Search Results (7,887)

Semantic segmentation technology based on Inverse Synthetic Aperture Radar (ISAR) images can provide crucial perception and analytical capabilities for intelligent safety maintenance of on-orbit spacecraft. However, conventional semantic segmentation methods suffer from three main limitations: firstly, the lack of modeling for radar physical characteristics in the “image first, segment later” pipeline leads to loss of scattering information and phase details; secondly, reliance on extensive pixel-level manual annotation increases application costs; thirdly, ineffective utilization of spacecraft structural priors fails to guide networks to focus on the main body and edges of spacecraft segmentation. To address these issues, this paper proposes a complex-domain semantic segmentation framework named One-Stop Segmentation (OSS) based on ISAR echoes. The framework incorporates two innovative modules: an Automatic ISAR Labeling (AIL) method designed based on ISAR scattering characteristics to generate labels corresponding to ISAR echoes, and a complex-domain semantic segmentation network named One-Stop Segmentation Network (OSSNet) that performs semantic segmentation directly on echoes, avoiding information loss from imaging while shortening the data processing chain. Core contributions of OSSNet include: (1) a Domain Alignment Module (DAM) to effectively mitigate domain mismatch caused by data distribution differences between raw echo signals and labels; (2) a Multi-Perspective Attention (MPA) framework incorporating a Sliding Correlation Attention (SCA) module and a Subdomain Balanced Attention (SBA) module, lever-aging spacecraft structural priors to guide the network’s focus on main structures and edge details from complementary perspectives, significantly improving segmentation ac-curacy. Experimental results on a simulated ground-based radar dataset demonstrate that the proposed OSS framework achieves a mean Intersection over Union (mIoU) of 92.13% and a mean F1-score of 95.75% in ISAR spacecraft semantic segmentation tasks, outperforming existing methods. Full article

Against the background of the ongoing transition toward green livestock production and the implementation of “dual-carbon” goals, reducing the environmental footprint of ruminant production while maintaining animal performance has become a key focus in nutritional regulation research. This review summarizes and critically discusses the mechanisms and recent progress of nutritional strategies for emission mitigation and improved nutrient utilization in ruminants, with particular emphasis on major environmental challenges, including enteric methane emissions and nitrogen and phosphorus losses. The review discusses the underlying mechanisms through which nutritional interventions contribute to emission reduction, focusing on ruminal hydrogen flux allocation, microbial community remodeling, host metabolic responses, and changes in nutrient utilization efficiency. In addition, by integrating functional feed additives, diet formulation optimization, precision protein supply, and mineral nutrition regulation, this review compares the application characteristics and practical challenges of different strategies in mitigating methane emissions, reducing nutrient excretion, and sustaining production performance. Furthermore, research approaches such as in vitro screening, multi-omics analysis, and evidence synthesis approaches are discussed to highlight the shift of green nutritional technologies from single-target interventions toward integrated and system-level regulation. This review provides a theoretical basis and technical reference for developing green ruminant production systems that balance productivity, animal health, and environmental sustainability. Full article

Cybersecurity risk management is often complicated by fragmented solutions for threat identification and detection, vulnerability assessment, and control selection across multiple frameworks. This paper presents a unified, dynamically updated, threat-based cybersecurity control platform that addresses this challenge by integrating Information Technology (IT), Operational Technology (OT), and Internet of Things (IoT) standards, including ISO/IEC 27001:2022, National Institute of Standards and Technology Cybersecurity Framework (NIST CSF) 2.0, and IEC 62443-3-3. The platform enables (1) querying a selected threat to identify associated vulnerabilities, (2) recommending applicable security controls across multiple frameworks, and (3) identifying overlapping or unique controls to avoid redundant implementation. Automated integration of Common Vulnerabilities and Exposures (CVEs) from the NIST National Vulnerability Database (NVD) links vulnerabilities to mapped threats and controls, supporting proactive risk management. A structured evaluation was conducted across 100 threat scenarios spanning IT, OT, and IoT domains, producing approximately 1000 threat–control relationships across 3 integrated frameworks. Performance evaluation demonstrates that the platform is scalable. While integrating additional frameworks, it maintains an average query latency of 0.40 s to 0.43 s, which implies an insignificant incremental latency increase of 0.03 s, while its web-based interface provides dynamic querying and visualization in a user-friendly manner for technical and non-technical users. By unifying threat, vulnerability, and control data, the platform streamlines compliance, reduces control retrieval time, and ensures traceable, consistent, and cross-framework mitigation strategies, enhancing informed cybersecurity decision making. Full article

Background/Objectives: Iatrogenic nerve injury is a significant challenge in urologic surgery, with radical prostatectomy posing a high risk due to complex pelvic neural anatomy. Despite advances in robotic-assisted and nerve-sparing techniques, postoperative urinary incontinence and erectile dysfunction remain prevalent, adversely affecting patients’ quality of life and imposing substantial healthcare costs. Methods: A narrative review was conducted using PubMed, MEDLINE, and the Cochrane Library (searches through February 2026) for studies on radical prostatectomy epidemiology, mechanisms of nerve injury, functional outcomes, and economic burden. Emerging intraoperative fluorescence imaging technologies, surgical strategies to mitigate iatrogenic nerve injuries, and the financial costs of post-prostatectomy complications were assessed. Results: Robotic-assisted radical prostatectomy now accounts for >80% of procedures in the United States, and has been associated in observational studies with improved early recovery of erectile function compared with open and laparoscopic approaches. However, the lack of real-time nerve visualization remains a limiting factor. Recent milestones (January 2026) include the Food and Drug Administration Investigational New Drug clearance for the nerve-specific fluorophore LGW16-03 (NerveTrace), which enables real-time identification of sub-millimeter nerve branches, and the 510(k) premarket clearance of Dendrite imaging (November 2025). Conclusions: Enhanced intraoperative nerve discrimination via molecularly targeted imaging has the potential to reduce iatrogenic complications and improve long-term functional and economic outcomes in prostate cancer surgery, although these benefits have yet to be demonstrated in prospective clinical and health-economic studies. Full article

Background: The administration of intravenous contrast media using automated power injectors is fundamental for high-quality computed tomography (CT), particularly in CT angiography (CTA). The selection of an appropriate vascular access device (VAD) and adherence to technical safety standards are critical for ensuring diagnostic efficacy and patient safety. This systematic review aims to synthesize current scientific literature regarding the efficacy and safety of contrast media infusion across various vascular access routes, including peripheral intravenous cannulas (PIVC), central venous catheters (CVC), and totally implantable venous access devices (TIVAD). Methods: The review followed PRISMA 2020 guidelines. A comprehensive search was conducted in PubMed, CINAHL, Web of Science, and Scopus for studies published between 2000 and 2026. A total of 19 studies—including randomized controlled trials (RCTs), cohort studies, and systematic reviews—were analyzed. Methodological quality was assessed using Joanna Briggs Institute (JBI) appraisal tools. Results: Modern PIVCs utilizing diffuser technology (side holes) significantly reduce distal jet pressure, minimizing vessel wall damage during high-flow injections. For patients with difficult vascular access, “power-injectable” certified devices (e.g., PICCs or TIVADs) serve as a safe alternative. Standard, non-power-injectable central lines must be avoided due to the risk of catheter rupture. The selection of an appropriate vascular access device is particularly challenging in patients of older age or those with chronic conditions, such as peripheral vascular disease, obesity, or chemotherapy-related venous damage, which often lead to difficult intravenous access (DIVA). Conclusions: Utilizing certified power-injectable devices and advanced cannula designs improves the safety of high-pressure contrast administration. Adherence to technical protocols and the identification of high-risk patients are essential for mitigating complications such as contrast extravasation. Full article

The growing global demand for energy and rising greenhouse gas emissions require effective mitigation strategies, including carbon capture and storage (CCS) technologies. This study reviews 16 widely used simulation tools, including Aspen Plus, MATLAB, Fluent, and gPROMS, for steady-state and dynamic modeling of post-combustion, pre-combustion, and oxy-fuel combustion carbon capture processes. The tools are evaluated using five criteria: chemical process simulation capability, dynamic modeling functionality, thermodynamic property management, heat transfer accuracy, and tool integration features. The results reveal distinct strengths across platforms. Aspen Plus and Aspen Plus Dynamics perform strongly in chemical process simulation and thermodynamic property modeling, reflecting their robustness in reaction modeling and property estimation. gPROMS excels in dynamic modeling, demonstrating strong capability for time-dependent and transient process analysis. MATLAB achieves the highest score in tool integration, highlighting its flexibility in coupling with optimization solvers, control systems, and external programming environments. Fluent shows strong performance in heat transfer modeling, particularly for detailed thermal analysis in oxy-fuel combustion systems. Most existing studies focus on individual carbon capture technologies rather than simulation tool capabilities. Following the PRISMA 2020 guidelines, a systematic search of Scopus yielded 53 peer-reviewed papers on CCS simulation, which were analyzed to identify dominant tools and inform the AHP-based evaluation. This work addresses that gap by clarifying tool-specific advantages, supporting informed model selection to improve the efficiency and sustainability of CCS process design. Full article

The rapid expansion of wind and solar generation has significantly increased the share of variable renewable energy in power systems worldwide, introducing new operational challenges. Among these, the simultaneous growth of renewable energy curtailment and persistent blackout risk reveals structural limitations in energy planning and system flexibility. This study conducts a Systematic Literature Review (SLR) following the PRISMA protocol to examine how the scientific literature has addressed the relationship between curtailment, energy security, and optimization strategies in high-renewable power systems. A total of 53 Q1-indexed articles published between 2021 and 2025 were analyzed using bibliometric and qualitative content analysis techniques. The results indicate that curtailment should not be interpreted solely as an operational inefficiency but rather as a potential flexibility asset when integrated with energy storage systems, power-to-X technologies, demand-side management, and stochastic optimization frameworks. The findings also highlight a shift from deterministic planning approaches toward robust and distributionally aware models capable of managing renewable uncertainty. Despite significant advances, geographic imbalances in case studies and limited integration between regulatory mechanisms and technical optimization remain key research gaps. This review contributes by synthesizing mitigation strategies into a structured flexibility framework and by outlining research directions for enhancing reliability in renewable-dominated systems. Full article

The environmentally sustainable decontamination of chemical warfare agents (CWAs) remains a critical challenge. This study reports the solar-driven photocatalytic degradation of live CWAs—GD, HD, HN1, and HN2—using titanium dioxide (TiO₂) under natural sunlight. Experiments were conducted in an OPCW-designated laboratory to ensure authenticity and practical relevance. TiO₂ exhibited substantial photocatalytic activity, achieving 60% degradation of GD, 63% of HD, 76% of HN1, and 93% of HN2 after 6 h. High-resolution mass spectrometry (HR-MS) analysis suggested plausible degradation pathways for nitrogen mustards consistent with the higher apparent reactivity of HN2; detailed identification of intermediates and reactive oxygen species remains a subject for future investigation. These findings provide mechanistic insights into the photocatalytic behavior of nitrogen-based agents and address a notable gap in studies that have largely focused on sulfur mustards and nerve agents. Beyond military applications, this solar-assisted photocatalytic approach provides mechanistic information relevant to the green remediation of highly toxic organic contaminants and broader chemical hazard mitigation. This work contributes foundational knowledge toward eco-friendly decontamination technologies capable of mitigating diverse CWA threats. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

Supercooled large droplets (SLDs), typically defined as droplets with diameters exceeding 100 μm, represent a significant meteorological hazard to aviation safety. Unlike conventional cloud-sized droplets, SLDs have higher inertia and can follow more ballistic trajectories, leading to impingement well aft of leading-edge ice protection systems. SLD icing is further complicated by high-speed splashing, secondary-droplet re-impingement, delayed solidification, and surface water runback. This paper reviews recent progress in understanding SLD impact, splashing, surface water transport, and ice accretion. The review discusses droplet impact on dry and wet surfaces, oblique impingement, ambient-air effects, non-instantaneous solidification, runback dynamics, and downstream ice growth. Emerging ice protection technologies, including superhydrophobic, lubricant-infused, and compliant surfaces, are also evaluated. By synthesizing these developments, this review connects fundamental droplet-impact physics with practical aviation icing challenges and mitigation strategies. Full article

Transparent soil technology provides a non-invasive experimental approach for visualizing internal processes in geotechnical infrastructure systems, where soil deformation, seepage, erosion, and failure evolution are often difficult to observe using conventional model tests. This review examines the material systems and applicability of transparent soil with emphasis on infrastructure-related applications, including foundation engineering, underground construction, seepage and grouting, internal erosion, slope failure, disaster mitigation, and thermal monitoring. The discussion focuses on transparent sand and transparent clay, comparing their engineering relevance, typical application scenarios, and main limitations rather than treating transparency as the sole criterion for material selection. Based on the reviewed studies, a four-dimensional applicability framework is proposed, consisting of mechanical similarity, optical measurability, system compatibility, and scenario matching. This framework is used to clarify how transparent soil can support mechanism interpretation, model calibration, and scheme comparison in infrastructure-related geotechnical experiments. The review indicates that transparent soil is particularly useful for revealing displacement fields, flow paths, localized deformation, and progressive failure processes in foundations, tunnels, slopes, and other geotechnical systems. However, direct extrapolation of model test results to engineering design parameters remains constrained by material equivalence, optical measurement conditions, model scale, and similarity calibration. Overall, the proposed framework and synthesis provide a systematic reference for transparent soil material selection, infrastructure-oriented scenario matching, and the assessment of applicability boundaries in transparent soil model tests. Full article

Digital trade reflects the convergence of the new technological revolution and traditional trade. Investigating its effectiveness in pollution control and carbon mitigation (PCCM) is crucial for addressing global environmental challenges. This research exploits the rollout of cross-border e-commerce comprehensive pilot zones (CECPZs) as an exogenous policy shock, leveraging double machine learning (DML) methods to assess the impact of digital trade on PCCM using panel data from 280 Chinese prefecture-level cities from 2011 to 2023. The results reveal that digital trade significantly enhances PCCM, mainly by promoting technological innovation, intelligent industrial transformation, and public participation; government emphasis on new quality productive forces and digital government construction positively moderates the link between digital trade and PCCM, while intensified environmental regulation exerts a counteracting inhibitory effect. Heterogeneous outcomes reveal that the promoting effects of digital trade are more evident in large areas, as well as in cities that are neither traditional industrial bases nor resource-based. Further analysis shows that digital trade can deliver a triple dividend in the form of reduced pollution, lower carbon emissions, and sustained economic growth. These findings provide meaningful guidance for promoting a balanced and sustainable relationship between human activities and the natural environment in the digital era. Full article

Amid China’s transition from rapid urbanization to high-quality development, quantifying urban building metabolism is crucial for building resilient resource management systems. However, current research predominantly focuses on eastern cities, largely overlooking non-residential buildings. Here, we apply dynamic material flow analysis (dMFA) to quantify the material stocks of residential and non-residential buildings in two major economic hubs in western China, Xi’an and Chengdu. The stock patterns from 1950 to 2050 and the underlying drivers are further clarified. Model projections suggest that material stocks in both cities will peak around 2040, reaching 2.2 billion tons in Chengdu and 1.08 billion tons in Xi’an, under the intensive scenario. Chengdu reaches stock saturation 2 to 3 years earlier than Xi’an, and the total stocks are approximately twice those of Xi’an. Reinforced concrete and steel structures dominate future building development and increase the accumulation of cement and steel. Sand and gravel still account for the majority of building materials. Demand for new construction materials shows a pronounced double-peak pattern, occurring in 2016 and 2026. Construction waste is projected to rise sharply by mid-century; scenario analysis indicates that an 80% material recovery rate has the potential to largely offset new material demand. Sensitivity analysis identifies building lifetime extension and construction technology improvement as the strategies with the greatest potential for mitigating future waste generation. This study expands the scope of urban building material metabolism research and provides a scientific basis for low-carbon urban planning and construction waste management in China. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

With intensifying measures related to investor and policy requirements, corporate governance and sectoral environmental performance became a focal point for sustainability disclosure, especially in energy-intensive industries with high environmental externalities. This study evaluates whether corporate environmental governance practices in key sectors correspond to their pollution intensity and economic output, analysing a panel dataset across EU member states, for the 2000–2021 period. The empirical methodology includes ordinary least squares (OLS), fixed- and random-effects models, and dynamic system generalised method of moments (GMM) panel estimation to account for sectoral heterogeneity. Results prove that sectoral value added is an influential factor of greenhouse gas emissions, with carbon dioxide exhibiting the highest elasticity to economic activity, followed by methane emissions, and nitrous oxide displaying cross-country variations due to structural and regulatory differences. While services and manufacturing sectors partially decouple via cleaner technologies, overall growth positively correlates with emissions, and renewable energy offers limited mitigation due to scale and integration challenges. Conclusions emphasise robust governance frameworks in high-value energy sectors to meet EU climate-neutrality goals, as stronger environmental accountability attracts capital and supports sustainable development, underscoring the needs for targeted decarbonisation, regulatory coordination, and accelerated technological innovation within persistent industry disparities. Full article