15,686 research outputs found

    Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective

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    This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimation for vital production parameters, exclusive generation of point forecasts without considering model uncertainty, and lacking explanations regarding the sources of such uncertainty. Our approach employs Quantile Regression Forests for generating interval predictions, alongside both local and global variants of SHapley Additive Explanations for the examined predictive process monitoring problem. The practical applicability of the proposed methodology is substantiated through a real-world production planning case study, emphasizing the potential of prescriptive analytics in refining decision-making procedures. This paper accentuates the imperative of addressing these challenges to fully harness the extensive and rich data resources accessible for well-informed decision-making

    POLARIZATION OF LOCAL COMMUNITY PERCEPTION ON SOCIOCULTURAL DYNAMICS IN ECOTOURISM DEVELOPMENT OF BOPUNJUR, WEST JAVA

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    In addition to providing multiplier economic benefits, the tourism sector also has the potential to cause some latent and massive negative social impacts. For this reason, it is important to map out the orientation of the local community. This study analyzes the polarization of the local community's perceptions of sociocultural dynamics in the ecotourism development area. The local community that became the focus of the research consisted of five groups of respondents: traditional leaders, religious leaders, educational leaders, community leaders, and tourism actors. This research was conducted in the Bopunjur Ecotourism Area, Bogor Regency, West Java, precisely in seven ecotourism destinations, namely Ciawi, Caringin, Cibogo, Cipayung, Megamendung, Cisarua, and Tugu. This study used mixed methods, qualitative and quantitative approach. Data collection on social and cultural dynamics was done by distributing questionnaires to the respondents. The research instrument was a questionnaire designed closed-ended with guidance on one score-one indicator scoring system. The results showed that positive social situations, namely conducive situations, associations, cooperative situations, and productive collaborations were still more dominant than negative social situations: war, conflict, and dissociation. The polarization of the local community on sociocultural dynamics has a positive direction with a polarization scale that is aligned with each other so that there is an excellent opportunity to build productive collaboration among stakeholders in this are

    Out of Box Thinking to Tangible Science: A Benchmark History of 3D Bio-Printing in Regenerative Medicine and Tissues Engineering

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    Advancements and developments in the 3D bioprinting have been promising and have met the needs of organ transplantation. Current improvements in tissue engineering constructs have enhanced their applications in regenerative medicines and other medical fields. The synergistic effects of 3D bioprinting have brought technologies such as tissue engineering, microfluidics, integrated tissue organ printing, in vivo bioprinted tissue implants, artificial intelligence and machine learning approaches together. These have greatly impacted interventions in medical fields, such as medical implants, multi-organ-on-chip models, prosthetics, drug testing tissue constructs and much more. This technological leap has offered promising personalized solutions for patients with chronic diseases, and neurodegenerative disorders, and who have been in severe accidents. This review discussed the various standing printing methods, such as inkjet, extrusion, laser-assisted, digital light processing, and stereolithographic 3D bioprinter models, adopted for tissue constructs. Additionally, the properties of natural, synthetic, cell-laden, dECM-based, short peptides, nanocomposite and bioactive bioinks are briefly discussed. Sequels of several tissue-laden constructs such as skin, bone and cartilage, liver, kidney, smooth muscles, cardiac and neural tissues are briefly analyzed. Challenges, future perspectives and the impact of microfluidics in resolving the limitations in the field, along with 3D bioprinting, are discussed. Certainly, a technology gap still exists in the scaling up, industrialization and commercialization of this technology for the benefit of stakeholders

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Corporate Social Responsibility: the institutionalization of ESG

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    Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective

    The Great Green Wall Initiative in Mali - Country Review

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    Allergies to food and airborne allergens in children and adolescents : role of epigenetics in a changing environment

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    Allergic diseases today affect millions of children and adolescents worldwide. In this review, we focus on allergies to food and airborne allergens, and provide examples of prevalence trends during a time when climate change is of increasing concern. Profound environmental changes have affected natural systems in terms of biodiversity loss, air pollution levels and climate change. We discuss potential links between these changes and allergic diseases in children, as well as clinical implications. Several exposures of relevance for allergic disease also correlate with epigenetic changes such as DNA-methylation levels. We propose that epigenetics may offer a promising tool by which exposures and hazards related to a changing environment may be captured. Epigenetics may also provide promising biomarkers and help elucidation of mechanisms related to allergic disease initiation and progress. Key messages: • Allergic diseases affect millions of children and adolescents worldwide; between 5 and 30% of adolescents report rhino-conjunctivitis symptoms and up to 10 % report food allergy. • Links between climate change and allergic diseases are of increasing concern, and these include: extended and altered pollen seasons, spread of allergens to new areas along with changing and warmer climate, air pollution exposures changes, increasing exposure to heat events, and altered biodiversity. • These new climate change aspects of allergic diseases have clinical implications for prevention, diagnostics and treatment. • Epigenetic changes, exemplified by DNA methylation, are associated both with environmental exposures and allergic diseases, although causality needs to be explored further. • There is potential in the use of epigenetic signatures and omics profiles to detect and monitor aspects of environmental exposures of relevance for health and disease in children and adolescents.H2020 research program (TRIBAL, No 757919; EXPANSE project, No 874627; Prominent)Swedish Research CouncilSwedish Heart-Lung FoundationRegion StockholmUS National Institutes of Health (R01 AI118833, R01 AI147028, U01 AI160082, and U19 AI136053)ZON-MW (VICI grant)Netherlands Lung FoundationGSKVertexTEVA the NetherlandsNovo Nordisk Foundation Challenge Programme (#NNF17OC0027812)Accepte

    A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching

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    An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members' willingness to communicate on team efficiency, with the person-job matching score calculated using intuitionistic fuzzy numbers. Then, a reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP adopts the ensemble population strategies. Before the population evolution at each generation, the agent selects one from four population search modes according to the information obtained, thus realizing a sound balance of exploration and exploitation. In addition, surrogate models are used in the algorithm to evaluate the formation plans generated by individuals, which speeds up the algorithm learning process. Afterward, a series of comparison experiments are conducted to verify the overall performance of RL-GP and the effectiveness of the improved strategies within the algorithm. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams. This study reveals the advantages of reinforcement learning methods, ensemble strategies, and the surrogate model applied to the GP framework. The diversity and intelligent selection of search patterns along with fast adaptation evaluation, are distinct features that enable RL-GP to be deployed in real-world enterprise environments.Comment: 16 page
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