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The necessity for resource-efficient manufacturing technologies requires new developments within the field of plastic processing. Lightweight design using wood fibers as sustainable reinforcement for thermoplastics might be one solution. The processing of wood fibers requires special attention to the applied thermal load. Even at low processing temperatures, the influence of the dwell time, temperature and shear force is critical to ensure the structural integrity of fibers. Therefore, this article compares different compounding rates for polypropylene with wood fibers and highlights their effects on the olfactory, visual and mechanical properties of the injection-molded part. The study compares one-step processing, using an injection-molding compounder (IMC), with two-step processing, using a twin-scew-extruder (TSE), a heating/cooling mixer (HCM) and an internal mixer (IM) with subsequent injection molding. Although the highest fiber length was achieved by using the IMC, the best mechanical properties were achieved by the HCM and IM. The measured oxidation induction time and volatile organic compound content indicate that the lowest amount of thermal damage occurred when using the HCM and IM. The advantage of one-time melting was evened out by the dwell time. The reinforcement of thermoplastics by wood fibers depends more strongly on the structural integrity of the fibers compared to their length and homogeneity
The study at hand presents a methodology to determine the utilization of public charging infrastructure by the proportion of effective charging time and idle time. The results of the study can be used by decision makers to better understand the usage behavior of public charging infrastructure and to align the provision of charging infrastructure with the behavior of users. The user behavior is analyzed using charging areas and scenario-based analysis of charging sessions. More than 300,000 charging sessions at AC and DC charging stations in the urban area of Munich in 2020 were recorded as the data basis for the study. The methodology takes the available vehicle models in the study area as well as the characteristic charging behavior of plug-in electric vehicles (PEV) with a decreasing charging power at high states of charge of the battery into account. The results show a high proportion of idle time at AC charging stations. The outcome of a correlation analysis additionally indicates a correlation between the share of idle time and the proportion of high-density urban living in the survey area.
Recently, the SQL standardization Committee published a specification for support of the concept called Row Pattern Recognition in SQL. That way, the focus for the members of the Committee turned back to a kernel issue of the language, after standardizing the storage and manipulation of data formats, such as XML and JSON. In this paper we discuss the specified features using several examples and show to what extent different relational database systems as well as Data Analytics tools have integrated them. At the end of the paper, we describe the main inaccuracies of the proposal and the ways how to solve them. From our point of view, the following should be modified in one of the future proposals of the standard in relation to this concept: (a) Naming of several pattern navigation operations is inappropriate and should be changed; (b) The concept of implicit definition for row pattern variables in DEFINE should be changed to explicit; (c) The set of existing functions should be extended.
In today's big data era, a humongous amount of data are collected from various sources. In many cases, these data are incomplete, imprecise, and uncertain. An illustrative example is the OpenStreetMap project, where users all over the world contribute data on a more or less precise and complete level. This research shows whether these data are suited to support management decisions. A real-world example demonstrates the extent to which location decisions of a fast-food restaurant chain can be reproduced using techniques from the field of advanced analytics. The problem deals with classifying potential locations and comparing the predicted locations with the actual ones. The data used for this example are retrieved from the OpenStreetMap project. We find that the OpenStreetMap data are generally suitable for predicting location decisions. However, the choice of the data analytics technique is crucial. In our illustrative example case, boosted trees resulted in the best forecast, thereby outperforming neural networks, classic trees, and logit models.
Road freight transportation accounts for a great share of the anthropogenic greenhouse gas (GHG) emissions. In order to provide a common methodology for carbon accounting related to transport activities, the European Committee for Standardization has published the European Norm EN-16258. Unfortunately, EN-16258 contains gaps and ambiguities and leaves room for interpretation, which makes the comparison of the environmental performance of different logistics networks still difficult and hinders the identification of best practices. This research contributes to the identification of particularly meaningful principles for the allocation of GHG to shipments in road freight transportation by presenting an analytical framework for studying the performance of the EN-16258 allocation schemes with respect to accuracy, fairness, and the GHG minimizing incentive. In doing so, we continue previous studies that analyzed two important aspects of the EN-16258 allocation rules: accuracy and fairness. This study provides further insights into this allocation problem by investigating the incentive power of the different allocation schemes to opt for the GHG minimal way of running a road freight network. First, we complement the list of transport scenarios introduced in prior studies and present two novel scenarios. Second, we carry out a series of numerical experiments to compare the EN-16258 allocation rules with respect to accuracy, fairness, and the GHG minimizing incentive. We find that the results may differ significantly for the two scenarios, suggesting a case-by-case recommendation. This is particularly interesting because the first scenario confirms the results of the prior studies, while the second scenario rather contradicts them.
Many supply chains within developing countries lack transparency and are fraught with fraud, corruption, and a substantial number of intermediaries. For several decades, the cocoa sector has faced multiple social, economic, and environmental challenges, some of which include the issue of child labor and very low incomes for farmers, leading to poor living conditions. Blockchain technology has a high potential to reduce—or completely eradicate—some of these hurdles. In this article, we present a blockchain-based solution based on the open-source framework Hyperledger Fabric for the cocoa supply chain to promote transparency and reduce fraud. In doing so, we explicitly describe how farmers can be directly integrated into the whole blockchain solution considering the limited infrastructure, knowledge, and technologies available to them. Since about 70% of all cocoa worldwide is produced in West Africa, this case study uses the cocoa sector in Ghana as an example.
The applications for occupancy detection range from controlling building automation and systems, determining heat transfer coefficients and even assessing the risk of infection in rooms. Studies in the literature use various statistical models, physical models and machine learning techniques to detect occupancy. All these methods require data for training the occupancy detection models. However, data generation is time-consuming and expensive. This study demonstrates the feasibility of using simulated learning data. Using three different data sources, we tested the suitability of different methods for generating learning data. We conducted two experiments in two office spaces with a real user and an artificial user, and we generated a third data set using a building simulation model. In addition, this study compares two different machine learning approaches (Random Forest and LASSO) using environmental parameters. Both machine learning approaches could develop models with a sensitivity of at least 83 % and a specificity of at least 97 % with both training data sets. This work shows that it is possible to determine the presence in rooms using simulated data. The results compared to measured data were just slightly less accurate, and the added value due to the lower effort was considerable