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The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.
Im Anschluss an die arbeitsrechtlichen Jahresübersichten der vergangenen Jahre (NWB 5/2019 S. 266; NWB 8/2020 S. 557) gibt der nachfolgende Beitrag einen Überblick über relevante Entwicklungen im Arbeitsrecht des Jahres 2020. Der erste Teil beschäftigt sich hierbei mit gesetzlichen Änderungen. Neben Regelungen, die auf die COVID-19-Pandemie zurückgeführt werden können, hat der Gesetzgeber auch andere Vorhaben umgesetzt. So wurde u. a. das Arbeitnehmerentsenderecht reformiert. Für Diskussionen sorgt(e) zudem das Gesetzesvorhaben zur Regelung mobiler Arbeit. Im zweiten Teil werden für die Praxis wichtige höchstrichterliche Gerichtsentscheidungen zum Arbeitsrecht erläutert, alphabetisch sortiert von A (wie Antidiskriminierungsrecht) bis U (wie Urlaub).
Die Arbeitswelt ist im Umbruch. Der Bedarf an flexiblen Arbeitszeitmodellen nimmt stetig zu, wobei die Corona-Pandemie dieses Bedürfnis nochmals verschärft hat. Gerade auch in kleineren und mittleren Unternehmen wächst die Notwendigkeit, den Einsatz der Beschäftigten möglichst bedarfsgerecht zu steuern, also bei guter Auftragslage mehr Arbeitszeit abzurufen und bei ausbleibenden Aufträgen die Arbeitszeit zu reduzieren und somit bezahlte „Leerlaufzeiten“ zu vermeiden. Der Gesetzgeber stellt den Arbeitgebern hierfür das Instrument der sog. Abrufarbeit ( § 12 Teilzeit- und Befristungsgesetz – TzBfG ) zur Verfügung. In dem nachfolgenden Beitrag werden Möglichkeiten und Grenzen der Abrufarbeit skizziert und konkrete arbeitsvertragliche Gestaltungsmöglichkeiten aufgezeigt.
The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models.
Glucose oxidase (GOx) is an enzyme frequently used in glucose biosensors. As increased temperatures can enhance the performance of electrochemical sensors, we investigated the impact of temperature pulses on GOx that was drop-coated on flattened Pt microwires. The wires were heated by an alternating current. The sensitivity towards glucose and the temperature stability of GOx was investigated by amperometry. An up to 22-fold increase of sensitivity was observed. Spatially resolved enzyme activity changes were investigated via scanning electrochemical microscopy. The application of short (<100 ms) heat pulses was associated with less thermal inactivation of the immobilized GOx than long-term heating.
Stretch-shortening type actions are characterized by lengthening of the pre-activated muscle-tendon unit (MTU) in the eccentric phase immediately followed by muscle shortening. Under 1 g, pre-activity before and muscle activity after ground contact, scale muscle stiffness, which is crucial for the recoil properties of the MTU in the subsequent push-off. This study aimed to examine the neuro-mechanical coupling of the stretch-shortening cycle in response to gravity levels ranging from 0.1 to 2 g. During parabolic flights, 17 subjects performed drop jumps while electromyography (EMG) of the lower limb muscles was combined with ultrasound images of the gastrocnemius medialis, 2D kinematics and kinetics to depict changes in energy management and performance. Neuro-mechanical coupling in 1 g was characterized by high magnitudes of pre-activity and eccentric muscle activity allowing an isometric muscle behavior during ground contact. EMG during pre-activity and the concentric phase systematically increased from 0.1 to 1 g. Below 1 g the EMG in the eccentric phase was diminished, leading to muscle lengthening and reduced MTU stretches. Kinetic energy at take-off and performance were decreased compared to 1 g. Above 1 g, reduced EMG in the eccentric phase was accompanied by large MTU and muscle stretch, increased joint flexion amplitudes, energy loss and reduced performance. The energy outcome function established by linear mixed model reveals that the central nervous system regulates the extensor muscles phase- and load-specifically. In conclusion, neuro-mechanical coupling appears to be optimized in 1 g. Below 1 g, the energy outcome is compromised by reduced muscle stiffness. Above 1 g, loading progressively induces muscle lengthening, thus facilitating energy dissipation.
How different diversity factors affect the perception of first-year requirements in higher education
(2021)
In the light of growing university entry rates, higher education institutions not only serve larger numbers of students, but also seek to meet first-year students’ ever more diverse needs. Yet to inform universities how to support the transition to higher education, research only offers limited insights. Current studies tend to either focus on the individual factors that affect student success or they highlight students’ social background and their educational biography in order to examine the achievement of selected, non-traditional groups of students. Both lines of research appear to lack integration and often fail to take organisational diversity into account, such as different types of higher education institutions or degree programmes. For a more comprehensive understanding of student diversity, the present study includes individual, social and organisational factors. To gain insights into their role for the transition to higher education, we examine how the different factors affect the students’ perception of the formal and informal requirements of the first year as more or less difficult to cope with. As the perceived requirements result from both the characteristics of the students and the institutional context, they allow to investigate transition at the interface of the micro and the meso level of higher education. Latent profile analyses revealed that there are no profiles with complex patterns of perception of the first-year requirements, but the identified groups rather differ in the overall level of perceived challenges. Moreover, SEM indicates that the differences in the perception largely depend on the individual factors self-efficacy and volition.
Industrie 4.0 stellt viele Herausforderungen an produzierende Unternehmen und ihre Beschäf-tigten. Innovative und effektive Trainingsstrategien sind erforderlich, um mit den sich schnell verändernden Produktionsumgebungen und neuen Fertigungstechnologien Schritt halten zu können. Virtual Reality (VR) bietet neue Möglichkeiten für On-the-Job, On-Demand- und Off-Premise-Schulungen. Diese Arbeit stellt ein neues VR Schulungssystem vor, welches sich flexible an unterschiedliche Trainingsobjekte auf Grundlage von Rezepten und CAD Modellen anpassen lässt. Das Konzept basiert auf gerichteten azyklischen Graphen und einem Level-system. Es ermöglicht eine benutzerindividuelle Lerngeschwindigkeit mittels visueller Ele-mente. Das Konzept wurde für einen mechanischen Anwendungsfall mit Industriekomponen-ten implementiert und in der Industrie 4.0-Modellfabrik der FH Aachen umgesetzt.