3633 research outputs found
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Intelligent scheduling based on discrete-time simulation using machine learning
Get PDFDynamic planning is becoming more and more critical. The changing external and internal conditions for planning require a fast response. Due to the complexity, planning tasks cannot be solved in a closed loop. Instead, heuristics are used to solve the task. A frequently used method is the discrete-time simulation. However, this requires different simulation parameters to carry out the planning. The orchestration of the parameters is intricate because of many parameters, respectively, parameters combinations and their dependencies within the simulation. Machine Learning (ML) is an essential technology for adapting parameters under changing conditions. Using ML, this paper presents an approach that allows an optimized configuration of the planning parameters depending on the data. The method is evaluated based on real-world data sets from the industry
Claude rules: An evaluation of large language models’ applicability to solve cases in German business law
Get PDFIn the evolving field of legal information systems, Claude 3 and other advanced conversational agents (CAs) are emerging as transformative forces. This interdisciplinary study combines quantitative methods, legal analysis, and digital transformation approaches to evaluate the efficacy of leading commercially available CAs in the German legal environment. Employing a corpus of 200 unique legal tasks, the research benchmarks Claude 3 against notable systems such as Google Gemini and ChatGPT versions 4 and 3.5. Through automated evaluations of 1,600 responses generated by these CAs, Claude 3 is demonstrated to be the most effective system, capable of successfully addressing realistic legal challenges and passing a German business law examination with an overall score of 60%—significantly surpassing the 50% score of the previous performance leader ChatGPT-4. Despite its superior performance, Claude 3, along with other evaluated systems, exhibits considerable limitations that can be difficult to identify. Based on these insights, it is recommended that legal professionals thoroughly verify all CA-generated content before use. Additionally, caution is advised for novices utilizing CA-generated legal advice, due to the specialized knowledge required for proper evaluation. This study contributes to the ongoing study of digital transformation in the legal domain, offering insights for both academic and industry stakeholders
Nachhaltigkeitsmanagement in Kleinunternehmen pragmatisch umsetzen
No full textNachhaltigkeit im Unternehmen ist ein strategischer Erfolgsfaktor und die Erwartungen von Kunden, Lieferanten und Kapitalgebern steigen stetig. Doch viele kleine Unternehmen tun sich damit schwer und haben bis dato noch keine strategische Antwort auf die sie selbst betreffenden Nachhaltigkeitsfragen. Sie fühlen sich getrieben, wenn ihre gesetzlich dazu verpflichteten Großkunden aufwendige Anforderungen an sie weitergeben. Daher führt auch für sie kein Weg daran vorbei, Schritt für Schritt ein professionelles und ihrer Größe angemessenes Managementsystem aufzubauen. Jetzt
The development of a Raman spectroscopy-based workflow for the identification of salivary gland tumor tissue and the discussion of the barriers of translation of spectroscopic methods
No full textThe pre-, intra- and postoperative determination of the entity and dignity of salivary gland tumors (ST) based solely on histomorphological criteria is not reliably in all cases. The spectra of Raman spectroscopy (RS) contain information about the molecular composition of the examined tissue. The aim of the work was to establish an RS-based measurement setup and a workflow for the differentiation of salivary gland tumor tissue and salivary gland tissue. In addition, the barriers of translating RS in salivary gland diagnostics are discussed. 10 µm thick, native cryo-tissue sections of Warthin tumors (n=5) and pleomorphic adenomas (n=4) were examined using RS in both tumor tissue and healthy salivary gland tissue and the data were evaluated in a multivariate data analysis. All measurements were histomorphologically localized in a corresponding HE section. A "principal component" analysis (PCA) of the RS data and coupled discriminant analysis enabled both a distinction between tumor and non-tumor tissue as well as the differentiation of the various tumor entities (based on the histopathological assessment) with a high level of accuracy (93% ). In summary, it could be shown that the RS measurements could be used to reliably distinguish between ST and healthy salivary gland tissue. Another important result is that tissue processing is possible reliably using standard pathological methods. The high number of different ST entities represents a biostatistical challenge. Approaches to the solution include multi-level statistical models and simultaneous correlation with histomorphological criteria
Geschlechter verschmelzen immer mehr
No full textGerd Nufer ist Professor für Betriebswirtschaftslehre mit den Schwerpunkten Marketing, Handel und Sportmanagement an der ESB Business School der Hochschule Reutlingen. Im Interview spricht er zum Thema Gender-Marketing
Behavioral theory of the firm
No full textThis article discusses the behavioral theory of firms and how it lays the foundation for behavioral strategy, which combines elements of social psychology and cognitive frameworks with strategic management principles and practices. The goal is to highlight key contributions from various authors by comparing their work. Emphasis is placed on the behavioral theory of firms and its significance to behavioral strategy that integrates social psychology and cognitive aspects with strategic management practices. However, this area of study is still relatively untapped, and more research is needed to develop comprehensive approaches
From crop to click - organic and digital transformation of out-of- home catering value chains in Germany
No full textAn important aspect of achieving global climate neutrality and food security is transforming our food system. To support the goal, Germany has set a national target of reaching a 30% share in organic farming. When looking at the transformation process from conventional to organic farming, it becomes apparent that measures need to be taken to reach the anticipated goal. Using Design Science Research, we model and analyze the as-is farm-to-fork value chain of public out-of-home-eaten meals to identify the central barriers and drivers of organic transformation. From the insights gained in the modeling process, we derive a digital platform model that addresses the current issues. We propose a digitally supported value network instead of a hierarchical value chain to share the co-design opportunities for different stakeholders more equally. We then elaborate on the potential to overcome the barriers to organic transformation with the network-based platform. To specify the main functionalities of the digital platform architecture, we map user requirements with the proposed to-be value network. The results further emphasize the need for a change in the current value chain perspective. We conclusively propose to further develop existing approaches under consideration of our identified requirements and the overall sustainability goal, rather than focusing solely on individual dimensions or metrics
Software business : 14th International Conference, ICSOB 2023, Lahti, Finland, November 27–29, 2023, proceedings. - (Lecture notes in business information processing ; 500)
No full textThis open access book constitutes the refereed proceedings of the 23rd International Conference on Software Business, ICSOB 2023, which was held in Lahti, Finland, during November 27–29, 2023. The special theme of ICSOB 2023 was Digital Agility: Mastering Change in Software Business and Digital Services. The 27 full papers and 8 short papers presented in this book were carefully reviewed and selected from 79 submissions. They were organized in topical sections as follows: Requirements; software procurement; platforms, ecosystems and data; artificial intelligence; software startups; software product management; software and business co-development; and emerging digital world
Arbeitsrechtliche Aspekte hybrider Arbeit
No full textDie Arbeitswelt ist im Wandel. Hybride Arbeitsformen in Form von mobilem Arbeiten und Homeoffice gewinnen – angetrieben durch die Corona-Pandemie und die zunehmende Digitalisierung – rasant an Bedeutung. In zahlreichen Unternehmen gehört mobiles Arbeiten bereits zum Alltag. Arbeitnehmerinnen und Arbeitnehmer erlangen durch hybride Arbeitsformen mehr Zeitsouveränität und Flexibilität, was zu einer besseren Vereinbarkeit von Beruf, Freizeit und Familie und damit zu einer besseren Work-Life-Balance beiträgt (vgl. Lott et al. 2021, S. 7). Um für Bewerberinnen und Bewerber auch künftig attraktiv zu sein, kann sich daher kein Arbeitgeber diesem Thema verschließen. Gleichzeitig droht beim mobilen Arbeiten die Grenze zwischen Arbeit und Freizeit zu verschwimmen. Ständige Erreichbarkeit und soziale Isolation können ein Gesundheitsrisiko für die Beschäftigten darstellen. Ziel dieses Beitrags ist es, einen aktuellen Überblick über die rechtlichen Aspekte hybrider Arbeit zu geben. Außerdem soll aufgezeigt werden, wo derzeit rechtlicher Handlungsbedarf besteht, um den Chancen und Risiken hybrider Arbeit zu begegnen
Unsupervised question-retrieval approach based on topic keywords filtering and multi-task learning
No full textCurrently, the majority of retrieval-based question-answering systems depend on supervised training using question pairs. However, there is still a significant need for further exploration of how to employ unsupervised methods to improve the accuracy of retrieval-based question-answering systems. From the perspective of question topic keywords, this paper presents TFCSG, an unsupervised question-retrieval approach based on topic keyword filtering and multi-task learning. Firstly, we design the topic keyword filtering algorithm, which, unlike the topic model, can sequentially filter out the keywords of the question and can provide a training corpus for subsequent unsupervised learning. Then, three tasks are designed in this paper to complete the training of the question-retrieval model. The first task is a question contrastive learning task based on topic keywords repetition strategy, the second is questions and its corresponding sequential topic keywords similarity distribution task, and the third is a sequential topic keywords generation task using questions. These three tasks are trained in parallel in order to obtain quality question representations and thus improve the accuracy of question-retrieval task. Finally, our experimental results on the four publicly available datasets demonstrate the effectiveness of the TFCSG, with an average improvement of 7.1%, 4.4%, and 3.5% in the P@1, MAP, and MRR metrics when using the BERT model compared to the baseline model. The corresponding metrics improved by 5.7%, 3.5% and 3.0% on average when using the RoBERTa model. The accuracy of unsupervised similar question-retrieval task is effectively improved. In particular, the values of P@1, P@5, and P@10 are close, the retrieved similar questions are ranked more advance