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High-tech running shoes and spikes ("super-footwear") are currently being debated in sports. There is direct evidence that distance running super shoes improve running economy; however, it is not well established to which extent world-class performances are affected over the range of track and road running events.
This study examined publicly available performance datasets of annual best track and road performances for evidence of potential systematic performance effects following the introduction of super footwear. The analysis was based on the 100 best performances per year for men and women in outdoor events from 2010 to 2022, provided by the world governing body of athletics (World Athletics).
We found evidence of progressing improvements in track and road running performances after the introduction of super distance running shoes in 2016 and super spike technology in 2019. This evidence is more pronounced for distances longer than 1500 m in women and longer than 5000 m in men. Women seem to benefit more from super footwear in distance running events than men.
While the observational study design limits causal inference, this study provides a database on potential systematic performance effects following the introduction of super shoes/spikes in track and road running events in world-class athletes. Further research is needed to examine the underlying mechanisms and, in particular, potential sex differences in the performance effects of super footwear.
We revisit the quantitative analysis of the ultrafast magnetoacoustic experiment in a freestanding nickel thin film by Kim and Bigot [J.-W. Kim and J.-Y. Bigot, Phys. Rev. B 95, 144422 (2017)] by applying our recently proposed approach of magnetic and acoustic eigenmode decomposition. We show that the application of our modeling to the analysis of time-resolved reflectivity measurements allows for the determination of amplitudes and lifetimes of standing perpendicular acoustic phonon resonances with unprecedented accuracy. The acoustic damping is found to scale as ∝ω2 for frequencies up to 80 GHz, and the peak amplitudes reach 10−3. The experimentally measured magnetization dynamics for different orientations of an external magnetic field agrees well with numerical solutions of magnetoelastically driven magnon harmonic oscillators. Symmetry-based selection rules for magnon-phonon interactions predicted by our modeling approach allow for the unambiguous discrimination between spatially uniform and nonuniform modes, as confirmed by comparing the resonantly enhanced magnetoelastic dynamics simultaneously measured on opposite sides of the film. Moreover, the separation of timescales for (early) rising and (late) decreasing precession amplitudes provide access to magnetic (Gilbert) and acoustic damping parameters in a single measurement.
While most ultrafast time-resolved optical pump-probe experiments in magnetic materials reveal the spatially homogeneous magnetization dynamics of ferromagnetic resonance (FMR), here we explore the magneto-elastic generation of GHz-to-THz frequency spin waves (exchange magnons). Using analytical magnon oscillator equations, we apply time-domain and frequency-domain approaches to quantify the results of ultrafast time-resolved optical pump-probe experiments in free-standing ferromagnetic thin films. Simulations show excellent agreement with the experiment, provide acoustic and magnetic (Gilbert) damping constants and highlight the role of symmetry-based selection rules in phonon-magnon interactions. The analysis is extended to hybrid multilayer structures to explore the limits of resonant phonon-magnon interactions up to THz frequencies.
The technique of laser ultrasonics perfectly meets the need for noncontact, noninvasive, nondestructive mechanical probing of nanometer- to millimeter-size samples. However, this technique is limited to the excitation of low-amplitude strains, below the threshold for optical damage of the sample. In the context of strain engineering of materials, alternative optical techniques enabling the excitation of high-amplitude strains in a nondestructive optical regime are needed. We introduce here a nondestructive method for laser-shock wave generation based on additive superposition of multiple laser-excited strain waves. This technique enables strain generation up to mechanical failure of a sample at pump laser fluences below optical ablation or melting thresholds. We demonstrate the ability to generate nonlinear surface acoustic waves (SAWs) in Nb-SrTiO3 substrates, with associated strains in the percent range and pressures up to 3 GPa at 1 kHz repetition rate and close to 10 GPa for several hundred shocks. This study paves the way for the investigation of a host of high-strain SAW-induced phenomena, including phase transitions in conventional and quantum materials, plasticity and a myriad of material failure modes, chemistry and other effects in bulk samples, thin layers, and two-dimensional materials.
The utilisation of artificial intelligence (AI) is progressively emerging as a significant mechanism for innovation in human resource management (HRM). The capacity to facilitate the transformation of employee performance across numerous responsibilities. AI development, there remains a dearth of comprehensive exploration into the potential opportunities it presents for enhancing workplace performance among employees. To bridge this gap in knowledge, the present work carried out a survey with 300 participants, utilises a fuzzy set-theoretic method that is grounded on the conceptualisation of AI, KS, and HRM. The findings of our study indicate that the exclusive adoption of AI technologies does not adequately enhance HRM engagements. In contrast, the integration of AI and KS offers a more viable HRM approach for achieving optimal performance in a dynamic digital society. This approach has the potential to enhance employees’ proficiency in executing their responsibilities and cultivate a culture of creativity inside the firm.
Purpose
Although start-ups have gained increasing scholarly attention, we lack sufficient understanding of their entrepreneurial strategic posture (ESP) in emerging economies. The purpose of this study is to examine the processes of ESP of new technology venture start-ups (NTVs) in an emerging market context.
Design/methodology/approach
In line with grounded theory guidelines and the inductive research traditions, the authors adopted a qualitative approach involving 42 in-depth semi-structured interviews with Ghanaian NTV entrepreneurs to gain a comprehensive analysis at the micro-level on the entrepreneurs' strategic posturing. A systematic procedure for data analysis was adopted.
Findings
From the authors' analysis of Ghanaian NTVs, the authors derived a three-stage model to elucidate the nature and process of ESP Phase 1 spotting and exploiting market opportunities, Phase II identifying initial advantages and Phase III ascertaining and responding to change.
Originality/value
The study contributes to advancing research on ESP by explicating the process through which informal ties and networks are utilised by NTVs and NTVs' founders to overcome extreme resource constraints and information vacuums in contexts of institutional voids. The authors depart from past studies in demonstrating how such ties can be harnessed in spotting and exploiting market opportunities by NTVs. On this basis, the paper makes original contributions to ESP theory and practice.
Purpose
Although recent literature has examined diverse measures adopted by SMEs to navigate the COVID-19 turbulence, there is a shortage of evidence on how crisis-time strategy creation behaviour and digitalization activities increase (1) sales and (2) cash flow. Thus, predicated on a novel strategy creation perspective, this inquiry aims to investigate the crisis behaviour, sales and cash flow performance of 528 SMEs in Morocco.
Design/methodology/approach
Novel links between (1) aggregate wage cuts, (2) variable operating hours, (3) deferred payment to suppliers, (4) deferred payment to tax authorities and (5) sales performance are developed and tested. A further link between sales performance and cash flow is also examined and the analysis is conducted using a non-linear structural equation modelling technique.
Findings
While there is a significant association between strategy creation behaviours and sales performance, only variable operating hours have a positive effect. Also, sales performance increases cash flow and this relationship is substantially strengthened by e-commerce digitalization and innovation.
Originality/value
Theoretically, to the best of the authors’ knowledge, this is one of the first inquiries to espouse the strategy creation view to explain SMEs' crisis-time behaviour and digitalization. For practical purposes, to supplement Moroccan SMEs' propensity to seek tax deferrals, it is argued that debt and equity support measures are also needed to boost sales performance and cash flow.
An international study summarizes the threat situation in the OT environment under the heading "Growing security threats" [1]. According to this study, attacks on automation systems are likely to increase in the future. Accordingly, an automation system must be able to protect the integrity of the transmitted information in the future. This requirement is motivated, among other things, by the fact that the network-side isolation of industrial communication systems is no longer considered sufficient as the sole protective measure. This paper uses the example of PROFINET to show how the future requirements for a real-time communication protocol can be met and how they can be derived from the IEC 62443 standard.
Polyarticulated active prostheses constitute a promising solution for upper limb amputees. The bottleneck for their adoption though, is the lack of intuitive control. In this context, machine learning algorithms based on pattern recognition from electromyographic (EMG) signals represent a great opportunity for naturally operating prosthetic devices, but their performance is strongly affected by the selection of input features. In this study, we investigated different combinations of 13 EMG-derived features obtained from EMG signals of healthy individuals performing upper limb movements and tested their performance for movement classification using an Artificial Neural Network. We found that input data (i.e., the set of input features) can be reduced by more than 50% without any loss in accuracy, while diminishing the computing time required to train the classifier. Our results indicate that input features must be properly selected in order to optimize prosthetic control.
The main focus of this chapter is the theoretical and instrumental processes that underpin densitometric methods widely used in thin-layer chromatography (TLC). Densitometric methods include UV–vis, luminescence and fluorescence optical measurements as well as infrared and Raman spectroscopic measurements. The chapter is divided in two general parts: a theoretical part and a practical part. The systems for direct radioactivity measurements and the combination of TLC with mass spectrometry are also discussed. All these systems allow measuring an intensity distribution directly on a TLC plate. We call this “in situ detection” because no analyte is removed from the plate.
The main focus of this chapter is the theoretical and instrumental processes that underpin densitometric methods widely used in thin-layer chromatography (TLC). Densitometric methods include UV–vis, luminescence, and fluorescence optical measurements as well as infrared and Raman spectroscopic measurements. The chapter is divided in two general parts: a theoretical part and a practical part. The systems for direct radioactivity measurements and the combination of TLC with mass spectrometry are also discussed. All these systems allow measuring an intensity distribution directly on a TLC plate. We call this “in situ detection” because no analyte is removed from the plate.
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.
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.
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.
State-of-the-art models for pixel-wise prediction tasks such as image restoration, image segmentation, or disparity estimation, involve several stages of data resampling, in which the resolution of feature maps is first reduced to aggregate information and then sequentially increased to generate a high-resolution output. Several previous works have investigated the effect of artifacts that are invoked during downsampling and diverse cures have been proposed that facilitate to improve prediction stability and even robustness for image classification. However, equally relevant, artifacts that arise during upsampling have been less discussed. This is significantly relevant as upsampling and downsampling approaches face fundamentally different challenges. While during downsampling, aliases and artifacts can be reduced by blurring feature maps, the emergence of fine details is crucial during upsampling. Blurring is therefore not an option and dedicated operations need to be considered. In this work, we are the first to explore the relevance of context during upsampling by employing convolutional upsampling operations with increasing kernel size while keeping the encoder unchanged. We find that increased kernel sizes can in general improve the prediction stability in tasks such as image restoration or image segmentation, while a block that allows for a combination of small-size kernels for fine details and large-size kernels for artifact removal and increased context yields the best results.
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.
Motivated by the recent trend towards the usage of larger receptive fields for more context-aware neural networks in vision applications, we aim to investigate how large these receptive fields really need to be. To facilitate such study, several challenges need to be addressed, most importantly: (i) We need to provide an effective way for models to learn large filters (potentially as large as the input data) without increasing their memory consumption during training or inference, (ii) the study of filter sizes has to be decoupled from other effects such as the network width or number of learnable parameters, and (iii) the employed convolution operation should be a plug-and-play module that can replace any conventional convolution in a Convolutional Neural Network (CNN) and allow for an efficient implementation in current frameworks. To facilitate such models, we propose to learn not spatial but frequency representations of filter weights as neural implicit functions, such that even infinitely large filters can be parameterized by only a few learnable weights. The resulting neural implicit frequency CNNs are the first models to achieve results on par with the state-of-the-art on large image classification benchmarks while executing convolutions solely in the frequency domain and can be employed within any CNN architecture. They allow us to provide an extensive analysis of the learned receptive fields. Interestingly, our analysis shows that, although the proposed networks could learn very large convolution kernels, the learned filters practically translate into well-localized and relatively small convolution kernels in the spatial domain.
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
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/.
Differentiation between human and non-human objects can increase efficiency of human-robot collaborative applications. This paper proposes to use convolutional neural networks for classifying objects in robotic applications. The body temperature of human beings is used to classify humans and to estimate the distance to the sensor. Using image classification with convolutional neural networks it is possible to detect humans in the surroundings of a robot up to five meters distance with low-cost and low-weight thermal cameras. Using transfer learning technique we trained the GoogLeNet and MobilenetV2. Results show accuracies of 99.48 % and 99.06 % respectively.
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.
Following their success in visual recognition tasks, Vision Transformers(ViTs) are being increasingly employed for image restoration. As a few recent works claim that ViTs for image classification also have better robustness properties, we investigate whether the improved adversarial robustness of ViTs extends to image restoration. We consider the recently proposed Restormer model, as well as NAFNet and the "Baseline network" which are both simplified versions of a Restormer. We use Projected Gradient Descent (PGD) and CosPGD for our robustness evaluation. Our experiments are performed on real-world images from the GoPro dataset for image deblurring. Our analysis indicates that contrary to as advocated by ViTs in image classification works, these models are highly susceptible to adversarial attacks. We attempt to find an easy fix and improve their robustness through adversarial training. While this yields a significant increase in robustness for Restormer, results on other networks are less promising. Interestingly, we find that the design choices in NAFNet and Baselines, which were based on iid performance, and not on robust generalization, seem to be at odds with the model robustness.
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.
The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.
Grundzüge der Strömungslehre
(2023)
Dieses ausgereifte Lehrbuch stellt in prägnant kurzer und mathematisch verständlicher Darstellung die strömungstechnischen Grundlagen dar. Aufgaben mit Lösungen helfen den Lernstoff richtig anzuwenden und fördern das Verständnis. Das Buch eignet sich zur Begleitung und Vertiefung der Vorlesungen über Strömungslehre sowie zum Selbststudium. Die vorliegende Auflage geht auf die immer größer werdende Rolle des Energiehaushalts ein und trägt damit den aktuellen Entwicklungen Rechnung. Ergänzt wurden aktuelle Übungsaufgaben der Strömungsmechanik, zahlreiche Beispiele veranschaulichen den Energiesatz.
Automation devices or automation stations (AS) take on the task of controlling, regulating, monitoring and, if necessary, optimising building systems and their system components (e.g. pumps, compressors, fans) based on recorded process variables. For this purpose, a wide range of control and regulation methods are used, starting with simple on/off controllers, through classic PID controllers, to higher-order controllers such as adaptive, model-predictive, knowledge-based or adaptive controllers.
Starting with a brief introduction to automation technology (Sect. 7.1), the chapter goes into the structure and functionality of the usual compact controllers using the application examples of solar thermal systems and heat pump systems (Sect. 7.2). Finally, the integration of system automation into a higher-level building automation system and into the building management system is described using specific application examples (Sect. 7.3).
This central book chapter now details the implementation of automation of solar domestic hot water systems, solar assisted building heating, rooms, solar cooling systems, heat pump heating systems, geothermal systems and thermally activated building component systems. Hydraulic and automation diagrams are used to explain how the automation of these systems works. A detailed insight into the engineering and technical interrelationships involved in the use of these systems, as well as the use of simulation tools, enables effective control and regulation. System characteristic curves and systematic procedures support the automation engineer in his tasks.
Renewable energy sources such as solar radiation, geothermal heat and ambient heat are available for energy conversion. With the help of special converters, these resources can be put to use. These include solar collectors, geothermal probes and chillers. They collect the energy and convert it to a temperature level high enough to be suitable for heat purposes. In the case of refrigeration machines, a distinction is made between electrically and thermally driven machines.
The use of renewable energy sources for heating and cooling in buildings today offers the best opportunities to avoid the use of fossil fuels and the associated climate-damaging emissions. However, unlike fossil fuels, renewable energy sources such as solar radiation are not available at the push of a button, but occur uncontrollably depending on weather conditions, the location of the building and the time of year. Their use is free of charge. However, complex converters and systems usually have to be installed in order to use them. These must be carefully planned and operated in order to avoid unnecessary costs and to generate the maximum possible yield. The regenerative energy systems are usually integrated into existing conventional systems. When designing the control and regulation equipment, it is crucial to design the automation of the systems in such a way that primarily renewable energy sources are used and the share of fossil energy sources is minimized.
This textbook helps use regenerative systems for heating and cooling effectively. Integration and automation schemes provide a quick overview. Practical examples clearly show standard solutions for the integration of regenerative energy sources. For the 2nd edition, improvements have been made to the text and illustrations, and references to standards have been updated. Control questions at the end of the main chapters serve to consolidate the understanding of the content.
Public educational institutions are increasingly confronted with a decline in the number of applicants, which is why competition between colleges and universities is also intensifying. For this reason, it is important to position oneself as an institution in order to be perceived by the various target groups and to differentiate oneself from the competition. In this context, the brand and thus its perception and impact play a decisive role, especially in view of the desired communication of the institution's own values and its self-image, the brand identity. To this end, emotions serve as an approach to creating positive stimulation and brand loyalty.
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.
In this study, circular economy (CE) relevance in Germany will be discussed based on LinkedIn readily available data. LinkedIn company profiles located in Germany with ‘circular economy’ in their description or any other field were selected and used as a data source to analyze their CE relation. Overall, 514 German companies were analyzed in reference to the 15 German regions they belong. Most companies are located in the federal state of Berlin (126), followed by North Rhine-Westphalia (96) and Bavaria (77). In terms of the industry sector, they are self-classified to environmental services (64), management consulting (50), renewables & environment (33), research (31), and computer software (18) etc. Regarding their employees with LinkedIn profiles, 22,621 people are affiliated with these companies, ranging from one to 7,877. All examined companies have a total of 819,632 followers on LinkedIn, ranging from none to 88,167. An increase in CE-related companies was recorded in 13 of the 16 federal states of Germany over a one-year period. This work provides essential insights into the increasing relevance and trends of the circular economy in German enterprises and will help conduct further national studies with readily available data from LinkedIn.
Human interaction frequently includes decision-making processes during which interactants call on verbal and non-verbal resources to manage the flow of interaction. In 2017, Stevanovic et al. carried out pioneering work, analyzing the unfolding of moment-by-moment dynamics by investigating the behavioral matching during search and decision-making phases. By studying the similarities in the participant's body sway during a conversation task in Finnish, the authors showed higher behavioral matching during decision phases than during search phases. The purpose of this research was to investigate the whole-body sway and its coordination during joint search and decision-making phases as a replication of the study by Stevanovic et al. (2017) but based on a German population. Overall, 12 dyads participated in this study and were asked to decide on 8 adjectives, starting with a pre-defined letter, to describe a fictional character. During this joint-decision task (duration: 206.46 ± 116.08 s), body sway of both interactants was measured using a 3D motion capture system and center of mass (COM) accelerations were computed. Matching of body sway was calculated using a windowed cross correlation (WCC) of the COM accelerations. A total of 101 search and 101 decision phases were identified for the 12 dyads. Significant higher COM accelerations (5.4*10−3 vs. 3.7*10−3 mm/s2, p < 0.001) and WCC coefficients (0.47 vs. 0.45, p = 0.043) were found during decision-making phases than during search phases. The results suggest that body sway is one of the resources humans use to communicate the arrival at a joint decision. These findings contribute to a better understanding of interpersonal coordination from a human movement science perspective.
Femtosecond (fs) time-resolved magneto-optics is applied to investigate laser-excited ultrafast dynamics of one-dimensional nickel gratings on fused silica and silicon substrates for a wide range of periodicities Λ = 400–1500 nm. Multiple surface acoustic modes with frequencies up to a few tens of GHz are generated. Nanoscale acoustic wavelengths Λ/n have been identified as nth-spatial harmonics of Rayleigh surface acoustic wave (SAW) and surface skimming longitudinal wave (SSLW), with acoustic frequencies and lifetimes being in agreement with theoretical calculations. Resonant magnetoelastic excitation of the ferromagnetic resonance (FMR) by SAW’s third spatial harmonic, and, most interestingly fingerprints of the parametric resonance at 1/2 SAW frequency have been observed. Numerical solutions of Landau–Lifshitz–Gilbert (LLG) equation magnetoelastically driven by complex polychromatic acoustic fields quantitatively reproduce all resonances at once. Thus, our results provide a solid experimental and theoretical base for a quantitative understanding of ultrafast fs-laser-driven magnetoacoustics and tailoring the magnetic-grating-based metasurfaces at the nanoscale.
Die Erfindung betrifft in einem ersten Aspekt eine Vorrichtung zur transkutanen Aufbringung eines elektrischen Stimulationsreizes auf ein Ohr. Die Vorrichtung umfasst einen Schaltungsträger, mindestens zwei Elektroden sowie eine Steuerungseinheit, wobei die Steuerungseinheit dazu konfiguriert ist, anhand von Stimulationsparametern ein elektrisches Stimulationssignal an den Elektroden zu erzeugen. Dabei ist die Vorrichtung, insbesondere eine Oberfläche des Schaltungsträgers der Vorrichtung, auf eine anatomische Form eines Ohres angepasst, sodass Elektroden auf der Oberfläche des Schaltungsträgers aufgebracht sind und ausgewählte Bereiche des Ohres kontaktieren Die Vorrichtung ist dadurch kennzeichnet, dass diese weiterhin einen Sensor zur Erkennung mindestens eines physiologischen Parameter umfasst und eine Steuerungseinheit dazu konfiguriert ist, anhand des mindestens einen physiologischen Parameters die Stimulationsparameter für den Stimulationsreiz anzupassen.In einem weiteren Aspekt betrifft die Erfindung ein Verfahren zur Herstellung der erfindungsgemäßen Vorrichtung.
Die Erfindung betrifft ein Verfahren zum Maximieren der von einer analogen Entropiequelle abgeleiteten Entropie, wobei das Verfahren folgende Schritte aufweist:- Bereitstellen von Eingabedaten für die analoge Entropiequelle (2);- Erzeugen von Rückgabewerten durch die analoge Entropiequelle basierend auf den Eingabedaten (3); und- Gruppieren der Rückgabewerte, wobei das Gruppieren der Rückgabewerte ein Anwenden von Versätzen auf Rückgabewerte aufweist (4).
Bei vielen Schulungen, Unterrichten und Weiterbildungen kommen Präsentationen zum Einsatz, um ausbildungsrelevante Inhalte zu vermitteln. Oft sind diese jedoch nicht interessant und zielführend gestaltet, was sich z. B. durch ein Übermaß an Text auszeichnet. Die Autoren stellen alternativ eine visualisierte Aufbereitung von Inhalten vor. Ziel ist es, komplexe Sachverhalte als einfache Bilder und Skizzen komprimiert darzustellen. Mit Hilfe der vorgestellten Methoden können beispielsweise Übungen effizienter vorbereitet, Einsätze übersichtlich erfasst, aber auch alltägliche Situationen vereinfacht kommuniziert werden.
Sustainable Production
(2023)
Visual programming languages (VPL) let users develop software programs by combining visual program elements, like lists of objects, loops or conditional statements rather than by specifying them textually.
Humanoid robots programming is a very attractive and motivating application domain for students, especially for programming beginners. Humanoid robots are constructed in such a way that they mimic the human body by using actuators that perform like muscles. Typically, a humanoid robot consists of sensors and actuators, i.e. torso, a head, two arms, and two legs, though some humanoid robots may replicate only part of the body, for example, from the waist up. In some cases, humanoid robots are equipped with heads designed to replicate additional human facial features such as eyes. Additional sensors are needed by a robot to gather information about the conditions of the environment to allow the robot to make necessary decisions about its position or certain actions that the situation requires, e.g. an arm movement or an open/close hand action. Other examples for sensor are reflective infrared sensors used to detect objects in proximity.
In this work, we introduce a use-case centered approach based on sensors and actors of a robot and a workflow model to visually describe the sequence of actions including conditional actions or concurrent actions. We provide an in-depth discussion of a new VPL based teaching method for programming humanoid robots based on VPLs. Open research challenges, limits and perspectives for further development of our teaching approach are discussed as well.
Sensors and actuators enable creation of context-aware applications in which applications can discover and take advantage of contextual information, such as user location, nearby people and objects. In this work, we use a general context definition, which can be applied to various devices, e.g., robots and mobile devices. Developing context-based software applications is considered as one of the most challenging application domains due to the sensors and actuators as part of a device. We introduce a new development approach for context-based applications by using use-case descriptions and Visual Programming Languages (VPL). The introduction of web-based VPLs, such as Scratch and Snap, has reinvigorated the usefulness of VPLs. We provide an in-depth discussion of our new VPL based method, a step by step development process to enable development of context-based applications. Two case studies illustrate how to apply our approach to different problem domains: Context-based mobile apps and context-based humanoid robot applications.
The main advantage of mobile context-aware applications is to provide effective and tailored services by considering the environmental context, such as location, time, nearby objects and other data, and adapting their functionality according to the changing situations in the context information without explicit user interaction. The idea behind Location-Based Services (LBS) and Object-Based Services (OBS) is to offer fully-customizable services for user needs according to the location or the objects in a mobile user's vicinity. However, developing mobile context-aware software applications is considered as one of the most challenging application domains due to the built-in sensors as part of a mobile device. Visual Programming Languages (VPL) and hybrid visual programming languages are considered to be innovative approaches to address the inherent complexity of developing programs. The key contribution of our new development approach for location and object-based mobile applications is a use case driven development approach based on use case templates and visual code templates to enable even programming beginners to create context-aware mobile applications. An example of the use of the development approach is presented and open research challenges and perspectives for further development of our approach are formulated.
Due to globalization and the resulting increase in competition on the market, products must be produced more and more cheaply, especially in series production, because buyers expect new variants or even completely new products in ever shorter cycles. Injection molding is the most important production process for manufacturing plastic components in large quantities. However, the conventional production of a mold is extremely time-consuming and costly, which creates a contradiction to the fast pace of the market. Additive tooling is an area of application of additive manufacturing, which in the field of injection molding is preferably used for the prototype production of mold inserts. This allows injection molding tools to be produced faster and more cheaply than through the subtractive manufacturing of metal tools. Material Jetting processes using polymers (MJT-UV/P), also called Polyjet Modeling (PJM), have a great potential for use in additive tooling. Due to the poorer mechanical and thermal properties compared to conventional mold insert materials, e.g. steel or aluminum, the previously used design principles cannot be applied. Accordingly, new design guidelines are necessary, which are developed in this paper. The necessary information is obtained with the help of a systematic literature research. The design guidelines are mapped in a uniform design guide, which is structured according to the design process of injection molds. The guidelines do not only refer to the constructive design of the injection mold or the polymer mold insert, but to the entire design process and describe the four phases of planning, conception, development and realization. Particular attention is paid to the special geometric designs of a polymer mold insert and the thermomechanical properties of the mold insert materials. As a result, design guidelines are available that are adapted to the special requirements of additive tooling of molds inserts made of plastics for injection molding.
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.
Günter Knieps hat das Forschungsgebiet der Netzökonomie in Deutschland maßgeblich geprägt. Ein in seinen Forschungsarbeiten immer wiederkehrendes Thema ist die Frage nach der richtigen Balance zwischen Wettbewerb und Regulierung in Netzsektoren. Unter den vielen wissenschaftlichen Beiträgen, die Günter Knieps bislang vorgelegt hat, genießt ein Beitrag einen besonderen Stellenwert: sein im August 1997 in der Zeitschrift Kyklos erschienener Aufsatz „Phasing out Sector-Specific Regulation in Competitive Telecommunications“. Der 25. Jahrestag des Erscheinens dieses Aufsatzes wurde von der Herausgeberin und den Herausgebern des vorliegenden Sammelbandes zum Anlass genommen, den Versuch zu unternehmen, das wissenschaftliche Werk und das Wirken von Günter Knieps als Forscher und Hochschullehrer mit einer Festschrift zu würdigen. Mit Beiträgen von (in der Reihenfolge der Kapitel): Johannes M. Bauer, Falk von Bornstaedt, Manfred J. Holler & Florian Rupp, Hans-Ulrich Küpper, Kay Mitusch, Friedrich Schneider, Viktor J. Vanberg, Achim Wambach, Bernhard Wieland und Patrick Zenhäusern sowie einem Geleitwort von Carl Christian von Weizsäcker.
One of the most important questions about smart metering systems for the end users is their data privacy and security. Indeed, smart metering systems provide a lot of advantages for distribution system operators (DSO), but functionalities offered to users of existing smart meters are still limited and society is becoming increasingly critical. Smart metering systems are accused of interfering with personal rights and privacy, providing unclear tariff regulations which not sufficiently encourage households to manage their electricity consumption in advance. In the specific field of smart grids, data security appears to be a necessary condition for consumer confidence without which they will not be able to give their consent to the collection and use of personal data concerning them.
Precisely synchronized communication is a major precondition for many industrial applications. At the same time, hardware cost and power consumption need to be kept as low as possible in the Internet of Things (IoT) paradigm. While many wired solutions on the market achieve these requirements, wireless alternatives are an interesting field for research and development. This article presents a novel IEEE802.11n/ac wireless solution, exhibiting several advantages over state-of-the-art competitors. It is based on a market-available wireless System on a Chip with modified low-level communication firmware combined with a low-cost field-programmable gate array. By achieving submicrosecond synchronization accuracy, our solution outperforms the precision of low-cost products by almost four orders of magnitude. Based on inexpensive hardware, the presented wireless module is up to 20 times cheaper than software-defined-radio solutions with comparable timing accuracy. Moreover, it consumes three to five times less power. To back up our claims, we report data that we collected with a high sampling rate (2000 samples per second) during an extended measurement campaign of more than 120 h, which makes our experimental results far more representative than others reported in the literature. Additional support is provided by the size of the testbed we used during the experiments, composed of a hybrid network with nine nodes divided into two independent wireless segments connected by a wired backbone. In conclusion, we believe that our novel Industrial IoT module architecture will have a significant impact on the future technological development of high-precision time-synchronized communication for the cost-sensitive industrial IoT market.
Artificial Intelligence (AI) can potentially transform many aspects of modern society in various ways, including automation of tasks, personalization of products and services, diagnosis of diseases and their treatment, transportation, safety, and security in public spaces, etc. Recently, AI technology has been transforming the financial industry, offering new ways to analyse data and automate processes, reduce costs, increase efficiency, and provide more personalized services to customers. However, it also raised important ethical and regulatory questions that need to be addressed by the industry and society as a whole. The aim of the Erasmus+ project Transversal Skills in Applied Artificial Intelligence - TSAAI (KA220-HED - Cooperation Partnerships in higher education) has been to establish a training platform that will incorporate teaching guidelines based on a curriculum covering different areas of application of AI technology. In this work, we will focus on applying AI models in the financial and insurance sectors.
Printed electronics can add value to existing products by providing new smart functionalities, such as sensing elements over large-areas on flexible or non-conformal surfaces. Here we present a hardware concept and prototype for a thinned ASIC integrated with an inkjet-printed temperature sensor alongside in-built additional security and unique identification features. The hybrid system exploits the advantages of inkjet-printable platinum-based sensors, physically unclonable function circuits and a fluorescent particle-based coating as a tamper protection layer.
PROFINET Security: A Look on Selected Concepts for Secure Communication in the Automation Domain
(2023)
We provide a brief overview of the cryptographic security extensions for PROFINET, as defined and specified by PROFIBUS & PROFINET International (PI). These come in three hierarchically defined Security Classes, called Security Class 1,2 and 3. Security Class 1 provides basic security improvements with moderate implementation impact on PROFINET components. Security Classes 2 and 3, in contrast, introduce an integrated cryptographic protection of PROFINET communication. We first highlight and discuss the security features that the PROFINET specification offers for future PROFINET products. Then, as our main focus, we take a closer look at some of the technical challenges that were faced during the conceptualization and design of Security Class 2 and 3 features. In particular, we elaborate on how secure application relations between PROFINET components are established and how a disruption-free availability of a secure communication channel is guaranteed despite the need to refresh cryptographic keys regularly. The authors are members of the PI Working Group CB/PG10 Security.
Wireless communication networks are crucial for enabling megatrends like the Internet of Things (IoT) and Industry 4.0. However, testing these networks can be challenging due to the complex network topology and RF characteristics, requiring a multitude of scenarios to be tested. To address this challenge, the authors developed and extended an automated testbed called Automated Physical TestBed (APTB). This testbed provides the means to conduct controlled tests, analyze coexistence, emulate multiple propagation paths, and model dependable channel conditions. Additionally, the platform supports test automation to facilitate efficient and systematic experimentation. This paper describes the extended architecture, implementation, and performance evaluation of the APTB testbed. The APTB testbed provides a reliable and efficient solution for testing wireless communication networks under various scenarios. The implementation and performance verification of the testbed demonstrate its effectiveness and usefulness for researchers and industry practitioners.
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.
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.
Fused Filament Fabrication (FFF) is a widespread additive manufacturing technology, mostly in the field of printable polymers. The use of filaments filled with metal particles for the manufacture of metallic parts by FFF presents specific challenges regarding debinding and sintering. For aluminium and its alloys, the sintering temperature range overlaps with the temperature range of thermal decomposition of many commonly used “backbone” polymers, which provide stability to the green parts. Moreover, the high oxygen affinity of aluminium necessitates the use of special sintering regimes and alloying strategies. Therefore, it is challenging to achieve both low porosity and low levels of oxygen and carbon impurities at the same time. Feedstocks compatible with the special requirements of aluminium alloys were developed. We present results on the investigation of debinding/sintering regimes by Fourier Transform Infrared spectroscopy (FTIR) based In-Situ Process Gas Analysis and discuss optimized thermal treatment strategies for Al-based FFF.
A smart energy concept was designed and implemented for a cluster of 5 existing multi-family houses, which combines heat pumps, photovoltaic (PV) modules and combined heat and power units (CHP) to achieve energy- and cost-efficient operation. Measurement results of the first year of operation show that the local power generation by PV modules and CHP unit has a positive effect on the electrical self-sufficiency by reducing electricity import from the grid. In winter, when the CHP unit operates continuously for long periods, the entire electricity for the heat pump and 91 % of the total electricity demand of the neighborhood are supplied locally. In summer, only 53 % is generated within the neighborhood. The use of a specifically developed energy management system EMS is intended to further increase this share. CO2 emissions for heating and electricity of the neighborhood are evaluated and amount to 18.4 kg/(m2a). Compared to the previous energy system consisting of gas boilers (29.1 kg/(m2a)), savings of 37 % are achieved with electricity consumption from the grid being reduced by 65 %. In the second construction stage, an additional heat pump, CHP unit and PV modules will be added. The measurement results indicate that the final district energy system is likely to achieve the ambitious CO2 reduction goal of -50% and further increase the self-sufficiency of the district.
This book constitutes the proceedings of the 23rd International TRIZ Future Conference on Towards AI-Aided Invention and Innovation, TFC 2023, which was held in Offenburg, Germany, during September 12–14, 2023. The event was sponsored by IFIP WG 5.4.
The 43 full papers presented in this book were carefully reviewed and selected from 80 submissions. The papers are divided into the following topical sections: AI and TRIZ; sustainable development; general vision of TRIZ; TRIZ impact in society; and TRIZ case studies.
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.
Der vorliegende Leitfaden entstand im Rahmen der wissenschaftlichen Querspange »LowEx-Bestand Analyse« des thematischen Projektverbunds »LowEx-Konzepte für die Wärmeversorgung von Mehrfamilien-Bestandsgebäuden (LowEx-Bestand)« zusammen. In diesem Verbund arbeiteten die drei Forschungsinstitute Fraunhofer ISE, KIT und Universität Freiburg (INATECH) mit Herstellern von Heizungs- und Lüftungstechnik und mit Unternehmen der Wohnungswirtschaft zusammen. Gemeinsam wurden Lösungen entwickelt, analysiert und demonstriert, die den effizienten Einsatz von Wärmepumpen, Wärmeübergabesystemen und Lüftungssystemen bei der energetischen Modernisierung von Mehrfamiliengebäuden zum Ziel haben.
In der Studie "Technisch-wissenschaftliche Analyse zur Energieeffizienz unterschiedlicher Trinkwasser-Erwärmungssysteme im Vergleich" im Auftrag der Viega GmbH & Co. KG werden verschiedene Trinkwasser-Erwärmungssysteme hinsichtlich ihrer Energieeffizienz in Wärmepumpensystemen vergleichend untersucht. Neben Aufbau und Parametrierung eines Simulationsmodells sowie Integration von Lastreihen nach Norm umfasst die Studie eine detaillierte Abbildung aller untersuchten Systeme. Dabei liegt ein Schwerpunkt auf der Einordnung des Energieeinsparpotenzials durch eine Warmwassertemperaturreduktion mit dem Viega AVS Trinkwasser Management System. Die untersuchten Varianten sind: Referenzsystem 1: Durchflusstrinkwassererwärmer DTE (1 stufig) mit Rücklaufeinschichtung. System 2: Viega DTE (2 stufig). System 3: Viega AVS Trinkwasser Management System mit DTE (2 stufig) und Ultrafiltrationsmodul im Zirkulationsrücklauf UFC. System 4: Wohnungsstation, 4-Leiter-System. System 5: Wohnungsstation, 2-Leiter-System. System 6: Elektrischer Durchlauferhitzer. Die Studie ergab, dass sich bei Einsatz einer Niedertemperatur-Wärmepumpe mit maximaler Vorlauftemperatur von 58 °C das Viega AVS System mit DTE und UFC, dezentrale elektrische Durchlauferhitzer sowie das 4-Leiter-System bei einer Trinkwassertemperatur von 45°C im Vergleich als energetisch am besten erweisen. Bei einer Wärmepumpe mit einer höheren maximalen Vorlauftemperatur von 64 °C kann auch das 4-Leiter-System bei einer Trinkwassertemperatur von 50°C sinnvoll eingesetzt werden. Die Ergebnisse zeigten auch, dass je höher die durch die Wärmepumpe bereitgestellte Temperatur (maximale Vorlauftemperatur), desto besser lassen sich auch die anderen Systeme einsetzen, da sich dadurch der Einsatz des Backup-Systems minimieren lässt. Das Viega Aqua VIP System mit Temperaturabsenkung schneidet im Vergleich sehr gut hinsichtlich des Einsatzes der Endenergie und der zu erreichenden Jahresarbeitszahl ab. Der Einsatz dieses Systems in Kombination mit einer Wärmepumpe bietet Potenzial für den Einsatz erneuerbarer Energien.
LowEx-Konzepte für die Wärmeversorgung von Mehrfamilien-Bestandsgebäuden ("LowEx-Bestand Analyse")
(2023)
Der vorliegende Abschlussbericht fasst die Ergebnisse der wissenschaftlichen Querspange »LowEx-Bestand Analyse« des thematischen Projektverbunds »LowEx-Konzepte für die Wärmeversorgung von Mehrfamilien-Bestandsgebäuden (LowEx-Bestand)« zusammen. In diesem Verbund arbeiteten drei Forschungsinstitute mit Herstellern von Heizungs- und Lüftungstechnik und mit Unternehmen der Wohnungswirtschaft zusammen. Gemeinsam wurden Lösungen entwickelt, analysiert und demonstriert, die den effizienten Einsatz von Wärmepumpen, Wärmeübergabesystemen und Lüftungssystemen bei der energetischen Modernisierung von Mehrfamiliengebäuden zum Ziel haben. LowEx-Systeme arbeiten durch geringe Temperaturdifferenzen zwischen Heizmedium und Nutzwärmebesonders effizient. Wärmepumpen haben dabei erhebliches Potenzial zur Absenkung der spezifischen CO2-Emissionen bei der Wärmebereitstellung. Für die energetische Modernisierung von Mehrfamiliengebäuden ist der Einsatz solcher Systeme mit besonderen Herausforderungen und Anforderungen an die Übergabe der Raumwärme, die Warmwasserbereitung und die Nutzung von Umweltwärme verbunden. Diese Herausforderungen werden in LowEx-Bestand adressiert.
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.
Optimization of energetic refurbishment roadmaps for multi-family buildings utilizing heat pumps
(2023)
A novel methodology for calculating optimized refurbishment roadmaps is developed in this paper. The aim of the roadmaps is to determine when and how should which component of the building envelope and heat generation system be refurbished to achieve the lowest net present value. The integrated optimization approach couples a particle swarm optimization algorithm with a dynamic building simulation of the building envelope and the heat supply system. Due to a free selection of implementation times and refurbishment depth, the optimization method achieves the lowest net present value and high CO2 reduction and is therefore an important contribution to achieve climate neutrality in the building stock.
The method is exemplarily applied to a multi-family house built in 1970. In comparison to a standard refurbishment roadmap, cost savings of 6–16 % and CO2 savings of 6–59 % are possible. The sensitivity of the refurbishment roadmap measures is analyzed on the basis of a parametric analysis. Robust optimization results are obtained with a mean refurbishment level of approx. 50 kWh/m2/a of the building envelope. The preferred heat generation system is a bivalent brine-heat pump system with a share of 70 % of the heat load being covered by the electric heat pump.
Elektrische Wärmepumpen sind eine Schlüsseltechnologie für klimafreundliche Gebäude. In Mehrfamilienhäusern ist ihr Einsatz noch eine Herausforderung und entsprechend wenig verbreitet. Im Rahmen des Verbundprojekts "HEAVEN" haben Forschende nun ein Mehrquellen-Wärmepumpensystem entwickelt, das an die Anforderungen größerer Wohngebäude angepasst ist. Getestet wurde es im Rahmen des Verbundprojekts "Smartes Quartier Durlach" in einem Karlsruher Gebäude. Daten zum ersten Betriebsjahr liegen nun vor.
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Am 1. Juli 2022 trafen sich im Rahmen des Abschlusskolloquiums des Projekts ACA-Modes rund 60 Teilnehmende aus Forschung, Lehre und Industrie zu einer internationalen Konferenz an der Hochschule Offenburg. Hier wurden die Projektergebnisse rund um die erfolgreiche Implementierung modellprädiktiver Regelstrategien vorgestellt, aktuelle Fragestellungen diskutiert und Entwicklungspfade hin zu einem netzdienlichen Betrieb von Energieverbundsystemen skizziert.
Heat pumps play a central role in decarbonizing the heat supply of buildings. However, in this article, implementing heat pumps in existing buildings, a significant challenge is still presented due to high temperature requirements. In this article, a systematic analysis of the effects of heat source temperatures, maximum heat pump condenser temperatures, and system temperatures on the seasonal performance of heat pump (HP) systems is presented. The quantitative performance analysis encompasses over 50 heat pumps installed in residential buildings, revealing correlations between the building characteristics, observed temperatures, and heat pump type. The performance of an HP system retrofitted to a 30-dwelling multifamily building is presented in more detail. The bivalent HP system combines air and ground as heat sources and achieves a seasonal performance factor of 3.25 with a share of the gas boiler of 27% in its first year of operation. In these findings, the technical feasibility of retrofitting heat pumps is demonstrated in existing buildings and insights are provided into overcoming the challenges associated with high temperature requirements.
Wärmepumpen sind eine Schlüsseltechnologie der Wärmewende. Durch die Nutzbarmachung von Umweltwärme und den Antrieb mit Elektrizität, die zunehmend aus erneuerbaren Energien gewonnen wird, kann die CO2-Intensität der Wärmeversorgung gesenkt werden. Eine Herausforderung besteht in der Anwendung in größeren Mehrfamilienbestandsgebäuden. Lösungsansätze und beispielhafte Umsetzungen werden hierzu vorgestellt.
Currently, immersive technologies are enjoying great popularity. This trend is reflected in technological advances and the emergence of new products for the mass market, such as augmented reality glasses. The range of applications for immersive technologies is growing with more efficient and affordable technologies and student adoption. Especially in education, the use will improve existing learning methods. Immersive application use visual, audio and haptic sensors to fully engage the user in a virtual environment. This impression is reinforced with the help of realistic visualizations and the opportunity for interaction. In particular, Augmented reality is characterized by a high degree of integration between reality and the inserted virtual objects. An augmented interactive simulation for the determination of the specific charge of an electron will be used as an example to demonstrate how such immersion can be created for users. A virtual Helmholtz coil is used to measure and calculate the e/m constant. The voltage at the cathode for generating the electron beam, but also the voltage of the homogeneous magnetic field for deflecting the electron beam, can be variably controlled by haptic user input. Based on these voltages, an immersive virtual electron beam is calculated and visualized. In this paper, the authors present the conceptual steps of this immersive application and address the challenges associated with designing and developing an augmented and interactive simulation.
Redesigning a curriculum for teaching media technology is a major challenge. Up-to-date teaching and learning concepts are necessary that meet the constant technological progress and prepare students specifically for their professional life. Teaching and studying should be characterized by a student-oriented teaching and learning culture. In order to achieve this goal, consistent evaluation is essential. The aim of the evaluation concept presented here is to generate structured information regarding the quality of content-related, didactic and organizational aspects of teaching. The exchange of opinions between students and lecturers should be encouraged in order to continuously improve the teaching and learning processes.
The paper will focus on the activities of the International Year of Light and Optical Technologies 2015 (IYL) with their impact in life, science, art, culture, education and outreach as well as the importance in promoting the objectives for sustainable development. It describes our activities carried out in the run-up to or during the IYL, as well as reports on the generic projects that led to the success of the IYL. The success of the IYL is illustrated by examples and statistics. Relating to the potential and success of the IYL, the impact and the genesis of the International Day of Light (IDL) is presented. Impressions from the opening ceremony of the IYL in Paris at UNESCO headquarters and the Inaugural Ceremony of the IDL will then be covered. A second focus is placed on the interdisciplinary media projects realized by the students of our university dedicated to these events. Finally, an analysis of the impact and legacy of IYL and IDL will be presented.
In recent times, 5G has found applications in several public as well as private networks. There is a growing need to make it compatible with diverse services without compromising security. Current security options for authenticating devices into a home network are 5G Authentication and Key Agreement (5G-AKA) and Extensible Authentication Protocol (EAP)-AKA'. However, for specific use cases such as private networks, more customizable and convenient authentication mechanisms are required. The current mobile networks use authentication based only on SIM cards, but as 5G is being applied in fields like IIoT and automation, even in Non-Public-Networks (NPNs), there is a need for a simpler method of authentication. Certificate-based authentication is one such mechanism that is passwordless and works solely on the information present in the digital certificate that the user holds. The paper suggests an authentication mechanism that performs certificate-based mutual authentication between the UE and the Home network. The proposed concept identifies both the user and network with digital certificates and intends to carry out primary authentication with the help of it. In this work we conduct a study on presently available authentication protocols for 5G networks, both theoretically and experimentally in hardware as well as virtual environments. On the basis of the analysis a series of proposed steps for certificate primary authentication are presented.
The Transport Layer Security protocol is a widespread cryptographic protocol designed to provide secure communication over insecure networks by providing authenticity, integrity, and confidentiality. As a first step, in the TLS Handshake Protocol a common master secret is negotiated. In many configurations, this step makes considerable use of asymmetric cryptographic algorithms. It seems to be a prevalent assumption that the use of such asymmetric cryptographic algorithms is unsuitable for resource-constrained devices. Therefore, the work at hand analyzes the runtime performance of the TLS vl.2 session establishments on an embedded ARM Cortex-M4 platform. We measure the execution time to generate and parse session establishment messages for the client and server sides. In particular, we study the impact of different elliptic curves used for the ephemeral Diffie-Hellman key exchange and the impact of different lengths and subject public key algorithms of certification paths. Our analysis shows that the use of asymmetric cryptographic algorithms is well possible on resource-constrained devices, if carefully chosen and well implemented. This allows the use of the well-proven TLS protocol also for applications from the (Industrial) Internet of Things, including Fieldbus communication.
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.
The often-occurring short-term orders of manufactured products require a high machine availability. This requirement increases the importance of predictive maintenance solutions for bearings used in machines. There are, among others, hybrid solutions that rely on a physical model. For their usage, knowing the different degradation stages of bearings is essential. This research analyzes the underlying failure mechanisms of these stages theoretically and in a practical example of the well-known FEMTO dataset used for the IEEE PHM 2012 Data Challenge to provide this knowledge. In addition, it shows for which use cases the usage of low-frequency accelerometers is sufficient. The analysis provides that the degradation stages toward the end of the bearing life can also be detected with low-frequency accelerometers. Further, the importance of high-frequency accelerometers to detect bearing faults in early degradation stages is pointed out. These aspects have not been paid attention to by industry and research until now, despite providing a considerable cost-saving potential.
As cyber-attacks and functional safety requirements increase in Operational Technology (OT), implementing security measures becomes crucial. The IEC/IEEE 60802 draft standard addresses the security convergence in Time-Sensitive Networks (TSN) for industrial automation.We present the standard’s security architecture and its goals to establish end-to-end security with resource access authorization in OT systems. We compare the standard to our abstract technology-independent model for the management of cryptographic credentials during the lifecycles of OT systems. Additionally, we implemented the processes, mechanisms, and protocols needed for IEC/IEEE 60802 and extended the architecture with public key infrastructure (PKI) functionalities to support complete security management processes.
The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps. We describe a novel approach, in which both steps - detection and classification - are executed in one task through a deep neural network. Therefore, training data is not annotated by hand, but manufactured artificially from the underlying forms and yet existing datasets. It can be demonstrated that this single-task approach is superior in comparison to the state-of-the-art two task approach. The current study focuses on hand-written Latin letters and employs the EMNIST data set. However, limitations were identified with this data set, necessitating further customization. Finally, an overall recognition rate of 88.28% was attained on real data obtained from a written exam.
Training deep neural networks using backpropagation is very memory and computationally intensive. This makes it difficult to run on-device learning or fine-tune neural networks on tiny, embedded devices such as low-power micro-controller units (MCUs). Sparse backpropagation algorithms try to reduce the computational load of on-device learning by training only a subset of the weights and biases. Existing approaches use a static number of weights to train. A poor choice of this so-called backpropagation ratio limits either the computational gain or can lead to severe accuracy losses. In this paper we present TinyProp, the first sparse backpropagation method that dynamically adapts the back-propagation ratio during on-device training for each training step. TinyProp induces a small calculation overhead to sort the elements of the gradient, which does not significantly impact the computational gains. TinyProp works particularly well on fine-tuning trained networks on MCUs, which is a typical use case for embedded applications. For typical datasets from three datasets MNIST, DCASE2020 and CIFAR10, we are 5 times faster compared to non-sparse training with an accuracy loss of on average 1%. On average, TinyProp is 2.9 times faster than existing, static sparse backpropagation algorithms and the accuracy loss is reduced on average by 6 % compared to a typical static setting of the back-propagation ratio.
This paper presents a system that uses a multi-stage AI analysis method for determining the condition and status of bicycle paths using machine learning methods. The approach for analyzing bicycle paths includes three stages of analysis: detection of the road surface, investigation of the condition of the bicycle paths, and identification of substrate characteristics. In this study, we focus on the first stage of the analysis. This approach employs a low-threshold data collection method using smartphone-generated video data for image recognition, in order to automatically capture and classify surface condition and status.
For the analysis convolutional neural networks (CNN) are employed. CNNs have proven to be effective in image recognition tasks and are particularly well-suited for analyzing the surface condition of bicycle paths, as they can identify patterns and features in images. By training the CNN on a large dataset of images with known surface conditions, the network can learn to identify common features and patterns and reliably classify them.
The results of the analysis are then displayed on digital maps and can be utilized in areas such as bicycle logistics, route planning, and maintenance. This can improve safety and comfort for cyclists while promoting cycling as a mode of transportation. It can also assist authorities in maintaining and optimizing bicycle paths, leading to more sustainable and efficient transportation system.
In this paper we present the concept of the "KI-Labor Südbaden" to support regional companies in the use of AI technologies. The approach is based on the "Periodic Table of AI" and is extended with both new dimensions for sustainability, and the impact of AI on the working environment. It is illustrated on the basis of three real-world use cases: 1. The detection of humans with lowresolution infrared (IR) images for collaborative robotics; 2. The use of machine data from specifically designed vehicles; 3. State-of-the-art Large Language Models (LLMs) applied to internal company documents. We explain the use cases, thereby demonstrating how to apply the Periodic Table of AI to structure AI applications.
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.
Die Erfindung betrifft ein 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, wobei in der Mitte einer Schaltperiode (Tp) keine Symmetrie erzeugt wird.
Die Erfindung betrifft ein Verfahren zum Betrieb eines batterieelektrischen Fahrzeugs mit einer elektrischen Maschine zum Antrieb des Fahrzeugs und einem Inverter (1) zum Ansteuern eine Stators (2) der elektrischen Maschine, wobei der Inverter (1) eine dreiphasige Brückenschaltung mit einer Anzahl von als Halbleiter ausgebildeten Schaltern (3) umfasst, wobei im Inverter (1) und/oder in der elektrischen Maschine entstehende Verluste zum Heizen eines Innenraums des Fahrzeugs und/oder zum Temperieren einer Batterie und/oder zum Temperieren von Getriebeöl verwendet werden, wobei während des Stillstands des Fahrzeugs ein von einem Permanentmagneten der elektrischen Maschine verursachter Permanentmagnetfluss durch Einstellen einer nichtdrehmomentbildenden Statorstromkomponente (Id) in Höhe des negativen Quotienten aus einem Statorfluss (&psgr;PM) und einer d-Komponente einer Statorinduktivität (Ld) so stark geschwächt wird, dass der magnetische Fluss kompensiert wird, wobei ein sehr hochfrequenter Wechselstrom als drehmomentbildende Statorstromkomponente (Iq) eingestellt wird.
Die Erfindung betrifft ein Verfahren zum Betrieb eines batterieelektrischen Fahrzeugs mit einer elektrischen Maschine zum Antrieb des Fahrzeugs und einem Inverter (1) zum Ansteuern eines Stators (2) der elektrischen Maschine, wobei der Inverter (1) eine dreiphasige Brückenschaltung mit einer Anzahl von als Halbleiter ausgebildeten Schaltern (3) umfasst, wobei im Inverter (1) und/oder in der elektrischen Maschine entstehende Verluste zum Heizen eines Innenraums des Fahrzeugs und/oder zum Temperieren einer Batterie und/oder zum Temperieren von Getriebeöl verwendet werden, wobei eine als Wechselstrom ausgebildete nichtdrehmomentbildende Statorstromkomponente (Id) in die elektrische Maschine eingeprägt wird, wobei im Stillstand eine drehmomentbildende Statorstromkomponente (Iq) zu Null geregelt wird, wobei im Fahrbetrieb ein Kompensationsstrom als drehmomentbildende Statorstromkomponente (Iq) eingeprägt wird, der ein durch die Variation der nichtdrehmomentbildenden Statorstromkomponente (Id) entstehendes Drehmoment kompensiert.
This study focuses on the autonomous navigation and mapping of indoor environments using a drone equipped only with a monocular camera and height measurement sensors. A visual SLAM algorithm was employed to generate a preliminary map of the environment and to determine the drone's position within the map. A deep neural network was utilized to generate a depth image from the monocular camera's input, which was subsequently transformed into a point cloud to be projected into the map. By aligning the depth point cloud with the map, 3D occupancy grid maps were constructed by using ray tracing techniques to get a precise depiction of obstacles and the surroundings. Due to the absence of IMU data from the low-cost drone for the SLAM algorithm, the created maps are inherently unscaled. However, preliminary tests with relative navigation in unscaled maps have revealed potential accuracy issues, which can only be overcome by incorporating additional information from the given sensors for scale estimation.
Modern industrial production is heavily dependent on efficient workflow processes and automation. The steady flow of raw materials as well as the separation of vital parts and semi-finished products are at the core of these automated procedures. Commonly used systems for this work are bowl feeders, which separate the parts and material by a combination of mechanical vibration and friction. The production of these tools, especially the design of the ramping spiral, is delicate and time-consuming work, as the shape, slope, and material must be carefully adjusted for the corresponding parts. In this work, we propose an automated approach, making use of optimization procedures from artificial intelligence, to design the spiral ramps of the bowl feeders. Therefore, the whole system and considered parts are physically simulated and the optimized geometry is subsequently exported into a CAD system for the actual building, respectively printing. The employment of evolutionary optimization gives the need to develop a mathematical model for the whole setup and find an efficient representation of integral features.
Im Automobilbau bietet der Einsatz der Multimaterialbauweise ein signifikantes Potenzial zur Gewichtsreduktion. Zugleich erfordert diese Bauweise eine große Anzahl von Fügeverfahren für die Verbindung der unterschiedlichen Werkstoffe und Werkstoffklassen. Dabei muss eine Vielzahl an konstruktiven und materialseitigen Anforderungen berücksichtigt werden. Um in diesem Auswahlprozess den Aspekt des Leichtbaus beim Fügeverfahren selbst systematisch zu integrieren, wurde eine Methodik entwickelt, welche die Fügeverfahren im Hinblick auf ihr jeweiliges Leichtbaupotenzial bewertet.
Landing heel first has been associated with elevated external knee abduction moments (KAM), thereby potentially increasing the risk of sustaining a non-contact ACL injury. Apart from the foot strike angle, knee valgus angle (VAL) and vertical center of mass velocity at initial ground contact (IC) have been associated with increased KAM in females across different sidestep cuts. While real-time biofeedback training has been proven effective for gait retraining [4], the highly dynamic, non-cyclical nature of cutting maneuvers makes real-time feedback unsuitable and alternative approaches necessary. This study aimed at assessing the efficacy of immediate software-aided feedback on cutting technique in reducing KAM during handball-specific cutting maneuvers.
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.
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.
Encapsulant-free N.I.C.E. modules have strong ecological advantages compared to conventional laminated modules but suffer generally from lower electrical performance. Via long-term outdoor monitoring of fullsize industrial modules of both types with identical solar cells, we investigated if the performance difference remains constant over time and which parameters influence its value. After assessing about a full year’s data, two obvious levers for N.I.C.E. optimization are identified: The usage of textured glass and transparent adhesives on the module rear side. Also, the performance loss could be alleviated using tracking systems due to lower AOI values. Our measurements show additionally that N.I.C.E. module surfaces are in average about 2.5°C cooler compared to laminated modules. With these findings, we lay out a roadmap to reduce today’s LIV gap of about 5%rel by different optimizations.