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The data presented here contain information on cheating behavior from experiments and general self-reported attitudes related to honesty-related social norms and trust, together with individual-level demographic variables. Our sample included 493 university students in five countries, namely, Germany, Vietnam, Taiwan, China, and Japan. The experiment was monetarily incentivized based on the performance on a matrix task. The participants also answered a survey questionnaire. The dataset is valuable for academic researchers in sociology, psychology, and economics who are interested in honesty, norms, and cultural differences.
The concept of Circular Economy (CE) is becoming increasingly important in the pursuit of more sustainable societies. CE strategies are being applied in the sustainable management of a plethora of areas, such as energy, water, food and eco-industrial parks. The present paper focuses on the question of how CE principles can support the sustainable management of water in the agricultural sector around the world, considering different legislative environments, water resources management guidelines, environmental stressors, and CE practices. Considering these practices and circumstances, seven countries were compared: Brazil, Germany, Japan, Mexico, Morocco, Portugal, and Taiwan. Together, CE experts in the seven countries developed a set of 44 criteria to assess each of these areas. Broader establishment and respect of water resources legislation was found to be strongly correlated with lower agricultural water use. While the application of CE practices was found to not be correlated with lower consumption, this is still novel in most countries. Based on the studied countries, it can be concluded that a global CE agenda has not been reached for water resources. Further application and variety of practices is required to better represent the impact of CE on a national scale, but local success stories could support the wider application of CE in agriculture. The findings and the framework of the study can be applied to other countries in directing CE strategies for more sustainable water use in agriculture. Increasing CE implementation, motivated by legislation and better management can help ensure water security throughout nations.
Disconnection in a left-hemispheric temporo-parietal network impairs multiplication fact retrieval
(2023)
Arithmetic fact retrieval has been suggested to recruit a left-lateralized network comprising perisylvian language areas, parietal areas such as the angular gyrus (AG), and non-neocortical structures such as the hippocampus. However, the underlying white matter connectivity of these areas has not been evaluated systematically so far. Using simple multiplication problems, we evaluated how disconnections in parietal brain areas affected arithmetic fact retrieval following stroke. We derived disconnectivity measures by jointly considering data from n = 73 patients with acute unilateral lesions in either hemisphere and a white-matter tractography atlas (HCP-842) using the Lesion Quantification Toolbox (LQT). Whole-brain voxel-based analysis indicated a left-hemispheric cluster of white matter fibers connecting the AG and superior temporal areas to be associated with a fact retrieval deficit. Subsequent analyses of direct gray-to-gray matter disconnections revealed that disconnections of additional left-hemispheric areas (e.g., between the superior temporal gyrus and parietal areas) were significantly associated with the observed fact retrieval deficit. Results imply that disconnections of parietal areas (i.e., the AG) with language-related areas (i.e., superior and middle temporal gyri) seem specifically detrimental to arithmetic fact retrieval. This suggests that arithmetic fact retrieval recruits a widespread left-hemispheric network and emphasizes the relevance of white matter connectivity for number processing.
Background: In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient’s case.
Objective: The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values.
Methods: We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting–based algorithm and compared their performance on both data sets.
Results: The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%.
Conclusions: In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients’ diagnosis and treatment.
For a detailed discussion of process mining, the objective of this paper is the analysis of the successful implementation of process mining in the practical fields of supply chain management. The research comprises the investigation of use cases in companies that are already actively using process mining.
Purpose: This research aims to highlight the applicability of process mining in the supply chain management business field.
Research Methodology: In order to examine the applicability of process mining in supply chain management a research study was conducted among experts in this business field. Further, theoretical findings were compared to the results and evaluated.
Results: Process Mining can be applied very well in the SCM area. The advantages that arise primarily reflect significant potential benefits and improved process throughput times. The information that can be gained from the operational areas supported by process mining is suitable for reliable decisions, both in the tactical and strategic areas.
Limitations: The results on the application of process mining show a certain generalization and have to be adapted and adjusted to the respective application case.
Contribution: This study is useful, especially for the purchasing and logistics business area.
The integration of genetic algorithms to optimize the networks of value chains could enormously improve the performance of supply chains. For this reason, this paper describes in more detail the application of genetic algorithms in the value chains of the automotive industry. For this purpose, a theoretical model is built up to evaluate whether the application of the model can optimize the value chain. This option is described, analyzed and its restrictions are shown. Instead of looking at the entire network, individual finished goods and their bill of material are used as a basis for optimization, which greatly reduces the complexity of the original problem. The original complexity of the supply chain networks can thus be reduced and considered based on the bill of material.
The future of German pharmacy business models with retail clinics as USP against online pharmacies
(2022)
Purpose: This study aims to discuss the chances of in-store pharmacies through in-store health services, e. g. vaccinations or Covid-19 tests, in Germany. As a result, retail clinics could lead to a USP against online pharmacies. Additionally, the study will show how possible retail clinics could look like and how the acceptance in the population is.
Research Methodology: To conduct the survey, Google Forms was used together with MS Excel for the analysis. Various studies were reviewed, and care was taken to work as closely as possible to practice and its figures from e. g. associations, companies, or statista.
Results: The launch of retail clinics could give German stationary pharmacies a new boost and differentiate them from online pharmacies on the market. Pharmaceutical services are in great demand among the population, hopefully, the legal framework will soon be in place, and the proper services will have to be offered to be able to fully generate the large sales potential.
Discussion and Conclusion: The study only points to a general overview of how health services can help store pharmacies in the competition with online pharmacies in Germany. Yet there are also other possible USPs for store pharmacies, which are not concluded in that study. In addition, the legal framework under which pharmacies operate must be analyzed in detail by legal experts to obtain a precise overview of what is possible for pharmacies in the area of pharmaceutical services and retail clinics. The study is useful for pharmacists, business economists in general as well as in health care management.
A study of industry 4.0 technologies in the John Deere and Company and their impact on company operations is presented in this paper. Deere and Company’s implementation of Industry 4.0 to its factories and its factors was the focus of the research. The literature review with the systematic approach as well as a comprehensive review of current John Deere and Company’s developments is used in the current study. Also, it relied on freely available information on the company website. Public and investor relations have also been used as credible sources of information. An analysis found that adopting industry 4.0 technologies to agriculture manufacturing results in higher quality products, increased productivity, safety, and wider acceptance among stakeholders. This study assumes full implementation of these technologies in all agriculture manufacturing companies, and it also emphasizes up-to-date technologies. Studying this topic can be useful for engineers in mechanical and agricultural fields, managers in business, and marketers.
The objective of this study is to allow a better understanding of the role of industry 4.0 technologies, especially filament extrusion technology in the reduction of costs, environmental impact, energy consumption, and the possibility to expand the range of printable materials. The study focuses on the desktop Filament Extruders available in the market now, where these machines are assessed and future possible modifications for these apparatuses are presented. The research leading to the publication of this study consists of a review of the existing literature, in addition, information from different extruders manufacturers’ websites has been used. The study has demonstrated that the extrusion of material at home is still not an exact science, and the process ends up costing the user large sums of money over time. However, there are still limitations to the use of this technology such as the lack of standardized extrusion settings, the necessity of pre-drying the pellets, and the complexity of the extruder cleaning process after each use.
Implementation strategies of a modern showroom concept for retailers with a wide range of products
(2022)
This paper suggests a new business model based on modern technology for retail. In the age of digitalization, stationary retail is losing market shares to online retail. Therefore, there is an obvious need for change in businesses. The concept developed in this paper combines the strengths of online and stationary retail to benefit stationary retail. In the approach taken in this paper, the basis for change is modern technology. Finding innovative ways to use technologies like NFC, AI, and robotics is regarded as the key factor to sustainable success. As the implementation of modern technologies entails a particular investment, the customers’ opinion on structural changes like these has been included in the consideration. Therefore, a survey has been conducted to find out which level of innovation current customers are willing to accept thus emphasizing the need for certain changes and dissuading specific others. The result of this paper is the modern showroom concept which takes the customers’ opinion into account while implementing the right amount of technology that should pave the way to a sustainable future for stationary retail.