Search Results (1,347)

This review aims to assess the clinical performance and application results of oral wearable devices in in vivo trials. Following a systematic search of PubMed, Cochrane, Embase, and Scopus databases up to 15 October 2025, and strict screening in accordance with PRISMA 2020 guidelines, 13 in vivo human trials were finally included for analysis. These were analyzed across four clinical functions: diagnosis, treatment, monitoring, and prevention. These devices have evolved from bulky prototypes into miniaturized, wireless systems with diverse diagnostic and therapeutic functions. Their applications now extend beyond common conditions like caries and bruxism to postoperative recovery and pediatric dental anxiety intervention. The findings show that some devices already offer practical value for clinical screening and auxiliary diagnosis. They demonstrate significant potential in early disease detection and medical cost control. However, development still faces many challenges. Technical issues include limited battery life, insufficient mechanical durability, and wireless transmission constraints within the oral environment. Furthermore, clinical evidence levels remain low, indications are narrow, and dedicated ethical and regulatory frameworks are lacking. Inconsistent regulatory standards, production costs, and clinician adoption hurdles slow its commercial development. In the future, the integration of AI, breakthroughs in energy harvesting, and the creation of digital health platforms will be key to overcoming technical bottlenecks. Full article

Carcass growth and development are crucial evaluation indicators influencing the economic efficiency of goats (Capra hircus). This study aimed to screen the nucleotide variation sites (SNPs) of the APOE gene in Guizhou white goats and explore the correlation between APOE gene variations and body size traits, as APOE had been identified as a key candidate gene regulating growth and development in this breed through transcriptome sequencing screening. A total of 324 Guizhou white goats were used in this study for SNP detection, population genetic analysis, real-time fluorescence quantitative PCR (RT-qPCR) and association analysis. The results showed that one nucleotide mutation site (g.353 A > G) was detected in the APOE gene, which yielded two alleles (A and G) and three genotypes (AA, AG and GG). The site exhibited moderate polymorphism and conformed to Hardy–Weinberg equilibrium. The mRNA expression level of APOE in longissimus dorsi muscle was significantly higher in males than in females. Association analysis revealed a sex-specific effect of this locus on body size traits. The A allele and AA genotype were significantly associated with increased body weight and heart girth in females, whereas no significant effect was detected in males. Therefore, the identified APOE gene mutation site can serve as a candidate molecular marker for the early selection of growth traits in Guizhou white goats. Full article

The increasing global demand for sustainable water purification technologies has accelerated research on catalytic degradation and advanced oxidation processes for the removal of refractory pollutants. This study provides a comprehensive bibliometric analysis of global research trends in catalytic water and wastewater treatment from 2010 to 2025, combining quantitative mapping with a qualitative synthesis of emerging technological directions. Bibliographic data were retrieved from the Scopus database and screened using the PRISMA framework, followed by analysis using VOSviewer (v1.6.20) and OriginPro (version 2023, OriginLab Corporation, Northampton, MA, USA) to examine publication growth, citation patterns, international collaboration networks, and thematic evolution. A total of 1550 publications, including 1265 research articles and 285 review papers, were analyzed. The results show a significant increase in research output after 2015, reflecting growing global attention to water sustainability and environmental remediation. China, the United States, and India were identified as the leading contributors, with strong international collaboration networks. Keyword co-occurrence analysis revealed three dominant research themes: photocatalytic degradation and semiconductor engineering, Fenton and Fenton-like advanced oxidation processes, and emerging hybrid catalytic systems involving carbon-based materials and metal–organic frameworks. The analysis also indicates a recent shift toward multifunctional hybrid catalysts designed to improve efficiency, stability, and performance in complex wastewater systems. These findings highlight key scientific developments and suggest future research priorities, including green catalyst synthesis, reactor and process scale-up, AI-assisted catalyst design, and life-cycle sustainability assessment to support the transition from laboratory research to practical water treatment applications. Full article

Textile-to-textile recycling is strongly constrained by upstream pre-processing, where post-consumer clothing must be identified, separated, and prepared under high variability in materials, appearance, and contamination. This paper presents a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic literature review of intelligent and automated technologies for textile recycling pre-processing covering the interval between 2015 to 2025. After screening and quality assessment, 21 primary studies published between 2020 and 2025 were included. The literature is synthesized across three task families: (i) identificationof fiber/material, composition, or color; (ii) sorting, considered only when explicit separation strategies are defined to operationalize identification outcomes into routing actions or output streams; and (iii) contaminant detection and/or removal, targeting non-recyclable items. Results show that identification dominates the field (19/21 studies), supported by Red–Green–Blue (RGB) and red–green–blue plus depth (RGB-D) imaging and material-signature sensing, including near-infrared (NIR) spectroscopy, hyperspectral imaging (HSI), and Raman spectroscopy. In contrast, sorting as a defined separation stage is less frequent (4/21), and contaminant-related automation remains sparse (3/21). Most studies are validated in laboratory conditions, with limited semi-industrial evidence, highlighting a persistent perception-to-action gap. Overall, the review indicates that robust separation strategies, representative datasets, and end-to-end system integration remain key bottlenecks for scalable automated textile recycling pre-processing. Full article

Hydrogen is increasingly adopted as a clean energy carrier for storing and transporting low-carbon energy. Achieving a practical volumetric energy density for real-world deployment typically requires compression to several hundred bar, which in turn demands dedicated high-pressure infrastructure. Because valves are indispensable for isolation and flow control within this infrastructure, durable sealing valve materials become a key reliability and safety requirement. This assembly-level screening study compares two valve configurations with different polymer assemblies: EPDM O-rings with PEEK seats/bushing and NBR O-rings with POM seats/bushing. Four new identical 500-bar ball valves were tested (two EPDM/PEEK and two NBR/POM). For each seal configuration, one valve was cycled 5000 times at 500 bar in helium (inert baseline), and a second identical valve was cycled 5000 times at 500 bar in hydrogen to isolate hydrogen effects from mechanical/metallic wear. Leakage was tracked during cycling, and seals were analyzed by SEM/EDX after testing. The EPDM/PEEK configuration remained leak-tight in both gases, with no cracking observed in the elastomer or thermoplastic components. The NBR/POM configuration exhibited POM bushing fracture during cycling and minor external leakage at the stem during the hydrogen phase, accompanied by micro-fissures on the NBR O-ring surface. EDX indicated composition changes after cycling, including oxygen and fluorine enrichment and occasional metallic transfer species, consistent with surface films and deposits. Under the present valve geometry and cycling protocol, EPDM/PEEK provided robust sealing, whereas NBR/POM showed failure modes relevant to high-pressure service. These findings are intended as configuration-level screening evidence to be used in valves rather than as a full qualification of the individual materials. Full article

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market. Full article

Background/Objective: Advances in intensive care medicine have substantially improved the survival of critically ill patients; however, they have also revealed the growing burden of neurological complications that affect both short-term outcomes and long-term functioning. Neurological complications in the intensive care unit (ICU) include a wide spectrum of disorders, ranging from acute brain dysfunction such as delirium, coma, and encephalopathy to persistent cognitive impairment after discharge, which represents a key component of Post-Intensive Care Syndrome (PICS). Delirium affects approximately one-third of ICU patients and is independently associated with increased mortality, prolonged hospitalization, and worse long-term neurocognitive outcomes. Due to the limited effectiveness of pharmacological therapies, current clinical approaches emphasize prevention, early diagnosis, and non-pharmacological strategies in line with PADIS guidelines. This narrative review aims to provide a clinically relevant synthesis of neurological complications in adult ICU patients, conceptualized as a continuum from acute brain dysfunction to long-term cognitive impairment. Methods: A narrative review of the literature was conducted, focusing on studies addressing epidemiology, pathophysiology, risk factors, diagnostic strategies, and prevention of neurological complications in critically ill adults. Attention was given to delirium, ICU-acquired cognitive impairment, and their association with PICS, as well as to current guideline-based and non-pharmacological interventions. Results: Available evidence indicates that neurological complications in the ICU are multifactorial and result from the interaction between patient vulnerability, severity of illness, systemic inflammation, sedative exposure, and environmental factors. Delirium remains the most common manifestation of acute brain dysfunction and is strongly associated with adverse outcomes. Increasing evidence supports the effectiveness of structured screening, early mobilization, sleep optimization, and multidisciplinary care bundles in reducing delirium incidence and duration. Moreover, growing attention is directed toward post-ICU follow-up and rehabilitation to reduce long-term cognitive decline. Conclusions: Neurological complications should be considered a central component of critical illness and a continuum extending beyond ICU discharge. Early identification of high-risk patients, implementation of preventive strategies, and integration of acute and post-ICU care are essential to improve survival and long-term cognitive outcomes. Further research should focus on personalized preventive and neuroprotective approaches in critically ill patients. Full article

This study presents a GIS-driven multi-criteria decision analysis (MCDA) framework for regional suitability screening of data center (DC) development in the United Kingdom. The methodology integrates spatial exclusion of constrained zones, raster standardization of climate and infrastructure indicators, Analytic Hierarchy Process (AHP) weighting, and Weighted Linear Combination (WLC) to generate a national suitability surface at 1 km resolution. Climate indicators (temperature, air frost days, humidity, and solar radiation) and infrastructure and environmental constraint indicators (grid access, transport proximity, environmental protections, and population distribution) were standardized and combined within a GIS-based decision framework. Hard constraints such as protected areas and flood zones were applied through binary exclusion, while climatic and infrastructure factors were evaluated using weighted suitability scoring. Five candidate regions were identified from the suitability analysis: the Scottish Highlands, Northeast England, Southwest England (Cornwall), Northwest England, and Eastern England. These regions were further evaluated against key requirements including power infrastructure accessibility, workforce and connectivity availability, and exposure to environmental and hydro-climate constraints. The final comparison identified Lincolnshire as the most suitable region due to strong grid accessibility, favorable composite climate suitability, adequate population proximity, and limited overlap with protected areas. The proposed framework demonstrates how climate-driven cooling suitability can be integrated with infrastructure accessibility and environmental constraints within a unified spatial decision model for national-scale digital infrastructure planning. Full article

Background: Against the backdrop of global population aging, understanding the mechanisms of age-related cognitive decline has become crucial for improving the health and quality of life in older adults. Methods: This study employed a multimodal approach to investigate the neural modulations induced by a motor cognitive dual task and their relationship with age-related decline. By integrating behavioral assessments, electroencephalography (EEG), and body composition analysis, we comprehensively evaluated performance and neural correlates in 19 younger and 18 older adults. Specifically, EEG analyses focused on comparing pre-task and post-task resting-state recordings to investigate the immediate impact of a single acute cognitive-motor dual-task session on neural oscillations and brain network organization. Results: Key findings include: (1) older adults exhibited significantly inferior performance in task accuracy, reaction time, and composite performance score compared to younger adults (p < 0.001); (2) neural oscillatory analysis of resting-state data revealed a localized increase in gamma-band power at posterior-temporal sites (PO4/T6) in older adults following the dual-task, while younger adults exhibited widespread multi-band (delta to beta) power modulation across frontal, central, and temporal regions in younger adults; (3) brain network analysis demonstrated synergistic enhancement of multi-band (Theta, Alpha, Beta, Gamma) connectivity and optimized topological organization in younger adults post-task, contrasting with network rigidity and localized compensatory patterns in older adults; (4) correlation analyses indicated significant associations between dual-task performance and MoCA-B scores in older adults (r = 0.861, p < 0.001). Conclusions: This study innovatively elucidates the neurophysiological characteristics of brain aging. The motor-cognitive dual-task paradigm proves to be a sensitive tool for capturing early cognitive changes, holding significant promise for clinical screening. Full article

Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

Background/Objectives: The emergence of antimicrobial resistance threatens advances achieved by medicine in the last century. This situation has been exacerbated by the suboptimal outcome of screening campaigns to provide novel antimicrobials. Methods: An alternative strategy was employed to identify new chemical scaffolds with antimicrobial activity. A collection of photoactive compounds originally synthesized for industrial purposes was screened for antibacterial activity. Results: 4H-pyran-4-ylidenes were identified as active against Gram-positive bacteria. Compounds belonging to this family displayed dose-dependent bactericidal activity against both wild-type and methicillin-resistant Staphylococcus aureus. No cytotoxicity was observed in the HepG2 hepatic cell line at the concentrations required for antimicrobial activity against S. aureus. Resistance to 4H-pyran-4-ylidenes in S. aureus was associated with point mutations in the rny locus, which encodes for a ribonuclease that plays a key role in RNA homeostasis. Conclusions: These findings indicate that chemical libraries not originally intended for drug discovery can be an innovative source of chemical diversity for the development of novel antimicrobials. Full article

The deployment of distributed renewable energy systems at the neighborhood scale is a key lever for urban decarbonization. In Europe, the regulatory framework now enables collective self-consumption, allowing multiple end-users to share locally produced energy. However, the complexity and early-stage uncertainties of such projects, especially in new district development, pose challenges for feasibility assessment and investor confidence. This study proposes a method to identify the impact of numerous technical, economic, and social parameters that may affect the feasibility of a project and that are uncertain at the early design stage, across multiple key performance indicators, thus addressing the concerns of various stakeholders. A key objective is to provide an integrated method applicable during the early stages of district development, when the integration of a collective self-consumption scheme is under consideration. The developed tools and methods are compatible with the available data at this stage and provide a basis for multi-criteria analysis. The simulation workflow was built around URBANopt and enhanced with probabilistic occupancy modeling, energy sharing mechanisms, and financial analysis modules. It was further complemented by sensitivity and risk analysis layers. The method was applied to a pre-design case study, illustrating how key design and operational uncertainties influence project viability. The results showed that despite the uncertainties on a wide array of parameters, reliable risk assessment per KPI could be performed on only a handful of parameters, which were identified through a sensitivity analysis using the Morris screening method. Full article

Hydropower plays a key regulating role in new-type power systems, and both forecasting accuracy and interpretability are critical for power dispatch. However, cascade hydropower forecasting is constrained by strong spatiotemporal coupling among multi-dimensional features, flow propagation delays, as well as the limited transparency of deep learning models. To tackle these issues, this paper develops a hybrid framework integrating Maximal Information Coefficient (MIC), the Long- and Short-term Time-series Network (LSTNet), and the SHapley Additive exPlanations (SHAP) interpretability method. First, an MIC-based nonlinear screening mechanism is employed to remove redundant noise and construct a high-quality input space. Second, an LSTNet model is developed to deeply extract spatiotemporal coupling features among cascade stations and flow evolution patterns, achieving high-accuracy forecasting of both system-level and station-level outputs. Finally, SHAP is used for global and local interpretability analysis to perform physics-consistency verification with respect to the model’s decision-making rationale. Experimental results indicate that the proposed approach achieves low errors in total output forecasting, reducing error levels by approximately 57–88% compared with Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Informer. Moreover, SHAP feature-dependence analysis reveals a nonlinear response change of station D around 7.8 MW, providing evidence for the physical consistency of the model outputs and improving model interpretability. Full article

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands. Full article

This paper presents a qualitative sustainability assessment of an innovative, water-based, partially bio-based, and potentially removable screen-printing ink designed to replace conventional PVC-based inks in the textile industry. The assessment is conducted in alignment with the European Commission’s tiered Safe and Sustainable by Design (SSbD) framework, applying a simplified screening approach suitable for innovations with limited sustainability data availability. The evaluation is conducted using the LCBROM (Life Cycle Based Risk and Opportunity Mapping) methodology, which is a structured approach designed to identify potential environmental, economic, and social drawbacks and benefits throughout the product’s life cycle, from production and use to end of life. The screening incorporates the MET+Ec+S matrix (Material, Energy, Toxicity, and Economic and Social dimensions), providing a comprehensive overview of the sustainability performance of the removable PVC-free ink at each stage of its life cycle. The novel removable PVC-free ink formulation incorporates bio-based pigments, thickeners, and plasticisers, and is designed to facilitate recyclability and reuse in textile applications. Compared to traditional plastisol inks, the screening indicates potential reductions in toxicity and environmental persistence compared to PVC-based plastisol inks, subject to validation in future quantitative studies. However, key trade-offs include reliance on fossil-based ingredients (as bio-based alternatives are still being developed), increased material costs, and durability concerns. Despite these issues, the removable PVC-free ink’s compatibility with existing printing infrastructure and alignment with emerging EU sustainability regulations indicate its potential relevance for circular textile production, subject to validation through quantitative life-cycle assessment and pilot-scale implementation. The results do not constitute a quantitative life cycle assessment but instead provide a structured qualitative basis for guiding further development, data collection, and future LCA modeling. By explicitly positioning the work within a simplified SSbD tier, this study demonstrates how early-stage screening can support innovation design while transparently addressing uncertainty and trade-offs. Full article