10 research outputs found
A Review of the integrated WEF nexus modeling platform in the NENA region: Morocco case study
No full textNowadays, the world is recognizing the water-food-energy (WEF) as a conceptual framework that aims to the implementation of the Sustainable Development Goals (SDGs). The WEF Nexus is being developed to support the integrity of national and more importantly local projects in the cross-sectoral partnerships and coordination to enhance the sustainable development outcomes from the different projects as well as avoiding the trade-offs. Most importantly, building a great synergy between the sectors is challenged by sectorial boundaries such as policy decisions, scaling investments, and cross-sectorial consequences, which leads to anticipation biases when it comes to social, economic, and environmental costs. This paper aims to review the latest integrated WEF Nexus modeling platform that has been developed in the NENA region during the project "Implementing the 2030 Agenda for Water Efficiency/Productivity and Water Sustainability in NENA Countries" (WEPS-NENA), led by the Food and Agriculture Organization of the United Nations (FAO) and supported by the Swedish International Development Cooperation Agency
A Smarter Way to Procure: Exploring the Use of Smart Contracts
No full textPurpose:This article delves into the transformative potential of smart contracts in revolutionizing procurement within supply chain management, with a special focus on the healthcare industry. In line with the challenges posed by complex procurement processes, the study explores how self-executing digital contracts can enhance efficiency, security, and transparency. Theoretical framework:The article is grounded in the concept of smart contracts and their applications in procurement, serving as a bridge between emerging technology and supply chain management innovation. It draws upon an extensive body of literature and research related to smart contracts, supply chain management, and technology adoption. The study is designed to build upon this theoretical foundation, using two comprehensive case studies to provide real-world insights and practical applications. These case studies, situated within the healthcare industry, serve as tangible examples of how the theoretical concepts surrounding smart contracts manifest in actual procurement processes. Design/methodology/approach:To investigate the application of smart contracts in healthcare procurement, we employed a qualitative research approach. This encompassed an extensive literature review of academic papers, industry reports, and relevant articles, uncovering the unique advantages and challenges in procurement & supply chain management. Building upon these findings, we developed a groundbreaking smart contract-based procurement system, presented through sequence diagrams. To validate our solution, we implemented and rigorously tested it within a real-world Ethereum environment. Findings:Our research reveals that integrating smart contracts into procurement processes results in streamlined operations, diminished reliance on intermediaries, and heightened transparency and traceability. Moreover, the proposed solution showcases significant potential for enhancing procurement efficiency in the healthcare sector. Research, Practical & Social Implications: These findings present valuable insights with far-reaching implications and hold substantial implications for stakeholders. Healthcare organizations can harness smart contracts to optimize their procurement procedures, yielding improved efficiency, transparency, and security. In a rapidly digitalizing landscape, our research empowers companies to maintain their competitive edge while delivering enhanced value to partners and customers. Originality/value:This article contributes significantly to the existing literature by offering a comprehensive examination of smart contract integration within procurement function, set within the broader context of supply chain management. It introduces a pioneering solution and provides a validated methodology, paving the way for in-depth exploration of smart contracts' impact on diverse stakeholders across healthcare supply chain
Towards Fault Detection in Industrial Equipment through Energy Consumption Analysis: Integrating Machine Learning and Statistical Methods
No full textAccurately forecasting the energy consumption of industrial equipment and linking these forecasts to equipment health has become essential in modern manufacturing. This capability is crucial for advancing predictive maintenance strategies to reduce energy consumption and greenhouse gas emissions. In this study, we propose a hybrid model that combines Long Short-Term Memory (LSTM) for energy consumption prediction with a statistical change-point detection algorithm to identify significant shifts in consumption patterns. These shifts are then correlated with the equipment’s health status, providing a comprehensive overview of energy usage and potential failure points. In our case study, we began by evaluating the prediction model to confirm the performance of LSTM, comparing it with several machine learning models commonly used in the literature, such as Random Forest, Support Vector Machines (SVM), and GRU. After assessing different loss functions, the LSTM model achieved the strongest prediction accuracy, with an RMSE of 0.07, an MAE of 0.0188, and an R2 of 92.7%. The second part of the model, which focuses on detecting change points in consumption patterns, was evaluated by testing several cost functions combined with binary segmentation and dynamic programming. Applied to a real-world case, it successfully detected a change point two months before equipment failure, offering the potential to reduce energy consumption by 27,052 kWh. This framework not only clarifies the relationship between equipment health and CO2 emissions but also provides actionable insights into emission reduction, contributing to both economic and environmental sustainability
Towards Fault Detection in Industrial Equipment through Energy Consumption Analysis: Integrating Machine Learning and Statistical Methods
No full textAccurately forecasting the energy consumption of industrial equipment and linking these forecasts to equipment health has become essential in modern manufacturing. This capability is crucial for advancing predictive maintenance strategies to reduce energy consumption and greenhouse gas emissions. In this study, we propose a hybrid model that combines Long Short-Term Memory (LSTM) for energy consumption prediction with a statistical change-point detection algorithm to identify significant shifts in consumption patterns. These shifts are then correlated with the equipment’s health status, providing a comprehensive overview of energy usage and potential failure points. In our case study, we began by evaluating the prediction model to confirm the performance of LSTM, comparing it with several machine learning models commonly used in the literature, such as Random Forest, Support Vector Machines (SVM), and GRU. After assessing different loss functions, the LSTM model achieved the strongest prediction accuracy, with an RMSE of 0.07, an MAE of 0.0188, and an R2 of 92.7%. The second part of the model, which focuses on detecting change points in consumption patterns, was evaluated by testing several cost functions combined with binary segmentation and dynamic programming. Applied to a real-world case, it successfully detected a change point two months before equipment failure, offering the potential to reduce energy consumption by 27,052 kWh. This framework not only clarifies the relationship between equipment health and CO2 emissions but also provides actionable insights into emission reduction, contributing to both economic and environmental sustainability
Linktree* for makers: app for contemporary craftspeople. The application of ergonomics to support emerging young makers in the passion economy, optimizing their selling journey
No full textLAUREA MAGISTRALEIn risposta all’epoca antropocentrica in cui viviamo, negli ultimi anni si è registrato un rinnovato interesse nel mondo dell’artigianato, che ha assunto la forma di un movimento definito come artigianato contemporaneo.
Una nuova generazione di artigiani si sta consolidando, che vede l’abbandono della tradizionale definizione di artigiano in favore di talenti dai background eterogenei e dalle capacità trasversali, in comunione con un nuovo concetto, ibrido e fluido, di maker contemporaneo. In questo scenario, emergono straordinari esempi di micro-imprenditoralità digitale che, rivolgendosi ad un pubblico giovane e appassionato, promuove un consumo etico e un nuovo concetto di nuovo, non più correllato all’inutilizzo del prodotto, come accade per quelli industriali, ma determinato dalla sua unicità e irripetibilità.
Spesso, tuttavia, gli strumenti e le piattaforme a disposizione dei makers non rispondono alle loro reali necessità, così che piattaforme progettate per scopi differenti vengano convertite in un certo senso, non sempre con risultati di successo.
Questo ha condotto alla domanda di ricerca: come un’interazione ottimale, basata sull’applicazione dell’ergonomia e dell’usabilità, può supportare questa nuova generazione di makers? La tesi mira perciò a progettare un’applicazione che supporti questi nuovi makers ibridi emergenti, rispondendo ai loro bisogni e permettendo di aggregare una community giovane interessata ai temi dell’artigianato e della sostenibilità.In response to the anthropocentric era in which we live, there has been a new interest in the world of craftsmanship in recent years, which took the form of a movement that the author defined as contemporary craftsmanship.
A new generation of artisans is emerging with it, which overturns the traditional definition of the artisan in favor of talents with heterogeneous backgrounds and transversal skills, according to a new, fluid and hybrid concept of the contemporary maker. At this juncture, there are extraordinary examples of digital micro-entrepreneurship that, by addressing a young and passionate audience, promotes ethical consumption and a new concept of newness, no longer determined by the fact that the product is unused, as with industrial ones, but by its uniqueness and unrepeatability.
Often, however, the tools and platforms used by these makers do not meet their real needs, so some platforms created for different purposes are converted in a certain sense, not always with successful results.
It leads to the research question: how can optimal interaction based on the application of ergonomics and usability support this new generation of makers? The project aims to create an application to support emerging hybrid makers, meet their needs, and aggregate a young community engaged in craftsmanship and sustainability themes
Space Optimization for Raw Material Transportation: Reducing Environmental Impact
No full textIn today’s industrial landscape, adhering to lean manufacturing principles and achieving operational excellence is increasingly vital due to rising competition. Eliminating waste, or Muda is essential for attaining a high level of lean manufacturing. One significant area for improvement is space optimization, which can be applied to production areas and logistic warehouses on a macroeconomic scale, or to trucks and pallets on a microeconomic scale. This study focuses on optimising truck space for the transportation of raw materials. An in-depth analysis of heuristics and metaheuristics methods was conducted to select the most suitable approach for this context. The chosen metaheuristic was then tailored in terms of parameters and design for this specific application. Simulation results indicate the optimal number of trucks required to meet demand and provide a 3D layout for storing raw material-filled boxes on pallets and pallets in trucks. The proposed solution is versatile and can be adapted for various storage optimization challenges within a given space
Space Optimization for Raw Material Transportation: Reducing Environmental Impact
No full textIn today’s industrial landscape, adhering to lean manufacturing principles and achieving operational excellence is increasingly vital due to rising competition. Eliminating waste, or Muda is essential for attaining a high level of lean manufacturing. One significant area for improvement is space optimization, which can be applied to production areas and logistic warehouses on a macroeconomic scale, or to trucks and pallets on a microeconomic scale. This study focuses on optimising truck space for the transportation of raw materials. An in-depth analysis of heuristics and metaheuristics methods was conducted to select the most suitable approach for this context. The chosen metaheuristic was then tailored in terms of parameters and design for this specific application. Simulation results indicate the optimal number of trucks required to meet demand and provide a 3D layout for storing raw material-filled boxes on pallets and pallets in trucks. The proposed solution is versatile and can be adapted for various storage optimization challenges within a given space
Experimental investigation on the desiccation cracking process in date palm fiber reinforced clayey soil using digital image correlation
No full textThis paper presents an experimental study that aims to enhance the cracking resistance of clay soil. Laboratory tests were performed in order to assess the effect of fiber content and length on the desiccation cracking of clay soil. During these tests, a digital image acquisition system was used to depict the development and spread of cracks. The results were analyzed using the DIC digital image correlation technique for the full-strain obtaining and image processing analysis for the crack feature measurements. The results show that date palm fibers have a considerable impact on the wide spread of cracks in clay soil during desiccation, in addition to their area and width. The DIC approach was useful for monitoring the displacement evolution and strain field detection on the surface of the specimen. The addition of fibers slows the water evaporation rate of the specimens. Optimum crack reduction ratio of about 47% was found in fiber-reinforced clay with 0.75% fiber content at 10 mm fiber length.</p
