Search Results (2,966)

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. Full article

Against the backdrop of the global energy transition, the efficient exploitation of marine renewable energy has become a key pathway toward achieving carbon neutrality. Wind–wave hybrid systems (WWHSs) have attracted growing attention due to their resource complementarity, efficient spatial utilization, and shared infrastructure. However, most existing studies focus on single components or local optimization, while systematic integration of the full technology chain remains limited. This gap hinders the transition from demonstration projects to commercial deployment. This review provides a comprehensive overview of the technological evolution and key characteristics of offshore wind turbine (OWT) foundations and wave energy converters (WECs). Fixed-bottom foundations remain the mainstream solution for near-shore development. Floating offshore wind turbines (FOWTs) represent the core direction for deep-sea deployment. Among WEC technologies, oscillating buoy (OB) WECs are the dominant research pathway. Yet high costs and poor performance under extreme sea states remain major barriers to commercialization. On this basis, the paper summarizes three major integration modes of WWHSs. Among them, hybrid configurations have become the research focus due to their structural sharing, hydrodynamic coupling, and significant cost and energy synergies. Furthermore, the review synthesizes optimization strategies for both technology design and spatial layout, aiming to enhance energy capture, structural stability, and overall economic performance. Finally, the paper critically identifies the main research gaps and technical bottlenecks and outlines key development pathways required to achieve future commercial viability. These include the development of high-performance adaptive power take-off (PTO) systems, deeper understanding of multi-physics coupling mechanisms, intelligent operation and maintenance enabled by digital twins, and comprehensive life-cycle techno-economic and environmental assessments. Through this integrated perspective, the review seeks to provide a systematic reference for the development of multi-energy offshore systems and to support future progress in integrated energy utilization in deep-sea environments. Full article

The global energy transition to renewable energy sources requires a rigorous assessment of the environmental impacts of all system components, including power electronics converters (PECs), which play a critical role in adapting generated energy to grid and load requirements. This paper presents a comprehensive comparative assessment of conventional PECs used in renewable energy systems, with a focus on DC-AC, DC-DC, and AC-DC converters. The study combines life cycle assessment (LCA) with the Circular Energy Sustainability Index (CESI) to evaluate both environmental performance and material circularity. The LCA is conducted using a functional unit defined as a representative converter, within consistent system boundaries that encompass material extraction, manufacturing, and end-of-life stages. This approach enables comparability among converter topologies but introduces limitations related to the exclusion of application-specific design optimizations, such as maximum efficiency, spatial constraints, and thermal management. CESI is subsequently applied as a decision-support tool to rank converter technologies according to sustainability and circularity criteria. The results reveal substantial differences among converter types: the controlled rectifier exhibits the lowest environmental impact and the highest circularity score (95.3%), followed by the uncontrolled rectifier (69.3%), whereas the inverter shows the highest environmental burden and the lowest circularity performance (38.6%), primarily due to its higher structural complexity and the material and manufacturing intensity associated with its switching architecture. Full article

Markerless motion capture is increasingly used in pediatric neurorehabilitation, yet its ability to detect patient-specific gait abnormalities in small and heterogeneous cohorts remains unclear. This study evaluated a smartphone-based markerless workflow (OpenCap integrated with OpenSim) as a clinical assessment tool to support individualized planning in the context of robot-assisted gait rehabilitation (RAGT) by characterizing individualized gait deviations in four pediatric patients with neurological gait disorders, referenced against normative data from 30 healthy individuals. Sagittal hip, knee, and ankle kinematics were extracted, normalized, and converted into gait-cycle–dependent Z-scores. Group-level comparisons using one-sample Statistical Parametric Mapping (SPM) revealed no significant deviations between patient-group means and normative trajectories (p ≥ 0.05). In contrast, individualized deviation profiling—including Z-score heatmaps, phase-wise Z-score analysis, and per-patient kinematic overlays—identified distinct, clinically meaningful abnormalities in every patient, such as excessive swing-phase hip and knee flexion, mid-stance knee extension deficits, reduced terminal-stance hip extension, and markedly diminished late-stance ankle plantarflexion and push-off. Several deviations exceeded |2–5| SD from the normative dataset, indicating substantial impairments that were obscured by group averaging. These individualized patterns were consistent with each patient’s clinical presentation and could be interpreted in relation to modifiable gait features that are commonly considered during planning and phase-specific adjustment of robot-assisted gait rehabilitation, rather than serving as direct evidence of therapeutic efficacy. Overall, the findings demonstrate that smartphone-based markerless motion capture enables sensitive, individualized gait assessment even when group-level statistics remain nonsignificant, supporting its use as an exploratory, decision-support framework rather than as an outcome measure of RAGT. Full article

Convolutional neural networks (CNNs) have been the standard for computer vision tasks including applications in Alzheimer’s disease (AD). Recently, Vision Transformers (ViTs) have been introduced, which have emerged as a strong alternative to CNNs. A common precursor stage of AD is a syndrome called mild cognitive impairment (MCI). However, not all individuals diagnosed with MCI progress to AD. In this exploratory investigation, we aimed to assess whether a ViT can reliably classify converters versus non-converters. A transfer learning approach was used for model training by applying a pretrained ViT model, fine-tuned on the ADNI dataset. The cohort comprised 575 individuals (299 stable MCIs; 276 progressive MCIs who converted within 36 months) from whom axial T1-weighted MRI slices covering the hippocampal region were used as model inputs. Results showed an average area under the receiver operating characteristic curve (AUC-ROC) on the test set of 0.74 ± 0.02 (mean ± SD), an accuracy of 0.69 ± 0.03, a sensitivity of 0.65 ± 0.07, a specificity of 0.72 ± 0.06, and an F1-score for the progressive MCI class of 0.67 ± 0.04. These findings demonstrate that a ViT approach achieves reasonable accuracy for classifying AD converters vs. non-converters, though its generalizability and clinical utility require further validation. Full article

Offshore wind energy is emerging as a vital component of the global transition to renewable energy, leveraging consistent wind conditions and higher power density compared to onshore systems. Integrating variable offshore wind power with hydrogen production via electrolysis provides a strategic pathway to convert surplus electricity into a storable and transportable energy carrier, thereby mitigating grid congestion, curtailment, and variability challenges. This review systematically examines the integration of offshore wind farms and hydrogen production technologies. Key components of the review include a comparative analysis of electrolyzer technologies, their suitability for offshore deployment, and the implications for energy storage and transport. The analysis employs a multi-step framework: (1) extensive search of the literature in scientific databases, (2) qualitative and quantitative assessment of system performance, and (3) synthesis of findings to identify trends and research gaps, enabling a thorough examination of technical challenges in the marine environment, and economic and policy barriers. The review highlights recent advancements, technical challenges, and economic considerations related to deployment of offshore wind-to-hydrogen systems. This review provides a comprehensive understanding of the current state of offshore hydrogen production, identifies research gaps, and outlines policy recommendations to accelerate its deployment. Offshore wind-powered hydrogen emerges as a cornerstone of a resilient, low-carbon energy future. The systematic approach ensures actionable insights and robust conclusions, facilitating the alignment of technological advancements with global decarbonization goals. Full article

The food industry generates large volumes of nutrient-rich by-products that remain underutilised despite their considerable biochemical potential. These materials originate predominantly from the fruit and vegetable, dairy, meat, and fish and seafood sectors and represent a substantial opportunity for sustainable valorisation. Fermentation has emerged as a powerful platform for converting such by-products into high-value ingredients, including bioactive compounds, functional metabolites, enzymes, antimicrobials, and nutritionally enriched fractions. This review synthesises recent advances in microbial fermentation strategies—spanning lactic acid bacteria, filamentous fungi, yeasts, and mixed microbial consortia—and highlights their capacity to enhance the bioavailability, stability, and functionality of recovered compounds across diverse substrate streams. Key technological enablers, including substrate pre-treatments, precision fermentation, omics-guided strain selection and improvement, and bioprocess optimisation, are examined within the broader framework of circular bioeconomy integration. Despite significant scientific progress, major challenges remain, particularly related to substrate heterogeneity, process scalability, regulatory alignment, safety assessment, and consumer acceptance. The review identifies critical research gaps and future directions, emphasising the need for standardised analytical frameworks, harmonised compositional databases, AI-driven fermentation control, integrated biorefinery concepts, and pilot-scale validation. Overall, the evidence indicates that integrated fermentation-based approaches—especially those combining complementary by-product streams, tailored microbial consortia, and system-level process integration—represent the most promising pathway toward the scalable, sustainable, and economically viable valorisation of food industry by-products. Full article

This article focuses on power supply control issues in high-speed switched reluctance motors (SRMs). The primary scientific objective of this study was to determine whether and to what extent, the controller itself imposes limitations on SRM drive operation at very high rotational speeds, and to identify the maximum achievable speed range resulting from these limitations. Unlike most existing studies, which focus mainly on motor or power electronics constraints, this work explicitly analyses the dynamic limitations introduced by the control system architecture. An analysis of the essential controller functionalities required for implementing the SRM drive control algorithm with a C-dump converter was performed. The control system, composed of specialised hardware modules operating concurrently, was implemented in an field-programmable gate array (FPGA) device. Simulation and experimental investigations were conducted to evaluate signal propagation delays within the FPGA and their impact on the motor control process. Key functional modules contributing to the maximum signal propagation delays were identified, enabling a direct determination of the maximum motor speed at which correct power supply operation can be ensured. Furthermore, delays introduced by the power electronic components were characterized for the developed test controller, allowing a comprehensive assessment of both control and hardware-induced speed limitations. The research concluded that the FPGA-based controller introduces no significant limitations to the drive’s maximum speed. The maximum speed is limited by the mechanical constraints of the rotor and the inertia of the phase windings. Furthermore, expanding the controller with additional functionality does not significantly slow down the control algorithm’s execution. Full article

As heritage museums shift toward more experience-oriented development, fragmented layouts and discontinuous visitor flows can reduce both spatial efficiency and the coherence of on-site experience. This study proposes an immersive experience-centred evaluation framework for exhibition layout in heritage museums, intended to translate experience goals into practical and diagnosable criteria for spatial optimization. An indicator system was refined through two rounds of Delphi consultation with an interdisciplinary expert panel, resulting in a hierarchical framework comprising five dimensions and multiple indicators. To support intervention prioritization in design and operations, weights were derived using the Group Analytic Hierarchy Process (GAHP), with Aggregation of Individual Judgments (AIJs) and consistency checks applied to control group judgement quality. A CV–entropy procedure was further used to support prioritization at the third-indicator level. Importance–Performance Analysis (IPA) was then employed to convert “importance–fit” assessments into an actionable sequence of optimization priorities. The results indicate that narrative and scene design carries the greatest weight (0.2877), followed by circulation and spatial organization (0.2281), sensory experience and atmosphere (0.1981), authenticity and sense of place (0.1644), and interactivity and participation (0.1217), suggesting that a “narrative–circulation–atmosphere” chain forms the core support for immersive layout design. A feasibility application using the Yinxu Museum demonstrates the framework’s value for benchmarking and diagnosis, helping decision-makers enhance visitor experience while respecting conservation constraints and more precisely target spatial investment priorities. Full article

Accurate prediction of postoperative complications, such as acute myocardial injury (AMI) and acute kidney injury (AKI), is essential for informed clinical decision-making and improved patient outcomes. However, conventional machine learning approaches often exhibit degraded performance in this setting due to severe class imbalance and the need for extensive feature preprocessing. To address these challenges, we propose a retrieval-based disease prediction (RBD) framework that leverages language models for postoperative risk assessment. The proposed framework converts heterogeneous preoperative and intraoperative clinical data into textual representations and retrieves relevant disease information by comparing patient-specific descriptions with predefined disease definitions of AMI and AKI. This retrieval-based formulation reduces the dependence on complex data normalization and resampling strategies commonly required by traditional models. Experimental results demonstrate that the RBD framework consistently outperforms existing machine learning methods in predicting postoperative complications under imbalanced data conditions. These findings indicate that retrieval-based language model analytics provide a promising approach for clinical decision support in postoperative care. Full article

To improve the fire safety performance of fire protection renovation projects for existing public buildings, this paper systematically sorts out and analyzes relevant research studies, accident reports, and fire protection renovation codes and guidelines. It constructs a fire performance evaluation system for such projects, including 4 first-level indicators—”Building Characteristics”, “Building Fire Protection and Rescue”, “Fire Facilities and Equipment”, and “Heating, Ventilation, Air Conditioning (HVAC) and Electrical Systems”—and 19 second-level indicators such as “Building Usage Function”. The subjective–objective combined weighting method of Analytic Hierarchy Process (AHP)-CRITIC is adopted to determine the weights of indicators at all levels. Four high-weight second-level indicators are selected as core remediation objects: average fire load density, floor layout, automatic fire alarm and linkage control system, and electrical systems. Meanwhile, the evaluation system is converted into a Bayesian Network model, with an empirical verification analysis carried out on a shopping mall in Chaoyang District, Beijing, as a case study. Results show that the approach of combining partial codes with the rectification of high-weight indicators can reduce the fire occurrence probability of the mall from 78%, before renovation, to 24%. Therefore, the constructed evaluation system and Bayesian Network model can realize the accurate quantification of fire risks, provide scientific and feasible technical schemes for the fire protection renovation of existing public buildings, and lay a foundation for enriching and improving fire protection assessment theories. Full article

Aligned with circular bioeconomy principles, which aim to establish closed-loop systems that maximize resource utilization and renewal while minimizing waste, this study developed and characterized innovative catalysts derived from waste almond shells. These shells were carbonized and functionalized to create active surfaces containing Lewis and Brønsted acid sites. Modification was achieved through treatment with ZnCl₂ to introduce Lewis acid (LA) sites and with sulfuric acid to generate Brønsted acid (BA) sites. Detailed instrumental analyses enabled assessment of catalyst morphology, textural parameters, and surface functional groups. A physical mixture of the two catalysts was used to convert glucose into 5-hydroxymethylfurfural (HMF), yielding a maximum HMF yield of 76.8%. The results indicate that the collaborative action of Lewis and Brønsted acid sites, along with oxygen-containing surface groups, contributes to catalyst efficiency. These insights facilitate targeted catalyst optimization by adjusting surface texture and functional groups. Full article

The rapid increase in organic waste generation poses significant environmental challenges and highlights the limitations of conventional waste management practices. In this context, black soldier fly (Hermetia illucens) larvae (BSFL) have emerged as a promising biological tool for valorizing organic residues within circular bioeconomy frameworks. This review provides an integrated analysis of BSFL-based bioconversion systems, focusing on the biological characteristics of BSFL, suitable organic waste streams, and the key process parameters influencing waste reduction efficiency, larval biomass production, and frass (the residual material from larval bioconversion) yield. The performance of BSFL in converting organic waste is assessed with emphasis on substrate characteristics, environmental conditions, larval density, and harvesting strategies. Environmental and economic implications are discussed in comparison with conventional treatments such as landfilling, composting, and anaerobic digestion. Special attention is given to the nutritional composition of BSFL and the valorization of larvae as sustainable protein and lipid sources for animal feed and emerging human food applications, while frass is highlighted as a nutrient-rich organic fertilizer and soil amendment. Finally, current challenges related to scalability, safety, regulation, and social acceptance are highlighted. By linking waste management, resource recovery, and sustainable protein production, this review clarifies the role of BSFL and frass in resilient and resource-efficient food and waste management systems. Full article

This paper proposes a novel method, the Convergent Radiation Algorithm (CRA), aimed at multi-attribute group decision-making (MAGDM) in circular intuitionistic fuzzy settings. The approach is aimed at reaching geometric consensus among experts, with uncertainties and hesitancies expressed via circular intuitionistic fuzzy numbers (CIFNs). First, the qualitative judgment in professionals is converted into a geometric space where experts’ assessments are represented as spatial points that reflect the differences between the opinions. All these points are gradually combined with the help of a radiation–reflection–convergence mechanism, which iteratively finds the Optimal Consensus Point (OCP) to minimize the overall weighted divergence over the evaluations. After that, a projection-based scoring method is used to locate good and bad optimal solutions, and the alternatives are ranked based on a comparison of their projection distance. It presents a numerical example with data supplied by the Hubei agro-ecological zone to demonstrate that the offered method helps to capture collective agreement and convergence behavior that is consistent, and makes the decision results readable and reliable. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article