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Background
To assess the in-field walking mechanics during downhill hiking of patients with total knee arthroplasty five to 14 months after surgery and an age-matched healthy control group and relate them to the knee flexor and extensor muscle strength.
Methods
Participants walked on a predetermined hiking trail at a self-selected, comfortable pace wearing an inertial sensor system for recording the whole-body 3D kinematics. Sagittal plane hip, knee, and ankle joint angles were evaluated over the gait cycle at level walking and two different negative slopes. The concentric and eccentric lower extremity muscle strength of the knee flexors and extensors isokinetically at 50 and 120°/s were measured.
Findings
Less knee flexion angles during stance have been measured in patients in the operated limb compared to healthy controls in all conditions (level walking, moderate downhill, steep downhill). The differences increased with steepness. Muscle strength was lower in patients for both muscle groups and all measured conditions. The functional hamstrings to quadriceps ratio at 120°/sec correlated with knee angle during level and downhill walking at the moderate slope in patients, showing higher ratios with lower peak knee flexion angles.
Interpretation
The study shows that even if rehabilitation has been completed successfully and complication-free, five to 14 months after surgery, the muscular condition was still insufficient to display a normal gait pattern during downhill hiking. The muscle balance between quadriceps and hamstring muscles seems related to the persistence of a stiff knee gait pattern after knee arthroplasty. LoE: III.
Purpose
This study aims to investigate a systematic approach to the production and use of additively manufactured injection mould inserts in product development (PD) processes. For this purpose, an evaluation of the additive tooling design method (ATDM) is performed.
Design/methodology/approach
The evaluation of the ATDM is conducted within student workshops, where students develop products and validate them using AT-prototypes. The evaluation process includes the analysis of work results as well as the use of questionnaires and participant observation.
Findings
This study shows that the ATDM can be successfully used to assist in producing and using AT mould inserts to produce valid AT prototypes. As a reference for the implementation of AT in industrial PD, extracts from the work of the student project groups and suitable process parameters for prototype production are presented.
Originality/value
This paper presents the application and evaluation of a method to support AT in PD that has not yet been scientifically evaluated.
Verfahren zum Betrieb eines batterieelektrischen Fahrzeugs mit einer elektrischen Maschine zum Antrieb des Fahrzeugs und einem Inverter (1) zum Ansteuern der elektrischen Maschine, wobei der Inverter (1) eine dreiphasige Brückenschaltung mit einer Anzahl von als Halbleiter ausgebildeten Schaltern (3) umfasst, wobei im Inverter (1) entstehende Verluste zum Heizen eines Innenraums des Fahrzeugs und/oder zum Temperieren einer Batterie und/oder zum Temperieren von Getriebeöl verwendet werden, wobei der Inverter (1) mittels Raumzeigermodulation gesteuert wird, wobei ein nicht-optimales Schaltverhalten des Inverters (1) herbeigeführt wird, indem nicht optimale Spannungs-Raumzeiger (e, eu, ev, ew, e1, e2, -e1, -e2) eingestellt werden, wobei eine Skalierung der Spannungs-Raumzeiger (e, e1, e2) über die Schaltung von Nullspannungsvektoren, die je nach zeitlichem Anteil die Spannung reduzieren, oder durch Zuhilfenahme eines jeweils gegenüberliegenden Spannungs-Raumzeigers (-e1, -e2) erfolgt, so dass eine Schaltfolge mit einer maximalen Anzahl von Schaltzyklen realisiert wird, dadurch gekennzeichnet, dass in der Mitte einer Schaltperiode (Tp) keine Symmetrie erzeugt wird.
Hintergrund
In diesem Artikel wird ein Überblick und Vergleich der am häufigsten verwendeten zementierten Hüftschäfte, gruppiert in die verschiedenen Schafttypen und Zementmanteldicken, gegeben, um zu sehen, welche Kombination gut abschneidet.
Methodik
Aus dem Endoprothesenregister Deutschland wurden die Revisionsraten zementierter Schaftarten kategorisiert und die Revisionsraten von 3 und 5 Jahren erfasst und analysiert. Für die Recherche lag die Konzentration auf den Schäften Exeter, C‑Stem, MS-30, Excia, Bicontact, Charnley, Müller Geradschaft, Twinsys, Corail, Avenir, Quadra und dem Lubinus SP II. Ein wichtiger Aspekt lag darin, welcher Schaft favorisiert implantiert wird und welche Zementiertechnik in Hinblick auf die geplante Zementmanteldicke angewendet wird. Um einen Trend in der zementierten Hüftendoprothetik herauszufinden, wurden zusätzlich die Daten des dänischen, schwedischen, norwegischen, schweizerischen, neuseeländischen, englischen und australischen Endoprothesenregister verglichen.
Ergebnisse und Schlussfolgerung
Die meisten Länder nutzen zementierte Prothesen nach dem Kraftschlussprinzip (Exeter, MS30, C‑Stem etc.) oder dem Formschlussprinzip (Charnley, Excia, Bicontact), welche mit einer Zementmanteldicke von 2–4 mm implantiert werden. Jedoch hat sich in Deutschland und der Schweiz ein Trend zur Line-to-Line-Technik, mit einer geplanten Zementmanteldicke von 1 mm (Twinsys, Corail, Avenir, Quadra) aufgezeigt, dem Prinzip der Müller-Geradschaft-Prothese und der Kerboul-Charnley-Prothese folgend, auch wenn diese an sich als „french paradoxon“ postuliert werden. In den EPRD-5-Jahres-Ergebnissen scheinen die neueren Line-to-Line-Prothesen etwas schlechter abzuschneiden. Die besten Ergebnisse erzielt der „MS 30“ in Deutschland und der „Exeter“ in England. Hierbei handelt es sich um polierte Geradschäfte mit Zentraliser und Subsidence-Raum an der Spitze mit einem 2–4 mm Zementmantel in guter Zementiertechnik.
Analysing and predicting the advance rate of a tunnel boring machine (TBM) in hard rock is integral to tunnelling project planning and execution. It has been applied in the industry for several decades with varying success. Most prediction models are based on or designed for large-diameter TBMs, and much research has been conducted on related tunnelling projects. However, only a few models incorporate information from projects with an outer diameter smaller than 5 m and no penetration prediction model for pipe jacking machines exists to date. In contrast to large TBMs, small-diameter TBMs and their projects have been considered little in research. In general, they are characterised by distinctive features, including insufficient geotechnical information, sometimes rather short drive lengths, special machine designs and partially concurring lining methods like pipe jacking and segment lining. A database which covers most of the parameters mentioned above has been compiled to investigate the performance of small-diameter TBMs in hard rock. In order to provide sufficient geological and technical variance, this database contains 37 projects with 70 geotechnically homogeneous areas. Besides the technical parameters, important geotechnical data like lithological information, unconfined compressive strength, tensile strength and point load index is included and evaluated. The analysis shows that segment lining TBMs have considerably higher penetration rates in similar geological and technical settings mostly due to their design parameters. Different methodologies for predicting TBM penetration, including state-of-the-art models from the literature as well as newly derived regression and machine learning models, are discussed and deployed for backward modelling of the projects contained in the database. New ranges of application for small-diameter tunnelling in several industry-standard penetration models are presented, and new approaches for the penetration prediction of pipe jacking machines in hard rock are proposed.
This report examines exporters’ challenges and possible solutions for public intervention to promote foreign trade. Based on fieldwork conducted in Georgia, we explore which policy approaches can help to stimulate Georgian exports further. Our outcomes show that exporters face substantial barriers such as navigating complex trade regulations, lack of knowledge about target markets, trade finance gaps, as well as new export promotion programs (EPPs) in competitor countries. Other upper-middle-income countries can learn from our results that exporters can significantly benefit from a comprehensive export promotion strategy combined with an ecosystem-based “team” approach. EPPs related to awareness and capacity building in Georgia should be part of this strategy, focusing on challenges such as a lack of knowledge about trade practices and international business skills. Other EPPs must help to mitigate related market failures, as information gathering is costly, and firms have no incentive to share this information with competitors. Furthermore, targeted marketing support and customer matchmaking can answer Georgian exporters’ challenges, such as lack of market access and low sector visibility. Our results also show that public intervention through financial support and risk mitigation is essential for firms with an international orientation. The high-quality, rich outcomes provide significant value for other upper-middle-income countries by exploring the example of Georgia’s contemporary circumstances in an in-depth manner based on extensive interviews and document analysis. Limitations include that our work primarily relies on qualitative data and further research could involve a quantitative study with a diverse range of sectors.
The last decades have seen the evolution of industrial production into more sophisticated processes. The development of specialized, high-end machines has increased the importance of predictive maintenance of mechanical systems to produce high-quality goods and avoid machine breakdowns. Predictive maintenance has two main objectives: to classify the current status of a machine component and to predict the maintenance interval by estimating its remaining useful life (RUL). Nowadays, both objectives are covered by machine learning and deep learning approaches and require large training datasets that are often not available. One possible solution may be transfer learning, where the knowledge of a larger dataset is transferred to a smaller one. This thesis is primarily concerned with transfer learning for predictive maintenance for fault classification and RUL estimation. The first part presents the state-of-the-art machine learning techniques with a focus on techniques applicable to predictive maintenance tasks (Chapter 2). This is followed by a presentation of the machine tool background and current research that applies the previously explained machine learning techniques to predictive maintenance tasks (Chapter 3). One novelty of this thesis is that it introduces a new intermediate domain that represents data by focusing on the relevant information to allow the data to be used on different domains without losing relevant information (Chapter 4). The proposed solution is optimized for rotating elements. Therefore, the presented intermediate domain creates different layers by focusing on the fault frequencies of the rotating elements. Another novelty of this thesis is its semi and unsupervised transfer learning-based fault classification approach for different component types under different process conditions (Chapter 5). It is based on the intermediate domain utilized by a convolutional neural network (CNN). In addition, a novel unsupervised transfer learning loss function is presented based on the maximum mean discrepancy (MMD), one of the state-of-the-art algorithms. It extends the MMD by considering the intermediate domain layers; therefore, it is called layered maximum mean discrepancy (LMMD). Another novelty is an RUL estimation transfer learning approach for different component types based on the data of accelerometers with low sampling rates (Chapter 6). It applies the feature extraction concepts of the classification approach: the presented intermediate domain and the convolutional layers. The features are then used as input for a long short-term memory (LSTM) network. The transfer learning is based on fixed feature extraction, where the trained convolutional layers are taken over. Only the LSTM network has to be trained again. The intermediate domain supports this transfer learning type, as it should be similar for different component types. In addition, it enables the practical usage of accelerometers with low sampling rates during transfer learning, which is an absolute novelty. All presented novelties are validated in detailed case studies using the example of bearings (Chapter 7). In doing so, their superiority over state-of-the-art approaches is demonstrated.
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.
Socially assistive robots (SARs) are becoming more prevalent in everyday life, emphasizing the need to make them socially acceptable and aligned with users' expectations. Robots' appearance impacts users' behaviors and attitudes towards them. Therefore, product designers choose visual qualities to give the robot a character and to imply its functionality and personality. In this work, we sought to investigate the effect of cultural differences on Israeli and German designers' perceptions of SARs' roles and appearance in four different contexts: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. The key insight is that although Israeli and German designers share similar perceptions of visual qualities for most of the robotics roles, we found differences in the perception of the COVID-19 officer robot's role and, by that, its most suitable visual design. This work indicates that context and culture play a role in users' perceptions and expectations; therefore, they should be taken into account when designing new SARs for diverse contexts.
With the expansion of IoT devices in many aspects of our life, the security of such systems has become an important challenge. Unlike conventional computer systems, any IoT security solution should consider the constraints of these systems such as computational capability, memory, connectivity, and power consumption limitations. Physical Unclonable Functions (PUFs) with their special characteristics were introduced to satisfy the security needs while respecting the mentioned constraints. They exploit the uncontrollable and reproducible variations of the underlying component for security applications such as identification, authentication, and communication security. Since IoT devices are typically low cost, it is important to reuse existing elements in their hardware (for instance sensors, ADCs, etc.) instead of adding extra costs for the PUF hardware. Micro-electromechanical system (MEMS) devices are widely used in IoT systems as sensors and actuators. In this thesis, a comprehensive study of the potential application of MEMS devices as PUF primitives is provided. MEMS PUF leverages the uncontrollable variations in the parameters of MEMS elements to derive secure keys for cryptographic applications. Experimental and simulation results show that our proposed MEMS PUFs are capable of generating enough entropy for a complex key generation, while their responses show low fluctuations in different environmental conditions.
Keeping in mind that the PUF responses 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 the second part of this thesis, we elaborate on different key generation schemes and their advantages and drawbacks. We propose the PUF output positioning (POP) and integer linear programming (ILP) methods, which are novel methods for grouping the PUF outputs in order to maximize the extracted entropy. To implement these methods, the key enrollment and key generation algorithms are presented. The proposed methods are then evaluated by applying on the responses of the MEMS PUF, where it can be practically shown that the proposed method outperforms other existing PUF key generation methods.
The final part of this thesis is dedicated to the application of the MEMS PUF as a security solution for IoT systems. We select the mutual authentication of IoT devices and their backend system, and propose two lightweight authentication protocols based on MEMS PUFs. The presented protocols undergo a comprehensive security analysis to show their eligibility to be used in IoT systems. As the result, the output of this thesis is a lightweight security solution based on MEMS PUFs, which introduces a very low overhead on the cost of the hardware.
Die pädagogische Wende
(2024)
Die Corona-Pandemie hat die Grenzen des digital gestützten Unterrichts deutlich gemacht: Gerade durch Fernunterricht, Schulclouds, Learning Analytics und Lernmanagementsysteme haben der Präsenzunterricht und das Lernen in Gemeinschaft ihre fundamentale Relevanz belegt, hat die Schule als sozialer Ort der Beziehung und Bindung an Bedeutung gewonnen. Dieses Buch versammelt praxisnahe Konzepte für Lehrkräfte und Schulen, die sich eine stärkere (Rück-)Besinnung auf das interpersonale Unterrichten wünschen, ohne deshalb auf digitale Medien verzichten zu müssen. Die zentrale Botschaft: Für Pädagoginnen und Pädagogen bleibt das Unterrichten das Kerngeschäft. Medien und Technik können Lehr- und Lernprozesse bei Bedarf unterstützen, aber nicht ersetzen. Schule und Unterricht bleiben notwendig interpersonale Prozesse. Diese Botschaft wird mit den Erkenntnissen aus der Pandemie und einem kritischen Blick auf die bisherigen Ergebnisse des Digitalpakts Schule unterfüttert.
Marketing in Kommunalverwaltungen muss sich an einige besondere Rahmenbedingungen anpassen – sei es rechtlicher, organisatorischer oder gesellschaftspolitischer Art. Dies bedeutet auch, dass das Marketinginstrument „Social Media“ bewusst und taktisch klug angewandt werden sollte. Die Relevanz dieses Instruments für die Kommunalverwaltungen ist groß. So hilft es nicht nur, Social Media strategisch einzubetten, sondern auch die Ziele klar zu setzen, sich der Zielgruppen und Rollen im Kontext Social Media bewusst zu sein und diese zielführend einzusetzen.
Social Media hat für Unternehmen der Medienbranche mehrere Dimensionen. Es sind Wettbewerbsmedien im Kampf um Marktanteile im Nutzer- und Werbemarkt, aber auch neue Kommunikationskanäle und Umfelder für die Entwicklung und Vermarktung neuer eigener Social-Media-Produkte. Entsprechend ist die Integration der Möglichkeiten sozialer Medien in die strategische Planung von Medienunternehmen seit der Jahrtausendwende mehr und mehr zum Erfolgsfaktor geworden; ein Thema, das in die digitale Transformationsstrategie eingebettet werden muss.
Ultra-low-power passive telemetry systems for industrial and biomedical applications have gained much popularity lately. The reduction of the power consumption and size of the circuits poses critical challenges in ultra-low-power circuit design. Biotelemetry applications like leakage detection in silicone breast implants require low-power-consuming small-size electronics. In this doctoral thesis, the design, simulation, and measurement of a programmable mixed-signal System-on-Chip (SoC) called General Application Passive Sensor Integrated Circuit (GAPSIC) is presented. Owing to the low power consumption, GAPSIC is capable of completely passive operation. Such a batteryless passive system has lower maintenance complexity and is also free from battery-related health hazards. With a die area of 4.92 mm² and a maximum analog power consumption of 592 µW, GAPSIC has one of the best figure-of-merits compared to similar state-of-the-art SoCs. Regarding possible applications, GAPSIC can read out and digitally transmit the signals of resistive sensors for pressure or temperature measurements. Additionally, GAPSIC can measure electrocardiogram (ECG) signals and conductivity.
The design of GAPSIC complies with the International Organization for Standardization (ISO) 15693/NFC (near field communication) 5 standard for radio frequency identification (RFID), corresponding to the frequency range of 13.56 MHz. A passive transponder developed with GAPSIC comprises of an external memory storage and very few other external components, like an antenna and sensors. The passive tag antenna and reader antenna use inductive coupling for communication and energy transfer, which enables passive operation. A passive tag developed with GAPSIC can communicate with an NFC compatible smart device or an ISO 15693 RFID reader. An external memory storage contains the programmable application-specific firmware.
As a mixed-signal SoC, GAPSIC includes both analog and digital circuitries. The analog block of GAPSIC includes a power management unit, an RFID/NFC communication unit, and a sensor readout unit. The digital block includes an integrated 32-bit microcontroller, developed by the Hochschule Offenburg ASIC design center, and digital peripherals. A 16-kilobyte random-access memory and a read-only 16-kilobyte memory constitute the GAPSIC internal memory. For the fabrication of GAPSIC, one poly, six-metal 0.18 µm CMOS process is used.
The design of GAPSIC includes two stages. In the first stage, a standalone RFID/NFC frontend chip with a power management unit, an RFID/NFC communication unit, a clock regenerator unit, and a field detector unit was designed. In the second stage, the rest of the functional blocks were integrated with the blocks of the RFID/NFC frontend chip for the final integration of GAPSIC. To reduce the power consumption, conventional low-power design techniques were applied extensively like multiple power supplies, and the operation of complementary metal-oxide-semiconductor (CMOS) transistors in the sub-threshold region of operation, as well as further innovative circuit designs.
An overvoltage protection circuit, a power rectifier, a bandgap reference circuit, and two low-dropout (LDO) voltage regulators constitute the power management unit of GAPSIC. The overvoltage protection circuit uses a novel method where three stacked transistor pairs shunt the extra voltage. In the power rectifier, four rectifier units are arranged in parallel, which is a unique approach. The four parallel rectifier units provide the optimal choice in terms of voltage drop and the area required.
The communication unit is responsible for RFID/NFC communication and incorporates demodulation and load modulation circuitry. The demodulator circuit comprises of an envelope detector, a high-pass filter, and a comparator. Following a new approach, the bandgap reference circuit itself acts as the load for the envelope detector circuit, which minimizes the circuit complexity and area. For the communication between the reader and the RFID/NFC tag, amplitude-shift keying (ASK) is used to modulate signals, where the smallest modulation index can be as low as 10%. A novel technique involving a comparator with a preset offset voltage effectively demodulates the ASK signal. With an effective die area of 0.7 mm² and power consumption of 107 µW, the standalone RFID/NFC frontend chip has the best figure-of-merits compared to the state-of-the-art frontend chips reported in the relevant literature. A passive RFID/NFC tag developed with the standalone frontend chip, as well as temperature and pressure sensors demonstrate the full passive operational capability of the frontend chip. An NFC reader device using a custom-built Android-based application software reads out the sensor data from the passive tag.
The sensor readout circuit consists of a channel selector with two differential and four single-ended inputs with a programmable-gain instrumentation amplifier. The entire sensor readout part remains deactivated when not in use. The internal memory stores the measured offset voltage of the instrumentation amplifier, where a firmware code removes the offset voltage from the measured sensor signal. A 12-bit successive approximation register (SAR) type analog-to-digital-converter (ADC) based on a charge redistribution architecture converts the measured sensor data to a digital value. The digital peripherals include a serial peripheral interface, four timers, RFID/NFC interfaces, sensor readout unit interfaces, and 12-bit SAR logic.
Two sets of studies with custom-made NFC tag antennas for biomedical applications were conducted to ascertain their compatibility with GAPSIC. The first study involved the link efficiency measurements of NFC tag antennas and an NFC reader antenna with porcine tissue. In a separate experiment, the effect of a ferrite compared to air core on the antenna-coupling factor was investigated. With the ferrite core, the coupling factor increased by four times.
Among the state-of-the-art SoCs published in recent scientific articles, GAPSIC is the only passive programmable SoC with a power management unit, an RFID/NFC communication interface, a sensor readout circuit, a 12-bit SAR ADC, and an integrated 32-bit microcontroller. This doctoral research includes the preliminary study of three passive RFID tags designed with discrete components for biomedical and industrial applications like measurements of temperature, pH, conductivity, and oxygen concentration, along with leakage detection in silicone breast implants. Besides its small size and low power consumption, GAPSIC is suitable for each of the biomedical and industrial applications mentioned above due to the integrated high-performance microcontroller, the robust programmable instrumentation amplifier, and the 12-bit analog-to-digital converter. Furthermore, the simulation and measurement data show that GAPSIC is well suited for the design of a passive tag to monitor arterial blood pressure in patients experiencing Peripheral Artery Disease (PAD), which is proposed in this doctoral thesis as an exemplary application of the developed system.
In many application areas, Deep Reinforcement Learning (DRL) has led to breakthroughs. In Curriculum Learning, the Machine Learning algorithm is not randomly presented with examples, but in a meaningful order of increasing difficulty. This has been used in many application areas to further improve the results of learning systems or to reduce their learning time. Such approaches range from learning plans created manually by domain experts to those created automatically. The automated creation of learning plans is one of the biggest challenges.In this work, we investigate an approach in which a trainer learns in parallel and analogously to the student to automatically create a learning plan for the student during this Double Deep Reinforcement Learning (DDRL). Three Reward functions, Friendly, Adversarial, and Dynamic based on the learner’s reward are compared. The domain for evaluation is kicking with variable distance, direction and relative ball position in the SimSpark simulated soccer environment.As a result, Statistic Curriculum Learning (SCL) performs better than a random curriculum with respect to training time and result quality. DDRL reaches a comparable quality as the baseline and outperforms it significantly in shorter trainings in the distance-direction subdomain reducing the number of required training cycles by almost 50%.
Printed circuit boards (PCB) are a foundation of electronical devices in modern society. The fabrication of these boards requires various processes and machines. The utilisation of a robot with multiple tools can shorten the process chain compared to screen printing. In this paper a system is presented, which utilises an industrial six axis robot to manufacture
PCBs. The process flow and conversion process of the Gerber format into robot specific commands is presented. The advantages and challenges applying a robot to print circuits are discussed.
Injury prevention is essential in running due to the risk of overuse injury development. Tailoring running shoes to individual needs may be a promising strategy to reduce this risk. Novel manufacturing processes allow the production of individualised running shoes that incorporate features that meet individual biomechanical and experiential needs. However, specific ways to individualise footwear to reduce injury risk are poorly understood. Therefore, this scoping review provides an overview of (1) footwear design features that have the potential for individualisation; and (2) the literature on the differential responses to footwear design features between selected groups of individuals. These purposes focus exclusively on reducing the risk of overuse injuries. We included studies in the English language on adults that analysed: (1) potential interaction effects between footwear design features and subgroups of runners or covariates (e.g., age, sex) for running-related biomechanical risk factors or injury incidences; (2) footwear comfort perception for a systematically modified footwear design feature. Most of the included articles (n = 107) analysed male runners. Female runners may be more susceptible to footwear-induced changes and overuse injury development; future research should target more heterogonous sampling. Several footwear design features (e.g., midsole characteristics, upper, outsole profile) show potential for individualisation. However, the literature addressing individualised footwear solutions and the potential to reduce biomechanical risk factors is limited. Future studies should leverage more extensive data collections considering relevant covariates and subgroups while systematically modifying isolated footwear design features to inform footwear individualisation.
Plastics are used today in many areas of the automotive, aerospace and mechanical engineering industries due to their lightweight potential and ease of processing. Additive manufacturing is applied more and more frequently, as it offers a high degree of design freedom and eliminates the need for complex tools. However, the application of additively manufactured components made of plastics have so far been limited due to their comparatively low strength. For this reason, processes that offer additional reinforcement of the plastic matrix using fibers made of high-strength materials have been developed. However, these components represent a composite of different materials produced on the basis of fossil raw materials, which are difficult to recycle and generally not biodegradable.
Therefore, this paper will explore the potential for new composite materials whose matrix consists of a bio-based plastic. In this investigation, it is assumed that the matrix is reinforced with a fibrous material made of natural fiber to significantly increase the strength. This potential material should offer a lightweight yet strong structure and be biodegradable after use under controlled conditions. Therefore, the state of the art in the use of bio-based materials in 3D printing is first presented. In order to determine the economic boundary conditions, the growth potentials for bio-based materials are analyzed. Also, the recycling prospects for bio-based plastics will also be highlighted. The greenhouse gas emissions and land use to be expected when using bio-based materials are also estimated. Finally, the degradability of the composites is discussed.
The mathematical representations of data in the Spherical Harmonic (SH) domain has recently regained increasing interest in the machine learning community. This technical report gives an in-depth introduction to the theoretical foundation and practical implementation of SH representations, summarizing works on rotation invariant and equivariant features, as well as convolutions and exact correlations of signals on spheres. In extension, these methods are then generalized from scalar SH representations to Vectorial Harmonics (VH), providing the same capabilities for 3d vector fields on spheres.
In this paper, the J-integral is derived for temperature-dependent elastic–plastic materials described by incremental plasticity. It is implemented using the equivalent domain integral method for assessment of three-dimensional cracks based on results of finite-element calculations. The J-integral considers contributions from inhomogeneous temperature fields and temperature-dependent elastic and plastic material properties as well as from gradients in the plastic strains and the hardening variables. Different energy densities are considered, the Helmholtz free energy and the stress-working density, providing a physical meaning of the J-integral as a fracture criteria for crack growth. Results obtained for a plate with two different crack configurations each loaded by a cool-down thermal shock show domain-independence of the incremental J-integral for different energy densities even for high temperature gradients and significant temperature-dependence of the yield stress and the hardening exponent in the presence of large scale yielding. Hence, the derived J-integral is an appropriate parameter for the assessment of cracks in thermomechanically loaded components.
Team description papers of magmaOffenburg are incremental in the sense that each year we address a different topic of our team and the tools around our team. In this year’s team description paper we focus on the architecture of the software. It is a main factor for being able to keep the code maintainable even after 15 years of development. We also describe how we make sure that the code follows this architecture.
Projektmanagement und mit ihm die PM-Prozesse, Methoden und Werkzeuge entwickeln sich stetig weiter, in kleinen, kaum spürbaren Schritten oder in großen unübersehbaren Veränderungen. In den letzten Jahren war der Diskurs über das Pro & Contra agiler Vorgehensweisen so allgegenwärtig, dass andere Aspekte nicht immer die notwendige Aufmerksamkeit bekamen. Erkannte Notwendigkeiten der PM-Entwicklung konnten noch nicht in spürbare Fortschritte umgewandelt werden. Einflüsse der Globalisierung und der IT, aber auch die aus der zunehmenden Forderung nach Nachhaltigkeit resultierenden Veränderungen in der Projektarbeit sollen daher genauer betrachtet werden. Ist erst einmal die Sensibilität für relevante Trends beim Projektpersonal geschaffen, rücken ein aktualisiertes Kompetenzprofil und ein erweiterter Methodenkanon in greifbare Nähe.
Die fortschreitende Digitalisierung der Schulen macht es möglich, die Lerndaten der Schülerinnen und Schüler in einer zentralen Cloud zu speichern. Die Befürworter versprechen sich davon eine bessere individuelle Förderung und fordern eine bundesweite Lösung, um möglichst viele Daten auswerten zu können. Die Gegner befürchten eine automatisierte Steuerung des Lernens.
Ensuring that software applications present their users the most recent version of data is not trivial. Self-adjusting computations are a technique for automatically and efficiently recomputing output data whenever some input changes.
This article describes the software architecture of a large, commercial software system built around a framework for coarse-grained self-adjusting computations in Haskell. It discusses advantages and disadvantages based on longtime experience. The article also presents a demo of the system and explains the API of the framework.
Eco-innovations in chemical processes should be designed to use raw materials, energy and water as efficiently and economically as possible to avoid the generation of hazardous waste and to conserve raw material reserves. Applying inventive principles identified in natural systems to chemical process design can help avoid secondary problems. However, the selection of nature-inspired principles to improve technological or environmental problems is very time-consuming. In addition, it is necessary to match the strongest principles with the problems to be solved. Therefore, the research paper proposes a classification and assignment of nature-inspired inventive principles to eco-parameters, eco-engineering contradictions and eco-innovation domains, taking into account environmental, technological and economic requirements. This classification will help to identify suitable principles quickly and also to realize rapid innovation. In addition, to validate the proposed classification approach, the study is illustrated with the application of nature-inspired invention principles for the development of a sustainable process design for the extraction of high-purity silicon dioxide from pyrophyllite ores. Finally, the paper defines a future research agenda in the field of nature-inspired eco-engineering in the context of AI-assisted invention and innovation.
Die Erfindung betrifft eine Vorrichtung zur biologischen Methanisierung von CO und/oder CO2 mittels methanogener Mikroorganismen durch Umsetzung von H2 und CO und/oder CO2, die eine Begasungskolonne und eine Entgasungskolonne, jeweils mit einer Bodenseite und einer der Bodenseite gegenüberliegenden oberen Seite, ein in der Begasungskolonne und der Entgasungskolonne bereitgestelltes Medium mit methanogenen Mikroorganismen, eine Zuführeinrichtung zum Zuführen eines H2 enthaltenden Gases in das Medium der Begasungskolonne, eine Abführeinrichtung zum Abführen eines CH4 enthaltenden Gases aus der Entgasungskolonne, eine Verbindungsleitung zwischen Begasungskolonne und Entgasungskolonne im Bereich der Bodenseiten, eine Pumpe zum Überführen von Medium über die Verbindungsleitung von der Begasungskolonne in die Entgasungskolonne, und eine Rückführleitung zwischen der Begasungskolonne und der Entgasungskolonne im Bereich der oberen Seiten zum Rückführen von Medium aus der Entgasungskolonne in die Begasungskolonne aufweist. Die Erfindung betrifft auch ein Verfahren zur biologischen Methanisierung von CO und/oder CO2 in einer Vorrichtung mittels methanogener Mikroorganismen als Teil eines in der Vorrichtung bereitgestellten Mediums, wobei das Medium in einem Kreislauf über eine Begasungskolonne und eine Entgasungskolonne geführt wird, wobei die Kolonnen jeweils über eine Verbindungsleitung im Bereich ihrer Bodenseiten und über eine Rückführleitung im Bereich der den Bodenseiten gegenüberliegenden oberen Seiten miteinander verbunden sind, worin das Medium sich in der Begasungskolonne absteigend und in der Entgasungskolonne aufsteigend bewegt, worin dem Medium in der Begasungskolonne ein H2 enthaltendes Gas zugeführt wird.
Assessing the robustness of deep neural networks against out-of-distribution inputs is crucial, especially in safety-critical domains like autonomous driving, but also in safety systems where malicious actors can digitally alter inputs to circumvent safety guards. However, designing effective out-of-distribution tests that encompass all possible scenarios while preserving accurate label information is a challenging task. Existing methodologies often entail a compromise between variety and constraint levels for attacks and sometimes even both. In a first step towards a more holistic robustness evaluation of image classification models, we introduce an attack method based on image solarization that is conceptually straightforward yet avoids jeopardizing the global structure of natural images independent of the intensity. Through comprehensive evaluations of multiple ImageNet models, we demonstrate the attack's capacity to degrade accuracy significantly, provided it is not integrated into the training augmentations. Interestingly, even then, no full immunity to accuracy deterioration is achieved. In other settings, the attack can often be simplified into a black-box attack with model-independent parameters. Defenses against other corruptions do not consistently extend to be effective against our specific attack.
Project website: https://github.com/paulgavrikov/adversarial_solarization
Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling
(2023)
Convolutional neural networks encode images through a sequence of convolutions, normalizations and non-linearities as well as downsampling operations into potentially strong semantic embeddings. Yet, previous work showed that even slight mistakes during sampling, leading to aliasing, can be directly attributed to the networks' lack in robustness. To address such issues and facilitate simpler and faster adversarial training, [12] recently proposed FLC pooling, a method for provably alias-free downsampling - in theory. In this work, we conduct a further analysis through the lens of signal processing and find that such current pooling methods, which address aliasing in the frequency domain, are still prone to spectral leakage artifacts. Hence, we propose aliasing and spectral artifact-free pooling, short ASAP. While only introducing a few modifications to FLC pooling, networks using ASAP as downsampling method exhibit higher native robustness against common corruptions, a property that FLC pooling was missing. ASAP also increases native robustness against adversarial attacks on high and low resolution data while maintaining similar clean accuracy or even outperforming the baseline.
The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research [1] has shown how such detection can generally be enabled by deep learning methods, but appears to be very limited regarding the overall amount of detected vulnerabilities. We analyse to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardised LLVM Intermediate Representation. Te vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, our proposed technical approach and methodology enables an accurate detection of 23 (compared to 4 [1]) vulnerabilities.
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
(2023)
Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification.Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes.The code for our experiments is provided at https://github.com/deepfake-study/deepfake-multiLID.
Erlang is a functional programming language with dynamic typing. The language offers great flexibility for destructing values through pattern matching and dynamic type tests. Erlang also comes with a type language supporting parametric polymorphism, equi-recursive types, as well as union and a limited form of intersection types. However, type signatures only serve as documentation; there is no check that a function body conforms to its signature.
Set-theoretic types and semantic subtyping fit Erlang’s feature set very well. They allow expressing nearly all constructs of its type language and provide means for statically checking type signatures. This article brings set-theoretic types to Erlang and demonstrates how existing Erlang code can be statically type checked without or with only minor modifications to the code. Further, the article formalizes the main ingredients of the type system in a small core calculus, reports on an implementation of the system, and compares it with other static type checkers for Erlang.
In recent years, predictive maintenance tasks, especially for bearings, have become increasingly important. Solutions for these use cases concentrate on the classification of faults and the estimation of the Remaining Useful Life (RUL). As of today, these solutions suffer from a lack of training samples. In addition, these solutions often require high-frequency accelerometers, incurring significant costs. To overcome these challenges, this research proposes a combined classification and RUL estimation solution based on a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. This solution relies on a hybrid feature extraction approach, making it especially appropriate for low-cost accelerometers with low sampling frequencies. In addition, it uses transfer learning to be suitable for applications with only a few training samples.
With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global level explain model behavior and on a local level explain sample predictions. We propose a visual analytics workflow to support seamless transitions between global and local explanations, focusing on attributions and counterfactuals on time series classification. In particular, we adapt local XAI techniques (attributions) that are developed for traditional datasets (images, text) to analyze time series classification, a data type that is typically less intelligible to humans. To generate a global overview, we apply local attribution methods to the data, creating explanations for the whole dataset. These explanations are projected onto two dimensions, depicting model behavior trends, strategies, and decision boundaries. To further inspect the model decision-making as well as potential data errors, a what-if analysis facilitates hypothesis generation and verification on both the global and local levels. We constantly collected and incorporated expert user feedback, as well as insights based on their domain knowledge, resulting in a tailored analysis workflow and system that tightly integrates time series transformations into explanations. Lastly, we present three use cases, verifying that our technique enables users to (1)~explore data transformations and feature relevance, (2)~identify model behavior and decision boundaries, as well as, (3)~the reason for misclassifications.
Entity Matching (EM) defines the task of learning to group objects by transferring semantic concepts from example groups (=entities) to unseen data. Despite the general availability of image data in the context of many EM-problems, most currently available EM-algorithms solely rely on (textual) meta data. In this paper, we introduce the first publicly available large-scale dataset for "visual entity matching", based on a production level use case in the retail domain. Using scanned advertisement leaflets, collected over several years from different European retailers, we provide a total of ~786k manually annotated, high resolution product images containing ~18k different individual retail products which are grouped into ~3k entities. The annotation of these product entities is based on a price comparison task, where each entity forms an equivalence class of comparable products. Following on a first baseline evaluation, we show that the proposed "visual entity matching" constitutes a novel learning problem which can not sufficiently be solved using standard image based classification and retrieval algorithms. Instead, novel approaches which allow to transfer example based visual equivalent classes to new data are needed to address the proposed problem. The aim of this paper is to provide a benchmark for such algorithms.
Information about the dataset, evaluation code and download instructions are provided under https://www.retail-786k.org/.
Following the traditional paradigm of convolutional neural networks (CNNs), modern CNNs manage to keep pace with more recent, for example transformer-based, models by not only increasing model depth and width but also the kernel size. This results in large amounts of learnable model parameters that need to be handled during training. While following the convolutional paradigm with the according spatial inductive bias, we question the significance of \emph{learned} convolution filters. In fact, our findings demonstrate that many contemporary CNN architectures can achieve high test accuracies without ever updating randomly initialized (spatial) convolution filters. Instead, simple linear combinations (implemented through efficient 1×1 convolutions) suffice to effectively recombine even random filters into expressive network operators. Furthermore, these combinations of random filters can implicitly regularize the resulting operations, mitigating overfitting and enhancing overall performance and robustness. Conversely, retaining the ability to learn filter updates can impair network performance. Lastly, although we only observe relatively small gains from learning 3×3 convolutions, the learning gains increase proportionally with kernel size, owing to the non-idealities of the independent and identically distributed (\textit{i.i.d.}) nature of default initialization techniques.
Modern CNNs are learning the weights of vast numbers of convolutional operators. In this paper, we raise the fundamental question if this is actually necessary. We show that even in the extreme case of only randomly initializing and never updating spatial filters, certain CNN architectures can be trained to surpass the accuracy of standard training. By reinterpreting the notion of pointwise ($1\times 1$) convolutions as an operator to learn linear combinations (LC) of frozen (random) spatial filters, we are able to analyze these effects and propose a generic LC convolution block that allows tuning of the linear combination rate. Empirically, we show that this approach not only allows us to reach high test accuracies on CIFAR and ImageNet but also has favorable properties regarding model robustness, generalization, sparsity, and the total number of necessary weights. Additionally, we propose a novel weight sharing mechanism, which allows sharing of a single weight tensor between all spatial convolution layers to massively reduce the number of weights.
This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.
Die Erfindung betrifft eine Vorrichtung zur biologischen Methanisierung von CO und/oder CO2mittels methanogener Mikroorganismen durch Umsetzung von H2und CO und/oder CO2, die eine Begasungskolonne und eine Entgasungskolonne, jeweils mit einer Bodenseite und einer der Bodenseite gegenüberliegenden oberen Seite, ein in der Begasungskolonne und der Entgasungskolonne bereitgestelltes Medium mit methanogenen Mikroorganismen, eine Zuführeinrichtung zum Zuführen eines H2enthaltenden Gases in das Medium der Begasungskolonne, eine Abführeinrichtung zum Abführen eines CH4enthaltenden Gases aus der Entgasungskolonne, eine Verbindungsleitung zwischen Begasungskolonne und Entgasungskolonne im Bereich der Bodenseiten, eine Pumpe zum Überführen von Medium über die Verbindungsleitung von der Begasungskolonne in die Entgasungskolonne, und eine Rückführleitung zwischen der Begasungskolonne und der Entgasungskolonne im Bereich der oberen Seiten zum Rückführen von Medium aus der Entgasungskolonne in die Begasungskolonne aufweist. Die Erfindung betrifft auch ein Verfahren zur biologischen Methanisierung von CO und/oder CO2in einer Vorrichtung mittels methanogener Mikroorganismen als Teil eines in der Vorrichtung bereitgestellten Mediums, wobei das Medium in einem Kreislauf über eine Begasungskolonne und eine Entgasungskolonne geführt wird, wobei die Kolonnen jeweils über eine Verbindungsleitung im Bereich ihrer Bodenseiten und über eine Rückführleitung im Bereich der den Bodenseiten gegenüberliegenden oberen Seiten miteinander verbunden sind, worin das Medium sich in der Begasungskolonne absteigend und in der Entgasungskolonne aufsteigend bewegt, worin dem Medium in der Begasungskolonne ein H2enthaltendes Gas zugeführt wird.
Selbsttests in Lernmanagementsystemen (LMS) ermöglichen es Studierenden, den eigenen Lernfortschritt einzuschätzen. Im Gegensatz zur Einreichung und Korrektur vollständig ausformulierter Aufgabenlösungen nutzen LMS überwiegend die Eingabe der Lösung im Antwort-Auswahl-Verfahren (Single-Choice). Nach didaktischen Ansatz „Physik durch Informatik“ geben die Lernenden stattdessen ihre Aufgabenlösungen in einer Programmiersprache ins LMS ein, was eine automatisierte Rückmeldung erleichtert und das Erreichen einer höheren Kompetenzstufe fördert. Es wurden zehn LMS-Selbsttests erstellt, bei denen die Lösungen zu einer Lehrbuch-Aufgabenstellung jeweils durch Eingabe in einer Programmiersprache und von einer Kontrollgruppe im Antwort-Auswahl-Verfahren abgefragt wurden. Ergebnisse aus dem ersten Einsatz dieser Selbsttests für die Lehrveranstaltung Physik im Studiengang Biotechnologie werden vorgestellt.
Die Verwendung von markenbezogenen nutzer-generierten Inhalten auf den unternehmenseigenen Social-Media-Kanälen ist ein äußerst vielversprechender Ansatz im Content-Marketing. Dabei können durch die authentischen, vom Nutzer bereitgestellten Inhalte zahlreiche Kommunikationsziele erreicht werden. Hierzu gehören etwa die Verstärkung des Nutzerengagements oder aber auch die Förderung von Verkäufen. Daneben müssen allerdings auch Risiken, wie etwa rechtliche Aspekte, beachtet werden. Damit Unternehmen die Potentiale von markenbezogenen nutzer-generierten Inhalten für sich nutzen können, wird im nachstehenden Beitrag ein Strukturierungsrahmen vorgestellt. Dieser fasst die wesentlichen Aspekte dieser durchaus komplexen Thematik strukturiert zusammen. Der hier entwickelte Strukturierungsrahmen wurde durch Experteninterviews überprüft.
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.
TSN, or Time Sensitive Networking, is becoming an essential technology for integrated networks, enabling deterministic and best effort traffic to coexist on the same infrastructure. In order to properly configure, run and secure such TSN, monitoring functionality is a must. The TSN standard already has some preparations to provide such functionality and there are different methods to choose from. We implemented different methods to measure the time synchronisation accuracy between devices as a C library and compared the measurement results. Furthermore, the library has been integrated into the ControlTSN engineering framework.
Marketing and sales have high expectations of new methods such as Big Data, artificial intelligence, machine learning, and predictive analytics. But following the “garbage in—garbage out” principle, the results leave much to be desired. The reason is often insufficient quality in the underlying customer data. This article sheds light on this problem using the data quality and value pyramid as an example. The higher up the value-added pyramid the data is located, the higher its quality and the more value it generates for a company. In addition, we show how the use of monitoring systems, such as a data quality scorecard, makes data quality visible and improvements measurable. In this way, the actual value of data for companies becomes obvious and manageable.
Wirtschaftliche Krisenzeiten implizieren häufig Liquiditätsengpässe und bei kompletter Zahlungsunfähigkeit auch Insolvenzen. Das Instrument des Working Capital Management hilft bei der schnelleren Freisetzung von gebundenem Kapital. Sofern ein datengetriebenes Management unter Einsatz von Business-Analytics-Techniken und mit der dafür notwendigen technisch-organisatorischen Infrastruktur eingesetzt wird, entstehen neue Möglichkeiten von Einsichten in die Prozesslandschaft und die Optimierung von Durchlaufzeiten. Das Ziel ist der Aufbau eines Working-Capital- Analytics-Ansatzes.
Mathematik lässt sich in vielen Objekten finden. Sei es die lineare Steigung eines Handlaufs zum Schulgebäude oder die nahezu zylindrische Form einer Litfaßsäule in der Innenstadt. Das Bestreben, Schüler*innen diese Zusammenhänge entdecken zu lassen, steht im Zentrum des MathCityMap Projekts (Ludwig et al., 2013). Auf sogenannten mathematischen Wanderpfaden (bzw. Mathtrails) werden Schüler*innen durch eine App zu Mathematikaufgaben an realen Objekten bzw. in realen Situationen ihrer Umwelt geleitet. Um die Aufgaben zu lösen, werden Daten erhoben, z. B. durch Messungen oder Zählen. Entscheidend ist, dass die Aufgaben so gestellt sind, dass der Schritt der Datenbeschaffung nur vor Ort stattfinden kann und somit direkt mit dem Objekt bzw. der Situation verknüpft wird.
Turbocharger housings in internal combustion engines are subjected to severe mechanical and thermal cyclic loads throughout their life-time or during engine testing. The combination of thermal transients and mechanical load cycling results in a complex evolution of damage, leading to thermo-mechanical fatigue (TMF) of the material. For the computational TMF life assessment of high temperature components, the DTMF model can provide reliable TMF life predictions. The model is based on a short fatigue crack growth law and uses local finite-element (FE) results to predict the number of cycles to failure for a technical crack. In engine applications, it is nowadays often acceptable to have short cracks as long as they do not propagate and cause loss of function of the component. Thus, it is necessary to predict not only potential crack locations and the corresponding number of cycles for a technical crack, but also to determine subsequent crack growth or even a possible crack arrest. In this work, a method is proposed that allows the simulation of TMF crack growth in high temperature components using FE simulations and non-linear fracture mechanics (NLFM).
A NLFM based crack growth simulation method is described. This method starts with the FE analysis of a component. In this paper, the method is demonstrated for an automotive turbocharger housing subjected to TMF loading. A transient elastic-viscoplastic FE analysis is used to simulate four heating and cooling cycles of an engine test. The stresses, inelastic strains, and temperature histories from the FEA are then used to perform TMF life predictions using the standard DTMF model. The crack position and the crack plane of critical hotspots are then identified. Simulated cracks are inserted at the hotspots. For the model demonstrated, cracks were inserted at two hotspot locations. The ΔJ integral is computed as a fracture mechanics parameter at each point along the crack-front, and the crack extension of each point is then evaluated, allowing the crack to grow iteratively. The paper concludes with a comparison of the crack growth curves for both hotspots with experimental results.
Learning programming fundamentals is considered as one of the most challenging and complex learning activities. Some authors have proposed visual programming language (VPL) approaches to address part of the inherent complexity [1]. A visual programming language lets users develop programs by combining program elements, like loops graphically rather than by specifying them textually. Visual expressions, spatial arrangements of text and graphic symbols are used either as syntax elements or secondary notation. VPLs are normally used for educational multimedia, video games, system development, and data warehousing/business analytics purposes. For example, Scratch, a platform of Massachusetts Institute of Technology, is designed for kids and after school programs.
Design of mobile software applications is considered as one of the most challenging application domains due to the build in sensors as part of a mobile device, like GPS, camera or Near Field Communication (NFC). Sensors enable creation of context-aware mobile applications in which applications can discover and take advantage of contextual information, such as user location, nearby people and objects, and the current user activity. As a consequence, context-aware mobile applications can sense clues about the situational environment making mobile devices more intelligent, adaptive, and personalized. Such context aware mobile applications seem to be motivating and attractive case studies, especially for programming beginners (“my own first app”).
In this work, we introduce a use-case centered approach as well as clear separation of user interface design and sensor-based program development. We provide an in-depth discussion of a new VPL based teaching method, a step by step development process to enable programming beginners the creation of context aware mobile applications. Finally, we argue that addressing challenges for programming beginners by our teaching approach could make programming teaching more motivating, with an additional impact on the final software quality and scalability.
The key contributions of our study are the following:
- An overview of existing attempts to use VPL approaches for mobile applications
- A use case centered teaching approach based on a clear separation of user interface design and sensor-based program development
- A teaching case study enabling beginners a step by step creation of context-aware mobile applications based on the MIT App Inventor (a platform of Massachusetts Institute of Technology)
- Open research challenges and perspectives for further development of our teaching approach
References:
[1] Idrees, M., Aslam, F. (2022). A Comprehensive Survey and Analysis of Diverse Visual Programming Languages, VFAST Transactions on Software Engineering, 2022, Volume 10, Number 2, pp 47-60.
Neural networks have a number of shortcomings. Amongst the severest ones is the sensitivity to distribution shifts which allows models to be easily fooled into wrong predictions by small perturbations to inputs that are often imperceivable to humans and do not have to carry semantic meaning. Adversarial training poses a partial solution to address this issue by training models on worst-case perturbations. Yet, recent work has also pointed out that the reasoning in neural networks is different from humans. Humans identify objects by shape, while neural nets mainly employ texture cues. Exemplarily, a model trained on photographs will likely fail to generalize to datasets containing sketches. Interestingly, it was also shown that adversarial training seems to favorably increase the shift toward shape bias. In this work, we revisit this observation and provide an extensive analysis of this effect on various architectures, the common L_2-and L_-training, and Transformer-based models. Further, we provide a possible explanation for this phenomenon from a frequency perspective.
Seismic data processing relies on multiples attenuation to improve inversion and interpretation. Radon-based algorithms are often used for multiples and primaries discrimination. Deep learning, based on convolutional neural networks (CNNs), has shown encouraging applications for demultiple that could mitigate Radon-based challenges. In this work, we investigate new strategies to train a CNN for multiples removal based on different loss functions. We propose combined primaries and multiples labels in the loss for training a CNN to predict primaries, multiples, or both simultaneously. Moreover, we investigate two distinctive training methods for all the strategies: UNet based on minimum absolute error (L1) training, and adversarial training (GAN-UNet). We test the trained models with the different strategies and methods on 400 synthetic data. We found that training to predict multiples, including the primaries …
In 4D printing an additively manufactured component is given the ability to change its shape or function under the influence of an external stimulus. To achieve this, special smart materials are used that are able to react to external stimuli in a specific way. So far, a number of different stimuli have already been investigated and initial applications have been impressively demonstrated, such as self-folding bodies and simple grippers. However, a methodical specification for the selection of the stimuli and their implementation was not yet in the foreground of the development.
The focus of this work is therefore to develop a methodical approach with which the technology of 4DP can be used in a solution- and application-oriented manner. The developed approach is based on the conventional design methodology for product development to solve given problems in a structured way. This method is extended by specific approaches under consideration of the 4D printing and smart materials.
To illustrate the developed method, it is implemented in practice using a problem definition in the form of an application example. In this example, which represents the recovery of an object from a difficult-to-access environment, the individual functions of positioning, gripping and extraction are implemented using 4D printing. The material extrusion process is used for additive manufacturing of all components of the example. Finally, the functions are successfully tested. The developed approach offers an innovative and methodical approach to systematically solve technical complex problems using 4DP and smart materials.
Sweaty has already participated several times in RoboCup soccer competitions (Adult Size). Now the work is focused coordinating the play of two robots. Moreover, we are working on stabilizing the gait by adding additional sensor information. An ongoing work is the optimization of the control strategy by balancing between impedance and position control. By minimizing the jerk, gait and overall gameplay should improve significantly.
Die Kundenbindung ist für Publisher aus dem Pressesektor seit Jahrzehnten ein Thema, da nicht nur bei auf Wiederholkäufe angelegten Einzelverkaufstiteln, sondern gerade bei Abonnementprodukten neben der erfolgreichen konzeptionellen Ausrichtung der Produkte an den Kundenbedürfnissen der Kontakt zu den Leser*innen zunehmend an Bedeutung gewonnen hat. Produkte, die im Zeitungsbereich oft mit Tradition und Gewohnheit verbunden waren, stehen im digitalen Wettbewerbsumfeld in einem sehr viel intensiveren direkten Wettbewerb, sodass die traditionellen Marketingmaßnahmen der Kundenbindung und -gewinnung für Subscriber-Pakete, aber auch die Maßnahmen der customer centricity in der digitalen Live-Optimierung der Produkte zur entscheidenden Kompetenz werden, um mit Methoden des Content-Marketings und Engagement-Marketings oder mit gänzlich anderen Ansätzen wie Membership-Modellen die Verluste des auslaufenden klassischen Print-Abonnements zu kompensieren.
Gamification wird in vielen Bereichen, die auch den Bildungssektor einschließen, zur Motivations- und Leistungssteigerung eingesetzt. Dieser Beitrag beschreibt das Design, die Umsetzung und Evaluierung eines Gamification-Konzeptes für die Vorlesung „Software Engineering" an der Hochschule Offenburg. Gamification soll nach Intention der Lehrenden eine kontinuierliche und tiefergehende Auseinandersetzung mit den Themen der Vorlesung forcieren sowie einen positiven Einfluss auf die Motivation der Studierenden haben, um den Lernprozess zu unterstützen. Zentral für das Gamification-Design sind dabei eine freiwillige Teilnahme, die Wahrnehmung der Bedeutung der Lerninhalte und ein zielorientierter Einsatz von Gamification-Elementen. Das entwickelte Konzept wurde in der Lernplattform Moodle realisiert, über drei Semester eingesetzt und parallel evaluiert. Die Ergebnisse dieser Evaluierungen zeigen, dass die Studierenden den gamifizierten Kurs intensiv und oft über das gesamte Semester nutzten und aus eigenem Antrieb eine Vielzahl von Übungen absolvierten.
Public export credits and trade insurance require a global framework of institutions, rules and regulations to avoid subsidies and a race to the bottom. The extensive modernisation of the Arrangement on Officially Supported Export Credits (Arrangement) of the Organisation for Economic Co-operation and Development intends to re-level the playing field. This Practitioner Commentary describes the demand for adequate government interventions, considers the need for the reform and discusses key aspects of the new Arrangement. We argue that there is a breakthrough in several important areas such as tenors, repayment terms and green finance. However, we also find that the modernisation falls short in areas such as the interplay between different rulebooks, pre-shipment instruments' regulations and climate action.
Künstliche Intelligenz (KI) durchdringt unser Leben immer stärker. Studierende werden im Alltag und an Hochschulen zunehmend mit KI-Anwendungen konfrontiert. An der Hochschule Offenburg werden deshalb KI-bezogene Lehrangebote curricular verankert, um Studierende im Erwerb von KI-Kompetenz zu unterstützen.
Der Beitrag stellt ein Konzept für die Entwicklung von Lehrveranstaltungen nach der Idee des pädagogischen Makings zur Förderung von KI-Kompetenz in der Hochschullehre vor. Konkretisiert wird das Konzept anhand eines Moduls zum Thema Chatbots, dessen Lehrinhalte interdisziplinär aus verschiedenen Perspektiven ausgearbeitet werden.
This thesis deals with the redesign of manufacturing systems by simulation and optimization. Material flow simulation is a common tool for solving problems in system design. Limitations are the high requirements in time and knowledge to execute simulation studies, evaluate results and solve design problems. New chances arrives with the technologies of industry 4.0 and the digital shadow, providing data for simulation. However, the methods to use production data for the redesign of production systems are not available yet. Purpose of this work is providing the methods to automate simulation from digital shadow, use simulation to optimize and solve problems in system design. Two case studies are used to support the action research approach of this work. The result of this work is a framework for the application of the digital shadow in optimization and problem-solving.
This article provides an overview of the legal framework for website marketing. The presentation of the numerous legal provisions, which are spread over several areas of law, is oriented towards business challenges and measures. After placing the website in the context of marketing, the article focuses on the legal framework relating to the establishment, design and operation of a website. If, in addition to its communication function, a website also has a sales function, i. e. in e-commerce (online trade), additional specific legal conditions must be taken into account.
Voice User Experience
(2023)
Sprachassistenten wie Alexa, Google Assistant, Siri, Cortana, Magenta und Bixby erfreuen sich dank ihrer intuitiven, schnellen und bequemen Interaktionsmöglichkeiten zunehmender Beliebtheit und bieten deshalb spannende Möglichkeiten für die Weiterentwicklung des digitalen Kundendialogs. Doch ob die Technologie wirklich breite Akzeptanz finden wird, hängt nicht nur mit ihrer technischen Qualität oder Usability zusammen. Auch die User Experience, die neben den Reaktionen der Nutzer*innen während der Anwendung auch ihre Erwartungen und Wahrnehmungen vor und nach der Anwendung umfasst, spielt eine zentrale Rolle. Die Messung der Qualität der Voice User Experience (Voice UX) ist daher von großem Interesse für die Bewertung und Optimierung von Sprachapplikationen. Die Frage, wie die Voice UX von sprachgesteuerten Systemen gemessen werden kann, ist jedoch noch offen. Aktuelle Methoden stützen sich häufig auf UX-Forschung zu grafischen Benutzeroberflächen, obwohl die sprachbasierten Interaktionsformen in der Regel weder visuell noch haptisch greifbar sind. In unserem Beitrag möchten wir den aktuellen Status quo der deutschen Voice User Experience untersuchen. Folgende Fragen stehen dabei im Mittelpunkt: Wie können Sprachanwendungen zu einem erfolgreichen Kundendialog beitragen? Welche Nutzerirritationen treten aktuell bei der Anwendung von Sprachassistenten auf? Mit welchen Methoden lässt sich die Voice User Experience messen?
The present paper addresses the research question: What recommendations for action and potential adjustments should an online magazine for beauty and fashion implement in order to make affiliate articles in these sections even more appealing to the target group and provide added value for them?
To be able to answer this research question, three hypotheses were defined and tested with using qualitative and quantitative research. The qualitative research consisted of user experience testings, where four affiliate articles in the fields of beauty and fashion were tested with 13 participants. The quantitative research involved collecting, analyzing and evaluating data from the four affiliate articles conducted with the company's real-life target group. Based on these results, recommendations for action were derived, which should not only improve the quality of the content in the future, but also increase the efficiency of the implementation of those articles.
Design Thinking wird im Wesentlichen als Methode zur Lösung komplexer Probleme und Entwicklung neuer Ideen verstanden.
Doch das mit dem Design Thinking einhergehende Mindset kann für breite und vielfältige Fragestellungen nutzbar sein und geht weit über die Methodik zur Lösung komplexer Probleme und Entwicklung neuer Ideen hinaus. Es besteht aus den Komponenten Empathie, non-lineare und lineare Denkweise, Experimentierfreude und Offenheit.
Die wissenschaftliche Auseinandersetzung mit praktischen Anwendungsbeispielen zeigt, wie durch das Design Thinking Mindset der Denkhorizont in Richtung Zukunft erweitert wird. Der Herausgeber Robert Gücker und fünf Autorinnen geben einen fundierten Einblick in das “Design Thinking Mindset”. In einem breiten Spektrum umfassen die Themen des vorliegenden Sammelbandes den Zusammenhang von Design Thinking und Unternehmenskultur, Arbeitszufriedenheit der Mitarbeitenden und Resilienz, die Möglichkeiten der Erreichung des Design Thinking Mindsets durch Workshops und gezielte Führungsentwicklungsmaßnahmen bis hin zur Betrachtung des Herausgebers, der die Metapher der qualitativen Forschung für Design Thinking vorschlägt.
Die wissenschaftliche Auseinandersetzung der fünf Autorinnen mit der Thematik erfolgt auf Grundlage von umfangreichen Literaturrecherchen und Expertenbefragungen, deren Erkenntnisse auf den Praxiseinsatz übertragen werden. Im Sammelband finden Sie damit in kompakten Beiträgen einen fundierten Überblick an Möglichkeiten und Chancen, wie Unternehmen anhand der Anwendung von Design Thinking bei ihren Mitarbeitenden und Führungskräften einen Mindshift in Richtung Zukunft schaffen und welche Rahmenbedingungen nötig sind, um zu einer resilienten Organisation mit zufriedenen Mitarbeitenden zu werden.
Die Ergebnisse der Beiträge bestätigen eine positive Wirkung in Richtung Zukunftskompetenzen, die sich aus einer Haltung ergeben, welches dem Design Thinking Mindset entspricht.
Mit praktischen Anwendungsbeispielen bietet das Buch nicht nur Personalverantwortlichen sowie Organisationsentwicklern wissenschaftlich fundiert und praxisnah neue Ansätze.
Background
Internal tibial loading is influenced by modifiable factors with implications for the risk of stress injury. Runners encounter varied surface steepness (gradients) when running outdoors and may adapt their speed according to the gradient. This study aimed to quantify tibial bending moments and stress at the anterior and posterior peripheries when running at different speeds on surfaces of different gradients.
Methods
Twenty recreational runners ran on a treadmill at 3 different speeds (2.5 m/s, 3.0 m/s, and 3.5 m/s) and gradients (level: 0%; uphill: +5%, +10%, and +15%; downhill: –5%, –10%, and –15%). Force and marker data were collected synchronously throughout. Bending moments were estimated at the distal third centroid of the tibia about the medial–lateral axis by ensuring static equilibrium at each 1% of stance. Stress was derived from bending moments at the anterior and posterior peripheries by modeling the tibia as a hollow ellipse. Two-way repeated-measures analysis of variance were conducted using both functional and discrete statistical analyses.
Results
There were significant main effects for running speed and gradient on peak bending moments and peak anterior and posterior stress. Higher running speeds resulted in greater tibial loading. Running uphill at +10% and +15% resulted in greater tibial loading than level running. Running downhill at –10% and –15% resulted in reduced tibial loading compared to level running. There was no difference between +5% or –5% and level running.
Conclusion
Running at faster speeds and uphill on gradients ≥+10% increased internal tibial loading, whereas slower running and downhill running on gradients ≥–10% reduced internal loading. Adapting running speed according to the gradient could be a protective mechanism, providing runners with a strategy to minimize the risk of tibial stress injuries.
Footwear plays a critical role in our daily lives, affecting our performance, health and overall well-being. Well-designed footwear can provide protection, comfort and improved foot functionality, while poorly designed footwear can lead to mobility problems and declines in physical activity. The overall goal of footwear research is to provide a scientific basis for professionals in the field to provide an optimal footwear solution for a given person, for a given task, in a given environment, while using sustainable manufacturing processes. This article suggests potential directions for future research with a focus on athletic footwear biomechanics. Directions include the evidence-based individualisation of footwear, the interaction between design and prolonged use, and improving the sustainability of footwear. The authors also provide a speculative outlook on methodological developments that may provide greater insight into these areas. These developments may include: (1) the use of larger scale, real-world and representative data, (2) the use of 3D printing to create experimental footwear, (3) the advancement of in silico research methods, and (4) furthering multidisciplinary collaboration. If successfully applied in the future, footwear research will contribute to active and healthy lifestyles across the lifespan.
Material flow simulation is a core technology of Industry 4.0. It can analyze and improve large-scale production systems through experimentation with digital simulation models. However, modeling in discrete event simulation is considered as an effortful and time-consuming activity and challenges especially small and medium-sized enterprises. Systematic experiments and what-if-analysis require a large number of models. Modeling and simulation becomes a repetitive activity and the ability to model and simulate instantly becomes crucial for industry, 4.0. However, model generation typically uses specific methods to build models with individual properties for specific physical systems. A general literature review cannot sufficiently describe the current state of model generation. This study aims to provide an analysis of model generation based on the modeling strategy, modeling view, and production system type, as well as model properties and limitations.
Die meisten Webseiten im World Wide Web werden mit Web-Content-Management-Systemen (WCMS) realisiert. In diesem Beitrag werden Ziele, Prinzipien, Funktionen und Architektur dieser wichtigen Infrastruktursysteme für unser tägliches Online-Leben und -Arbeiten vorgestellt.
Nach der Definition der grundlegenden Begriffe werden die Basisprinzipien Content Life Cycle und Trennung von Inhalt, Struktur und Darstellung erläutert. Es folgt die Vorstellung der wichtigen Komponenten von WCMS und deren Funktionalität, wie etwa Asset Management, Benutzerverwaltung und Workflow-Maschine. Zum Schluss wird ein Einblick in die aktuellen Entwicklungen von WCMS gegeben.
Schulen müssen bei ihrer Profilbildung mehr leisten als die reine Marketingpositionierung erfordert. Sie müssen für alle am Schulleben Beteiligten einen Sinn stiften. Es geht letztlich um eine Veränderung der Schulkultur, indem die Grundüberzeugungen und der Sinn und Zweck der Schule klar herausgearbeitet werden. Methoden aus dem Bereich der Entwicklung von Unternehmens- und Organisationskultur können hier wirksam zum Einsatz kommen.
Kundendaten im E-Commerce – Optimierungspotenzial im Checkout-Prozess des deutschen Online-Handels
(2023)
Die Gestaltung eines benutzungsfreundlichen Checkout-Prozesses ist für den Erfolg des E-Commerce von großer Bedeutung. Die Abfrage der Kundendaten bildet einen wichtigen Teil der Customer Journey. Auf der einen Seite wollen die Handelsunternehmen so viel wie möglich über ihre Kundschaft erfahren, um möglichst zielgenaue Angebote und Marketingmaßnahmen ausspielen und das perfekte Einkaufserlebnis generieren zu können. Auf der anderen Seite möchten sich die Kundinnen und Kunden beim Online-Shopping auf den Kauf konzentrieren und erwarten einen reibungslosen Ablauf. Der Checkout-Prozess ist in diesem Zusammenhang ein kritischer Punkt. Dies spiegelt sich auch in den hohen Warenkorbabbruchraten wider. Um Online-Shoppende nachhaltig zu begeistern, gibt es noch viel Raum für Verbesserungen. Mit dem Ziel, den Status quo im deutschen Online-Handel besser zu verstehen und Usability und User Experience für eine höhere Konvertierungsrate zu optimieren, untersuchte die hier vorgestellte Forschungsarbeit den Anmelde- und Checkout-Prozess der 100 umsatzstärksten Online-Shops in Deutschland. Es werden die Ergebnisse der Studie präsentiert und aufgezeigt, an welchen Stellen Optimierungspotenzial besteht – bspw. bei zu komplizierten Formularen, unnötigen Datenabfragen oder erzwungenen Registrierungen – sowie Vorschläge für die Praxis des Online-Handels diskutiert.
Additive manufacturing enables the production of lightweight and resilient components with extensive design freedom. In the low-cost sector, material extrusion (e.g. Fused Deposition Modeling - FDM) has been the main method used to date. Thus, robust 3D printers and inexpensive 3D materials (polymer filaments) can be used. However, the printing times for FDM are very long and the quality of the dimensions and surfaces is limited. Recently, new processes from the field of Vat polymerization have entered the market. For example, masked stereolithography (mSLA) offers a significant improvement in component quality and build speed through the use of resins and large-area curing at still reasonable costs. Currently, there is only limited knowledge available on the optimal design of components using this young process. In this contribution, design guidelines are developed to determine the possibilities and limitations of mSLA from a design point of view. For this purpose, a number of test geometries are designed and investigated to obtain systematic insights into important design features, such as wall thickness, grooves and holes. In addition, typical problems in additive manufacturing, such as the design of overhangs and fits or the hollowing of components, are investigated. The evaluation of practical 3D printing tests thus provides important parameters that can be transferred to design guidelines of components for additive manufacturing using mSLA.
Die Bildungswelt ist im Wandel. Soziale Umbrüche, Digitalisierung, nicht zuletzt die Folgen von Corona-Pandemie und globalen Krisen zwingen Bildungseinrichtungen in Veränderungsprozesse, die neue Anforderungen an Planung, Organisation und Durchführung des Lehrbetriebs stellen. Schulleitungen stehen vor der Herausforderung, die Gegenwart und Zukunft mit allen Unsicherheiten und Einschränkungen neu zu gestalten. Das Buch erläutert die Rahmenbedingungen für eine bewusste Führungskultur, die den Rahmen für eine zukunftsorientierte Organisations- und Lernkultur bildet. Es richtet sich sowohl an Mitglieder von Schulleitungsteams als auch an alle Mitwirkenden im pädagogischen Bereich von Bildungseinrichtungen sowie an Studierende, Referendarinnen und Referendare.
International trade requires sufficient, reliable, and affordable sources of financing. Export credit agencies (ECAs) fill trade finance gaps by offering financing, insurance and guarantees to provide liquidity or mitigate risks. They help to create or secure jobs in the domestic economy. However, comprehensive government support is required to create significant impact. This includes ‘full faith and credit’ of the state. In the context of public foreign trade promotion, full faith and credit is defined as an explicit, direct or indirect, irrevocable, legal commitment to accept all liabilities of an ECA as unconditional obligations of the respective government. Our policy recommendations for countries with relatively young ECAs, for example in Ukraine, Armenia, and Malawi, are to establish a full guarantee in addition to an efficient legal set-up, sufficient capital, and sound risk management of the respective agency. Without full faith and credit, policy goals of fostering economic growth through foreign trade fall short.
In this paper, we propose an approach for gait phase detection for flat and inclined surfaces that can be used for an ankle-foot orthosis and the humanoid robot Sweaty. To cover different use cases, we use a rule-based algorithm. This offers the required flexibility and real-time capability. The inputs of the algorithm are inertial measurement unit and ankle joint angle signals. We show that the gait phases with the orthosis worn by a human participant and with Sweaty are reliably recognized by the algorithm under the condition of adapted transition conditions. E.g., the specificity for human gait on flat surfaces is 92 %. For the robot Sweaty, 95 % results in fully recognized gait cycles. Furthermore, the algorithm also allows the determination of the inclination angle of the ramp. The sensors of the orthosis provide 6.9 and that of the robot Sweaty 7.7 when walking onto the reference ramp with slope angle 7.9.
Bewegungsanalysesysteme in der Forschung und für niedergelassene Orthopädinnen und Orthopäden
(2023)
Hintergrund
Komplexe biomechanische Bewegungsanalysen können für eine Vielzahl orthopädischer Fragestellungen wichtige Informationen liefern. Bei der Beschaffung von Bewegungsanalysesystemen sind neben den klassischen Messgütekriterien (Validität, Reliabilität, Objektivität) auch räumliche und zeitliche Rahmenbedingungen sowie Anforderungen an die Qualifikation des Messpersonals zu berücksichtigen.
Anwendung
In der komplexen Bewegungsanalyse werden Systeme zur Bestimmung der Kinematik, der Kinetik und der Muskelaktivität (Elektromyographie) eingesetzt. Der vorliegende Artikel gibt einen Überblick über Methoden der komplexen biomechanischen Bewegungsanalyse für den Einsatz in der orthopädischen Forschung oder in der individuellen Patientenversorgung. Neben dem Einsatz zur reinen Bewegungsanalyse wird auch der Einsatz von Bewegungsanalyseverfahren im Bereich des Biofeedbacktrainings diskutiert.
Beschaffung
Für die konkrete Anschaffung von Bewegungsanalysesystemen empfiehlt sich die Kontaktaufnahme mit Fachgesellschaften (z. B. Deutsche Gesellschaft für Biomechanik), Hochschulen und Universitäten mit vorhandenen Bewegungsanalyseeinrichtungen oder Vertriebsfirmen im Bereich der Biomechanik.
In an extensive research project, we have assessed the application of different service models by export credit agencies (ECAs) and export-import banks (EXIMs). We conducted interviews with 35 representatives of ECAs and EXIMs from 27 countries. The question guiding this study is: How do ECAs and EXIMs adopt public service models for supporting exporters? We conducted a holistic multiple case study, investigating if and how these organisations apply public service models developed by Schedler and Guenduez, and which roles of the state are relevant. We find that there is a variety of different service models used by ECAs and EXIMs, and that the service model approaches have great potential to learn from each other and innovate existing services.
Kein Mensch lernt digital: Über die Automatisierung von Schule und Unterricht - und Alternativen
(2023)
Complex tourism products with intangible service components are difficult to explain to potential customers. This research elaborates the use of virtual reality (VR) in the field of shore excursions. A theoretical research model based on the technology acceptance model was developed, and hypotheses were proposed. Cruise passengers were invited to test 360° excursion images on a landing page. Data was collected using an online questionnaire. Finally, data was analyzed using the PLS-SEM method. The results provide theoretical implications on technology acceptance model (TAM) research in the field of cruise tourism. Furthermore, the results and implications indicate the potential of virtual 360° shore excursion presentations for the cruise industry.
Robotic Process Automation (RPA) is a technology for automating business processes and connecting systems by means of software robots in organizations that is gaining traction and growing out of its infancy. Thus, it is no longer just a question of what is technologically feasible, but rather how this technology can be used most profitably. However, business models for RPA remain underinvestigated in literature. Existing work is highly heterogenous, lacking structure and applicability in practice. To close this gap, we present an approach to sustainably establish RPA as a driver of digitization and automation within a company based on an iterative, holistic view of business models with the Business Model Canvas as analysis tool.
Seit mehr als 40 Jahren wiederholen sich Diskussionen und Kontroversen über Sinn und Unsinn von Informationstechnik (IT) in Bildungseinrichtungen. Wurde bislang über das Arbeiten an und mit PC, Laptop oder Tablet debattiert, drehen sich aktuelle Diskussionen verstärkt um netzbasierte Anwendungen mit Rückkanal für Schülerdaten. Das Schüler*innenverhalten wird per Software ausgewertet, um Lehrinhalte automatisiert und „individualisiert“ anzupassen. Ergänzt werden solche Lernprogramme um Anwendungen der sogenannten „Künstliche Intelligenz“ (KI), die als „Lernbegleiter“ fungieren und zumindest perspektivisch fehlende Lehrkräfte ersetzen (sollen). Damit werden technische Systeme in Schulen etabliert, von denen nicht einmal mehr die Entwickler wissen, was diese Algorithmen genau tun.
Das erfordert einen kritisch-reflektierenden Diskurs. Dafür vertritt Ralf Lankau im vorliegenden Aufsatz die These, dass essenzielle Elemente der Bildung, wie die Erziehung zu Selbstbewusstsein, Reflexion und einer kritischen Bürgerschaft, mit solchen Lernprogrammen verloren gehen.
Cast aluminum cylinder blocks are frequently used in gasoline and diesel internal combustion engines because of their light-weight advantage. However, the disadvantage of aluminum alloys is their relatively low strength and fatigue resistance which make aluminum blocks prone to fatigue cracking. Engine blocks must withstand a combination of low-cycle fatigue (LCF) thermal loads and high-cycle fatigue (HCF) combustion and dynamic loads. Reliable computational methods are needed that allow for accurate fatigue assessment of cylinder blocks under this combined loading. In several publications, the mechanism-based thermomechanical fatigue (TMF) damage model DTMF describing the growth of short fatigue cracks has been extended to include the effect of both LCF thermal loads and superimposed HCF loadings. This approach is applied to the finite life fatigue assessment of an aluminum cylinder block. The required material properties related to LCF are determined from uniaxial LCF tests. The additional material properties required for the assessment of superimposed HCF are obtained from the literature for similar materials. The predictions of the model agree well with engine dyno test results. Finally, some improvements to the current process are discussed.
Staatliche Exportkreditagenturen und Export-Import-Banken finanzieren, versichern und garantieren jährlich fast 1 Bio. US-Dollar – mehr als 3 % der globalen Güterexporte. Ihre Interventionen sind an internationale Rahmenbedingungen gebunden, insbesondere an das WTO-Subventionsübereinkommen (ASCM) und den OECD-Konsensus. Das komplexe Zusammenspiel beider Rechtsrahmen sorgt seit langem für Herausforderungen, vor allem hinsichtlich des Anwendungsbereichs des “safe haven” des ASCM und des “Matching”-Mechanismus der OECD. In den vergangenen Jahren hinzugekommen ist die Problematik neuer Instrumente der Exportvor- sowie der Klimafinanzierung. Der folgende Beitrag erörtert Herausforderungen und Lösungsansätze. Er zeigt auf, dass der neue OECD-Konsensus trotz zahlreicher Verbesserungen zentrale rechtliche Probleme nicht behebt.