1,087 research outputs found

    Application of MT method of Mahalanobis-Taguchi system in methadone flexi dispensing program

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    Patient under methadone flexi dispensing (MFlex) program are required to perform blood tests like lipid profile. To verify the patient does have a lipid disorder, a doctor analyses 3 parameters such as cholesterol, HDL cholesterol, and LDL cholesterol. However, the present system lacks a robust ecology for categorization and optimization due to imprecise measuring methods and a lack of rationale for major elements that impact diagnostic accuracy. The goal is to implement the Mahalanobis-Taguchi system (MTS) into the MFlex programme. The data was acquired at the Bandar Pekan clinic and included 34 lipid profile measures. For classification and optimization, two categories of MTS techniques are being used, which are RT-Method and T-Method. As a result of the lipid profile analysis, the healthy Mahalanobis distance (MD) is 1.0000, whereas the unhealthy MD is 79.5876. Positive contributions are made by parameters 1, 3, 4, 6, 7, 8, 9, 11, 12, 17, 18, 23, 26, 27, 28, 30, 31, 33, and 34. 15 unknown samples were diagnosed with varying degrees of positive and negative contribution to obtain a smaller MD. The best recommended way has been typed 5 from overall 6 modifications. Finally, the pharmacist acknowledged that MTS could tackle the issue of MFlex programme categorization

    Evaluating the sustainability and resiliency of local food systems

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    With an ever-rising global population and looming environmental challenges such as climate change and soil degradation, it is imperative to increase the sustainability of food production. The drastic rise in food insecurity during the COVID-19 pandemic has further shown a pressing need to increase the resiliency of food systems. One strategy to reduce the dependence on complex, vulnerable global supply chains is to strengthen local food systems, such as by producing more food in cities. This thesis uses an interdisciplinary, food systems approach to explore aspects of sustainability and resiliency within local food systems. Lifecycle assessment (LCA) was used to evaluate how farm scale, distance to consumer, and management practices influence environmental impacts for different local agriculture models in two case study locations: Georgia, USA and England, UK. Farms were grouped based on urbanisation level and management practices, including: urban organic, peri-urban organic, rural organic, and rural conventional. A total of 25 farms and 40 crop lifecycles were evaluated, focusing on two crops (kale and tomatoes) and including impacts from seedling production through final distribution to the point of sale. Results were extremely sensitive to the allocation of composting burdens (decomposition emissions), with impact variation between organic farms driven mainly by levels of compost use. When composting burdens were attributed to compost inputs, the rural conventional category in the U.S. and the rural organic category in the UK had the lowest average impacts per kg sellable crop produced, including the lowest global warming potential (GWP). However, when subtracting avoided burdens from the municipal waste stream from compost inputs, trends reversed entirely, with urban or peri-urban farm categories having the lowest impacts (often negative) for GWP and marine eutrophication. Overall, farm management practices were the most important factor driving environmental impacts from local food supply chains. A soil health assessment was then performed on a subset of the UK farms to provide insight to ecosystem services that are not captured within LCA frameworks. Better soil health was observed in organically-farmed and uncultivated soils compared to conventionally farmed soils, suggesting higher ecosystem service provisioning as related to improved soil structure, flood mitigation, erosion control, and carbon storage. However, relatively high heavy metal concentrations were seen on urban and peri-urban farms, as well as those located in areas with previous mining activity. This implies that there are important services and disservices on farms that are not captured by LCAs. Zooming out from a focus on food production, a qualitative methodology was used to explore experiences of food insecurity and related health and social challenges during the COVID-19 pandemic. Fourteen individuals receiving emergency food parcels from a community food project in Sheffield, UK were interviewed. Results showed that maintaining food security in times of crisis requires a diverse set of individual, household, social, and place-based resources, which were largely diminished or strained during the pandemic. Drawing upon social capital and community support was essential to cope with a multiplicity of hardship, highlighting a need to develop community food infrastructure that supports ideals of mutual aid and builds connections throughout the food supply chain. Overall, this thesis shows that a range of context-specific solutions are required to build sustainable and resilient food systems. This can be supported by increasing local control of food systems and designing strategies to meet specific community needs, whilst still acknowledging a shared global responsibility to protect ecosystem, human, and planetary health

    NEMISA Digital Skills Conference (Colloquium) 2023

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    The purpose of the colloquium and events centred around the central role that data plays today as a desirable commodity that must become an important part of massifying digital skilling efforts. Governments amass even more critical data that, if leveraged, could change the way public services are delivered, and even change the social and economic fortunes of any country. Therefore, smart governments and organisations increasingly require data skills to gain insights and foresight, to secure themselves, and for improved decision making and efficiency. However, data skills are scarce, and even more challenging is the inconsistency of the associated training programs with most curated for the Science, Technology, Engineering, and Mathematics (STEM) disciplines. Nonetheless, the interdisciplinary yet agnostic nature of data means that there is opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Creating shared value:An operations and supply chain management perspective

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    Focusing solely on short-term profits has caused social, environmental, and economic problems. Creating shared value integrates profitability with social and environmental objectives, offering a holistic solution. This dissertation examines two areas where this integration is crucial. The first topic explores servicizing business models for a transition to a more circular economy, emphasizing environmental benefits and firm profitability. Initially, we focus on pricing policies, comparing pricing schemes across consumer segments to identify win-win-win strategies that meet all people, planet, and profit objectives. Our research reveals that pay-per-use schemes outperform pay-per-period schemes for cost-inefficient or small-scale providers. A win-win (profit and planet) strategy can be achieved by offering a pay-per-use policy to high usage-valuation consumers, but a win-win-win strategy is unattainable. We then investigate consumer choices in servicizing models by conducting a conjoint experiment on payment scheme, price, minimum contract duration, and entry label attributes. The payment scheme emerges as the most influential attribute, with purchasing and pay-per-use schemes being popular options. The second topic focuses on drug shortages. Specifically, we examine the impact of tendering on shortages. Our findings demonstrate that tendering reduces prices but increases shortages, particularly at the beginning of contracts. However, shortages are less severe when alternative suppliers are available, and the market is less concentrated. To address this issue, we propose allowing multiple winners, regionalizing tenders, increasing the time between tender and contract initiation, and incorporating a reliability measure as a winning criterion to mitigate shortages

    Systemic Circular Economy Solutions for Fiber Reinforced Composites

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    This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

    Funktionsorientierte Qualitätsregelung in Produktionsnetzwerken - Qualitätsmanagement in der Produktion hochpräziser Produkte durch netzwerkweite Datenintegration

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    Die Produktion hochpräziser Produkte findet an der Grenze des technisch Machbaren statt. Dennoch kann es durch ungünstige Kombinationen einzelner Komponenten zu einer Nichterfüllung der Funktion des Endproduktes kommen, obwohl sich die Komponenten innerhalb der geforderten Toleranzen befinden. Durch die Anwendung funktionsorientierter Qualitätsregelstrategien, wie bspw. der selektiven Montage, innerhalb der eigenen Werksgrenzen, kann die technologische Komplexität reduziert werden. Dies ermöglicht die wirtschaftliche Produktion hochpräziser Produkte. Standort- und unternehmensübergreifend finden derartige Qualitätsregelstrategien jedoch bisher keine Anwendung, da u.a. kein ausreichender Informationsaustausch stattfindet. In der Unternehmenspraxis werden daher oft sehr enge Toleranzen für die zugelieferten Hochpräzisionskomponenten gesetzt, um die Qualität der Endprodukte sicherzustellen. Dies kann einen hohen Ausschuss auf Seiten des Lieferanten zur Folge haben. Langfristig sorgt diese Ineffizienz im Produktionsnetzwerk für Wettbewerbsnachteile aller beteiligten Partner. Eine weitere, besondere Herausforderung bei der verteilten Produktion in Produktionsnetzwerken besteht darin, dass Komponenten nicht als Einzelteile weitergereicht, sondern in Losen produziert und zu Transporteinheiten gebündelt auf Ladungsträgern zwischengelagert werden. Diese Lose sind individuellen Einflüssen ausgesetzt. Somit ergeben sich losspezifische Verteilungen der funktionskritischen Merkmalsausprägungen der Komponenten. Dies erhöht die Komplexität in der Produktion und wird in Qualitätsregelstrategien bisher nicht berücksichtigt. Um diesen Defiziten zu begegnen, wird in der vorliegenden Arbeit ein Ansatz zur funktionsorientierten Qualitätsregelung in Produktionsnetzwerken entwickelt. Dieser ermöglicht die unternehmensübergreifende Anwendung bestehender und neuer Qualitätsregelstrategien, welche die Logistik einbeziehen und die Autonomie externer Partner berücksichtigen. Das entwickelte Qualitätsregelungssystem ist in der Lage, auf die losspezifischen Eigenschaften der verschiedenen Komponenten durch Anpassung der Prozessparameter korrespondierender Komponenten zu reagieren, die Lose gezielt zusammenzubringen und sogar Toleranzen individuell anzupassen. Durch die Entwicklung echtzeitfähiger, interoperabler Funktionsmodelle zur Prognose der Produktfunktion, kann die Funktionsorientierung in allen Phasen des Produktentstehungsprozesses erzielt werden. Mithilfe eines simulativen Entscheidungsunterstützungssystems gelingt es, die unternehmensübergreifenden, funktionsorientierten Qualitätsregelstrategien hinsichtlich der resultierenden Qualitätsverbesserung und ihrer netzwerkweiten Wirtschaftlichkeit zu bewerten. Somit lässt sich der Mehrwert einer netzwerkweiten Datenintegration quantifizieren. Der Ansatz kann sowohl in bestehenden, als auch bei der Planung neuer Produktionsnetzwerke verwendet werden. Der Ansatz wurde in einem Produktionsnetzwerk zur Herstellung hochpräziser Dieselinjektoren validiert. In Simulationsstudien konnte dabei, selbst unter Aufweitung sämtlicher Toleranzen, die Qualität verbessert (in Form des netzwerkweiten First Pass Yield und der Verteilungen in den End-of-Line-Funktionsprüfpunkten) sowie der Gesamtgewinn des Produktionsnetzwerkes signifikant erhöht werden. Dadurch können Ineffizien-zen im Produktionsnetzwerk abgebaut werden. Eine Erprobung in realer Produktionsumgebung konnte die Ergebnisse bestätigen. Es kann somit gezeigt werden, dass eine toleranzfreie Serienproduktion möglich ist
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