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The digitalisation of production has a positive impact on manufacturing processes in terms of resource efficiency and environmental impact, particularly in the form of increased efficiency as well as cost and resource savings. However, the use of digitalisation technologies is also associated with efforts such as costs, CO2 emissions, and raw material consumption. When planning or deciding on the digitalisation of manufacturing systems, it is therefore necessary to assess whether these technologies pay off in terms of sustainability over their life cycle. This literature review (based on the PRISMA guidelines) analyses the relevance of sustainability assessment and its methods for the digitalisation of production in research. The review reveals that research focuses on the benefits of digitalisation technologies in manufacturing, while the assessment of efforts and their benefits is in its infancy. There is a need for further research on holistic assessment methods for digitalisation technologies. In particular, there is a lack of assessment methods that consistently link the economic and environmental dimensions of sustainability, and there is also a lack of guidance for the application of assessment methods in production.
Manufacturing industries require the efficient and voluminous production of high-quality finished goods. In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlled product quality with high precision. In general, automation based on computer vision is a promising solution to prevent bottlenecks at the product quality checkpoint. We considered recent advancements in machine learning to improve visual defect localization, but challenges persist in obtaining a balanced feature set and database of the wide variety of defects occurring in the production line. Hence, this paper proposes a defect localizing autoencoder with unsupervised class selection by clustering with k-means the features extracted from a pretrained VGG16 network. Moreover, the selected classes of defects are augmented with natural wild textures to simulate artificial defects. The study demonstrates the effectiveness of the defect localizing autoencoder with unsupervised class selection for improving defect detection in manufacturing industries. The proposed methodology shows promising results with precise and accurate localization of quality defects on melamine-faced boards for the furniture industry. Incorporating artificial defects into the training data shows significant potential for practical implementation in real-world quality control scenarios.
The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy, but also for increasing the overall efficiency. Among the different job scheduling problems, the Job Shop Scheduling Problem (JSSP) is addressed in this work. JSSP falls into the category of NP-hard Combinatorial Optimization Problem (COP), in which solving the problem through exhaustive search becomes unfeasible. Simple heuristics such as First-In, First-Out, Largest Processing Time First and metaheuristics such as taboo search are often adopted to solve the problem by truncating the search space. The viability of the methods becomes inefficient for large problem sizes as it is either far from the optimum or time consuming. In recent years, the research towards using Deep Reinforcement Learning (DRL) to solve COPs has gained interest and has shown promising results in terms of solution quality and computational efficiency. In this work, we provide an novel approach to solve the JSSP examining the objectives generalization and solution effectiveness using DRL. In particular, we employ the Proximal Policy Optimization (PPO) algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs. We incorporated a new method called Order Swapping Mechanism (OSM) in the environment to achieve better generalized learning of the problem. The performance of the presented approach is analyzed in depth by using a set of available benchmark instances and comparing our results with the work of other groups.
Die Berechnung des bewerteten Norm-Trittschallpegels im Bau L'n,w erfolgt aktuell in der DIN 4109-2:2018 sowohl für den Massivbau als auch für den Holz-, Leicht- und Trockenbau unter Berücksichtigung relativ pauschaler Korrekturen für die flankierende Schallübertragung. Darüber hinaus unterliegen die derzeitigen Verfahren einigen weiteren gravierenden Beschränkungen des Anwendungsbereiches, die eine Überarbeitung der Verfahren wünschenswert erscheinen lassen. Basierend auf dem neuen vereinfachten Verfahren der DIN EN ISO 12354-2:2017 werden deshalb Berechnungsverfahren auf der Basis von Einzahlangaben vorgestellt, mit denen die direkte und die flankierende Trittschallübertragung, wie beim Luftschall, getrennt berechnet und anschließend aufsummiert werden. Hierdurch ergeben sich deutlich bessere Möglichkeiten, die tatsächliche Bausituation im Rechenmodell abzubilden und somit eine bessere Übereinstimmung zwischen dem prognostizierten und einem gemessenen Wert zu erhalten. Im Massivbau kann beispielsweise eine Vorsatzkonstruktion im Empfangsraum vor dem trennenden oder dem flankierenden Bauteil entsprechend ihrer schalltechnischen Wirkung berücksichtigt werden. Weiterhin können auch horizontale oder diagonale Übertragungssituationen oder die Trittschallübertragung von Treppen abhängig von der jeweiligen baulichen Situation berechnet werden. Die neuen Berechnungsverfahren ermöglichen damit eine wirtschaftlichere Dimensionierung der Bauteile.
An underrepresentation of women in executive positions has persisted for decades. This paper aims to analyze the financial impact of women in executive positions in German companies by examining the economic value added and exploring the effects of Environmental, Social, and Governance (ESG) factors and female supervisory board members on female board members. The results indicate that in the 200 largest German companies, the share of female executives increased between 2018 and 2022. Regardless of industry, female executives started at very low levels at around 4% in 2018; in 2022, this figure reached around 11%. Thereby, the financial sector showed the highest share of female executives at around 14% in 2022, and the industrial sector at around 9%. A closer look reveals that large companies have higher shares of female executives than smaller companies. Large companies show an average of 17% compared to small ones, and in 2022, only 8%. There is a positive correlation between the share of female supervisory board members and female executives, arguing that female supervisory board members seem to hire more female executives. Companies with more female executives tend to have lower ESG controversy scores, and companies with more female executives, measured by the economic value added, perform better financially than companies with few or no women. Companies with female executives show about 2 percentage points higher economic value added than those with the lowest share of females (no or few female executives). Thus, it seems that female executives matter and make a difference in companies.
In the following article, we construct an interaction model of general language change. This contributes in particular to quantitative studies on reversible language change initiated by G. Altmann by adding explanatory character in tracing global features of general language change back to the individual interaction of speakers. Although the corresponding coupled differential equations are (presumably) non-integrable, we use methods from the theory of dynamical systems to deduce the long-term behaviour (depending on four interaction parameters) of the model for any given initial constellation of speakers. Subsequent numerical analysis of real data on language change is used to justify the relevance of the constructed model for the practicing quantitative linguist. We show how data-fitting methods can be used to determine the four interaction parameters and predict from them the long-term behaviour of the system, i.e. if complete language change or reversible language change will take place.
This research examines the potential outputs, outcomes, and impacts of the German Act on Corporate Due Diligence Obligations in Supply Chains (LkSG) on the smallholder cocoa farmers in West Africa. The study primarily relies on a literature review and an impact pathway to conduct a systematic analysis to identify the potential effects of the LkSG on smallholder cocoa farmers. The findings indicate that some, but not all of the risks addressed by the LkSG align with those faced by smallholder cocoa farmers and their families. Additionally, the research also reveals weaknesses, particularly in managing environmental risks, which the LkSG does not adequately cover. Our findings show that in the short- and medium-term, the LkSG has no potential effects on smallholder cocoa farmers. Furthermore, the potential positive impacts of the law on smallholder cocoa farmers will take a long time to realize, as the LkSG considers primarily tier-1 suppliers. Companies in Germany might reassess their supply chains to strive for an LkSG-risk-free supply chain, which could in the long term have sustained impacts on smallholder cocoa farmers. However, we recommend a comprehensive risk analysis of the cocoa supply chain to enhance the human rights of cocoa farmers.
Environmental pollution by plastics is a global issue of increasing concern. However, microplastic analysis in complex environmental matrices, such as soil samples, remains an analytical challenge. Destructive mass-based methods for microplastic analysis do not determine plastics’ shape and size, which are essential parameters for reliable ecological risk assessment. By contrast, nondestructive particle-based methods produce such data but require elaborate, time-consuming sample preparation. Thus, time-efficient and reliable methods for microplastic analysis are needed. The present study explored the potential of frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) for rapidly and reliably identifying as well as differentiating plastics and natural materials from terrestrial environments. We investigated the fluorescence spectra of ten natural materials from terrestrial environments, tire wear particles, and eleven different transparent plastic granulates <5 mm to determine the optimal excitation wavelength for identification and differentiation via FD-FLIM under laboratory conditions. Our comparison of different excitation wavelengths showed that 445 nm excitation exhibited the highest fluorescence intensities. 445 nm excitation was also superior for identifying plastic types and distinguishing them from natural materials from terrestrial environments with a high probability using FD-FLIM. We could demonstrate that FD-FLIM analysis has the potential to contribute to a streamlined and time-efficient direct analysis of microplastic contamination. However, further investigations on size-, shape-, color-, and material-type detection limitations are necessary to evaluate if the direct identification of terrestrial environmental samples of relatively low complexity, such as a surface inspection soil, is possible.