Search Results (15,694)

Deep learning has shown strong performance in ECG domain adaptation; however, its decision-making process remains opaque, particularly when operating on input spectrograms. Traditional fuzzy inference offers interpretability but is structurally limited to tabular or multi-channel data, making it difficult to apply directly to single-channel two-dimensional spectrograms. To address this limitation, we propose the Fuzzy Energy–Frequency Spectrogram Network (FEFSN), a new fuzzy–deep learning hybrid framework that enables direct fuzzy rule generation in the spectrogram domain. In FEFSN, the Fuzzy Rule Image Generation Module (FRIGM) decomposes an STFT-transformed ECG spectrogram into multiple energy-based channels using an Energy–density Membership Function (EMF), and then applies a Frequency Membership Function (FMF) to produce AND and OR fuzzy rule images for each energy–frequency combination. The generated rule images are subsequently normalized, activated, and combined through learned weights to form a rule-based domain-adapted spectrogram, which is then processed by a CNN. To evaluate the proposed approach, we used the PhysioNet ECG-ID dataset and compared the performance of a standard CNN with and without the FRIGM under identical training conditions. The results show that FEFSN maintains or slightly improves adaptation performance compared to the baseline CNN, despite introducing only a small number of additional parameters. More importantly, FEFSN provides ante hoc interpretability, allowing direct visualization of which energy–frequency regions were emphasized or suppressed during adaptation—an ability that conventional post hoc methods such as Grad-CAM cannot offer. Overall, FEFSN demonstrates that fuzzy logic can be effectively integrated with deep learning to achieve both reliable performance and transparent, rule-based interpretability in ECG spectrogram domain adaptation. Full article

The process industry (PI) plays a vital role in the global economy and faces mounting pressure to enhance sustainability, operational agility, and resource efficiency amid tightening regulatory and market demands. Although artificial intelligence (AI) has been explored in this domain for decades, its adoption in industrial practice remains limited. Recently, machine learning (ML) has gained momentum, particularly when integrated with core PI systems such as process control, instrumentation, quality management, and enterprise platforms. Among ML techniques, reinforcement learning (RL) has emerged as a promising approach to tackle complex operational challenges. In contrast to conventional data-driven methods that focus on prediction or classification, RL directly addresses sequential decision making under uncertainty, a defining characteristic of dynamic process operations. Given RL’s growing relevance, this study conducts a systematic literature review to evaluate its current applications in the PI, assess methodological developments, and identify barriers to broader industrial adoption. The review follows the PRISMA methodology, a structured framework for identifying, screening, and selecting relevant publications. This approach ensures alignment with a clearly defined research question and minimizes bias, focusing on studies that demonstrate meaningful industrial applications of RL. The findings reveal that RL is transitioning from a theoretical construct to a practical tool, particularly in the chemical sector and for tasks such as process control and scheduling. Methodological maturity is improving, with algorithm selection increasingly tailored to problem-specific requirements and a trend toward hybrid models that integrate RL with established control strategies. However, most implementations remain confined to simulated environments, underscoring the need for real-world deployment, safety assurances, and improved interpretability. Overall, RL exhibits the potential to serve as a foundational component of next-generation smart manufacturing systems. Full article

Rapid urbanization in China has reshaped retail spatial structures, creating challenges of accessibility and service equity. This study employs a Geographic Information Systems (GIS)-based analytical framework to examine the spatial distribution and driving mechanisms of retail outlets in Taiyuan, a resource-based transition city in central China. Using 2023 Point of Interest (POI) data and a 2 km × 2 km grid system, kernel density estimation (KDE), Average Nearest Neighbor (ANN) Analysis, Location Quotient (LQ), and spatial autocorrelation were applied to identify clustering patterns and functional specialization. The GeoDetector (Word version, downloaded 2025) model further quantified the explanatory power of twelve natural, social, economic, and transportation variables. Results reveal a polycentric retail structure, with high-density clusters in Yingze and Xiaodian districts and under-supply in Jiancaoping and Jinyuan. Population density, nighttime light (NTL) intensity, and school distribution emerged as the strongest drivers, while topography constrained expansion. By integrating GIS-based spatial statistics with GeoDetector, the study demonstrates a transferable framework for analyzing urban retail spatial patterns. The findings extend retail geography to transition cities and provide practical guidance for optimizing retail allocation, enhancing service equity, and supporting spatial decision-making for sustainable urban development. Full article

Background/Objectives: Cardiovascular disease (CVD) remains a leading cause of mortality and disability worldwide, with timely diagnosis critical for preventing long-term functional impairment. Electrocardiograms (ECGs) provide essential biomarkers of cardiac function, but their interpretation is often complex, particularly across multi-institutional datasets. Methods: This study presents an explainable federated learning framework with long short-term memory (FL-LSTM) for multi-class heart disease classification, capable of distinguishing arrhythmia, ischemia, and healthy states while preserving patient privacy. Results: The model was trained and evaluated on three heterogeneous ECG datasets, achieving 92% accuracy, 99% AUC, and 91% F1 score, outperforming existing federated approaches. Model interpretability is provided via SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), highlighting clinically relevant ECG biomarkers such as P-wave height, R-wave height, QRS complex, RR interval, and QT interval. Conclusions: By integrating temporal modeling, federated learning, and interpretable AI, the framework enables secure and collaborative cardiac diagnosis while supporting transparent clinical decision-making in distributed healthcare settings. Full article

Embodied carbon (EC) of buildings has been gaining attention among researchers and the industry to achieve the carbon targets by 2050. With this interest, the development of technology-based EC tracking and monitoring systems for buildings has increased. The existing literature lacks a comprehensive review of technology-based EC tracking and monitoring systems, their benefits, limitations, and adoption challenges related to buildings. Thus, this study conducted a systematic literature review, with studies published between 1996 and 2025. The results revealed 16 systems, most of which are integrated with the Internet of Things (IoT) and Building Information Modelling (BIM). The results identified 6 benefits, 7 key limitations, 17 adoption challenges, and future research directions. By integrating these findings, a conceptual framework was developed that highlights the strategic roles of key stakeholders in the effective implementation of these systems. Findings revealed that the key limitations are included in lack of a feasible EC emission reduction target, lack of an early-stage EC emissions reduction decision-making process, difficulty in tracing the responsible stakeholders to reduce the EC throughout the whole supply chain of buildings, limited automated third-party verification process and transparency issues, uncertainty of the use data, limited system boundary and the scope of works and lack of industry-level applications to test the developed systems. The challenges include data quality, scalability and cost, technology, organisational, and external challenges. The findings can serve as a benchmark for academics, researchers and practitioners to guide future developments in effectively tracking and monitoring the EC in buildings. Full article

Plant tissues exhibit a layered architecture that makes spatial context decisive for interpreting transcriptional changes. This review explains why the location of gene expression is as important as its magnitude and synthesizes advances uniting single-cell/nucleus RNA-seq with spatial transcriptomics in plants. Surveyed topics include platform selection and material preparation; plant-specific sample processing and quality control; integration with epigenomic assays such as single-nucleus Assay for Transposase-Accessible Chromatin using sequencing (ATAC) and Multiome; and computational workflows for label transfer, deconvolution, spatial embedding, and neighborhood-aware cell–cell communication. Protoplast-based single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling but introduces dissociation artifacts and cell-type biases, whereas ingle-nucleus RNA sequencing (snRNA-seq) improves the representation of recalcitrant lineages and reduces stress signatures while remaining compatible with multiomics profiling. Practical guidance is provided for mitigating ambient RNA, interpreting organellar and intronic metrics, identifying doublets, and harmonizing batches across chemistries and studies. Spatial platforms (Visium HD, Stereo-seq, bead arrays) and targeted imaging (Single-molecule fluorescence in situ hybridization (smFISH), Hairpin-chain-reaction FISH (HCR-FISH), Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) ) are contrasted with plant-specific adaptations and integration pipelines that anchor dissociated profiles in anatomical coordinates. Recent atlases in Arabidopsis, soybean, and maize illustrate how cell identities, chromatin accessibility, and spatial niches reveal developmental trajectories and stress responses jointly. A roadmap is outlined for moving from atlases to interventions by deriving gene regulatory networks, prioritizing cis-regulatory targets, and validating perturbations with spatial readouts in crops. Together, these principles support a transition from descriptive maps to mechanism-informed, low-pleiotropy engineering of agronomic traits. Full article

In 2023, the bicycle industry in Taiwan reached a historic high. However, concerns about carbon emissions persist, particularly during the material acquisition and manufacturing stages of bicycle production. This study utilizes the Life Cycle Assessment (LCA) method, using SimaPro 9.5 for cradle-to-gate carbon emission data analysis. This study thoroughly examines the complete life cycle of a bicycle brake cable product through a carbon reduction evaluation tool, identifying carbon hotspots in the product’s life cycle. The data reveals that packaging accounts for the highest proportion of factory carbon emissions in the brake cable product analysis (34.42%), followed by the product’s casing (30.60%), with the leading materials being metal, plastic, and paper. Throughout the cradle-to-gate process, we collaborated with product developers to utilize the LCA carbon reduction evaluation tool to analyze the life cycle of the brake cable product. By aligning market and development needs, we supported manufacturers in identifying additional carbon reduction strategies at the material selection, mechanical design, and manufacturing process stages. These strategies include using natural raw materials, reducing packaging volume, developing lightweight products, and investing in integrated equipment. By implementing these measures, companies can reduce the product’s carbon footprint and enhance resource efficiency during production. This assessment tool serves as a communication bridge between designers and engineers, translating LCA quantitative data into references for design and management decision-making. It also functions as a simplified analytical tool for SMEs to conduct preliminary diagnosis of carbon emission hotspots and plan improvement directions, particularly suitable for manufacturers lacking consulting resources and carbon inventory capabilities. The research findings not only help companies integrate carbon reduction thinking early in product development, forming a closed-loop system of quantitative analysis and design actions, but also provide concrete references for Taiwan’s bicycle industry to promote supply chain collaboration, achieve green transformation, and meet global carbon reduction goals. Full article

This research examines a multinational supply chain inventory problem involving one manufacturer and multiple retailers across a range of carbon emission combinations and an incomplete production system. It aims to identify the optimal strategies for material use, production, delivery, replenishment, and pricing to maximize the integrated total profits under various situations. Three particle swarm optimization techniques are used to solve all the models. Numerical examples and sensitivity analyses on parameter changes are provided. The findings indicate that in a multinational supply chain, currency appreciation in individual retailers’ countries decreases their optimal order quantities and the manufacturer’s optimal material purchase quantity, but increases the optimal quantity for other retailers. In summary, this study offers valuable guidance to enterprises and supply chain decision-makers, especially those operating in a multinational framework, aiming to effectively balance carbon reduction and profitability within the context of global trends in carbon emission reduction. Full article

Two new active disturbance rejection control (ADRC) structures for nonlinear systems are introduced: a locally linearized variant and a fully nonlinear formulation. Both approaches incorporate model knowledge to enhance performance but differ in how nonlinear dynamics are integrated into the control and observer design. The first proposed structure employs a state-dependent local approximation of the nonlinear model to generate dynamic controller and observer gains, aiming to balance robustness and accuracy. In contrast, the second one directly embeds the full nonlinear dynamics into both the control law and extended state observer, tightly coupling performance to model fidelity. The proposed methods were experimentally validated on a magnetic levitation system, known for its strong nonlinearity, and compared with a classical linear ADRC (LADRC). Furthermore, stability analysis of the methods was conducted using Lyapunov theory. Results show that the linearized structure consistently improves regulation performance over LADRC and, in most cases, achieves similar results to nonlinear ADRC with lower computational effort. However, the performance of the nonlinear approach may degrade under modeling inaccuracies and limited observer bandwidth. This study highlights that the way model information is integrated–rather than its level of detail–has a decisive impact on control quality. Finally, practical design guidelines are provided to assist in selecting an appropriate ADRC structure for nonlinear applications where robustness, computational efficiency, and limited model knowledge must be balanced. Full article

Sustainable development is currently a key priority in improving production systems, requiring an integrated approach that combines economic efficiency, environmental responsibility, and rational energy management. In response to these challenges, this article presents a novel application of computer simulation as a tool for comprehensively assessing the impact of technological improvements in the machining process. The study introduces and compares two models: a baseline model representing the actual state of the machinery fleet with conventional machine tools, and an innovative alternative model incorporating modern numerically controlled (CNC) machines. The results demonstrate, for the first time in this context, that the implementation of CNC technology not only significantly reduces process time and energy demand but also improves resource efficiency, thereby lowering CO₂ emissions and operating costs. This research highlights the innovative use of computer simulation to support decision-making in sustainable manufacturing, offering a practical framework for evaluating technological modernization options and promoting the sustainable development of production enterprises. Full article

Engineering education continues to grapple with the shift from lecture-centered instruction to approaches that connect theory with practice and strengthen transferable competencies. This study examines an educational intervention in the Bachelor’s Degree in Mechanical Engineering at Miguel Hernández University of Elche. Our objective was to evaluate the impact of a challenge-based learning (CBL) strategy, supported by optional artificial intelligence (AI) tools and aligned with the Sustainable Development Goals (SDGs). The intervention took the form of a design challenge in which 48 students, working in teams, developed a mechanical artifact using laboratory resources, prepared a technical report, and justified design, material, and process decisions. Data were collected through student surveys to assess perceptions of skill development, AI use, and SDG awareness. Findings indicate improved understanding of manufacturing processes, more critical and selective use of AI, stronger sustainability awareness, and gains in transferable competencies such as creativity, decision-making, and technical communication. These results suggest that integrating CBL with emerging technologies can enhance learning outcomes and motivation in technical degree programs, while offering a practical model that other engineering courses can adapt. Full article

Background: Cerebellar infarction complicated by obstructive hydrocephalus is a life-threatening condition. External ventricular drainage (EVD) has traditionally been regarded as hazardous due to concerns about precipitating upward transtentorial herniation, whereas suboccipital decompressive craniectomy (SDC) remains the definitive life-saving treatment. The optimal role and sequencing of these interventions remain controversial. Methods: A scoping review was conducted in accordance with PRISMA-ScR guidelines. PubMed/MEDLINE was systematically searched from inception to September 2025. Eligible studies included adult patients with cerebellar infarction and acute obstructive hydrocephalus managed with EVD, with or without SDC. Data on study design, patient characteristics, interventions, complications, and outcomes were extracted and narratively synthesized. Results: Forty studies were included, encompassing multicenter registries, retrospective cohorts, case series, and international guidelines. Evidence suggests that EVD alone can be effective in selected patients with preserved or moderately impaired consciousness, while outcomes in comatose patients are improved with SDC or combined approaches. Importantly, this scoping review integrates current evidence with a representative institutional case to provide a practical clinical context. Radiographic signs of upward transtentorial herniation before EVD were common, but clinically significant deterioration was infrequent. Prognostic factors for surgical decision-making included infarct volume (practical threshold 25–35 mL), location (vermian or bilateral infarcts), brainstem involvement, and level of consciousness. International guidelines increasingly recognize EVD as a valid treatment option, particularly as initial therapy for hydrocephalus. Conclusions: EVD should no longer be regarded as an absolute contraindication in cerebellar infarction with obstructive hydrocephalus. Controlled drainage can suffice in carefully selected patients, whereas SDC remains indispensable in cases with severe mass effect or brainstem compression. A pragmatic stepwise approach—beginning with cautious EVD and escalating to SDC when indicated—may optimize outcomes. Further multicenter studies are required to refine patient selection criteria and establish standardized management algorithms. Full article

Greenhouse gas (GHG) emissions from rail infrastructure are increasingly examined in response to climate policy demands. Yet current assessment methods, such as ISO-based LCAs, FTIP, “Standardisierte Bewertung”, EN 15804 with c-PCR 023, and EIB’s Climate Proofing, differ substantially in assumptions and comparability. This study investigates the transferability of systematic criteria from semi-quantitative risk assessment as defined in the German pre-standard DIN V VDE V 0831-101 to GHG assessment methods. A two-step analysis was conducted. First, risk assessment criteria, including scope definition, granularity, conservatism, justification, system definition, sensitivity, monotonicity, transparency, calibration, variable interdependency, and result applicability, were reviewed for relevance to GHG assessment. Second, these criteria were applied to existing GHG methods to assess their coverage and identify shortcomings. The findings indicate that many systematic criteria are transferable and are largely fulfilled in LCA-based approaches, although LCAs are often very time and cost-intensive, especially regarding data collection and analysis. Current semi-quantitative frameworks, such as FTIP, lack granularity, justification, and calibration. The results suggest that a semi-quantitative GHG assessment method integrating systematic, legal, and topic-specific requirements could offer a harmonized, transparent, and practical tool for infrastructure planning. Such an approach promises balanced rigor and usability, facilitating more consistent decision-making and comparability across and within projects. Full article

Background/Objectives: Viral diseases remain a major threat to global public health, particularly during outbreaks when limited therapeutic resources must be rapidly and fairly distributed to large populations. Although Convalescent Plasma (CP) transfusion has shown clinical promise, existing allocation frameworks treat patient prioritization, donor selection, and validation as separate processes. This study proposes a credible, converged smart framework integrating multicriteria decision-making (MCDM) and regression-based validation within a telemedicine environment to enable transparent, data-driven CP allocation. Methods: The proposed framework consists of three stages: (i) Analytic Hierarchy Process (AHP) for weighting five clinically relevant biomarkers, (ii) dual prioritization of patients and donors using Order Preference by Similarity to Ideal Solution (TOPSIS) and Višekriterijumsko Kompromisno Rangiranje (VIKOR) with Group Decision-Making (GDM), and (iii) regression-based model selection to identify the most robust prioritization model. An external dataset of 80 patients and 80 donors was used for independent validation. Results: The external GDM AHP-VIKOR prediction model demonstrated strong predictive performance and internal consistency, with R² = 0.971, MSE = 0.0010, RMSE = 0.032, and MAE = 0.025. Correlation analysis confirmed biomarker behavior consistency and stability in ranking, thereby reinforcing the reliability of the prioritization outcomes. Conclusions: The proposed patient–donor matching framework is accurate, interpretable, and timely. This work presents an initial step toward realizing safe AI-enabled transfusion systems within telemedicine, supporting transparent and equitable CP allocation in future outbreak settings. Full article

The transfer of agricultural land has significant effects on farmers’ livelihoods and welfare. This study aims to explore the utility and obstacles of rural land transfer. The research found that in the process of agricultural transformation in developing countries, rural land transfer played a positive role in improving farmers’ welfare. Rural land transfer enables land lessors to obtain physical rent or implicit rent, which increases household income or enhances relationships with relatives and neighbors, generating a positive impact on farmers’ welfare. Land transfer was a comprehensive decision-making of households based on the optimal allocation of factor resources such as land, labor, and capital. Risks associated with land transfer and social security arrangements after transferring land rights have emerged as prominent obstacles. These factors tend to induce anxiety among land-leasing households regarding the livelihood risks their families might face post-transfer, thus making them hesitant and reluctant to engage in land transfer due to lingering concerns over both immediate and long-term interests. The welfare-enhancing effects of land transfer on farmers vary significantly depending on the local rural governance context, household’s social status within the community, and relative importance of internal family opinions in decision-making processes. This study demonstrates that the allocation of production factors should be examined within the overarching framework of urban–rural integration and provides empirical evidence and theoretical insights for central and local governments to refine relevant policy documents. Full article