one of this process, suitable metrics are selected and target values are defined based on these objectives. In phase two, data are collected and analysed. Finally, actions are defined. The developed process helps companies to measure the performance of their social media activities.
The aim of this review was to determine whether smartphone applications are reliable and valid to measure range of motion (RoM) in lower extremity joints. A literature search was performed up to October 2020 in the databases PubMed and Cochrane Library. Studies that reported reliability or validity of smartphone applications for RoM measurements were included. The study quality was assessed with the QUADAS-2 tool and baseline information, validity and reliability were extracted. Twenty-five studies were included in the review. Eighteen studies examined knee RoM, whereof two apps were analysed as having good to excellent reliability and validity for knee flexion (“DrGoniometer”, “Angle”) and one app showed good results for knee extension (“DrGoniometer”). Eight studies analysed ankle RoM. One of these apps showed good intra-rater reliability and excellent validity for dorsiflexion RoM (“iHandy level”), another app showed excellent reliability and moderate validity for plantarflexion RoM (“Coach’s Eye”). All other apps concerning lower extremity RoM had either insufficient results, lacked study quality or were no longer available. Some apps are reliable and valid to measure RoM in the knee and ankle joint. No app can be recommended for hip RoM measurement without restrictions.
Uphill training is applied to induce specific overload on the musculoskeletal system to improve sprinting mechanics. This study aimed to identify unique kinematic features of uphill sprinting at different slopes and to suggest practical implications based on comparisons we early stance phase. At take-off, steeper slopes induced significantly more extended joint angles and higher ROMs during the late stance phase. Compared with moderate slopes, more anti-phase coordination patterns were detected at steeper slopes. Thus, uphill sprinting at steeper slopes shares essential kinematic features with the early acceleration phase of level sprinting. Moderate inclinations induce biomechanical adaptations similar to those in the late acceleration phase of level sprinting. Hence, the specific transfer of uphill sprinting to acceleration depends on the slope inclinations.
Quantifying the midsole material characteristics of athletic footwear is a standard task in footwear research and development. Current material testing protocols primarily focus on the determination of cushioning properties of the heel region or the quantification of the midsole properties as one assembly. However, midsoles possess different spatial material properties that have not been quantified from previous methodologies. Therefore, new material testing methods are required to quantify the local material response of athletic footwear. We developed a cyclical force-controlled material testing protocol for the determination of non-homogeneously distributed material stiffness with a high spatial resolution. In five prototype shoes varying in their stiffness distribution, we found that the material properties can be reliably measured across the midsole. Furthermore, we observed a characteristic non-linear material response regardless of the midsole location. We found that the material stiffness increased with an increase of the applied force and that this effect is further intensified by higher testing cycles. Additionally, the obtained midsole stiffness depends on the geometry of the midsole. We explored different approaches to reduce the measurement time of the testing protocol and found that the number of measurements can be reduced by 70% using 2 D-interpolation procedures. Determining the spatial material properties of midsoles needs to be considered to understand foot-shoe interactions. Furthermore, this measurement protocol can be used for quality control within the footwear and can be adapted for considering the effects of different running styles or speeds on ground force application characteristics.
We present a video-densitometric high-performance thin-layer chromatography (HPTLC) quantification method for patulin in apple juice, developed in a vertical chamber from the starting point to a distance of 50 mm, using MTBE, n-pentane (9 + 5, v/v) as mobile phase. After separation the plate is sprayed with methyl-benzothiazolinone hydrazone hydrochloride monohydrate (MBTH) solution (40 mg in 20 mL methanol) and heated at 105 °C for 15 min. Patulin zones are transformed into yellow spots. The quantification is based on direct measurements using an inexpensive 48-bit flatbed scanner for color measurements (in red, green, and blue). Evaluation of the blue channel makes the measurements very specific. Quantification in fluorescence was also done by use of a 16-bit CCD-camera and UV-366 nm illumination as well as using a HPTLC DAD-scanner. For linearization the extended Kubelka–Munk expression for data transformation was used. The range of linearity covers more than two magnitudes and lies between 5 and 800 ng patulin. The extraction of 20 g apple juice and an extract application on plate up to 50 µL allows statistically defined checking the limit of detection (LOD) of 50 ng patulin per track, which is equivalent to 50 µg patulin per kg apple juice.
In the present paper, the influence of locally varying microstructures in case of an AlSi12 cast aluminium alloy is investigated by means of extracting the test pieces from different removal positions and low cycle fatigue tests. The temperature-dependent damage mechanisms, the material specific defect types, sizes and their influence on the fatigue properties of two AlSi7 and AlSi12 cast aluminium alloys are studied. An extreme value statistics methodology is applied to predict maximum defect sizes expected in a critical surface volume from two-dimensional metallographic micrographs. A damage map for the AlSi12 cast aluminium alloy is presented explaining the influence of the temperature- and load-dependent damage mechanisms on the observed isothermal and thermomechanical lifetime behavior.
In this work, the influence of superimposed high cycle fatigue on the LCF/HCF and TMF/HCF lifetime is investigated for two cast aluminium alloys of the types AlSi7 and AlSi12. The replica technique is used to examine the short crack growth behavior under pure LCF and LCF/HCF loading. The observed short crack growth evolution explains the observed lifetime reduction with increasing HCF amplitudes.
Detailed material investigations of the fatigue behavior of two cast aluminium alloys used in combustion engines are presented. The network of intermetallic phases of both aluminium alloys is characterized by means of detailed energy dispersive X-ray spectroscopy. In order to investigate the temperature-dependent fatigue behavior of the materials, tensile, low cycle and thermomechanical fatigue tests are performed over a wide temperature and loading range. The influence of the temperature dependence on the experimental results is discussed.
In this paper, the Bauschinger effect and latent hardening of single crystals are assessed in finite element calculations using a single crystal plasticity model with kinematic hardening. To this end, results of cyclic micro-bending experiments on single crystal Alloy 718 in different crystal orientations (single slip and multi slip) with respect to the loading direction are used to determine the slip system related material properties of the single crystal plasticity model. Two kinematic hardening laws are considered: a kinematic hardening law describing latent hardening and a kinematic hardening law without latent hardening. For the determination of material properties for both hardening laws, a gradient-based optimization method is used. The results show that the different strength levels observed for micro-bending tests on different crystal orientations can only be described with latent kinematic hardening well, whereas the pronounced Bauschinger effect is described well by both kinematic hardening laws. It is concluded that cyclic micro-bending experiments on single crystals using different crystal orientations give an appropriate data base for the determination of the slip system related material properties of the single crystal plasticity model with latent kinematic hardening.
Cyclic micro-bending tests on fcc single crystal Ni-base Alloy 718 cantilevers with different crystal orientations were performed to analyze the influence of activated slip systems on dislocation plasticity, latent hardening and the Bauschinger effect. The investigations indicate that plasticity in single crystal micro-cantilevers is significantly influenced by two phenomena - dislocation interaction and dislocation pile-up at the neutral plane. Both phenomena occur at the same time. Their ratio seems to be determined by the activated slip systems. Slip trace analysis indicates that the activation of only one slip system leads to a strong localization of plasticity to a limited number of parallel slip bands. This results in low dislocation interaction and consequently pronounced pile-ups at the neutral plane. In multi slip orientation, the second slip system leads to activation of significantly more dislocation sources, causing a much earlier and more homogeneous elastic-plastic transition zone. In stress-strain hysteresis loops during bending, pronounced dislocation interaction in multi slip orientation leads to a more pronounced latent hardening. The results suggest that on a microstructural length scale, plasticity behavior is strongly affected by activated slip systems, which determine local dislocation phenomena. Based on the results presented in this paper, a finite element analysis of latent hardening and the Bauschinger effect using a single crystal plasticity model with latent kinematic hardening is presented in Part II.
As PV enters the terawatt era, reliability, sustainability and low carbon footprint of solar modules are key requirements. The N.I.C.E.TM technology from Apollon Solar is a good candidate for significant improvements in these areas. As the second-generation pilot line is now functional with IEC certification underway, we present a holistic assessment of N.I.C.E.TM technology compared with conventional module technology with encapsulant. This includes electrical performance and cost/consumables, reliability, and degradation mechanisms as well as sustainability aspects. In addition, the new generation of N.I.C.E.-wire modules are presented that use thin round Cu wires instead of flat ribbons for interconnection. This candidate technology for an alternative to the Smart Wire Connection Technology (SWCT) is investigated experimentally as well as via numerical simulations.
A Simple and Reliable HPTLC Method for the Quantification of the Intense Sweetener Sucralose®
(2003)
This paper describes a simple and fast thin layer chromatography (TLC) method for the monitoring of the relatively new intense sweetener Sucralose® in various food matrices. The method requires little or no sample preparation to isolate or concentrate the analyte. The Sucralose® extract is separated on amino‐TLC‐plates, and the analyte is derivatized “reagent‐free” by heating the developed plate for 20 min at 190°C. Spots can be measured either in the absorption or fluorescence mode. The method allows the determination of Sucralose® at the levels of interest regarding foreseen European legislation (>50 mg/kg) with excellent repeatability (RSD = 3.4%) and recovery data (95%).
Quantification of astaxanthin in salmons by chemiluminescence and absorption after TLC separation
(2018)
Astaxanthin is a keto-carotenoid, belongs to the chemical class of terpenes and is a yellow lipid soluble compound. The compound is present in marine animals like salmons and crustacean. Its colour is due to conjugated double bonds and these double bonds are responsible for its antioxidant effect. Its antioxidant activity is ten times stronger than other carotenoids and nearly 500 fold stronger than vitamin-E. We present a new thin layer chromatography (TLC) method to measure astaxanthin on TLC-plates (Merck, 1.05554) in the visible absorption range as well as by using chemiluminescence. For separation a solvent mixture of cyclohexane and acetone (10 + 2.4, v/v) was used. The RF-value of astaxanthin is 0.14.The limit of detection in vis-absorption is 64 ng / band and the limit of quantification is 92 ng/band. In chemiluminescence the values are 90 ng / band and 115 ng/band. The method offers two independently working measurement modes on a single plate which increase the accuracy of the quantification.
Modern Franciscan Leadership
(2020)
This article combines two important areas of practical theology: Monastic rules and leadership in a cloistral organisation, using the Rule of Saint Francis as a prominent example. The aim of this research is to examine how living Christian tradition in a monastic order affects leadership today, discovering how the Rule and Franciscan spirituality impact managing a convent. The research question is answered within this inductive research applying the methodology of the ‘theology in four voices.’ Based on the results, it is possible to build a coherent leadership system based on Biblical and Franciscan sources.
The authors claim that location information of stationary ICT components can never be unclassified. They describe how swarm-mapping crowd sourcing is used by Apple and Google to worldwide harvest geo-location information on wireless access points and mobile telecommunication systems' base stations to build up gigantic databases with very exclusive access rights. After having highlighted the known technical facts, in the speculative part of this article, the authors argue how this may impact cyber deterrence strategies of states and alliances understanding the cyberspace as another domain of geostrategic relevance. The states and alliances spectrum of activities due to the potential existence of such databases may range from geopolitical negotiations by institutions understanding international affairs as their core business, mitigation approaches at a technical level, over means of cyber deterrence-by-retaliation.
Analysis of Miniaturized Printed Flexible RFID/NFC Antennas Using Different Carrier Substrates
(2020)
Antennas for Radio Frequency Identification (RFID) provide benefits for high frequencies (HF) and wireless data transmission via Near Field Communication (NFC) and many other applications. In this case, various requirements for the design of the reader and transmitter antennas must be met in order to achieve a suitable transmission quality. In this work, a miniaturized cost-effective RFID/NFC antenna for a microelectronic measurement system is designed and printed on different flexible carrier substrates using a new and low-cost Direct Ink Writing (DIW) technology. Various practical aspects such as reflection and impedance magnitude as well as the behavior of the printed RFID/NFC antennas are analyzed and compared to an identical copper-based antenna of the same size. The results are presented in this paper. Furthermore, the problems during the printing process itself on the different substrates are evaluated. The effects of the characteristics on the antenna under kink-free bending tests are examined and subsequently long-term measurements are carried out.
Time-Sensitive Networking (TSN) is the most promising time-deterministic wired communication approach for industrial applications. To extend TSN to "IEEE 802.11" wireless networks two challenging problems must be solved: synchronization and scheduling. This paper is focused on the first one. Even though a few solutions already meet the required synchronization accuracies, they are built on expensive hardware that is not suited for mass market products. While next Wi-Fi generation might support the required functionalities, this paper proposes a novel method that makes possible high-precision wireless synchronization using commercial low-cost components. With the proposed solution, a standard deviation of synchronization error of less than 500 ns can be achieved for many use cases and system loads on both CPU and network. This performance is comparable to modern wired real-time field busses, which makes the developed method a significant contribution for the extension of the TSN protocol to the wireless domain.
Propagation of acoustic waves is considered in a system consisting of two stiff quarter-spaces connected by a planar soft layer. The two quarter-spaces and the layer form a half-space with a planar surface. In a numerical study, surface waves have been found and analyzed in this system with displacements that are localized not only at the surface, but also in the soft layer. In addition to the semi-analytical finite element method, an alternative approach based on an expansion of the displacement field in a double series of Laguerre functions and Legendre polynomials has been applied.
It is shown that a number of branches of the mode spectrum can be interpreted and remarkably well described by perturbation theory, where the zero-order modes are the wedge waves guided at a rectangular edge of the stiff quarter-spaces or waves guided at the edge of a soft plate with rigid surfaces.
For elastic moduli and densities corresponding to the material combination PMMA–silicone–PMMA, at least one of the branches in the dispersion relation of surface waves trapped in the soft layer exhibits a zero-group velocity point.
Potential applications of these 1D guided surface waves in non-destructive evaluation are discussed.
Experimental Investigation of the Air Exchange Effectiveness of Push-Pull Ventilation Devices
(2020)
The increasing installation numbers of ventilation units in residential buildings are driven by legal objectives to improve their energy efficiency. The dimensioning of a ventilation system for nearly zero energy buildings is usually based on the air flow rate desired by the clients or requested by technical regulations. However, this does not necessarily lead to a system actually able to renew the air volume of the living space effectively. In recent years decentralised systems with an alternating operation mode and fairly good energy efficiencies entered the market and following question was raised: “Does this operation mode allow an efficient air renewal?” This question can be answered experimentally by performing a tracer gas analysis. In the presented study, a total of 15 preliminary tests are carried out in a climatic chamber representing a single room equipped with two push-pull devices. The tests include summer, winter and isothermal supply air conditions since this parameter variation is missing till now for push-pull devices. Further investigations are dedicated to the effect of thermal convection due to human heat dissipation on the room air flow. In dependence on these boundary conditions, the determined air exchange efficiency varies, lagging behind the expected range 0.5 < εa < 1 in almost all cases, indicating insufficient air exchange including short-circuiting. Local air exchange values suggest inhomogeneous air renewal depending on the distance to the indoor apertures as well as the temperature gradients between in- and outdoor. The tested measurement set-up is applicable for field measurements.
In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.
Diffracted waves carry high‐resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. However, the diffraction energy tends to be weak compared to the reflected energy and is also sensitive to inaccuracies in the migration velocity, making the identification of its signal challenging. In this work, we present an innovative workflow to automatically detect scattering points in the migration dip angle domain using deep learning. By taking advantage of the different kinematic properties of reflected and diffracted waves, we separate the two types of signals by migrating the seismic amplitudes to dip angle gathers using prestack depth imaging in the local angle domain. Convolutional neural networks are a class of deep learning algorithms able to learn to extract spatial information about the data in order to identify its characteristics. They have now become the method of choice to solve supervised pattern recognition problems. In this work, we use wave equation modelling to create a large and diversified dataset of synthetic examples to train a network into identifying the probable position of scattering objects in the subsurface. After giving an intuitive introduction to diffraction imaging and deep learning and discussing some of the pitfalls of the methods, we evaluate the trained network on field data and demonstrate the validity and good generalization performance of our algorithm. We successfully identify with a high‐accuracy and high‐resolution diffraction points, including those which have a low signal to noise and reflection ratio. We also show how our method allows us to quickly scan through high dimensional data consisting of several versions of a dataset migrated with a range of velocities to overcome the strong effect of incorrect migration velocity on the diffraction signal.
Extracting horizon surfaces from key reflections in a seismic image is an important step of the interpretation process. Interpreting a reflection surface in a geologically complex area is a difficult and time-consuming task, and it requires an understanding of the 3D subsurface geometry. Common methods to help automate the process are based on tracking waveforms in a local window around manual picks. Those approaches often fail when the wavelet character lacks lateral continuity or when reflections are truncated by faults. We have formulated horizon picking as a multiclass segmentation problem and solved it by supervised training of a 3D convolutional neural network. We design an efficient architecture to analyze the data over multiple scales while keeping memory and computational needs to a practical level. To allow for uncertainties in the exact location of the reflections, we use a probabilistic formulation to express the horizons position. By using a masked loss function, we give interpreters flexibility when picking the training data. Our method allows experts to interactively improve the results of the picking by fine training the network in the more complex areas. We also determine how our algorithm can be used to extend horizons to the prestack domain by following reflections across offsets planes, even in the presence of residual moveout. We validate our approach on two field data sets and show that it yields accurate results on nontrivial reflectivity while being trained from a workable amount of manually picked data. Initial training of the network takes approximately 1 h, and the fine training and prediction on a large seismic volume take a minute at most.
This work compares the performance of Bluetooth Mesh implementations on real chipsets against the ideal implementation of the specification. Measurements are taken in experimental settings and reveal non-idealities in the underlying Bluetooth Low Energy specification in real chipsets and in the implementation of Mesh, which introduces an unruly transmission as well as reception behavior. These effects lead to an impact on transmission rate, reception rate, latency, as well as a more significant impact on the average power consumption.
Printed electronics (PE) enables disruptive applications in wearables, smart sensors, and healthcare since it provides mechanical flexibility, low cost, and on-demand fabrication. The progress in PE raises trust issues in the supply chain and vulnerability to reverse engineering (RE) attacks. Recently, RE attacks on PE circuits have been successfully performed, pointing out the need for countermeasures against RE, such as camouflaging. In this article, we propose a printed camouflaged logic cell that can be inserted into PE circuits to thwart RE. The proposed cell is based on three components achieved by changing the fabrication process that exploits the additive manufacturing feature of PE. These components are optically look-alike, while their electrical behaviors are different, functioning as a transistor, short, and open. The properties of the proposed cell and standard PE cells are compared in terms of voltage swing, delay, power consumption, and area. Moreover, the proposed camouflaged cell is fabricated and characterized to prove its functionality. Furthermore, numerous camouflaged components are fabricated, and their (in)distinguishability is assessed to validate their optical similarities based on the recent RE attacks on PE. The results show that the proposed cell is a promising candidate to be utilized in camouflaging PE circuits with negligible overhead.