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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.
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.
The aim of this paper is to identify indicators at country level that could prove useful in improving the effectiveness of fraud detection in European Structural and Investment Funds. We analyse data for 454 funds, belonging to the period 2014-2020, from the 28 countries that were members of the European Union in 2014. Explanatory results suggest the convenience of tracking funds, especially in countries with higher GDP and higher transparency levels, and the lesser relevance of the number of irregularities for countries with higher GDP and those receiving larger funds. Fraud and fraud detection rates in individual funds vary significantly across states. Federal states, such as the Federal Republic of Germany, are comparatively successful in detecting fraud in EU funds.
Batteries typically consist of multiple individual cells connected in series. Here we demonstrate single-cell state of charge (SOC) and state of health (SOH) diagnosis in a 24 V class lithium-ion battery. To this goal, we introduce and apply a novel, highly efficient algorithm based on a voltage-controlled model (VCM). The battery, consisting of eight single cells, is cycled over a duration of five months under a simple cycling protocol between 20 % and 100 % SOC. The cell-to-cell standard deviations obtained with the novel algorithm were 1.25 SOC-% and 1.07 SOH-% at beginning of cycling. A cell-averaged capacity loss of 9.9 % after five months cycling was observed. While the accuracy of single-cell SOC estimation was limited (probably owed to the flat voltage characteristics of the lithium iron phosphate, LFP, chemistry investigated here), single-cell SOH estimation showed a high accuracy (2.09 SOH-% mean absolute error compared to laboratory reference tests). Because the algorithm does not require observers, filters, or neural networks, it is computationally very efficient (three seconds analysis time for the complete data set consisting of eight cells with approx. 780.000 measurement points per cell).
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.
Verfahren zum Betrieb eines batterieelektrischen Fahrzeugs mit einer elektrischen Maschine zum Antrieb des Fahrzeugs und einem Inverter (1) zum Ansteuern der elektrischen Maschine, wobei der Inverter (1) eine dreiphasige Brückenschaltung mit einer Anzahl von als Halbleiter ausgebildeten Schaltern (3) umfasst, wobei im Inverter (1) entstehende Verluste zum Heizen eines Innenraums des Fahrzeugs und/oder zum Temperieren einer Batterie und/oder zum Temperieren von Getriebeöl verwendet werden, wobei der Inverter (1) mittels Raumzeigermodulation gesteuert wird, wobei ein nicht-optimales Schaltverhalten des Inverters (1) herbeigeführt wird, indem nicht optimale Spannungs-Raumzeiger (e, eu, ev, ew, e1, e2, -e1, -e2) eingestellt werden, wobei eine Skalierung der Spannungs-Raumzeiger (e, e1, e2) über die Schaltung von Nullspannungsvektoren, die je nach zeitlichem Anteil die Spannung reduzieren, oder durch Zuhilfenahme eines jeweils gegenüberliegenden Spannungs-Raumzeigers (-e1, -e2) erfolgt, so dass eine Schaltfolge mit einer maximalen Anzahl von Schaltzyklen realisiert wird, dadurch gekennzeichnet, dass in der Mitte einer Schaltperiode (Tp) keine Symmetrie erzeugt wird.
An in-depth study of U-net for seismic data conditioning: Multiple removal by moveout discrimination
(2024)
Seismic processing often involves suppressing multiples that are an inherent component of collected seismic data. Elaborate multiple prediction and subtraction schemes such as surface-related multiple removal have become standard in industry workflows. In cases of limited spatial sampling, low signal-to-noise ratio, or conservative subtraction of the predicted multiples, the processed data frequently suffer from residual multiples. To tackle these artifacts in the postmigration domain, practitioners often rely on Radon transform-based algorithms. However, such traditional approaches are both time-consuming and parameter dependent, making them relatively complex. In this work, we present a deep learning-based alternative that provides competitive results, while reducing the complexity of its usage, and, hence simplifying its applicability. Our proposed model demonstrates excellent performance when applied to complex field data, despite it being exclusively trained on synthetic data. Furthermore, extensive experiments show that our method can preserve the inherent characteristics of the data, avoiding undesired oversmoothed results, while removing the multiples from seismic offset or angle gathers. Finally, we conduct an in-depth analysis of the model, where we pinpoint the effects of the main hyperparameters on real data inference, and we probabilistically assess its performance from a Bayesian perspective. In this study, we put particular emphasis on helping the user reveal the inner workings of the neural network and attempt to unbox the model.
A novel peptidyl-lys metalloendopeptidase (Tc-LysN) from Tramates coccinea was recombinantly expressed in Komagataella phaffii using the native pro-protein sequence. The peptidase was secreted into the culture broth as zymogen (~38 kDa) and mature enzyme (~19.8 kDa) simultaneously. The mature Tc-LysN was purified to homogeneity with a single step anion-exchange chromatography at pH 7.2. N-terminal sequencing using TMTpro Zero and mass spectrometry of the mature Tc-LysN indicated that the pro-peptide was cleaved between the amino acid positions 184 and 185 at the Kex2 cleavage site present in the native pro-protein sequence. The pH optimum of Tc-LysN was determined to be 5.0 while it maintained ≥60% activity between pH values 4.5—7.5 and ≥30% activity between pH values 8.5—10.0, indicating its broad applicability. The temperature maximum of Tc-LysN was determined to be 60 °C. After 18 h of incubation at 80 °C, Tc-LysN still retained ~20% activity. Organic solvents such as methanol and acetonitrile, at concentrations as high as 40% (v/v), were found to enhance Tc-LysN’s activity up to ~100% and ~50%, respectively. Tc-LysN’s thermostability, ability to withstand up to 8 M urea, tolerance to high concentrations of organic solvents, and an acidic pH optimum make it a viable candidate to be employed in proteomics workflows in which alkaline conditions might pose a challenge. The nano-LC-MS/MS analysis revealed bovine serum albumin (BSA)’s sequence coverage of 84% using Tc-LysN which was comparable to the sequence coverage of 90% by trypsin peptides.
Hintergrund
In diesem Artikel wird ein Überblick und Vergleich der am häufigsten verwendeten zementierten Hüftschäfte, gruppiert in die verschiedenen Schafttypen und Zementmanteldicken, gegeben, um zu sehen, welche Kombination gut abschneidet.
Methodik
Aus dem Endoprothesenregister Deutschland wurden die Revisionsraten zementierter Schaftarten kategorisiert und die Revisionsraten von 3 und 5 Jahren erfasst und analysiert. Für die Recherche lag die Konzentration auf den Schäften Exeter, C‑Stem, MS-30, Excia, Bicontact, Charnley, Müller Geradschaft, Twinsys, Corail, Avenir, Quadra und dem Lubinus SP II. Ein wichtiger Aspekt lag darin, welcher Schaft favorisiert implantiert wird und welche Zementiertechnik in Hinblick auf die geplante Zementmanteldicke angewendet wird. Um einen Trend in der zementierten Hüftendoprothetik herauszufinden, wurden zusätzlich die Daten des dänischen, schwedischen, norwegischen, schweizerischen, neuseeländischen, englischen und australischen Endoprothesenregister verglichen.
Ergebnisse und Schlussfolgerung
Die meisten Länder nutzen zementierte Prothesen nach dem Kraftschlussprinzip (Exeter, MS30, C‑Stem etc.) oder dem Formschlussprinzip (Charnley, Excia, Bicontact), welche mit einer Zementmanteldicke von 2–4 mm implantiert werden. Jedoch hat sich in Deutschland und der Schweiz ein Trend zur Line-to-Line-Technik, mit einer geplanten Zementmanteldicke von 1 mm (Twinsys, Corail, Avenir, Quadra) aufgezeigt, dem Prinzip der Müller-Geradschaft-Prothese und der Kerboul-Charnley-Prothese folgend, auch wenn diese an sich als „french paradoxon“ postuliert werden. In den EPRD-5-Jahres-Ergebnissen scheinen die neueren Line-to-Line-Prothesen etwas schlechter abzuschneiden. Die besten Ergebnisse erzielt der „MS 30“ in Deutschland und der „Exeter“ in England. Hierbei handelt es sich um polierte Geradschäfte mit Zentraliser und Subsidence-Raum an der Spitze mit einem 2–4 mm Zementmantel in guter Zementiertechnik.
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.
Photovoltaic-heat pump (PV-HP) combinations with battery and energy management systems are becoming increasingly popular due to their ability to increase the autarchy and utilization of self-generated PV electricity. This trend is driven by the ongoing electrification of the heating sector and the growing disparity between growing electricity costs and reducing feed-in tariffs in Germany. Smart control strategies can be employed to control and optimize the heat pump operation to achieve higher self-consumption of PV electricity. This work presents the evaluation results of a smart-grid ready controlled PV-HP-battery system in a single-family household in Germany, using 1-minute-high-resolution field measurement data. Within 12 months evaluation period, a self-consumption of 43% was determined. The solar fraction of the HP amounts to 36%, enabled also due to higher set temperatures for space heating and domestic hot water production. Accordingly, the SPF decreases by 4.0% the space heating and by 5.7% in the domestic hot water mode. The combined seasonal performance factor for the heat pump system increases from 4.2 to 6.7, when only considering the electricity taken from the grid and disregarding the locally generated electricity supplied from photovoltaic and battery units.
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.
Ultra-low-power passive telemetry systems for industrial and biomedical applications have gained much popularity lately. The reduction of the power consumption and size of the circuits poses critical challenges in ultra-low-power circuit design. Biotelemetry applications like leakage detection in silicone breast implants require low-power-consuming small-size electronics. In this doctoral thesis, the design, simulation, and measurement of a programmable mixed-signal System-on-Chip (SoC) called General Application Passive Sensor Integrated Circuit (GAPSIC) is presented. Owing to the low power consumption, GAPSIC is capable of completely passive operation. Such a batteryless passive system has lower maintenance complexity and is also free from battery-related health hazards. With a die area of 4.92 mm² and a maximum analog power consumption of 592 µW, GAPSIC has one of the best figure-of-merits compared to similar state-of-the-art SoCs. Regarding possible applications, GAPSIC can read out and digitally transmit the signals of resistive sensors for pressure or temperature measurements. Additionally, GAPSIC can measure electrocardiogram (ECG) signals and conductivity.
The design of GAPSIC complies with the International Organization for Standardization (ISO) 15693/NFC (near field communication) 5 standard for radio frequency identification (RFID), corresponding to the frequency range of 13.56 MHz. A passive transponder developed with GAPSIC comprises of an external memory storage and very few other external components, like an antenna and sensors. The passive tag antenna and reader antenna use inductive coupling for communication and energy transfer, which enables passive operation. A passive tag developed with GAPSIC can communicate with an NFC compatible smart device or an ISO 15693 RFID reader. An external memory storage contains the programmable application-specific firmware.
As a mixed-signal SoC, GAPSIC includes both analog and digital circuitries. The analog block of GAPSIC includes a power management unit, an RFID/NFC communication unit, and a sensor readout unit. The digital block includes an integrated 32-bit microcontroller, developed by the Hochschule Offenburg ASIC design center, and digital peripherals. A 16-kilobyte random-access memory and a read-only 16-kilobyte memory constitute the GAPSIC internal memory. For the fabrication of GAPSIC, one poly, six-metal 0.18 µm CMOS process is used.
The design of GAPSIC includes two stages. In the first stage, a standalone RFID/NFC frontend chip with a power management unit, an RFID/NFC communication unit, a clock regenerator unit, and a field detector unit was designed. In the second stage, the rest of the functional blocks were integrated with the blocks of the RFID/NFC frontend chip for the final integration of GAPSIC. To reduce the power consumption, conventional low-power design techniques were applied extensively like multiple power supplies, and the operation of complementary metal-oxide-semiconductor (CMOS) transistors in the sub-threshold region of operation, as well as further innovative circuit designs.
An overvoltage protection circuit, a power rectifier, a bandgap reference circuit, and two low-dropout (LDO) voltage regulators constitute the power management unit of GAPSIC. The overvoltage protection circuit uses a novel method where three stacked transistor pairs shunt the extra voltage. In the power rectifier, four rectifier units are arranged in parallel, which is a unique approach. The four parallel rectifier units provide the optimal choice in terms of voltage drop and the area required.
The communication unit is responsible for RFID/NFC communication and incorporates demodulation and load modulation circuitry. The demodulator circuit comprises of an envelope detector, a high-pass filter, and a comparator. Following a new approach, the bandgap reference circuit itself acts as the load for the envelope detector circuit, which minimizes the circuit complexity and area. For the communication between the reader and the RFID/NFC tag, amplitude-shift keying (ASK) is used to modulate signals, where the smallest modulation index can be as low as 10%. A novel technique involving a comparator with a preset offset voltage effectively demodulates the ASK signal. With an effective die area of 0.7 mm² and power consumption of 107 µW, the standalone RFID/NFC frontend chip has the best figure-of-merits compared to the state-of-the-art frontend chips reported in the relevant literature. A passive RFID/NFC tag developed with the standalone frontend chip, as well as temperature and pressure sensors demonstrate the full passive operational capability of the frontend chip. An NFC reader device using a custom-built Android-based application software reads out the sensor data from the passive tag.
The sensor readout circuit consists of a channel selector with two differential and four single-ended inputs with a programmable-gain instrumentation amplifier. The entire sensor readout part remains deactivated when not in use. The internal memory stores the measured offset voltage of the instrumentation amplifier, where a firmware code removes the offset voltage from the measured sensor signal. A 12-bit successive approximation register (SAR) type analog-to-digital-converter (ADC) based on a charge redistribution architecture converts the measured sensor data to a digital value. The digital peripherals include a serial peripheral interface, four timers, RFID/NFC interfaces, sensor readout unit interfaces, and 12-bit SAR logic.
Two sets of studies with custom-made NFC tag antennas for biomedical applications were conducted to ascertain their compatibility with GAPSIC. The first study involved the link efficiency measurements of NFC tag antennas and an NFC reader antenna with porcine tissue. In a separate experiment, the effect of a ferrite compared to air core on the antenna-coupling factor was investigated. With the ferrite core, the coupling factor increased by four times.
Among the state-of-the-art SoCs published in recent scientific articles, GAPSIC is the only passive programmable SoC with a power management unit, an RFID/NFC communication interface, a sensor readout circuit, a 12-bit SAR ADC, and an integrated 32-bit microcontroller. This doctoral research includes the preliminary study of three passive RFID tags designed with discrete components for biomedical and industrial applications like measurements of temperature, pH, conductivity, and oxygen concentration, along with leakage detection in silicone breast implants. Besides its small size and low power consumption, GAPSIC is suitable for each of the biomedical and industrial applications mentioned above due to the integrated high-performance microcontroller, the robust programmable instrumentation amplifier, and the 12-bit analog-to-digital converter. Furthermore, the simulation and measurement data show that GAPSIC is well suited for the design of a passive tag to monitor arterial blood pressure in patients experiencing Peripheral Artery Disease (PAD), which is proposed in this doctoral thesis as an exemplary application of the developed system.
In many application areas, Deep Reinforcement Learning (DRL) has led to breakthroughs. In Curriculum Learning, the Machine Learning algorithm is not randomly presented with examples, but in a meaningful order of increasing difficulty. This has been used in many application areas to further improve the results of learning systems or to reduce their learning time. Such approaches range from learning plans created manually by domain experts to those created automatically. The automated creation of learning plans is one of the biggest challenges.In this work, we investigate an approach in which a trainer learns in parallel and analogously to the student to automatically create a learning plan for the student during this Double Deep Reinforcement Learning (DDRL). Three Reward functions, Friendly, Adversarial, and Dynamic based on the learner’s reward are compared. The domain for evaluation is kicking with variable distance, direction and relative ball position in the SimSpark simulated soccer environment.As a result, Statistic Curriculum Learning (SCL) performs better than a random curriculum with respect to training time and result quality. DDRL reaches a comparable quality as the baseline and outperforms it significantly in shorter trainings in the distance-direction subdomain reducing the number of required training cycles by almost 50%.
Printed circuit boards (PCB) are a foundation of electronical devices in modern society. The fabrication of these boards requires various processes and machines. The utilisation of a robot with multiple tools can shorten the process chain compared to screen printing. In this paper a system is presented, which utilises an industrial six axis robot to manufacture
PCBs. The process flow and conversion process of the Gerber format into robot specific commands is presented. The advantages and challenges applying a robot to print circuits are discussed.
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.
Plastics are used today in many areas of the automotive, aerospace and mechanical engineering industries due to their lightweight potential and ease of processing. Additive manufacturing is applied more and more frequently, as it offers a high degree of design freedom and eliminates the need for complex tools. However, the application of additively manufactured components made of plastics have so far been limited due to their comparatively low strength. For this reason, processes that offer additional reinforcement of the plastic matrix using fibers made of high-strength materials have been developed. However, these components represent a composite of different materials produced on the basis of fossil raw materials, which are difficult to recycle and generally not biodegradable.
Therefore, this paper will explore the potential for new composite materials whose matrix consists of a bio-based plastic. In this investigation, it is assumed that the matrix is reinforced with a fibrous material made of natural fiber to significantly increase the strength. This potential material should offer a lightweight yet strong structure and be biodegradable after use under controlled conditions. Therefore, the state of the art in the use of bio-based materials in 3D printing is first presented. In order to determine the economic boundary conditions, the growth potentials for bio-based materials are analyzed. Also, the recycling prospects for bio-based plastics will also be highlighted. The greenhouse gas emissions and land use to be expected when using bio-based materials are also estimated. Finally, the degradability of the composites is discussed.
For the treatment of bone defects, biodegradable, compressive biomaterials are needed as replacements that degrade as the bone regenerates. The problem with existing materials has either been their insufficient mechanical strength or the excessive differences in their elastic modulus, leading to stress shielding and eventual failure. In this study, the compressive strength of CPC ceramics (with a layer thickness of more than 12 layers) was compared with sintered β-TCP ceramics. It was assumed that as the number of layers increased, the mechanical strength of 3D-printed scaffolds would increase toward the value of sintered ceramics. In addition, the influence of the needle inner diameter on the mechanical strength was investigated. Circular scaffolds with 20, 25, 30, and 45 layers were 3D printed using a 3D bioplotter, solidified in a water-saturated atmosphere for 3 days, and then tested for compressive strength together with a β-TCP sintered ceramic using a Zwick universal testing machine. The 3D-printed scaffolds had a compressive strength of 41.56 ± 7.12 MPa, which was significantly higher than that of the sintered ceramic (24.16 ± 4.44 MPa). The 3D-printed scaffolds with round geometry reached or exceeded the upper limit of the compressive strength of cancellous bone toward substantia compacta. In addition, CPC scaffolds exhibited more bone-like compressibility than the comparable β-TCP sintered ceramic, demonstrating that the mechanical properties of CPC scaffolds are more similar to bone than sintered β-TCP ceramics.
Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.
The mathematical representations of data in the Spherical Harmonic (SH) domain has recently regained increasing interest in the machine learning community. This technical report gives an in-depth introduction to the theoretical foundation and practical implementation of SH representations, summarizing works on rotation invariant and equivariant features, as well as convolutions and exact correlations of signals on spheres. In extension, these methods are then generalized from scalar SH representations to Vectorial Harmonics (VH), providing the same capabilities for 3d vector fields on spheres.
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.
Ziel dieser Studie ist es, den Markt von FinTech Unternehmen in Deutschland unter Berücksichtigung der beteiligten Marktkräfte darzustellen. Hierfür sollen die theoretischen Grundlagen einer Marktanalyse dargelegt und darauf aufbauend eine Marktanalyse durchgeführt werden. Betrachtet werden sollen in diesem Zusammenhang auch die rechtlichen Rahmenbedingungen, die für FinTech Unternehmen in Deutschland einschlägig sind, da auch diese Einfluss auf das Marktgeschehen haben. Ziel ist es, bestehende Rechtsgrundlagen sowie Entwürfe von künftigen Rechtsvorschriften mittels Recherche zu identifizieren und zu analysieren. Betrachtet werden sollen dabei das deutsche Recht sowie das für Deutschland als Mitgliedstaat der EU geltende EU-Recht.
Im Rahmen dieser Studie sollen Struktur und der Ablauf internationaler Carve-Out-Transaktionen dargestellt werden. Der Fokus liegt hierbei auf den rechtlichen Aspekten solcher Transaktionen. Nichtsdestotrotz, da internationale Carve-Out-Transaktionen gerade eine sehr enge und komplexe Verflechtung rechtlicher, organisatorischer und strategischer Aspekte ausmacht, soll die internationale Carve-Out-Transaktion als Ganzes beleuchtet werden.
Team description papers of magmaOffenburg are incremental in the sense that each year we address a different topic of our team and the tools around our team. In this year’s team description paper we focus on the architecture of the software. It is a main factor for being able to keep the code maintainable even after 15 years of development. We also describe how we make sure that the code follows this architecture.
Projektmanagement und mit ihm die PM-Prozesse, Methoden und Werkzeuge entwickeln sich stetig weiter, in kleinen, kaum spürbaren Schritten oder in großen unübersehbaren Veränderungen. In den letzten Jahren war der Diskurs über das Pro & Contra agiler Vorgehensweisen so allgegenwärtig, dass andere Aspekte nicht immer die notwendige Aufmerksamkeit bekamen. Erkannte Notwendigkeiten der PM-Entwicklung konnten noch nicht in spürbare Fortschritte umgewandelt werden. Einflüsse der Globalisierung und der IT, aber auch die aus der zunehmenden Forderung nach Nachhaltigkeit resultierenden Veränderungen in der Projektarbeit sollen daher genauer betrachtet werden. Ist erst einmal die Sensibilität für relevante Trends beim Projektpersonal geschaffen, rücken ein aktualisiertes Kompetenzprofil und ein erweiterter Methodenkanon in greifbare Nähe.
Die fortschreitende Digitalisierung der Schulen macht es möglich, die Lerndaten der Schülerinnen und Schüler in einer zentralen Cloud zu speichern. Die Befürworter versprechen sich davon eine bessere individuelle Förderung und fordern eine bundesweite Lösung, um möglichst viele Daten auswerten zu können. Die Gegner befürchten eine automatisierte Steuerung des Lernens.
Authentic corporate social responsibility: antecedents and effects on consumer purchase intention
(2023)
Purpose
The aim of the research is to identify the factors that create an authentic company's corporate social responsibility (CSR) engagement and to investigate whether an authentic CSR engagement influences the purchase intention. In addition, the study attempts to provide insights into the mediation role of attitude toward the company and frequency of purchase on purchase intention.
Design/methodology/approach
In this study, a theoretical framework is developed in which major antecedents of authentic CSR are identified. A specific example of a brand and its corporate social responsibility activities was used for the study. An online questionnaire was used to collect the data. To verify the hypothesis, structural equation modeling with the partial least squares method was used. A total of 240 people participated in the study.
Findings
The results of the study confirmed that CSR authenticity positively influences consumer purchase intention. Furthermore, the hypothesized impact of CSR authenticity on attitudes toward the company and frequency of purchase could be verified.
Originality/value
Although there is research on the antecedents influencing the consumer's perceived authenticity of CSR, it has not addressed differences in impact and has not presented a full picture of influencing antecedents. In addition, CSR proof as a new antecedent is investigated in the study. Moreover, research on outcomes of perceived CSR authenticity still lacks depth. The study therefore addresses this research gap by providing an extensive research framework including antecedents influencing CSR authenticity and outcomes of CSR authenticity.
Ensuring that software applications present their users the most recent version of data is not trivial. Self-adjusting computations are a technique for automatically and efficiently recomputing output data whenever some input changes.
This article describes the software architecture of a large, commercial software system built around a framework for coarse-grained self-adjusting computations in Haskell. It discusses advantages and disadvantages based on longtime experience. The article also presents a demo of the system and explains the API of the framework.
Eco-innovations in chemical processes should be designed to use raw materials, energy and water as efficiently and economically as possible to avoid the generation of hazardous waste and to conserve raw material reserves. Applying inventive principles identified in natural systems to chemical process design can help avoid secondary problems. However, the selection of nature-inspired principles to improve technological or environmental problems is very time-consuming. In addition, it is necessary to match the strongest principles with the problems to be solved. Therefore, the research paper proposes a classification and assignment of nature-inspired inventive principles to eco-parameters, eco-engineering contradictions and eco-innovation domains, taking into account environmental, technological and economic requirements. This classification will help to identify suitable principles quickly and also to realize rapid innovation. In addition, to validate the proposed classification approach, the study is illustrated with the application of nature-inspired invention principles for the development of a sustainable process design for the extraction of high-purity silicon dioxide from pyrophyllite ores. Finally, the paper defines a future research agenda in the field of nature-inspired eco-engineering in the context of AI-assisted invention and innovation.
Die Erfindung betrifft eine Vorrichtung zur biologischen Methanisierung von CO und/oder CO2 mittels methanogener Mikroorganismen durch Umsetzung von H2 und CO und/oder CO2, die eine Begasungskolonne und eine Entgasungskolonne, jeweils mit einer Bodenseite und einer der Bodenseite gegenüberliegenden oberen Seite, ein in der Begasungskolonne und der Entgasungskolonne bereitgestelltes Medium mit methanogenen Mikroorganismen, eine Zuführeinrichtung zum Zuführen eines H2 enthaltenden Gases in das Medium der Begasungskolonne, eine Abführeinrichtung zum Abführen eines CH4 enthaltenden Gases aus der Entgasungskolonne, eine Verbindungsleitung zwischen Begasungskolonne und Entgasungskolonne im Bereich der Bodenseiten, eine Pumpe zum Überführen von Medium über die Verbindungsleitung von der Begasungskolonne in die Entgasungskolonne, und eine Rückführleitung zwischen der Begasungskolonne und der Entgasungskolonne im Bereich der oberen Seiten zum Rückführen von Medium aus der Entgasungskolonne in die Begasungskolonne aufweist. Die Erfindung betrifft auch ein Verfahren zur biologischen Methanisierung von CO und/oder CO2 in einer Vorrichtung mittels methanogener Mikroorganismen als Teil eines in der Vorrichtung bereitgestellten Mediums, wobei das Medium in einem Kreislauf über eine Begasungskolonne und eine Entgasungskolonne geführt wird, wobei die Kolonnen jeweils über eine Verbindungsleitung im Bereich ihrer Bodenseiten und über eine Rückführleitung im Bereich der den Bodenseiten gegenüberliegenden oberen Seiten miteinander verbunden sind, worin das Medium sich in der Begasungskolonne absteigend und in der Entgasungskolonne aufsteigend bewegt, worin dem Medium in der Begasungskolonne ein H2 enthaltendes Gas zugeführt wird.
Assessing the robustness of deep neural networks against out-of-distribution inputs is crucial, especially in safety-critical domains like autonomous driving, but also in safety systems where malicious actors can digitally alter inputs to circumvent safety guards. However, designing effective out-of-distribution tests that encompass all possible scenarios while preserving accurate label information is a challenging task. Existing methodologies often entail a compromise between variety and constraint levels for attacks and sometimes even both. In a first step towards a more holistic robustness evaluation of image classification models, we introduce an attack method based on image solarization that is conceptually straightforward yet avoids jeopardizing the global structure of natural images independent of the intensity. Through comprehensive evaluations of multiple ImageNet models, we demonstrate the attack's capacity to degrade accuracy significantly, provided it is not integrated into the training augmentations. Interestingly, even then, no full immunity to accuracy deterioration is achieved. In other settings, the attack can often be simplified into a black-box attack with model-independent parameters. Defenses against other corruptions do not consistently extend to be effective against our specific attack.
Project website: https://github.com/paulgavrikov/adversarial_solarization
Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling
(2023)
Convolutional neural networks encode images through a sequence of convolutions, normalizations and non-linearities as well as downsampling operations into potentially strong semantic embeddings. Yet, previous work showed that even slight mistakes during sampling, leading to aliasing, can be directly attributed to the networks' lack in robustness. To address such issues and facilitate simpler and faster adversarial training, [12] recently proposed FLC pooling, a method for provably alias-free downsampling - in theory. In this work, we conduct a further analysis through the lens of signal processing and find that such current pooling methods, which address aliasing in the frequency domain, are still prone to spectral leakage artifacts. Hence, we propose aliasing and spectral artifact-free pooling, short ASAP. While only introducing a few modifications to FLC pooling, networks using ASAP as downsampling method exhibit higher native robustness against common corruptions, a property that FLC pooling was missing. ASAP also increases native robustness against adversarial attacks on high and low resolution data while maintaining similar clean accuracy or even outperforming the baseline.
Decentralized applications (dApp) have proliferated in recent years, but their long-term viability is a topic of debate. However, for dApps to be sustainable, and suitable for integration into a larger service networks, they need to attract users and promise reliable availability. Therefore, assessing their longevity is crucial. Analyzing the utilization trajectory of a service is, however, challenging due to several factors, such as demand spikes, noise, autocorrelation, and non-stationarity. In this study, we employ robust statistical techniques to identify trends in currently popular dApps. Our findings demonstrate that a significant proportion of dApps, across a range of categories, exhibit statistically significant positive overall trends, indicating that success in decentralized computing can be sustainable and transcends specific fields. However, there is also a substantial number of dApps showing negative trends, with a disproportionately high number from the decentralized finance (DeFi) category. Furthermore, a more detailed inspection of time series segments shows a clearly diminishing proportion of positive trends from mid-2021 to the present. In summary, we conclude that the dApp economy might have lost some momentum, and that there is a strong element of uncertainty regarding its future significance.
The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research [1] has shown how such detection can generally be enabled by deep learning methods, but appears to be very limited regarding the overall amount of detected vulnerabilities. We analyse to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardised LLVM Intermediate Representation. Te vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, our proposed technical approach and methodology enables an accurate detection of 23 (compared to 4 [1]) vulnerabilities.
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
(2023)
Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification.Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes.The code for our experiments is provided at https://github.com/deepfake-study/deepfake-multiLID.
As industrial networks continue to expand and connect more devices and users, they face growing security challenges such as unauthorized access and data breaches. This paper delves into the crucial role of security and trust in industrial networks and how trust management systems (TMS) can mitigate malicious access to these networks.The TMS presented in this paper leverages distributed ledger technology (blockchain) to evaluate the trustworthiness of blockchain nodes, including devices and users, and make access decisions accordingly. While this approach is applicable to blockchain, it can also be extended to other areas. This approach can help prevent malicious actors from penetrating industrial networks and causing harm. The paper also presents the results of a simulation to demonstrate the behavior of the TMS and provide insights into its effectiveness.
Erlang is a functional programming language with dynamic typing. The language offers great flexibility for destructing values through pattern matching and dynamic type tests. Erlang also comes with a type language supporting parametric polymorphism, equi-recursive types, as well as union and a limited form of intersection types. However, type signatures only serve as documentation; there is no check that a function body conforms to its signature.
Set-theoretic types and semantic subtyping fit Erlang’s feature set very well. They allow expressing nearly all constructs of its type language and provide means for statically checking type signatures. This article brings set-theoretic types to Erlang and demonstrates how existing Erlang code can be statically type checked without or with only minor modifications to the code. Further, the article formalizes the main ingredients of the type system in a small core calculus, reports on an implementation of the system, and compares it with other static type checkers for Erlang.
In recent years, predictive maintenance tasks, especially for bearings, have become increasingly important. Solutions for these use cases concentrate on the classification of faults and the estimation of the Remaining Useful Life (RUL). As of today, these solutions suffer from a lack of training samples. In addition, these solutions often require high-frequency accelerometers, incurring significant costs. To overcome these challenges, this research proposes a combined classification and RUL estimation solution based on a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. This solution relies on a hybrid feature extraction approach, making it especially appropriate for low-cost accelerometers with low sampling frequencies. In addition, it uses transfer learning to be suitable for applications with only a few training samples.
With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global level explain model behavior and on a local level explain sample predictions. We propose a visual analytics workflow to support seamless transitions between global and local explanations, focusing on attributions and counterfactuals on time series classification. In particular, we adapt local XAI techniques (attributions) that are developed for traditional datasets (images, text) to analyze time series classification, a data type that is typically less intelligible to humans. To generate a global overview, we apply local attribution methods to the data, creating explanations for the whole dataset. These explanations are projected onto two dimensions, depicting model behavior trends, strategies, and decision boundaries. To further inspect the model decision-making as well as potential data errors, a what-if analysis facilitates hypothesis generation and verification on both the global and local levels. We constantly collected and incorporated expert user feedback, as well as insights based on their domain knowledge, resulting in a tailored analysis workflow and system that tightly integrates time series transformations into explanations. Lastly, we present three use cases, verifying that our technique enables users to (1)~explore data transformations and feature relevance, (2)~identify model behavior and decision boundaries, as well as, (3)~the reason for misclassifications.
Entity Matching (EM) defines the task of learning to group objects by transferring semantic concepts from example groups (=entities) to unseen data. Despite the general availability of image data in the context of many EM-problems, most currently available EM-algorithms solely rely on (textual) meta data. In this paper, we introduce the first publicly available large-scale dataset for "visual entity matching", based on a production level use case in the retail domain. Using scanned advertisement leaflets, collected over several years from different European retailers, we provide a total of ~786k manually annotated, high resolution product images containing ~18k different individual retail products which are grouped into ~3k entities. The annotation of these product entities is based on a price comparison task, where each entity forms an equivalence class of comparable products. Following on a first baseline evaluation, we show that the proposed "visual entity matching" constitutes a novel learning problem which can not sufficiently be solved using standard image based classification and retrieval algorithms. Instead, novel approaches which allow to transfer example based visual equivalent classes to new data are needed to address the proposed problem. The aim of this paper is to provide a benchmark for such algorithms.
Information about the dataset, evaluation code and download instructions are provided under https://www.retail-786k.org/.
Following the traditional paradigm of convolutional neural networks (CNNs), modern CNNs manage to keep pace with more recent, for example transformer-based, models by not only increasing model depth and width but also the kernel size. This results in large amounts of learnable model parameters that need to be handled during training. While following the convolutional paradigm with the according spatial inductive bias, we question the significance of \emph{learned} convolution filters. In fact, our findings demonstrate that many contemporary CNN architectures can achieve high test accuracies without ever updating randomly initialized (spatial) convolution filters. Instead, simple linear combinations (implemented through efficient 1×1 convolutions) suffice to effectively recombine even random filters into expressive network operators. Furthermore, these combinations of random filters can implicitly regularize the resulting operations, mitigating overfitting and enhancing overall performance and robustness. Conversely, retaining the ability to learn filter updates can impair network performance. Lastly, although we only observe relatively small gains from learning 3×3 convolutions, the learning gains increase proportionally with kernel size, owing to the non-idealities of the independent and identically distributed (\textit{i.i.d.}) nature of default initialization techniques.
Modern CNNs are learning the weights of vast numbers of convolutional operators. In this paper, we raise the fundamental question if this is actually necessary. We show that even in the extreme case of only randomly initializing and never updating spatial filters, certain CNN architectures can be trained to surpass the accuracy of standard training. By reinterpreting the notion of pointwise ($1\times 1$) convolutions as an operator to learn linear combinations (LC) of frozen (random) spatial filters, we are able to analyze these effects and propose a generic LC convolution block that allows tuning of the linear combination rate. Empirically, we show that this approach not only allows us to reach high test accuracies on CIFAR and ImageNet but also has favorable properties regarding model robustness, generalization, sparsity, and the total number of necessary weights. Additionally, we propose a novel weight sharing mechanism, which allows sharing of a single weight tensor between all spatial convolution layers to massively reduce the number of weights.
This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.
In the framework of electro-elasticity theory and the finite element method (FEM), a model is set up for the computation of quantities in surface acoustic wave (SAW) devices accounting for nonlinear effects. These include second-order and third-order intermodulations, second and third harmonic generation and the influence of electro-acoustic nonlinearity on the frequency characteristics of SAW resonators. The model is based on perturbation theory, and requires input material constants, e.g., the elastic moduli up to fourth order for all materials involved. The model is two-dimensional, corresponding to an infinite aperture, but all three Cartesian components of the displacement and electrical fields are accounted for. The first version of the model pertains to an infinite periodic arrangement of electrodes. It is subsequently generalized to systems with a finite number of electrodes. For the latter version, a recursive algorithm is presented which is related to the cascading scheme of Plessky and Koskela and strongly reduces computation time and memory requirements. The model is applied to TC-SAW systems with copper electrodes buried in an oxide film on a LiNbO3 substrate. Results of computations are presented for the electrical current due to third-order intermodulations and the displacement field associated with the second harmonic and second-order intermodulations, generated by monochromatic input tones. The scope of this review is limited to methodological aspects with the goal to enable calculations of nonlinear quantities in SAW devices on inexpensive and easily accessible computing platforms.
Die Erfindung betrifft eine Vorrichtung zur biologischen Methanisierung von CO und/oder CO2 mittels methanogener Mikroorganismen durch Umsetzung von H2 und CO und/oder CO2, die eine Begasungskolonne und eine Entgasungskolonne, jeweils mit
einer Bodenseite und einer der Bodenseite gegenüberliegenden oberen Seite, ein in der Begasungskolonne und der Entgasungskolonne bereitgestelltes Medium mit methanogenen Mikroorganismen, eine Zuführeinrichtung zum Zuführen eines H2 enthaltenden Gases in das Medium der Begasungskolonne, wobei die Zuführeinrichtung im Bereich der Bodenseite der Begasungskolonne angeordnet ist, eine Abführeinrichtung zum Abführen eines CH4 enthaltenden Gases aus der Entgasungskolonne, eine Verbindungsleitung zwischen Begasungskolonne und Entgasungskolonne im Bereich der Bodenseiten, eine Pumpe zum Überführen von Medium über die Verbindungsleitung von der Begasungskolonne in die Entgasungskolonne, und eine Rückführleitung zwischen der Begasungskolonne und der Entgasungskolonne im Bereich der oberen Seiten zum Rückführen von Medium
aus der Entgasungskolonne in die Begasungskolonne aufweist. Die Erfindung betrifft auch ein Verfahren zur biologischen Methanisierung von CO und/oder CO2 in einer Vorrichtung mittels methanogener Mikroorganismen als Teil eines in der Vorrichtung bereitgestellten Mediums, wobei das Medium in einem Kreislauf über eine Begasungskolonne und eine Entgasungskolonne geführt wird, wobei die Kolonnen jeweils über eine Verbindungsleitung im Bereich ihrer Bodenseiten und über eine Rückführleitung im Bereich der den Bodenseiten gegenüberliegenden oberen Seiten miteinander verbunden sind, worin das Medium sich in der Begasungskolonne absteigend und in der Entgasungskolonne aufsteigend bewegt, worin dem Medium im Bereich der Bodenseite der Begasungskolonne ein H2 enthaltendes Gas zugeführt wird.
Die Erfindung betrifft eine Vorrichtung zur biologischen Methanisierung von CO und/oder CO2mittels methanogener Mikroorganismen durch Umsetzung von H2und CO und/oder CO2, die eine Begasungskolonne und eine Entgasungskolonne, jeweils mit einer Bodenseite und einer der Bodenseite gegenüberliegenden oberen Seite, ein in der Begasungskolonne und der Entgasungskolonne bereitgestelltes Medium mit methanogenen Mikroorganismen, eine Zuführeinrichtung zum Zuführen eines H2enthaltenden Gases in das Medium der Begasungskolonne, eine Abführeinrichtung zum Abführen eines CH4enthaltenden Gases aus der Entgasungskolonne, eine Verbindungsleitung zwischen Begasungskolonne und Entgasungskolonne im Bereich der Bodenseiten, eine Pumpe zum Überführen von Medium über die Verbindungsleitung von der Begasungskolonne in die Entgasungskolonne, und eine Rückführleitung zwischen der Begasungskolonne und der Entgasungskolonne im Bereich der oberen Seiten zum Rückführen von Medium aus der Entgasungskolonne in die Begasungskolonne aufweist. Die Erfindung betrifft auch ein Verfahren zur biologischen Methanisierung von CO und/oder CO2in einer Vorrichtung mittels methanogener Mikroorganismen als Teil eines in der Vorrichtung bereitgestellten Mediums, wobei das Medium in einem Kreislauf über eine Begasungskolonne und eine Entgasungskolonne geführt wird, wobei die Kolonnen jeweils über eine Verbindungsleitung im Bereich ihrer Bodenseiten und über eine Rückführleitung im Bereich der den Bodenseiten gegenüberliegenden oberen Seiten miteinander verbunden sind, worin das Medium sich in der Begasungskolonne absteigend und in der Entgasungskolonne aufsteigend bewegt, worin dem Medium in der Begasungskolonne ein H2enthaltendes Gas zugeführt wird.
Selbsttests in Lernmanagementsystemen (LMS) ermöglichen es Studierenden, den eigenen Lernfortschritt einzuschätzen. Im Gegensatz zur Einreichung und Korrektur vollständig ausformulierter Aufgabenlösungen nutzen LMS überwiegend die Eingabe der Lösung im Antwort-Auswahl-Verfahren (Single-Choice). Nach didaktischen Ansatz „Physik durch Informatik“ geben die Lernenden stattdessen ihre Aufgabenlösungen in einer Programmiersprache ins LMS ein, was eine automatisierte Rückmeldung erleichtert und das Erreichen einer höheren Kompetenzstufe fördert. Es wurden zehn LMS-Selbsttests erstellt, bei denen die Lösungen zu einer Lehrbuch-Aufgabenstellung jeweils durch Eingabe in einer Programmiersprache und von einer Kontrollgruppe im Antwort-Auswahl-Verfahren abgefragt wurden. Ergebnisse aus dem ersten Einsatz dieser Selbsttests für die Lehrveranstaltung Physik im Studiengang Biotechnologie werden vorgestellt.
Scheinselbständigkeit
(2023)
Im Rahmen dieser Untersuchung sollen Maßnahmen zur Reduzierung des Risikos von Scheinselbständigkeit für Unternehmen identifiziert und analysiert werden. Der Schwerpunkt liegt dabei auf der Erstellung einer zweiteiligen Checkliste für Unternehmen, die als Hilfsmittel bei der Beauftragung externer Drittdienstleister herangezogen werden kann.
Die Verwendung von markenbezogenen nutzer-generierten Inhalten auf den unternehmenseigenen Social-Media-Kanälen ist ein äußerst vielversprechender Ansatz im Content-Marketing. Dabei können durch die authentischen, vom Nutzer bereitgestellten Inhalte zahlreiche Kommunikationsziele erreicht werden. Hierzu gehören etwa die Verstärkung des Nutzerengagements oder aber auch die Förderung von Verkäufen. Daneben müssen allerdings auch Risiken, wie etwa rechtliche Aspekte, beachtet werden. Damit Unternehmen die Potentiale von markenbezogenen nutzer-generierten Inhalten für sich nutzen können, wird im nachstehenden Beitrag ein Strukturierungsrahmen vorgestellt. Dieser fasst die wesentlichen Aspekte dieser durchaus komplexen Thematik strukturiert zusammen. Der hier entwickelte Strukturierungsrahmen wurde durch Experteninterviews überprüft.
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.
TSN, or Time Sensitive Networking, is becoming an essential technology for integrated networks, enabling deterministic and best effort traffic to coexist on the same infrastructure. In order to properly configure, run and secure such TSN, monitoring functionality is a must. The TSN standard already has some preparations to provide such functionality and there are different methods to choose from. We implemented different methods to measure the time synchronisation accuracy between devices as a C library and compared the measurement results. Furthermore, the library has been integrated into the ControlTSN engineering framework.
Marketing and sales have high expectations of new methods such as Big Data, artificial intelligence, machine learning, and predictive analytics. But following the “garbage in—garbage out” principle, the results leave much to be desired. The reason is often insufficient quality in the underlying customer data. This article sheds light on this problem using the data quality and value pyramid as an example. The higher up the value-added pyramid the data is located, the higher its quality and the more value it generates for a company. In addition, we show how the use of monitoring systems, such as a data quality scorecard, makes data quality visible and improvements measurable. In this way, the actual value of data for companies becomes obvious and manageable.
Wirtschaftliche Krisenzeiten implizieren häufig Liquiditätsengpässe und bei kompletter Zahlungsunfähigkeit auch Insolvenzen. Das Instrument des Working Capital Management hilft bei der schnelleren Freisetzung von gebundenem Kapital. Sofern ein datengetriebenes Management unter Einsatz von Business-Analytics-Techniken und mit der dafür notwendigen technisch-organisatorischen Infrastruktur eingesetzt wird, entstehen neue Möglichkeiten von Einsichten in die Prozesslandschaft und die Optimierung von Durchlaufzeiten. Das Ziel ist der Aufbau eines Working-Capital- Analytics-Ansatzes.
Mathematik lässt sich in vielen Objekten finden. Sei es die lineare Steigung eines Handlaufs zum Schulgebäude oder die nahezu zylindrische Form einer Litfaßsäule in der Innenstadt. Das Bestreben, Schüler*innen diese Zusammenhänge entdecken zu lassen, steht im Zentrum des MathCityMap Projekts (Ludwig et al., 2013). Auf sogenannten mathematischen Wanderpfaden (bzw. Mathtrails) werden Schüler*innen durch eine App zu Mathematikaufgaben an realen Objekten bzw. in realen Situationen ihrer Umwelt geleitet. Um die Aufgaben zu lösen, werden Daten erhoben, z. B. durch Messungen oder Zählen. Entscheidend ist, dass die Aufgaben so gestellt sind, dass der Schritt der Datenbeschaffung nur vor Ort stattfinden kann und somit direkt mit dem Objekt bzw. der Situation verknüpft wird.
A report from the World Economic Forum (2019) stated loneliness as the third societal stressor in the world, mainly in western countries. Moreover, research shows that loneliness tends to be experienced more severely by young adults than other age groups (Rokach, 2000), which is the case of university students who face profound periods of loneliness when attending university in a new place (Diehl et al., 2018). Digital technology, especially mental health apps (MHapps), have been viewed as promising solutions to address this distress in universities, however, little evidence on this topic reveals uncertainty around how these resources impact individual well-being. Therefore, this research proposed to investigate how the gamified social mobile app Noneliness reduced loneliness rates and other associated mental health issues of students from a German university. As little work has focused on digital apps targeting loneliness, this project also proposed to describe and discuss the app’s design and development processes. A multimethod approach was adopted: literature review on high-efficacy MHapps design, gamification for mental health and loneliness interventions; User Experience Design and Human-centered Computing. Evaluations occurred according to the app’s development iterations, which assessed four versions (from prototype to Beta) through quantitative and qualitative studies with university students. The main results obtained regarding the design aspects were: users' preference for minimalistic interfaces; importance in maintaining privacy and establishing trust among users; students' willingness to use an online support space for emotional and educational support. Most used features were those related to group discussions, private chats and university social events. Preferred gamification elements were those that provided positive reinforcement to motivate social interactions (e.g. Points, Levels and Achievements). Results of a pilot randomized controlled trial with university students (N = 12), showed no statistically significant interactions in reducing loneliness among experimental group members (n = 7, x² = 3.500, p-value = 0.477, Cramer’s V = 0.27) who made continued use of the app for six weeks. On the other hand, the app showed effects of moderate magnitude on loneliness reduction in this group. The app also demonstrated relatively strong magnitude effects on other associated variables, such as depression and stress in the experimental group. In addition to motivating the conduct of further studies with larger samples, the findings point to a potential app effectiveness not only to reduce loneliness, but also other variables that may be associated with the distress.
Turbocharger housings in internal combustion engines are subjected to severe mechanical and thermal cyclic loads throughout their life-time or during engine testing. The combination of thermal transients and mechanical load cycling results in a complex evolution of damage, leading to thermo-mechanical fatigue (TMF) of the material. For the computational TMF life assessment of high temperature components, the DTMF model can provide reliable TMF life predictions. The model is based on a short fatigue crack growth law and uses local finite-element (FE) results to predict the number of cycles to failure for a technical crack. In engine applications, it is nowadays often acceptable to have short cracks as long as they do not propagate and cause loss of function of the component. Thus, it is necessary to predict not only potential crack locations and the corresponding number of cycles for a technical crack, but also to determine subsequent crack growth or even a possible crack arrest. In this work, a method is proposed that allows the simulation of TMF crack growth in high temperature components using FE simulations and non-linear fracture mechanics (NLFM).
A NLFM based crack growth simulation method is described. This method starts with the FE analysis of a component. In this paper, the method is demonstrated for an automotive turbocharger housing subjected to TMF loading. A transient elastic-viscoplastic FE analysis is used to simulate four heating and cooling cycles of an engine test. The stresses, inelastic strains, and temperature histories from the FEA are then used to perform TMF life predictions using the standard DTMF model. The crack position and the crack plane of critical hotspots are then identified. Simulated cracks are inserted at the hotspots. For the model demonstrated, cracks were inserted at two hotspot locations. The ΔJ integral is computed as a fracture mechanics parameter at each point along the crack-front, and the crack extension of each point is then evaluated, allowing the crack to grow iteratively. The paper concludes with a comparison of the crack growth curves for both hotspots with experimental results.
Enhancing engineering creativity with automated formulation of elementary solution principles
(2023)
The paper describes a method for the automated formulation of elementary creative stimuli for product or process design at different levels of abstraction and in different engineering domains. The experimental study evaluates the impact of structured automated idea generation on inventive thinking in engineering design and compares it with previous experimental studies in educational and industrial settings. The outlook highlights the benefits of using automated ideation in the context of AI-assisted invention and innovation.
Learning programming fundamentals is considered as one of the most challenging and complex learning activities. Some authors have proposed visual programming language (VPL) approaches to address part of the inherent complexity [1]. A visual programming language lets users develop programs by combining program elements, like loops graphically rather than by specifying them textually. Visual expressions, spatial arrangements of text and graphic symbols are used either as syntax elements or secondary notation. VPLs are normally used for educational multimedia, video games, system development, and data warehousing/business analytics purposes. For example, Scratch, a platform of Massachusetts Institute of Technology, is designed for kids and after school programs.
Design of mobile software applications is considered as one of the most challenging application domains due to the build in sensors as part of a mobile device, like GPS, camera or Near Field Communication (NFC). Sensors enable creation of context-aware mobile applications in which applications can discover and take advantage of contextual information, such as user location, nearby people and objects, and the current user activity. As a consequence, context-aware mobile applications can sense clues about the situational environment making mobile devices more intelligent, adaptive, and personalized. Such context aware mobile applications seem to be motivating and attractive case studies, especially for programming beginners (“my own first app”).
In this work, we introduce a use-case centered approach as well as clear separation of user interface design and sensor-based program development. We provide an in-depth discussion of a new VPL based teaching method, a step by step development process to enable programming beginners the creation of context aware mobile applications. Finally, we argue that addressing challenges for programming beginners by our teaching approach could make programming teaching more motivating, with an additional impact on the final software quality and scalability.
The key contributions of our study are the following:
- An overview of existing attempts to use VPL approaches for mobile applications
- A use case centered teaching approach based on a clear separation of user interface design and sensor-based program development
- A teaching case study enabling beginners a step by step creation of context-aware mobile applications based on the MIT App Inventor (a platform of Massachusetts Institute of Technology)
- Open research challenges and perspectives for further development of our teaching approach
References:
[1] Idrees, M., Aslam, F. (2022). A Comprehensive Survey and Analysis of Diverse Visual Programming Languages, VFAST Transactions on Software Engineering, 2022, Volume 10, Number 2, pp 47-60.
Neural networks have a number of shortcomings. Amongst the severest ones is the sensitivity to distribution shifts which allows models to be easily fooled into wrong predictions by small perturbations to inputs that are often imperceivable to humans and do not have to carry semantic meaning. Adversarial training poses a partial solution to address this issue by training models on worst-case perturbations. Yet, recent work has also pointed out that the reasoning in neural networks is different from humans. Humans identify objects by shape, while neural nets mainly employ texture cues. Exemplarily, a model trained on photographs will likely fail to generalize to datasets containing sketches. Interestingly, it was also shown that adversarial training seems to favorably increase the shift toward shape bias. In this work, we revisit this observation and provide an extensive analysis of this effect on various architectures, the common L_2-and L_-training, and Transformer-based models. Further, we provide a possible explanation for this phenomenon from a frequency perspective.
Seismic data processing relies on multiples attenuation to improve inversion and interpretation. Radon-based algorithms are often used for multiples and primaries discrimination. Deep learning, based on convolutional neural networks (CNNs), has shown encouraging applications for demultiple that could mitigate Radon-based challenges. In this work, we investigate new strategies to train a CNN for multiples removal based on different loss functions. We propose combined primaries and multiples labels in the loss for training a CNN to predict primaries, multiples, or both simultaneously. Moreover, we investigate two distinctive training methods for all the strategies: UNet based on minimum absolute error (L1) training, and adversarial training (GAN-UNet). We test the trained models with the different strategies and methods on 400 synthetic data. We found that training to predict multiples, including the primaries …
In 4D printing an additively manufactured component is given the ability to change its shape or function under the influence of an external stimulus. To achieve this, special smart materials are used that are able to react to external stimuli in a specific way. So far, a number of different stimuli have already been investigated and initial applications have been impressively demonstrated, such as self-folding bodies and simple grippers. However, a methodical specification for the selection of the stimuli and their implementation was not yet in the foreground of the development.
The focus of this work is therefore to develop a methodical approach with which the technology of 4DP can be used in a solution- and application-oriented manner. The developed approach is based on the conventional design methodology for product development to solve given problems in a structured way. This method is extended by specific approaches under consideration of the 4D printing and smart materials.
To illustrate the developed method, it is implemented in practice using a problem definition in the form of an application example. In this example, which represents the recovery of an object from a difficult-to-access environment, the individual functions of positioning, gripping and extraction are implemented using 4D printing. The material extrusion process is used for additive manufacturing of all components of the example. Finally, the functions are successfully tested. The developed approach offers an innovative and methodical approach to systematically solve technical complex problems using 4DP and smart materials.
Sweaty has already participated several times in RoboCup soccer competitions (Adult Size). Now the work is focused coordinating the play of two robots. Moreover, we are working on stabilizing the gait by adding additional sensor information. An ongoing work is the optimization of the control strategy by balancing between impedance and position control. By minimizing the jerk, gait and overall gameplay should improve significantly.
Enzyme‐assisted HPTLC method for the simultaneous analysis of inositol phosphates and phosphate
(2023)
Background
The analysis of myo‐inositol phosphates (InsPx) released by phytases during phytic acid degradation is challenging and time‐consuming, particularly in terms of sample preparation, isomer separation, and detection. However, a fast and robust analysis method is crucial when screening for phytases during protein engineering approaches, which result in a large number of samples, to ensure reliable identification of promising novel enzymes or target variants with improved characteristics, for example, pH range, thermal stability, and phosphate release kinetics.
Results
The simultaneous analysis of several InsPx (InsP1‐InsP4 and InsP5 + 6) as well as free phosphate was established on cellulose HPTLC plates using a buffered mobile phase. Inositol phosphates were subsequently stained using a novel enzyme‐assisted staining procedure. Immobilized InsPx were hydrolyzed by a phytase solution of Quantum® Blueliquid 5G followed by a molybdate reagent derivatization. Resulting blue zones were captured by DAD scan. The method shows good repeatability (intra‐day and intra‐lab) with maximum deviations of the Rf value of 0.01. The HPTLC method was applied to three commercially available phytases at two pH levels relevant to the gastrointestinal tract of poultry (pH 5.5 and pH 3.6) to observe their phytate degradation pattern and thus visualize their InsPx fingerprint.
Conclusion
This HPTLC method presents a semi‐high‐throughput analysis for the simultaneous analysis of phytic acid and the resulting lower inositol phosphates after its enzymatic hydrolysis and is also an effective tool to visualize the InsPx fingerprints and possible accumulations of inositol phosphates.
Die Kundenbindung ist für Publisher aus dem Pressesektor seit Jahrzehnten ein Thema, da nicht nur bei auf Wiederholkäufe angelegten Einzelverkaufstiteln, sondern gerade bei Abonnementprodukten neben der erfolgreichen konzeptionellen Ausrichtung der Produkte an den Kundenbedürfnissen der Kontakt zu den Leser*innen zunehmend an Bedeutung gewonnen hat. Produkte, die im Zeitungsbereich oft mit Tradition und Gewohnheit verbunden waren, stehen im digitalen Wettbewerbsumfeld in einem sehr viel intensiveren direkten Wettbewerb, sodass die traditionellen Marketingmaßnahmen der Kundenbindung und -gewinnung für Subscriber-Pakete, aber auch die Maßnahmen der customer centricity in der digitalen Live-Optimierung der Produkte zur entscheidenden Kompetenz werden, um mit Methoden des Content-Marketings und Engagement-Marketings oder mit gänzlich anderen Ansätzen wie Membership-Modellen die Verluste des auslaufenden klassischen Print-Abonnements zu kompensieren.
Building energy management systems (BEMSs), dedicated to sustainable buildings, may have additional duties, such as hosting efficient energy management systems (EMSs) algorithms. This duty can become crucial when operating renewable energy sources (RES) and eventual electric energy storage systems (ESSs). Sophisticated EMS approaches that aim to manage RES and ESSs in real time may need high computing capabilities that BEMSs typically cannot provide. This article addresses and validates a fuzzy logic-based EMS for the optimal management of photovoltaic (PV) systems with lead-acid ESSs using an edge computing technology. The proposed method is tested on a real smart grid prototype in comparison with a classical rule-based EMS for different weather conditions. The goal is to investigate the efficacy of islanding the building local network as a control command, along with ESS power control. The results show the implementation feasibility and performance of the fuzzy algorithm in the optimal management of ESSs in both operation modes: grid-connected and islanded modes.
Gamification wird in vielen Bereichen, die auch den Bildungssektor einschließen, zur Motivations- und Leistungssteigerung eingesetzt. Dieser Beitrag beschreibt das Design, die Umsetzung und Evaluierung eines Gamification-Konzeptes für die Vorlesung „Software Engineering" an der Hochschule Offenburg. Gamification soll nach Intention der Lehrenden eine kontinuierliche und tiefergehende Auseinandersetzung mit den Themen der Vorlesung forcieren sowie einen positiven Einfluss auf die Motivation der Studierenden haben, um den Lernprozess zu unterstützen. Zentral für das Gamification-Design sind dabei eine freiwillige Teilnahme, die Wahrnehmung der Bedeutung der Lerninhalte und ein zielorientierter Einsatz von Gamification-Elementen. Das entwickelte Konzept wurde in der Lernplattform Moodle realisiert, über drei Semester eingesetzt und parallel evaluiert. Die Ergebnisse dieser Evaluierungen zeigen, dass die Studierenden den gamifizierten Kurs intensiv und oft über das gesamte Semester nutzten und aus eigenem Antrieb eine Vielzahl von Übungen absolvierten.
In den letzten Jahren entstand ein regelrechter Hype um das Thema Kryptowährungen und sie sind in Gesellschaft, Politik und Wirtschaft kaum noch wegzudenken. Trotz der hohen Volatilität und des Risikos für Investoren werden Kryptowährungen teilweise auch als eine Alternative für herkömmliche Währungen angesehen. Daher stellt sich die Frage, ob die Einstellung zu Kryptowährungen und auch das Investitionsverhalten auf einer sorgfältigen Auseinandersetzung von Argumenten basiert. Dafür wurde die Forschungsfrage: „Basieren Kaufverhalten und Einstellung zu Kryptowährungen eher auf der intensiven Auseinandersetzung mit tatsächlichen Argumenten oder auf oberflächlichen Reizen?“ aufgestellt. Zur Untersuchung dieser wurde eine Umfrage mit 283 Teilnehmenden durchgeführt. Auf Basis theoretischer Überlegungen zur Einstellungsforschung durch das Elaboration-Likelihood-Model wurde ein Fragebogen entworfen, der den Einfluss der Elaboration auf die Einstellung und das Kaufverhalten empirisch messbar machen sollte. Durch eine Kausalanalyse mittels Strukturgleichungsmodell konnte ein teilweise signifikanter Einfluss von elaborationsbestimmenden Größen auf Einstellung und Kaufverhalten festgestellt werden. Eine Überprüfung der Gütekriterien des Fragebogens mittels explorativer und konfirmatorischer Faktoranalyse ergab in Hinsicht auf Reliabilität und Validität jedoch keine zufriedenstellenden Ergebnisse. Die Ergebnisse des Kausalmodells sollten deswegen mit Vorsicht betrachtet werden. In weiterführenden Forschungen könnte die Struktur der durch den Fragebogen erhobenen Konstrukte für Elaboration und Einstellung überarbeitet werden, um eine bessere Reliabilität und Validität zu erreichen und somit genauere Aussagen über die eigentlichen Beziehungen der Konstrukte treffen zu können.
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 performance of different continuous-time and discrete-time models of the electrical subsystem of induction machines and permanent-magnet synchronous machines as well as methods based on them for decoupling the direct and
quadrature axis components of the stator current are investigated and compared. The focus here is on inverter-fed, pulse width modulated drives when operated with a relatively large product of stator frequency and sampling time, where significant
differences between the models and decoupling methods used come to light. Recommendations for a discrete-time model to be used uniformly in the future are made, as well as statements on whether feedforward or feedback decoupling structures are better suited and whether state controllers improve decoupling measures for very steep speed ramps. Simulation studies and measurement results support the statements made above.
Künstliche Intelligenz (KI) durchdringt unser Leben immer stärker. Studierende werden im Alltag und an Hochschulen zunehmend mit KI-Anwendungen konfrontiert. An der Hochschule Offenburg werden deshalb KI-bezogene Lehrangebote curricular verankert, um Studierende im Erwerb von KI-Kompetenz zu unterstützen.
Der Beitrag stellt ein Konzept für die Entwicklung von Lehrveranstaltungen nach der Idee des pädagogischen Makings zur Förderung von KI-Kompetenz in der Hochschullehre vor. Konkretisiert wird das Konzept anhand eines Moduls zum Thema Chatbots, dessen Lehrinhalte interdisziplinär aus verschiedenen Perspektiven ausgearbeitet werden.
This thesis deals with the redesign of manufacturing systems by simulation and optimization. Material flow simulation is a common tool for solving problems in system design. Limitations are the high requirements in time and knowledge to execute simulation studies, evaluate results and solve design problems. New chances arrives with the technologies of industry 4.0 and the digital shadow, providing data for simulation. However, the methods to use production data for the redesign of production systems are not available yet. Purpose of this work is providing the methods to automate simulation from digital shadow, use simulation to optimize and solve problems in system design. Two case studies are used to support the action research approach of this work. The result of this work is a framework for the application of the digital shadow in optimization and problem-solving.
This article provides an overview of the legal framework for website marketing. The presentation of the numerous legal provisions, which are spread over several areas of law, is oriented towards business challenges and measures. After placing the website in the context of marketing, the article focuses on the legal framework relating to the establishment, design and operation of a website. If, in addition to its communication function, a website also has a sales function, i. e. in e-commerce (online trade), additional specific legal conditions must be taken into account.
Voice User Experience
(2023)
Sprachassistenten wie Alexa, Google Assistant, Siri, Cortana, Magenta und Bixby erfreuen sich dank ihrer intuitiven, schnellen und bequemen Interaktionsmöglichkeiten zunehmender Beliebtheit und bieten deshalb spannende Möglichkeiten für die Weiterentwicklung des digitalen Kundendialogs. Doch ob die Technologie wirklich breite Akzeptanz finden wird, hängt nicht nur mit ihrer technischen Qualität oder Usability zusammen. Auch die User Experience, die neben den Reaktionen der Nutzer*innen während der Anwendung auch ihre Erwartungen und Wahrnehmungen vor und nach der Anwendung umfasst, spielt eine zentrale Rolle. Die Messung der Qualität der Voice User Experience (Voice UX) ist daher von großem Interesse für die Bewertung und Optimierung von Sprachapplikationen. Die Frage, wie die Voice UX von sprachgesteuerten Systemen gemessen werden kann, ist jedoch noch offen. Aktuelle Methoden stützen sich häufig auf UX-Forschung zu grafischen Benutzeroberflächen, obwohl die sprachbasierten Interaktionsformen in der Regel weder visuell noch haptisch greifbar sind. In unserem Beitrag möchten wir den aktuellen Status quo der deutschen Voice User Experience untersuchen. Folgende Fragen stehen dabei im Mittelpunkt: Wie können Sprachanwendungen zu einem erfolgreichen Kundendialog beitragen? Welche Nutzerirritationen treten aktuell bei der Anwendung von Sprachassistenten auf? Mit welchen Methoden lässt sich die Voice User Experience messen?
The present paper addresses the research question: What recommendations for action and potential adjustments should an online magazine for beauty and fashion implement in order to make affiliate articles in these sections even more appealing to the target group and provide added value for them?
To be able to answer this research question, three hypotheses were defined and tested with using qualitative and quantitative research. The qualitative research consisted of user experience testings, where four affiliate articles in the fields of beauty and fashion were tested with 13 participants. The quantitative research involved collecting, analyzing and evaluating data from the four affiliate articles conducted with the company's real-life target group. Based on these results, recommendations for action were derived, which should not only improve the quality of the content in the future, but also increase the efficiency of the implementation of those articles.
Design Thinking wird im Wesentlichen als Methode zur Lösung komplexer Probleme und Entwicklung neuer Ideen verstanden.
Doch das mit dem Design Thinking einhergehende Mindset kann für breite und vielfältige Fragestellungen nutzbar sein und geht weit über die Methodik zur Lösung komplexer Probleme und Entwicklung neuer Ideen hinaus. Es besteht aus den Komponenten Empathie, non-lineare und lineare Denkweise, Experimentierfreude und Offenheit.
Die wissenschaftliche Auseinandersetzung mit praktischen Anwendungsbeispielen zeigt, wie durch das Design Thinking Mindset der Denkhorizont in Richtung Zukunft erweitert wird. Der Herausgeber Robert Gücker und fünf Autorinnen geben einen fundierten Einblick in das “Design Thinking Mindset”. In einem breiten Spektrum umfassen die Themen des vorliegenden Sammelbandes den Zusammenhang von Design Thinking und Unternehmenskultur, Arbeitszufriedenheit der Mitarbeitenden und Resilienz, die Möglichkeiten der Erreichung des Design Thinking Mindsets durch Workshops und gezielte Führungsentwicklungsmaßnahmen bis hin zur Betrachtung des Herausgebers, der die Metapher der qualitativen Forschung für Design Thinking vorschlägt.
Die wissenschaftliche Auseinandersetzung der fünf Autorinnen mit der Thematik erfolgt auf Grundlage von umfangreichen Literaturrecherchen und Expertenbefragungen, deren Erkenntnisse auf den Praxiseinsatz übertragen werden. Im Sammelband finden Sie damit in kompakten Beiträgen einen fundierten Überblick an Möglichkeiten und Chancen, wie Unternehmen anhand der Anwendung von Design Thinking bei ihren Mitarbeitenden und Führungskräften einen Mindshift in Richtung Zukunft schaffen und welche Rahmenbedingungen nötig sind, um zu einer resilienten Organisation mit zufriedenen Mitarbeitenden zu werden.
Die Ergebnisse der Beiträge bestätigen eine positive Wirkung in Richtung Zukunftskompetenzen, die sich aus einer Haltung ergeben, welches dem Design Thinking Mindset entspricht.
Mit praktischen Anwendungsbeispielen bietet das Buch nicht nur Personalverantwortlichen sowie Organisationsentwicklern wissenschaftlich fundiert und praxisnah neue Ansätze.
Purpose
To summarize the mechanical loading of the spine in different activities of daily living and sports.
Methods
Since the direct measurement is not feasible in sports activities, a mathematical model was applied to quantify spinal loading of more than 600 physical tasks in more than 200 athletes from several sports disciplines. The outcome is compression and torque (normalized to body weight/mass) at L4/L5.
Results
The data demonstrate high compressive forces on the lumbar spine in sport-related activities, which are much higher than forces reported in normal daily activities and work tasks. Especially ballistic jumping and landing skills yield high estimated compression at L4/L5 of more than ten times body weight. Jumping, landing, heavy lifting and weight training in sports demonstrate compression forces significantly higher than guideline recommendations for working tasks.
Conclusion
These results may help to identify acute and long-term risks of low back pain and, thus, may guide the development of preventive interventions for low back pain or injury in athletes.
Appraising the Methodological Quality of Sports Injury Video Analysis Studies: The QA-SIVAS Scale
(2023)
Background
Video analysis (VA) is commonly used in the assessment of sports injuries and has received considerable research interest. Until now, no tool has been available for the assessment of study quality. Therefore, the objective of this study was to develop and evaluate a valid instrument that reliably assesses the methodological quality of VA studies.
Methods
The Quality Appraisal for Sports Injury Video Analysis Studies (QA-SIVAS) scale was developed using a modified Delphi approach including expert consensus and pilot testing. Reliability was examined through intraclass correlation coefficient (ICC3,1) and free-marginal kappa statistics by three independent raters. Construct validity was investigated by comparing QA-SIVAS with expert ratings by using Kendall’s tau analysis. Rating time was studied by applying the scale to 21 studies and computing the mean time for rating per study article.
Results
The QA-SIVAS scale consists of an 18-item checklist addressing the study design, data source, conduct, report, and discussion of VA studies in sports injury research. Inter- and intra-rater reliability were excellent with ICCs > 0.97. Expert ratings revealed a high construct validity (0.71; p < 0.001). Mean rating time was 10 ± 2 min per article.
Conclusion
QA-SIVAS is a reliable and valid instrument that can be easily applied to sports injury research. Future studies in the field of VA should adhere to standardized methodological criteria and strict quality guidelines.
Novel approaches for the design of assistive technology controls propose the usage of eye tracking devices such as for smart wheelchairs and robotic arms. The advantages of artificial feedback, especially vibrotactile feedback, as opposed to their use in prostheses, have not been sufficiently explored. Vibrotactile feedback reduces the cognitive load on the visual and auditory channel. It provides tactile sensation, resulting in better use of assistive technologies. In this study the impact of vibration on the precision and accuracy of a head-worn eye tracking device is investigated. The presented system is suitable for further research in the field of artificial feedback. Vibration was perceivable for all participants, yet it does not produce any significant deviations in precision and accuracy.
Established robot manufacturers have developed methods to determine and optimize the accuracy of their robots. These methods vary from robot manufacturers to their competitors. Due to the lack of published data, a comparison of robot performance is difficult. The aim of this article is to find methods to evaluate important characteristics of a robot with an accurate and cost-effective setup. A laser triangulation sensor and geometric referenced spheres were used as a base to compare the robot performance.
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.