Fast-fashion supply chains are under pressure to deliver greater traceability, provenance, accountability, and measurable sustainability outcomes, yet the evidence base for blockchain-enabled transparency remains uneven. This scoping review mapped primary research on blockchain applications for supply-chain transparency in fast fashion and directly transferable textile-apparel contexts. In this review, fast fashion is defined by compressed product cycles, high stock-keeping-unit turnover, frequent sourcing changes, intense buyer-driven price pressure, and extensive subcontracting. The search concept from the retained review record was translated into six standard bibliographic databases, and the numerically verified screening set comprised 122 de-duplicated records. After title/abstract screening and full-text assessment, 31 primary studies published between 2020 and 2025 were included. Data were charted on study design, setting, blockchain architecture, transparency function, implementation stage, reported outcome, barrier, enabler, and sustainability implication. The synthesis distinguishes findings specific to fast-fashion or apparel supply chains from transferable evidence derived from adjacent textile-fiber settings. Blockchain most consistently enabled tamper-resistant provenance records, audit-ready documentation, product authentication, and more granular environmental accounting when combined with the Internet of Things (IoT), radio frequency identification (RFID), and quick response (QR) identifiers, enterprise systems, or digital product passports. However, direct evidence of social-sustainability effects, such as improved labor rights, wages, working conditions, or grievance resolution, remained limited. The review therefore supports a qualified conclusion: blockchain is a conditional transparency infrastructure rather than an isolated technological solution, and its value depends on credible source-data capture, supplier incentives, governance arrangements, and policy standards that connect material traceability with environmental, economic, and social accountability.
This study evaluates the contribution of the food and beverage industry and food and beverage exports toward sustainable agricultural development in Algeria. Considering the pressure for food security, resource scarcity, and the imperative for diversification away from hydrocarbons, the paper poses whether agro-industrial activities enhance the agricultural value added and addresses the food system resilience and sustainable development of the environment. The study analyzes the annual data of the National Office of Statistics of Algeria and uses the Autoregressive Distributed Lag (ARDL), bounds testing approach to analyze the short and long run relationships of agricultural value added, gross production of the food industries, and food and beverage exports. The findings affirm that there is a long-run equilibrium relationship among the stated variables. It is shown that in the long-run, a 1% increase in gross production of the food industries is associated with a 0.491% increase in agricultural value added, and a 1% increase in food and beverage exports would yield a 0.107% increase in agricultural value added. The error-correction coefficient showed that around 82% of short-run deviations from the long-run equilibrium are corrected within a year. This shows that the food-processing and export capacity may be associated with stronger the agricultural development of Algeria, sustainably framing agro-industrial growth, the reduction of post-harvest losses, and the environmentally sustainable food production policy.
Rapid urbanization and the expansion of artificial intelligence, digital platforms, connected infrastructure, and automated public services are reshaping how urban governments plan, coordinate, and deliver public services. In Indonesia, selecting an appropriate digital governance strategy remains challenging because expected service improvements must be considered alongside digital inclusion, public trust, algorithmic fairness, institutional capacity, environmental resilience, privacy risks, and implementation costs. This study investigates how alternative digital urban governance strategies can be evaluated within a transparent multi-criteria decision-support framework. Ten candidate strategies were evaluated against fifteen technical, social, institutional, environmental, and economic criteria using constructed role-based picture fuzzy assessments. Mutual information (MI) was applied to identify overlapping criteria and reduce redundancy, after which the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method was used to derive objective criterion weights. Ranking Comparison (RANCOM) weights were then generated from the LOPCOW-based criterion ordering and combined with the objective weights before the alternatives were ranked using the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. Ranking robustness was examined through parameter sensitivity analysis, 10,000-run Monte Carlo simulations, and comparisons with seven established multi-criteria decision-making (MCDM) methods, including the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA), Multi-Attributive Border Approximation Area Comparison (MABAC), Weighted Aggregated Sum Product Assessment (WASPAS), Evaluation based on Distance from Average Solution (EDAS), and Additive Ratio Assessment (ARAS). The results showed that mobile-first public services for remote and island communities ranked first, with a MARCOS appraisal score of 0.734412, followed closely by citizen participation and e-consultation at 0.723203. Privacy-preserving urban data exchange ranked third at 0.686119. The leading strategy remained stable across the tested parameter settings within the proposed MARCOS framework, although alternative weighting and ranking specifications produced greater variation in the identity of the first-ranked strategy. This result, together with the narrow gap between the two highest-ranked alternatives, indicates that local priorities, assessment inputs, and modelling choices can materially influence the final decision. The findings demonstrate that the proposed framework provides a traceable basis for comparing competing digital urban governance strategies while making the assumptions, trade-offs, and uncertainty underlying the ranking explicit. The approach offers urban authorities a structured decision-support tool for planning inclusive, accountable, resilient, and institutionally feasible public-service transformation.
Cryptocurrency mining in developing economies operates under intertwined economic, institutional, energy, and technological uncertainties, making strategic planning difficult for both policymakers and industry participants. This study investigates and prioritizes the factors shaping the sustainability of cryptocurrency mining in developing Middle Eastern economies. A structured decision framework combining the Delphi method with fuzzy pivot pairwise relative criteria importance assessment (PIPRECIA) was applied. Nineteen factors grouped into economic, governance, and information technology dimensions were identified from the literature and evaluated by 11 experts with experience in cryptocurrency-related projects. The Delphi process was used to validate the selected factors, while fuzzy PIPRECIA was employed to determine their relative importance under uncertain expert judgments. The results showed that unclear laws and regulations ranked first, with a final weight of 0.1335, followed by cryptocurrency market volatility at 0.1074 and energy costs at 0.0994. Monetary instability, inflation, cybersecurity threats, banking constraints, and international sanctions also received substantial weights. Economic factors were ranked above governance and information technology factors, indicating that access to technical infrastructure alone did not ensure sustainable mining operations. The findings demonstrate that the viability of cryptocurrency mining in developing Middle Eastern economies depends primarily on regulatory predictability, macroeconomic stability, energy-pricing conditions, and access to secure financial channels. The proposed framework provides a structured basis for strategic policy formulation, investment assessment, and resource allocation in cryptocurrency mining environments characterized by institutional and market uncertainty.
Low-carbon transformation of supply chains is important for climate-change mitigation, yet the mechanisms through which artificial intelligence (AI) contributes to low-carbon performance remain unclear. This study examines the associations between predictive, decision-making, and automated AI applications and low-carbon performance, considering the mediating role of green dynamic capability and the moderating role of low-carbon policies. Structural equation modeling (SEM) was applied to survey data collected from 398 managers and experts in Chinese enterprises using established measures of AI functions, green dynamic capability, low-carbon policies, and low-carbon performance. The results show that green dynamic capability fully mediates the associations of predictive and decision-making AI with low-carbon performance. For automated AI, the indirect effect through green dynamic capability is significant, while the direct effect is at the borderline of statistical significance, suggesting a possible partial mediation pattern that should be interpreted cautiously. Low-carbon policies positively moderate the relationships between all three AI functions and low-carbon performance. These findings suggest that AI is associated with supply chain decarbonization partly through firms’ green dynamic capabilities, while supportive policy conditions strengthen these relationships. The study therefore highlights the importance of integrating AI applications with organizational capabilities and supportive policy environments. Low-carbon performance in this study is perceived performance, reflecting respondents’ subjective assessments rather than independently verified absolute emission figures.
This study presents detailed modelling and comparative analysis of pouch lithium-ion cells (PLCs) configured in combined single and dual battery packs for electric vehicles (EVs), appraising their performance with and without active cooling systems. Pouch cells were selected due to their high volumetric energy density (VED), lightweight design, and suitability for high-power EV applications. Using SolidWorks for computer aided design (CAD) modelling, Analysis System (ANSYS) Fluent software with the multi-scale multi-domain (MSMD) approach, and equivalent circuit model (ECM), simulation based thermal and electrochemical behaviour during 1-hour charging process at specified currents was achieved. Four configurations were examined: single-pack and dual-pack, each with and without liquid active cooling. Main parameters included state of charge (SoC), temperature distribution, and thermal gradients. Results confirmed that dual-pack configuration significantly outperformed single-pack, achieving over 69.3% higher SoC (55.07% against 32.53%) under the same conditions. Without active cooling, maximum temperatures reached 541.11 K (single) and 462.77 K (dual), indicating notable hotspots in the single pack. Active cooling dramatically reduced temperatures to 295 K across both setups, with the dual pack displaying superior uniformity and a 0.45% lower average temperature, adequately averting thermal gradients and enhancing safety. Mathematical validation of SoC dynamics confirmed the dual configuration’s theoretical advantage in charging efficiency, tempered by heat losses. Findings reveal that dual battery packs integrated with active cooling render optimal equilibrium of faster charging, improved thermal management, and extended battery life, addressing limitations associated with conventional single-pack designs.
Accurate characterization of medicinal plant species based on leaf mor-phology is important for botanical documentation, biodiversity conser-vation, and the preservation of ethnobotanical knowledge, particularly in regions such as Assam, India, where diverse plant species are tradi-tionally used for medicinal purposes. A lightweight and interpretable computer vision framework, termed LeafSeg, was developed for leaf segmentation and morphological feature extraction from digital images of medicinal plants. In the proposed framework, images were sequen-tially processed through luminance normalization, Gaussian smoothing, Canny edge detection, topological contour extraction, and bounding-box-based segmentation, followed by the computation of primary di-mensions and invariant shape descriptors. The segmentation framework was evaluated on a 351-image validation subset of the MED117 dataset (three images per class across all 117 species) captured under variable natural-background conditions. Benchmarking against the dataset’s model-generated reference masks demonstrated strong segmentation agreement, achieving an average intersection over union of 0.832 and a Dice similarity coefficient of 0.908, with an average processing latency of 18.4 ms per image on a standard central processing unit. To assess the discriminative utility of the extracted morphological features, a downstream classification experiment was conducted using a random forest classifier across 15 plant classes, yielding an accuracy of 84.6%. These results indicate that computationally inexpensive morphological features retain substantial discriminative information for medicinal plant characterization without dependence on deep neural networks. By combining interpretable image-processing operations with quantitative morphological analysis, LeafSeg provides a resource-efficient frame-work that facilitates automated plant characterization in computationally constrained settings.
Owners, contractors, and consultants are among the stakeholders most vulnerable to high risk due to their intricate interactions over project phases. This study aims to identify and categorize the primary and secondary risk factors associated with these three parties and to examine the causal relationships among them to inform risk mitigation. A hybrid approach combining fuzzy theory and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was adopted to analyze 32 secondary risk factors grouped under 3 primary risk factors. Nine experts, each with at least fifteen-year experience in risk management in engineering organizations, were consulted to identify and examine the risk factors. Based on the fuzzy DEMATEL (FD) model, risks were analyzed through a weighted impact matrix, and all risks were prioritized by identifying the causes leading to their effects. Owner risk (Degree of Dispatching ($D$) $-$ Degree of Receiving ($R$) = +0.739) and contractor risk ($D - R$ = +0.108) were classified as causes, whereas consultant risk ($D - R$ = -0.84) was classified as an effect. The risk factors were coded RC1–RC32. From the consultant side, the second most prominent sub-factor ($D + R$ = 2.635) was regulatory non-compliance (RC15), which affected approval timelines and coordination with the consultant. As regards the owner, poor communication (RC5, $D + R$ = 5.756) was a causal sub-factor. For the contractor, regulatory non-compliance (RC26, $D + R$ = 2.690) was the most prominent causal sub-factor. The analysis revealed divergent risk perceptions among stakeholders, thus highlighting the importance of a collaborative risk management framework.
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