Search Results (2,578)

The special back feeding energization process involves several stages, including tie-transformer energization, GIS-busbar energization, and no-load line energization. These operations can generate transient overvoltages and impose considerable stress on equipment insulation. To investigate the associated transient characteristics, field tests were conducted at a 500 kV GIS substation. Transient signals were recorded during tie-transformer energization and GIS-busbar switching, and the responses at different stages were analyzed. An electromagnetic transient model was subsequently developed in PSCAD/EMTDC and validated against the field measurements. The validated model was then used to investigate the key factors affecting no-load line energization through a GIS outgoing bay. The results show that tie-transformer energization is dominated by nonlinear core excitation. As the system operating state is progressively established, the transient responses during GIS-busbar and transmission line energization are governed mainly by network parameters and switching conditions. During no-load line energization, the closing phase angle and closing resistor condition are the dominant factors affecting switching overvoltage. Degradation of the closing resistor weakens transient suppression and increases the insulation stress on connected equipment. These findings provide a technical basis for evaluating energization transients and optimizing operating conditions during the commissioning of 500 kV GIS installations. Full article

Background and Objectives: Artificial intelligence (AI) has generated numerous proof-of-concept applications in otology and neurotology, but relatively few have progressed to routine clinical use. This narrative review evaluates not only what AI can do across audiovestibular medicine, but also how close individual applications are to clinical translation and which barriers continue to limit implementation. Materials and Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched from database inception to 31 July 2026 using combinations of terms related to AI, machine learning, deep learning, hearing loss, cochlear implantation, tinnitus, vestibular disorders, nystagmus, temporal bone imaging, digital phenotyping, rehabilitation, and robotic surgery. Reference lists of relevant publications were also screened. Evidence was synthesized thematically and interpreted according to task maturity, external validation, clinical utility, workflow integration, and patient-safety requirements. Results: The most mature applications are narrow, well-defined tasks involving image or signal classification and anatomical segmentation, including audiogram pattern recognition, automated nystagmus extraction, temporal-bone segmentation, and vestibular schwannoma volumetry. By contrast, multimodal prognostic models, AI-guided treatment selection, digital phenotyping, autonomous decision support, and robotic or intraoperative systems remain less clinically established. Across domains, translation is constrained by small retrospective datasets, center- and device-specific acquisition patterns, inconsistent reference standards, limited external and prospective validation, inadequate reporting of calibration and failure modes, and scarce evidence of improved patient outcomes or workflow efficiency. Conclusions: The principal challenge for AI in otology and neurotology is no longer technical feasibility, but demonstration of generalizable clinical value. Future research should prioritize representative multicenter data, independent validation, prospective impact studies, clinically meaningful comparators, interoperability, explainability, fairness, and post-deployment surveillance. AI should be implemented as an auditable, human-supervised technology that strengthens specialist judgment and patient-centered care. Full article

Background: End-effector robotic devices are increasingly used in rehabilitation to retrain neural pathways through repetitive, task-specific exercise. However, the influence of movement kinematic variables remains unclear. This systematic review aimed to; (i) identify end-effector robotic systems used in upper- and lower-limb spinal cord injury rehabilitation, (ii) describe their technical specifications and operational use, and (iii) explore associations between device use characteristics and study outcomes. Methods: A systematic review of five databases was conducted from inception to June 2026. Results: Fifteen studies, ten lower-limb and five upper-limb, met the inclusion criteria. These covered five commercially available lower-limb devices and three upper-limb devices. Most lower-limb studies demonstrated moderate-to-large improvements in balance, ambulation (WISCI-II), walking endurance (6mWT), and independence (SCIM-III). End-effector training produced comparable outcomes to both conventional therapy and exoskeleton-based rehabilitation in controlled comparisons. Upper-limb studies showed substantial heterogeneity in outcome selection, reflecting diverse rehabilitation goals. Conclusions: WISCI-II and 10MWT were the most used lower-limb functional assessments, with SCIM-III used to assess independence for both upper- and lower-limb motor impairments. Reporting of kinematic settings are often limited and concentrated at study onset, limiting assessment of their contribution to outcomes. Reporting the progression of session-level metrics is needed to better understand how time-varying kinematics affect intervention effectiveness. Full article

Background/Objectives: Multimodal artificial intelligence (AI) is increasingly used in radiology to combine medical images with radiology reports, clinical narratives, structured health records, laboratory measurements, and other patient data. These systems may support more context-aware interpretation, reporting, and clinical decision-making than unimodal approaches. This narrative review examines recent technical and clinical advances in multimodal radiology AI and identifies barriers to responsible implementation. Methods: Relevant biomedical and technical literature was identified through targeted searches of PubMed, IEEE Xplore, ACM Digital Library, Web of Science, Scopus, arXiv, ScienceDirect, SpringerLink, and Google Scholar. A documented screening process identified 111 reviewed publications from 1154 records. Study selection and data extraction were conducted by one reviewer without independent verification. Original research, reviews, and commentaries, including selected preprints, were synthesized thematically. Results: The field has progressed from early and late feature fusion toward cross-modal attention, contrastive image–text pretraining, vision–language models, multimodal large language models, and general-purpose foundation models. Applications include diagnostic classification, prognostic modelling, image–text retrieval, visual question answering, clinical decision support, and automated radiology report generation. Despite promising technical results, comparison across studies remains difficult because of heterogeneous datasets, tasks, metrics, and validation designs. Clinical translation is further limited by scarce external and prospective validation, uncertain interpretability, hallucination and omission risks, privacy and fairness concerns, and limited real-world workflow evaluation. Conclusions: Multimodal AI may enable more clinically informed radiological interpretation and reporting, but progress in benchmark performance has outpaced evidence of safety, generalizability, and clinical utility. Future research should prioritize multicentre evaluation, clinically meaningful metrics, transparent reporting, robust safety assessment, and workflow-centred implementation. Full article

Oxide dispersion strengthened (ODS) austenitic steels exhibit superior high-temperature stability and mechanical properties, which are attributed to the strong pinning effect of nano-oxide particles on dislocations and grain boundaries. The microstructure and properties of ODS steels are intrinsically governed by their fabrication techniques. While powder metallurgy (PM) serves as the predominant synthesis route, the fabrication of austenitic ODS steels faces distinct technical challenges compared to their ferritic counterparts, primarily due to issues such as powder sticking to the milling media. Conversely, although traditional melting processes often lead to oxide agglomeration, coarsening, or flotation, which makes it difficult to achieve a uniform dispersion, they remain of significant interest because of their scalability and cost-effectiveness. Furthermore, emerging technologies such as additive manufacturing (AM) have also been employed for the preparation of ODS steels. This review provides a systematic and comprehensive overview of the recent progress in the fabrication technologies of ODS austenitic steels. The scope encompasses a critical analysis of the merits and limitations of techniques including PM, in situ internal oxidation, melting, and AM, alongside an examination of the potential impacts of adding process control agents (PCA) to mitigate powder sticking during mechanical alloying. Additionally, the microstructural characteristics resulting from different processing routes are discussed in detail, followed by a consolidated evaluation of their mechanical properties. Full article

If Recirculating Aquaculture Systems (RAS) are widely regarded as one of the most promising technologies for sustainable aquaculture, why has their commercial adoption remained relatively limited in countries such as Brazil? Despite the remarkable scientific progress achieved in RAS technology over the past decade, a significant gap remains between experimental advances and their widespread commercial implementation. Most published studies have focused on optimising individual system components, including biofiltration efficiency, water quality management, feeding strategies, functional additives, microbial communities, and fish performance under controlled experimental conditions. Although these studies have substantially advanced our understanding of RAS operation, relatively few have critically evaluated whether the reported technological improvements remain effective, economically viable, or operationally feasible when transferred to commercial production systems. Consequently, many innovations that demonstrate promising biological outcomes at laboratory or pilot scales have yet to be validated under the technical, economic, and logistical constraints faced by commercial aquaculture enterprises. The Brazilian evidence indicates a heterogeneous but increasingly mature scientific landscape. Shrimp production provides relatively strong evidence for intensive RAS, biofloc, low-water-exchange, and integrated systems, whereas marine fish and mollusc production remains more strongly concentrated on experimental and pilot-scale optimisation. The future of RAS will not depend solely on further technological innovation, but on the successful integration of biological performance, engineering efficiency, economic feasibility, and practical scalability into commercially viable production systems. Full article

Background: Chronic limb-threatening ischemia (CLTI) is associated with high rates of major adverse limb events and mortality, particularly in patients with diabetes mellitus (DM). The Global Limb Anatomic Staging System (GLASS) standardizes anatomical assessment and supports revascularization planning. This study evaluated the association of anatomical complexity and diabetes with immediate technical success after infrainguinal endovascular treatment. Methods: This single-center retrospective study included 136 patients undergoing infrainguinal endovascular procedures for CLTI between May 2021 and May 2023, stratified into diabetic (n = 85) and non-diabetic (n = 51) groups. Anatomical complexity was assessed using GLASS. Technical success was defined as restoration of inline flow to the foot. Logistic regression and receiver operating characteristic analyses were performed. Results: Technical success was achieved in 74.3% of procedures, without significant differences between diabetic and non-diabetic patients (75.3% vs. 72.5%, p = 0.723). Success declined progressively from 86.3% in GLASS stage I to 75.5% in stage II and 53.1% in stage III (p = 0.003; trend p = 0.001). In multivariable analysis, GLASS FP (femoropopliteal) grade ≥2 (adjusted OR 8.068, 95% CI 3.018–21.562; p < 0.001) and tibioperoneal trunk occlusion (adjusted OR 4.874, 95% CI 1.271–18.687; p = 0.021) were independently associated with technical failure, whereas diabetes was not. The model showed good discrimination (AUC 0.820, 95% CI 0.744–0.897; p < 0.001). Conclusions: Anatomical complexity was associated with immediate technical success in CLTI, whereas no independent association with diabetes was detected in this cohort. GLASS may provide clinically relevant preprocedural risk stratification and support endovascular planning. Full article

Bangladesh’s commitment to achieving Sustainable Development Goal 7 (SDG 7) and its aspiration to attain high-income status by 2041 depends heavily on securing a reliable, affordable, and sustainable energy supply. Despite sustained economic growth, the country’s power sector continues to face challenges associated with fuel import dependency, supply insecurity, policy inconsistencies, and limited technological innovation. Although renewable energy is increasingly recognised as a key component of the global energy transition, non-hydroelectric renewable sources contribute only a small share of Bangladesh’s electricity production, with wind energy remaining largely untapped despite its considerable potential. This study examines the prospects and challenges of wind energy development in Bangladesh, with particular emphasis on the policy, regulatory, economic, and institutional conditions influencing its deployment. A desk-based research methodology was employed, drawing upon government policies, regulatory frameworks, industry reports, international agency publications, and peer-reviewed literature. Through a systematic review and comparative analysis of international experiences, the study evaluates barriers to wind energy, mainly offshore wind expansion. The findings reveal that progress has been constrained by inadequate resource assessment, fragmented governance, limited investment incentives, fossil fuel subsidies, insufficient technical capacity, and underdeveloped financing mechanisms. Furthermore, institutional inexperience with competitive procurement schemes and the absence of a comprehensive offshore wind regulatory framework continue to hinder large-scale deployment. The study highlights the importance of integrated policy reforms, enhanced investment support, capacity-building initiatives, and long-term strategic planning. Strengthening these enabling conditions can accelerate renewable energy adoption, improve energy security, and position wind power as a critical pillar of Bangladesh’s sustainable energy transition. Full article

Exploration for hidden ore bodies is important and requires new prospecting methods. However, predicting the presence of deep hidden mineral resources is difficult because of the large prospective area and the numerous factors influencing underground geological structures and mineral resources; in addition, available information on geological structures and their relationships and changes over time is incomplete. This study focused on a region with typical Mn ore deposits in the Upper Yangtze Block, northeastern Guizhou Province, South China. A geological model based on basic geology and ore mineralogy knowledge was combined with model-free prediction based on the fourth paradigm of scientific research—which uses the multi-scale progressive technical strategy from 1P to 5P (xP refers to various scales of mineralization potential prediction)—to generate a data-and-model-driven big data mineral prospectivity model (BDMPM) that organizes big datasets and big data chains to predict Mn ore mineralization potential. The validation of BOA-AdaBoost in the Songtao Mn ore-concentrated areas demonstrated that the prediction model at the 3P scale achieved an accuracy of 0.936, precision of 0.948, recall of 0.925, F1 score of 0.928, Kappa of 0.862, and AUC of 0.967. This also indicated that the proposed BDMPM can provide technical support for the prospecting and exploration of hidden Mn ore deposits. Full article

Cross-regional greenhouse gas emission linkages have contributed to the formation of interconnected carbon emission networks among cities. However, whether a city’s position within such networks influences agricultural green total factor productivity (AGTFP) remains insufficiently understood. Using panel data from 40 prefecture-level cities in the Yangtze River Delta, China, from 2010 to 2024, this study measures AGTFP using a super-efficiency SBM model and constructs a carbon emission network based on a modified gravity model and social network analysis. Degree centrality is employed to capture cities’ network positions. Two-way fixed-effects, threshold, and moderation models are applied to examine the relationship between network centrality and AGTFP. The results show that carbon emission network centrality is positively associated with AGTFP, primarily through improvements in technical efficiency rather than technological progress. The relationship exhibits a nonlinear pattern: CENC is positively associated with AGTFP when the urban–rural income gap is below a certain threshold, but the association becomes negative when the gap exceeds this threshold. Moreover, stronger water resource management enhances the positive association between network centrality and AGTFP. These findings underscore the significance of network-based coordination and institutional capacity in driving agricultural green transformation. Full article

The world population is aging at an accelerating pace, and the rate of disability caused by chronic diseases and trauma is on the rise. The traditional rehabilitation model has some structural defects, such as subjective evaluation, homogeneity in treatment schemes, imbalance of resource allocation, and insufficient intervention accuracy. The deep integration of big data analysis, artificial intelligence (AI), edge computing, digital twins, federated learning and multimodal large models is promoting a shift in the rehabilitation system from an experience-driven paradigm to a data-driven paradigm. This review systematically summarizes the development and evolution of big data analysis and artificial intelligence pertaining to intelligent rehabilitation, proposes a four-tier progressive technical architecture, summarizes the standardized governance paradigm of heterogeneous rehabilitation big data, and analyzes the mechanism and application boundaries of artificial intelligence algorithms in rehabilitation scenarios, such as neurology, orthopedics, elderly balance, and speech cognition. By comparing typical intelligent rehabilitation platforms horizontally, the core bottlenecks, including data islands, lack of clinical interpretability, weak robustness in complex environments and lack of evidence-based support, were identified. A future development path for integrating federated learning, lightweight multimodal models, human digital twins, flexible wearable sensing and other technologies is proposed to provide theoretical support for the next generation of rehabilitation systems. Full article

Digital three-dimensional (3D) visualization is an evolving field that is being introduced into pediatric surgical oncology, where it addresses challenges related to complex anatomy and difficulties accurately defining surgical margins. This paper provides an overview of digital 3D modeling in pediatric surgical oncology and explores ways to further advance this field. Digital 3D models may improve various aspects of surgery, such as preoperative planning, intraoperative guidance, patient education, and surgical training. Traditionally, 3D models are manually or semi-automatically segmented. More recently, artificial intelligence (AI) can automatically segment pediatric solid tumors. For preoperative planning, 3D models improve anatomic understanding and help multidisciplinary communication. During surgery, these models can assist the surgeon in accurately localizing tumors and determining resection margins in preclinical research settings. In patient education, 3D models present an opportunity to improve patient understanding of disease, therapy, and surgical risks. Lastly, digital 3D models are used in surgical training, particularly for challenging cases. Although digital 3D technologies are evaluated using technical performance metrics, these measures do not necessarily result in improved clinical outcomes. Consequently, evidence demonstrating improved surgical outcomes remains limited, also due to the small patient cohorts. Several challenges continue to hinder clinical translation, including image quality and standardization, segmentation complexity, organ deformability during surgery, and insufficient clinical validation. Addressing these limitations will require progress in four key areas: AI integration, multicenter validation, the development of centralized infrastructures including digital twins, and scalable implementation strategies that support widespread clinical adoption. Full article

The complexity involved in predicting the progression of Alzheimer’s Disease (AD) occurs because of differences in neuroimaging data and the many inconsistencies in biomarker data and cognitive data alike. The current research examined an AI model that uses deep learning, organized data preprocessing, and post-experiment interpretations. A tracing examination of the provided computing material established that the experiments were not conducted using actual ADNI or OASIS-3 data but rather fabricated ADNI/OASIS-type data; therefore, the partition of OASIS was considered an independent synthetic test in the context of external data testing and not clinical validation. The complete dataset used in the research involved 603 records of MCI participants and used only good-quality MRI recordings. In the research, MRI data were combined with demographic, cognitive, APOE4-related, PET, and biomarker data. The output of the experiment was based on post-experiment explanation tools identified as SHAP and Grad-CAM. The internal AUROC of the gated model reached 0.676; sensitivity and specificity were equal to 0.524 and 0.690; the F1 score was equal to 0.489. The external domain testing results were AUROC 0.675; AUPRC 0.571; sensitivity 0.593; specificity 0.656; F1 score 0.551. The logistic regression model slightly surpassed the gated model in terms of internal AUROC; the simple multimodal concatenated model slightly surpassed the gated model in terms of external AUROC and AUPRC; therefore, the predictive edge cannot be claimed. The preprocessing benchmark yielded a throughput of approximately 708–738 records/s and a batch-8 inference latency of approximately 1.25 ms/record. These results support technical feasibility within the tested workload but do not establish large-scale big-data scalability or clinical applicability. Full article

Background: Unlike acute basilar artery occlusion, non-acute vertebrobasilar occlusion has limited evidence for intervention and requires individualized risk assessment. Case Presentation: A 62-year-old woman developed progressive vertigo, diplopia, dysarthria, and gait ataxia over ten days. Dual antiplatelet therapy was started after posterior circulation ischemia was suspected, but symptoms continued to progress. MRI showed acute to subacute left cerebellar infarction, and CTA showed absent antegrade basilar opacification with collateral distal filling. DSA showed distal left vertebral artery occlusion, retrograde basilar filling through posterior communicating artery collaterals, and hypoplastic right vertebral artery. Endovascular recanalization was performed after multidisciplinary review and informed consent. Intraluminal crossing, stepwise angioplasty, and stent reconstruction restored antegrade basilar flow without angiographic complications. NIHSS improved from 5 to 2 at discharge, and mRS was 1 at 30 days. No follow-up vascular imaging was available. Conclusions: This case illustrates technical feasibility in selected non-acute vertebrobasilar occlusion, but does not establish medical treatment failure, causal recovery, generalizable safety, or long-term durability. Full article

Background/Objectives: In 2016, the World Health Organization (WHO) set targets to eliminate viral hepatitis as a public health threat by 2030, seeking a 90% reduction in new chronic infections and a 65% reduction in mortality relative to a 2015 baseline. Despite effective vaccines and potent antivirals, viral hepatitis mortality is rising. This review assessed the global and regional status of, and impediments to, the 2030 HBV elimination goal and compared hepatitis B virus (HBV) with hepatitis C virus (HCV) elimination trajectories. Methods: A scoping review was conducted following the JBI methodology and reported in accordance with PRISMA-ScR. Peer-reviewed literature, global and regional surveillance reports, and grey literature published between 2015 and 2025 were searched across five databases and multiple institutional repositories. Data were charted against WHO impact and service-coverage indicators and mapped across the six WHO regions. Results: Ten core records that met the search criteria were synthesized. Overall, HBV-specific progress is uneven across the care continuum and differs sharply between regions. Routine infant three-dose vaccination (HepB3) reached 84% coverage globally in 2022, with 190/194 (98%) Member States having introduced HepB3, driving HBsAg prevalence below 1% in children under five. However, the timely birth dose (HepB-BD) stagnated at 46% globally and just 14–18% in the African Region, where only a minority of countries offer a universal birth dose. Only 13% of the 254 million people with chronic HBV were diagnosed, and 2.6% were treated. The total viral hepatitis toll increased from 1.1 million (2019) to 1.3 million (2022), with 83% attributed to HBV. Conclusions: HBV elimination is technically feasible but operationally off-track, and progress is regionally inequitable. Effective vaccines make prevention feasible, but current antiviral therapy remains suppressive rather than curative; the principal impediments are the birth-dose gap (most acute in Africa), a collapsed diagnosis–treatment cascade, centralized care, and inadequate domestic financing. Realigning with the 2030 targets requires integrating HBV services into antenatal and primary care, decentralizing testing and treatment through task-sharing, sustained domestic investment, and continued development of curative therapies. Full article