Search Results (145)

Transparency in business practices is essential for sustainability, ensuring that resources are used responsibly and that environmental and social impacts are properly measured and monitored, allowing the end consumer to make informed purchasing decisions without feeling cheated. The Digital Product Passport (DPP) promotes transparency by providing detailed information about a product’s origin, composition, and life-cycle activities, enabling more sustainable and responsible choices. The implementation of the DPP for textile and clothing items faces many challenges due to the large number and diversity of companies involved in the value chain of these products, combined with the large amount and variability of information that needs to be collected. Therefore, the integration and standardization of data from these companies is one of the largest present challenges. In this article, we study the use of Machine Learning (ML) algorithms for validating, in a homogeneous way, the quality of the data submitted by each company for the implementation of the DPP. We have studied four solutions that, using datasets organized in different ways and using different ML algorithms, enable selecting the solution that best suits each particular situation. Full article

This paper presents the development of handmade paper from fique residues, evaluating its technical and environmental viability through a scientific approach aimed at supporting low-income rural communities. The residues were characterized to assess their suitability for papermaking, with fiber crystallinity and chemical structure analyzed using X-ray diffraction (XRD) and ATR-FTIR spectroscopy. Pulps were produced from fique fibers and a 30:70 fique fiber–bagasse blend using a chemical-free mechanical pulping process, designed for easy implementation in rural settings. The effects of dyeing on pulp performance were also examined, and environmental impacts were assessed through a Life-Cycle Assessment (LCA). The average fiber length, diameter, and lumen of fique fibers were 1.83 mm, 26.5 μm, and 17.4 μm, respectively. Handsheets from fique pulp achieved a tensile index of 13.0 N·m/g and a burst index of 1.42 kPa·m²/g, while the fique fiber–bagasse blend reached 11.09 N·m/g and 1.05 kPa·m²/g. The corresponding sheet densities were 0.316 and 0.380 g/cm³. The dyeing process led to a reduction in the mechanical strength of the handmade paper. Environmental analysis indicated that fique tow fiber has a more favorable impact profile than other non-wood alternatives, such as aquatic weed fiber. Compared to results from similar studies, fique demonstrates strong potential as a high-quality, sustainable raw material for artisanal papermaking. These findings support its application in decentralized, eco-friendly production systems, contributing to rural development and circular economy strategies. Full article

Product-service systems contribute to sustainable development through innovative service integration and novel customer value creation. However, the competitive advantage of sustainable product lifecycle service delivery hinges critically on the operational efficiency of service networks. This study addresses dynamic service facility location and allocation challenges in a time-varying demand environment, focusing on the strategic deployment of multiple comprehensive service centers (CSCs) and their dynamic customer allocation across planning horizons. In this study, we develop a 0–1 integer programming model and propose a novel co-evolutionary adaptive multi-objective genetic algorithm (CA-MOGA) with four key enhancements: (1) optimized chromosome representation, (2) adaptive strategy incorporation, (3) genetic operators with gene repair mechanisms, and (4) elite trans-generation migration. Through real-world case validation, CA-MOGA demonstrates significant improvements over conventional genetic algorithms in both convergence speed and solution quality. The performance and adaptability of the proposed algorithm suggest strong potential for customizable applications in solving diverse complex optimization problems. Full article

Over 15 billion tonnes year⁻¹ of biomass is used globally, yet 14% is downcycled for energy, forfeiting billions in potential revenue for higher-value products. Robust metrics that couple cascading use with cradle-to-gate greenhouse gas (GHG) emissions and economic value are essential for identifying superior biomass pathways. The aim of this review is to systematically map biomass utilization indicators published between 2010 and 2025; compare their treatment regarding circularity, climate, and economic value; and introduce the enhanced Bioresource Utilization Index (eBUI). A PRISMA-aligned search of Scopus and Web of Science yielded 80,808 records, of which 33 met the eligibility criteria. Each indicator was scored on cascading, data intensity, and environmental and economic integration, as well as computational complexity and sector scope. The Material Circularity Indicator, Biomass Utilization Efficiency, the Biomass Utilization Factor, and legacy BUI satisfied no more than two criteria simultaneously, and none directly linked mass flows to both GHG emissions and net revenue. The eBUI concept integrates mass balance, lifecycle carbon intensity, and value coefficients into a single 0–1 score. An open-access calculator and data quality checklist accompany the metric, enabling policymakers and industry to prioritize biomass pathways that are circular, climate-smart, and economically attractive. Full article

This perspective article presents an innovative concept for a biomanufacturing Knowledge Hub (KH), designed as a data-driven learning platform supporting the entire lifecycle of biomedical products. By integrating advanced data sharing and processing technologies, the KH aspires to connect patients, bioengineers, clinicians, regulators, companies, and investors to accelerate product development, reduce redundancies, and ultimately fast-track the delivery of biomedical innovations to patients. We discuss current challenges in accessing and sharing data within biomanufacturing and outline novel approaches for building an ecosystem that links data stores, integrates digital twins, and leverages advanced analytics. The KH offers transformative capabilities, enabling the development of new products at a substantial increased speed. It is built as a secure, quantum-resistant platform that encrypts data and allows access through advanced algorithms, creating an intelligent, collaborative environment. Users can harness collective knowledge to enhance products, launch innovations, integrate technologies, and unlock revenue opportunities based on data quality and usage. This KH aims to revolutionize biomanufacturing, offering unprecedented opportunities for innovation, better patient outcomes, and commercialization with far reaching applications beyond biomanufacturing in the future. Full article

This study addresses this gap by proposing a multilevel fuzzy evaluation model combined with an analytic hierarchy process (AHP) to quantify the greenness of furniture products across their entire lifecycle. Focusing on an office desk as a case study, we developed an indicator system encompassing environmental attributes, resource efficiency, energy consumption, economic costs, and quality performance. Weighting results revealed that environmental attributes (27.2%) and resource efficiency (27.2%) dominated the greenness evaluation, with material recycling rate (33.5%) and solid waste pollution (24.3%) as critical sub-indicators. The prototype achieved a moderate greenness score of 70.38/100, highlighting optimization potential in renewable material adoption (10% current rate) and modular design for disassembly. Mechanically recycled materials could reduce lifecycle emissions by 18–25% in key categories. The model demonstrates scalability for diverse furniture types and informs policy-making by prioritizing high-impact areas such as toxic material reduction and energy-efficient manufacturing, thus amplifying its global and interdisciplinary multiplier effects. Full article

The development of E-commerce and digitalization drives the rapid change in logistics management practices and poses challenges to traditional talent training modes in logistics field. Nowadays, companies expect university graduates equipped with more practical logistics skills to connect tighter with the industry. This motivates universities to establish more practically relevant curriculums to enhance students’ career competitiveness. Under such background, industry–university collaboration courses are increasingly adopted in higher education institutes in logistics discipline. Due to the difference between this type of course and the traditionally taught courses, the learning outcome of it can be difficult to guarantee. Therefore, it is necessary to identify the influential factors of the learning outcomes of industry–university collaboration courses and establish the actionable strategies to enhance course quality. However, the current literature in logistics management education has little focus on this topic, resulting in gaps on clarifying the influential factors of learning outcomes of industry–university collaboration courses in this discipline. Applying a mixed method, this study conducted a case study for an industry–university collaboration course of a logistics discipline in a Chinese university. The interval-valued Pythagorean fuzzy (IVPF) numbers and the Weighted Aggregated Sum Product Assessment (WASPAS) methods were used. The results showed that there are 15 factors which can influence the outcomes of industry–university collaboration courses in logistics discipline. Among them, the most important factor is the working environment, followed by the students’ own ability. Also, the results indicated that students’ optimistic attitudes towards the course, whether students take the course seriously, and course evaluations can be influential factors for good learning outcomes. The sensitivity analysis was then conducted, showing that the results were robust. This study can contribute to the existing literature by providing a theoretical framework to understand and assess the quality of industry–university collaboration courses in logistics and relevant subjects, as well as offering new analytical tools for management educational studies. Moreover, this study can provide practical implications for educators to develop and maintain good industry–university collaboration courses and trainings. Specifically, a practical life-cycle view was suggested to put pertinent efforts in all periods before/during/after the course to achieve high course outcomes. Full article

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article

In the current data-driven era, effective data sharing is set to unlock billions in value for aerospace and complex manufacturing and their supply chains by enhancing product quality, boosting manufacturing and operational efficiency, and generating new value streams. However, current practices are hindered by fragmented data ecosystems, isolated silos, and reliance on paper-based documentation. Although the Digital Thread (DTh) initiative holds promise, its implementation remains impractical due to interoperability challenges, security and intellectual property risks, and the inherent difficulty of capturing and managing the overwhelming volume of data in such complex products as a holistic thread. This paper introduces the Manufacturing Digital Passport (MDP), a novel industry-driven concept that employs a product-centric, system-independent digital carrier to facilitate targeted, structured sharing of technical product data across the supply chain. The conceptual contribution of this work is the analytical formalisation of the MDP as a value-oriented carrier that shifts DTh thinking from costly, system-wide interoperability toward an incremental, ROI-driven record of lifecycle data. Rooted in real-world challenges and built on foundational principles of modularity, value creation, and model-based structures, the MDP, by design, enhances traceability, security, and trust through a bottom-up, incremental, use case-driven approach. The paper outlines its benefits through core design principles, definition, practical features, and integration strategies with legacy systems, laying the groundwork for a structured adoption roadmap in high-value manufacturing ecosystems. Full article

The increasing frequency of structural failures on construction sites emphasizes the critical role of rigorous supervision in ensuring the quality of both construction processes and materials. Current regulatory frameworks mandate the production of detailed supervision reports to provide comprehensive evaluations of construction quality, material compliance, and site records. This study proposes a novel approach to harnessing unstructured reports for automated quality assessment. Employing text mining techniques, a sentiment lexicon specifically tailored for quality performance evaluation was developed. A corpus-based manual classification was conducted on 291 relevant words and 432 sentences extracted from the supervision reports, assigning sentiment labels of negative, neutral, and positive. This sentiment lexicon was then utilized as fundamental information for the Quality Performance Evaluation Model (QPEM). To validate the efficacy of the QPEM, it was applied to supervision reports from 30 construction sites adhering to legal standards. Furthermore, a Pearson correlation analysis was performed with the actual outcomes based on the legal requirements, including quality test failure rate, material inspection failure rate, and inspection management performance. By leveraging the wealth of unstructured data continuously generated throughout a project’s lifecycle, this model can enhance the timeliness of inspection and management processes, ultimately contributing to improved construction performance. Full article

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain. Full article

Circulating fluidized bed fly ash (CFBFA) stockpiles release alkaline dust, high-pH leachate, and secondary CO₂/SO₂—an environmental burden that exceeds 240 Mt yr⁻¹ in China alone. Yet, barely 25% is recycled, because the high f-CaO/SO₃ contents destabilize conventional cementitious products. Here, we presents a pressurized flue gas heat curing (FHC) route to bridge this scientific deficit, converting up to 85 wt% CFBFA into structural lightweight gravel. The gypsum dosage was optimized, and a 1:16 (gypsum/CFBFA) ratio delivered the best compromise between early ettringite nucleation and CO₂-uptake capacity, yielding the highest overall quality. The optimal mix reaches 9.13 MPa 28-day crushing strength, 4.27% in situ CO₂ uptake, 1.75 g cm⁻³ bulk density, and 3.59% water absorption. Multi-technique analyses (SEM, XRD, FTIR, TG-DTG, and MIP) show that FHC rapidly consumes expansive phases, suppresses undesirable granular-ettringite formation, and produces a dense calcite/needle-AFt skeleton. The FHC-treated CFBFA composite gravel demonstrates 30.43% higher crushing strength than JTG/TF20-2015 standards, accompanied by a water absorption rate 28.2% lower than recent studies. Its superior strength and durability highlight its potential as a low-carbon lightweight aggregate for structural engineering. A life-cycle inventory gives a cradle-to-gate energy demand of 1128 MJ t⁻¹ and a process GWP of 226 kg CO₂-eq t⁻¹. Consequently, higher point-source emissions paired with immediate mineral sequestration translate into a low overall climate footprint and eliminate the need for CFBFA landfilling. Full article

This comprehensive review of the synergistic use of Quality by Design (QbD) and the Pi–Buckingham theorem explores an innovative approach to enhancing product development and process optimization within the pharmaceutical industry. QbD is a systematic, proactive methodology that integrates quality considerations throughout the product lifecycle to ensure that pharmaceutical products meet regulatory standards for safety and efficacy from the outset of development. The Pi–Buckingham theorem serves as a foundational principle in dimensional analysis, facilitating the simplification of complex models by transforming physical variables into dimensionless parameters. This synergy enables researchers to better understand and control the factors affecting critical quality attributes (CQAs), thereby improving manufacturing outcomes and minimizing variability. Full article

The transition to renewable energy requires sustainable solar manufacturing through optimized Production–Usage–Recycling (PUR) cycles, where electromagnetic (EM) sensing offers non-destructive monitoring solutions. This review categorizes EM methods into low- (<100 MHz) and medium-frequency (100 MHz–10 GHz) techniques for material evaluation, defect detection, and performance optimization throughout the solar lifecycle. During production, eddy current testing and impedance spectroscopy improve quality control while reducing waste. In operational phases, RFID-based monitoring enables continuous performance tracking and early fault detection of photovoltaic panels. For recycling, electrodynamic separation efficiently recovers materials, supporting circular economies. The analysis demonstrates the unique advantages of EM techniques in non-contact evaluation, real-time monitoring, and material-specific characterization, addressing critical sustainability challenges in photovoltaic systems. By examining capabilities and limitations, we highlight EM monitoring’s transformative potential for sustainable manufacturing, from production quality assurance to end-of-life material recovery. The frequency-based framework provides manufacturers with physics-guided solutions that enhance efficiency while minimizing environmental impact. This comprehensive assessment establishes EM technologies as vital tools for advancing solar energy systems, offering practical monitoring approaches that align with global sustainability goals. The review identifies current challenges and future opportunities in implementing these techniques, emphasizing their role in facilitating the renewable energy transition through improved resource efficiency and lifecycle management. Full article

This systematic literature review examines the development of a conceptual certification framework for solar panel reuse, positioned within the broader context of the circular economy. It emphasizes sustainable production and consumption in response to the climate crisis and resource depletion. This review was conducted using Scopus and Google Scholar, following a structured search strategy. A final set of 63 sources, including peer-reviewed journal articles, conference papers, and gray literature recommended by domain experts, were selected to analyze existing certification frameworks across various sectors, focusing on their relevance to solar panel reuse. Key aspects of product reuse such as safety, quality, and technical standards are explored, highlighting the unique challenges associated with the long lifespans and environmental exposure of solar panels. Through this analysis, this study reveals the core elements vital for an effective certification framework. While structured certification frameworks are essential for sustainability, empirical evidence on their effectiveness in the solar panel reuse remains scarce, and regulatory inconsistencies add complexity. Using established practices in electronics, batteries, and other high-liability sectors as an anchor, the proposed framework, emerging from this systematic review, aims to extend solar panels’ lifecycle, contributing to environmental sustainability and socio-economic equity. The findings provide valuable insights for policymakers, industry stakeholders, and researchers by addressing key certification gaps and identifying future research directions in solar panel reuse standardization. Full article