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A cooperative fruit fly optimization algorithm for energy-efficient scheduling of distributed permutation flow-shop with limited buffers
Get PDFThe scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environments. However, the energy efficiency metric is often ignored. The Energy-efficient Scheduling of Distributed Permutation Flow shop with Limited Buffer (EEDPFSP-LB) with the objectives of Makespan (Cmax) and Total Energy Consumption (TEC) is studied, and a Cooperative Fruit Fly Optimization Algorithm (CFOA) is proposed in this paper. First, the critical path of EEDPFSP-LB is identified, and energy-efficient operation are applied to non-critical paths to reduce the system’s energy consumption. Second, five acceptance criteria for multi-objective optimization are introduced to enhance the diversity of the population. Third, to select a superior next-generation population, a new congestion calculation method is introduced to resolve the issue of indeterminate positional relationships among non-dominated solutions with identical crowding distances at the same dominance level. Finally, CFOA is extensively tested and compared with state-of-the-art algorithms across 360 instances, demonstrating CFOA’s strong competitiveness in solving EEDPFSP-LB
Optofluidics : process analytical technology
No full text"Optofluidics : Process Analytical Technology" offers in its 2nd edition a distinctive foundational introduction to the realms of materials, photonics, fluidics, and sensors. The work serves to unify the disparate disciplines, integrating the requisite fundamental knowledge with applied science. It thus establishes a new standard and definition for both the academic and industrial fields.
It encompasses the requisite in-depth knowledge of smart materials, semiconductor processing, optical waveguiding and fluid dynamics. The objective of this distinctive publication is to present information in a readily comprehensible format that can be readily applied in everyday situations. It is truly interdisciplinary but not overloading with information, providing the highly required and relevant information to become an expert in this exciting area, which is gaining more and more relevance and recognition in the context of sensing, material science and automation in biotechnology and pharmaceutical manufacturing.
The concept of the book is to serve as a textbook for advanced beginners from all life science, engineering and physics disciplines, providing self-assessment questions and further reading recommendations for further guidance and in-depth learning
Enhancing individual self-efficacy through a self-growing memory artificial intelligence agent integrated with a diary application
Get PDFThis paper introduces an artificial intelligence (AI) interactive system featuring a self-growing memory network designed to enhance self-efficacy, reduce loneliness, and maintain social interaction among the elderly. The system dynamically analyzes and processes user-written diaries, generating empathic and personalized responses tailored to each individual. The system architecture includes an experience extraction model, a self-growing memory network that provides a contextual understanding of the user’s daily life, a chat agent, and a feedback loop that adaptively learns the user’s behavioral patterns and emotional states. By drawing on both successful and challenging experiences, the system crafts responses that reinforce the self-efficacy of the user, fostering a sense of accomplishment and engagement. This approach improves the psychological well-being of elderly users and promotes their mental health and overall quality of life through consistent interaction. To validate our proposed method, we developed a diary application to facilitate user interaction and collect diary entries. Over time, the system’s capacity to learn and adapt further refines the user experience, suggesting that AI-driven solutions hold significant potential for mitigating the effects of declining self-efficacy on mental health and social interactions. With the proposed system, we achieve an average system usability scale score of 77.3 (SD = 5.4) and a general self-efficacy scale score of 34.2 (SD = 3.5)
Virtual influencers in B2B marketing : future perspectives
No full textThis study explores the potential role of virtual influencers in future B2B marketing. Combining expert interviews and a literature review, the findings reveal the growing popularity of virtual influencers due to Web 3.0 and Metaverse advancements. The study suggests that virtual influencers may revolutionize brand-customer interactions in the digital space, with implications for B2B marketing strategies
Large language model application frameworks for domain-specific chatbots
No full textThe evolution of chatbots has been marked by ongoing efforts to bridge the gap between general language understanding and specialized domain knowledge. Previous efforts have primarily focused on improving language models for broader applicability, often overlooking the nuanced requirements of specific domains. A significant step forward is taken in the research by demonstrating how the Retrieval-Augmented Generation methodology can be seamlessly integrated with LangChain and LlamaIndex to overcome this limitation. This integration not only improves chatbot performance in specialized contexts but also sets a new precedent for the adaptability of chatbot technologies. The practical implications are vast, ranging from improved user experience in customer service to increased efficiency in data-sensitive environments such as healthcare and finance. A distinctive contribution to the field of generative Artificial Intelligence is marked by the innovative approach, paving the way for more sophisticated, context-aware chatbot applications
Selbstfreundschaft pflegen
No full textUm Menschen in ihrer Lebensführung unterstützen zu können, richtet sich Soziale Arbeit als normative Handlungswissenschaft letztlich am gelingenden und guten Leben aus. Alice Salomon bestimmte Soziale Arbeit als eine Kunst, das Leben zu lehren und das Leben selbst als die schwierigste Kunst. Diese Arbeit, welche sich dem ganzen Menschen annimmt, beansprucht auch die ganze Persönlichkeit. Damit wendet sich der professionelle Blick von der Sorge um andere zur besinnenden Sorge um sich selbst, um eben hilfreich für andere da sein zu können
Controlling fibrinogen adsorption on polyelectrolyte multilayer films by modifying self-assembly conditions
Get PDFThis study investigates the relationship between fibrinogen adsorption on polyelectrolyte multilayer (PEM) films and their surface properties. The films were constructed using weak polyelectrolytes, poly(acrylic acid) (PAA), and poly(allylamine hydrochloride) (PAH), with polyethyleneimine (PEI) as a precursor layer. Different deposition conditions, such as pH levels (3.5 and 7.0) and the type of outermost layer (PAA or PAH), were used to create films with tunable hydrophilicity and surface charge. The thickness of these films was measured using ellipsometry, and surface wettability was assessed via the contact angle method. Fibrinogen adsorption was quantified using quartz crystal microbalance with dissipation monitoring (QCM-D) and enzyme immunoassay (EIA) method, focusing on its D-domain exposure concerning surface thrombogenicity. Results indicated that PEM films are generally hydrophilic. Among them, PAH, the outermost layer, is the least hydrophilic and, therefore, has the lowest surface energy. Film thickness varied with the pH of the solutions, creating a mechanism to control layer parameters. Fibrinogen adsorption was more pronounced on less hydrophilic surfaces, which were thinner, viscoelastic, more hydrated, and preferentially positively charged. These findings suggest that by controlling surface properties, one can enhance hemocompatibility by influencing fibrinogen adsorption and subsequent platelet adhesion and activation
Application of AI maturity models to SMEs: insights into German enterprises
No full textArtificial intelligence is a key driver of the Fourth Industrial Revolution, with significant business implications and immense potential for small and medium-sized enterprises (SMEs). In Germany, where SMEs account for 99.6% of companies and drive economic output, Artificial Intelligence (AI) adoption is critical for maintaining global competitiveness. However, SMEs face financial, technical, and organizational challenges that hinder AI adoption. This study evaluates the feasibility of AI maturity models for SMEs in Germany’s manufacturing sector. We apply a case-study approach to two manufacturing SMEs through a comparative analysis of a theoretical framework and a model from practitioners. Findings reveal that while the theoretical model offers granular insights, its complexity limits usability, whereas the practitioner framework provides accessible benchmarking but lacks actionable guidance. The study identifies gaps in SME-specific AI Maturity Models (AIMMs) and proposes hybrid frameworks and modular self-assessment tools, ensuring that SMEs remain competitive in a rapidly evolving landscape
Westlicher Lebensstil, Nachhaltigkeit und das Menschenbild der Sozialen Arbeit
No full textProblematisch ist nicht, dass die Weltbevölkerung im Jahr 2023 die Acht-Milliarden-Marke überschritten hat, sondern vielmehr, dass global der Lebensstil westlicher Gesellschaften angestrebt wird. Der dort gelebte Wirtschafts- und Konsumstil strahlt mit seinem Versprechen eines materiell guten Lebens eine hohe Attraktivität aus. Dieser ressourcenverbrauchende Wohlstand ist zum globalen Exportschlager geworden, welcher durch exzessiven Konsum, Mobilität und Abfall zu Umweltzerstörung, Migration, sozialer Ungleichheit und Ressourcenkämpfen führt. Die Umkehr zu einem nachhaltigen Lebensstil setzt eine Änderung der Geisteshaltung voraus. Die hier behandelte Thematik ist weitläufig und komplex und wird in Bezug auf das Menschenbild und anvisierten Wertewandel wie folgt aufgegriffen: Im ersten Schritt wird der westliche Lebensstil in Bezug auf sein Konzept des guten Lebens und dessen inhärentes Menschenbild vorgestellt. Als Kontrast wird dann die Grundstruktur und das Menschenbild der Sozialen Arbeit skizziert, um anschließend eine allgemeine, breit anschlussfähige Positionierung der Sozialen Arbeit im Nachhaltigkeitsdiskurs sichtbar zu machen
A scoping review and quality assessment of machine learning techniques in identifying maternal risk factors during the peripartum phase for adverse child development
Get PDFMaternal exposure to environmental risk factors (e.g., heavy metal exposure) or mental health problems during the peripartum phase has been shown to lead to negative and lasting impacts on child development and life in adulthood. Given the importance of identifying early markers within highly complex and heterogeneous perinatal factors, machine learning techniques emerge as a promising tool. The main goal of the current scoping review was to summarize the evidence on the application of machine learning techniques in predicting or identifying risk factors during peripartum for child development. A critical appraisal was also conducted to evaluate various aspects, including representativeness, data leakage, validation, performance metrics, and interpretability. A systematic search was conducted in PubMed, Web of Science, Scopus, and Google Scholar to identify studies published prior to the 14th of January 2025. Review selection and data extraction were performed by three independent reviewers. After removing duplicates, the searches yielded 10,336 studies, of which 60 studies were included in the final report. Among these 60 machine learning studies, a majority were pattern-focused, using machine learning primarily as a tool to more accurately describe associations between variables, while 16 studies were prediction-focused (26.7%), exploring the predictive performance of their models. For prediction-focused machine learning studies, a diverse range of methodologies was observed. The quality assessment showed that all studies had some important criteria that were not fully met, with deviations ranging from minor to major, limiting the interpretability and generalizability of the reported findings. Future research should aim at addressing these limitations to enhance the robustness and applicability of machine learning models in this field