Search Results (32,422)

Accurate prediction of the International Roughness Index (IRI) is critical for road safety and maintenance decisions. In this study, we propose a novel Physics-Aware Informer (PA-Informer) model that integrates the efficiency of the Informer structure with physics constraints derived from partial differential equations (PDEs). The model addresses two key challenges: (1) performance degradation in short-sequence scenarios, and (2) the lack of physics constraints in conventional data-driven models. By embedding residual PDEs to link IRI with influencing factors such as temperature, precipitation, and joint displacement, and introducing a dynamic weighting strategy for balancing data-driven and physics-informed losses, the PA-Informer achieves robust and accurate predictions. Experimental results, based on data from four climatic regions in China, demonstrate its superior performance. The model achieves a Mean Squared Error (MSE) of 0.0165 and R² of 0.962 with an input window length of 30 weeks, and an MSE of 0.0152 and R² with an input window length of 120 weeks. Its accuracy is superior to that of other models, and the stability of the model when the input window length changes is far better than that of other models. Sensitivity analysis highlights joint displacement and internal stress as the most influential features, with stable sensitivity coefficients (S_p ≈ 0.89 and S_p ≈ 0.81). These findings validate the PA-Informer as a reliable and scalable tool for predicting pavement performance under diverse conditions, offering significant improvements over other IRI prediction models. Full article

Seen through a One Health perspective, plasmids act as global links, connecting human, animal, and environmental microbiomes while broadening the ecological scope of resistance and virulence. By combining knowledge about plasmid classification, mobility, resistance, virulence, and data sources, this review emphasizes their key role as drivers of bacterial evolution and worldwide health risks. Recognizing plasmids as connectors across microbiomes highlights both the urgency and opportunity to address plasmid-mediated resistance with integrated strategies. Current plasmid databases, such as NCBI RefSeq, PLSDB, IMG/PR, and PlasmidScope, have already greatly advanced our understanding of these connections, and they are likely to profoundly alter how we see plasmid biology and One Health relationships. Full article

Early intervention is pivotal for optimizing neurodevelopmental outcomes in children with language delay, where increased language stimulation can optimize therapeutic outcomes. Extending speech–language therapy from clinical settings to the home is a promising strategy; however, practical barriers and a lack of scalable, customizable home-based models limit the implementation of this approach. The integration of AI-powered digital interactive tools could bridge this gap. This pilot feasibility study adopted a single-arm pre–post (before–after) design within a two-phase, mixed-methods framework to evaluate a generative AI-powered interactive platform supporting home-based language therapy in children with either idiopathic language delay or autism spectrum disorder (ASD)-related language impairment: two conditions known to involve heterogeneous developmental profiles. The participants received clinical language assessments and engaged in home-based training using AI-enhanced tablet software, and 2000 audio recordings were collected and analyzed to assess pre- and postintervention language abilities. A total of 22 children aged 2–12 years were recruited, with 19 completing both phases. Based on 6-week cumulative usage, participants were stratified with respect to hours of AI usage into Groups A (≤5 h, n = 5), B (5 < h ≤ 10, n = 5), C (10 < h ≤ 15, n = 4), and D (>15 h, n = 5). A threshold effect was observed: only Group D showed significant gains between baseline and postintervention, with total words (58→110, p = 0.043), characters (98→192, p = 0.043), type–token ratio (0.59→0.78, p = 0.043), nouns (34→56, p = 0.043), verbs (12→34, p = 0.043), and mean length of utterance (1.83→3.24, p = 0.043) all improving. No significant changes were found in Groups A to C. These findings indicate the positive impact of extended use on the development of language. Generative AI-powered digital interactive tools, when they are integrated into home-based language therapy programs, can significantly improve language outcomes in children who have language delay and ASD. This approach offers a scalable, cost-effective extension of clinical care to the home, demonstrating the potential to enhance therapy accessibility and long-term outcomes. Full article

In response to the limitations of traditional single-signal approaches, which fail to comprehensively reflect fault conditions, and the difficulties of existing feature extraction methods in capturing subtle fault patterns in transformer fault diagnosis, this paper proposes an innovative fault diagnosis methodology. Initially, to address common severe faults in excitation transformers, Principal Component Analysis (PCA) is applied to reduce the dimensionality of multi-source feature data, effectively eliminating redundant information. Subsequently, to mitigate the impact of non-stationary noise interference in voiceprint signals, a Deep Belief Network (DBN) optimized using the Hunter–Prey Optimization (HPO) algorithm is employed to automatically extract deep features highly correlated with faults, thus enabling the detection of complex, subtle fault patterns. For temperature and electrical parameter signals, which contain abundant time-domain information, the Random Forest algorithm is utilized to evaluate and select the most relevant time-domain statistics. Nonlinear dimensionality reduction is then performed using an autoencoder to further reduce redundant features. Finally, a multi-classifier model based on Adaptive Boosting with Support Vector Machine (Adaboost-SVM) is constructed to fuse multi-source heterogeneous information. By incorporating a pseudo-label self-training strategy and integrating a working condition awareness mechanism, the model effectively analyzes feature distribution differences across varying operational conditions, selecting potential unseen condition samples for training. This approach enhances the model’s adaptability and stability, enabling real-time fault diagnosis. Experimental results demonstrate that the proposed method achieves an overall accuracy of 96.89% in excitation transformer fault diagnosis, outperforming traditional models such as SVM, Extreme Gradient Boosting with Support Vector Machine (XGBoost-SVM), and Convolutional Neural Network (CNN). The method proves to be highly practical and generalizable, significantly improving fault diagnosis accuracy. Full article

Background/Objectives: Gamification and game-based learning (GBL) have recently emerged as fresh and appealing ways of health education, and they have been shown to perform better in knowledge acquisition than traditional teaching approaches. Digital serious games are developing as innovative tools for cancer education and behaviour change, yet no review has systematically synthesized their use across key populations. This scoping review aimed to map evidence on serious games for cancer prevention, care, and survivorship among the public, patients, and healthcare professionals, framed through the Capability, Opportunity, Motivation-Behaviour (COM-B) model. Methods: Following Joanna Briggs Institute methodology, we searched Web of Science, MEDLINE, CINAHL, and PsycINFO. Eligible studies evaluated a serious game with a cancer focus and reported outcomes on knowledge, awareness, engagement, education, or behaviour. Data extraction and synthesis followed the PRISMA-ScR checklist. Results: Thirty-five studies met the inclusion criteria, covering diverse cancers, populations, and platforms. Most reported improvements in knowledge, engagement, self-efficacy, and communication. However, heterogeneity in study design and limited assessment of long-term behaviour change constrained comparability. Conclusions: Digital serious games show promise for enhancing cancer literacy and supporting behavioural outcomes across patients, professionals, and the public. By integrating multiple perspectives, this review highlights opportunities for theory-driven design, robust evaluation, and implementation strategies to maximize their impact in cancer education and awareness. Full article

Historic urban alleys encapsulate cultural identity and collective memory but are increasingly threatened by commercialization and context-insensitive redevelopment. Preserving their authenticity while enhancing environmental resilience requires design strategies that integrate both heritage and ecological values. This study explores the potential of generative artificial intelligence (AI) to support biophilic design in historic alleys, focusing on Daegu, South Korea. Four alley typologies—path, stairs, edge, and node—were identified through fieldwork and analyzed across cognitive, emotional, and physical dimensions of place identity. A Flux-based diffusion model was fine-tuned using low-rank adaptation (LoRA) with site-specific images, while a structured biophilic design prompt (BDP) framework was developed to embed ecological attributes into generative simulations. The outputs were evaluated through perceptual and statistical similarity indices and expert reviews (n = 8). Results showed that LoRA training significantly improved alignment with ground-truth images compared to prompt-only generation, capturing both material realism and symbolic cues. Expert evaluations confirmed the contextual authenticity and biophilic effectiveness of AI-generated designs, revealing typology-specific strengths: the path enhanced spatial legibility and continuity; the stairs supported immersive sequential experiences; the edge transformed rigid boundaries into ecological transitions; and the node reinforced communal symbolism. Emotional identity was more difficult to reproduce, highlighting the need for multimodal and interactive approaches. This study demonstrates that generative AI can serve not only as a visualization tool but also as a methodological platform for participatory design and heritage-sensitive urban regeneration. Future research will expand the dataset and adopt multimodal and dynamic simulation approaches to further generalize and validate the framework across diverse urban contexts. Full article

Background/Objectives: C-reactive protein (CRP) is widely used as a marker of perioperative inflammation, but its predictive value for cardiac surgical outcomes remains uncertain. Obstructive sleep apnea (OSA), a prevalent and underrecognized comorbidity, may independently contribute to postoperative complications through non-inflammatory mechanisms. This study aimed to reevaluate the prognostic role of CRP and determine the clinical impact of OSA severity on postoperative recovery, focusing on new-onset atrial fibrillation (AF), prolonged intubation time, and postoperative CPAP/AIRVO use as indicators of respiratory burden. Methods: In this prospective cohort of 142 elective cardiac surgery patients, preoperative polysomnography and serial CRP measurements were obtained. Multivariable regression, mediation analysis, and propensity score matching (PSM) were performed to evaluate associations between OSA severity, CRP, and perioperative outcomes (AF, intubation time, CPAP/AIRVO use). Results: OSA severity independently predicted prolonged intubation (β = 1.74, p = 0.0019) and new-onset AF (β = 0.85, p = 0.004), even after excluding patients with preexisting arrhythmia. CRP showed poor discriminatory power as a standalone biomarker (AUC for IOT > 14 h = 0.445) and did not mediate OSA–outcome associations. However, CRP > 2.1 mg/dL doubled the odds of moderate-to-severe OSA (OR = 2.05, p = 0.041). A composite score integrating AHI, BMI, and postoperative CRP strongly correlated with postoperative respiratory support (p < 0.0001). Conclusions: OSA exerts a stronger and more consistent influence on perioperative outcomes than CRP, challenging reliance on CRP for risk stratification. Incorporating objective OSA screening and spirometry into preoperative assessment may enhance perioperative risk prediction and guide personalized management strategies. Full article

Climate change and the El Niño Southern Oscillation (ENSO) events have been increasingly linked to infectious disease outbreaks. While growing evidence has connected climate variability with systemic illnesses, the ocular implications remain underexplored. This study aimed to assess the relationships between ENSO-driven climate events and infectious diseases with ophthalmic consequences. A narrative review of 255 articles was conducted, focusing on infectious diseases influenced by ENSO and their associated ocular findings. 39 articles met criteria for full review, covering diseases such as dengue, zika, chikungunya, malaria, leishmaniasis, leptospirosis, and Rift Valley fever. Warmer temperatures, increased rainfall, and humidity associated with ENSO events were found to enhance vector activity and disease transmission. Ocular complications included uveitis, retinopathy, and optic neuropathy, but the specific disease findings varied by infectious disease syndrome. The climactic variable changes in response to ENSO events differed across diseases and regions and were influenced by geography, local infrastructure, and socioeconomic factors. ENSO event-related climate shifts significantly impact the spread of infectious diseases with ocular symptoms. These findings highlight the need for region-specific surveillance and predictive models that may provide insight related to the risk of ophthalmic disease during ENSO events. Further research is needed to clarify long-term ENSO effects and develop integrated strategies for systemic and eye disease detection, prevention, and management. Full article

This study examines the effects of hailstorms on photovoltaic (PV) modules, focussing on damage mechanisms, testing standards, numerical simulations, damage detection techniques, and mitigation strategies. A comprehensive review of the recent literature (2017–2025), experimental results, and case studies is complemented by advanced simulation methods such as finite element analysis (FEA) and smoothed particle hydrodynamics (SPH). The research emphasises the crucial role of protective glass thickness, cell type, number of busbars, and quality of lamination in improving hail resistance. While international standards such as IEC 61215 specify test protocols, actual hail events often exceed these conditions, leading to glass breakage, micro-cracks, and electrical faults. Numerical simulations confirm that thicker glass and optimised module designs significantly reduce damage and power loss. Detection methods, including visual inspection, thermal imaging, electroluminescence, and AI-driven imaging, enable rapid identification of both visible and hidden damage. The study also addresses the financial risks associated with hail damage and emphasises the importance of insurance and preventative measures. Recommendations include the use of certified, robust modules, protective covers, optimised installation angles, and regular inspections to mitigate the effects of hail. Future research should develop lightweight, impact-resistant materials, improve simulation modelling to better reflect real-world hail conditions, and improve AI-based damage detection in conjunction with drone inspections. This integrated approach aims to improve the durability and reliability of PV modules in hail-prone regions and support the sustainable use of solar energy amidst increasing climatic challenges. Full article

Background/Objectives: The therapeutic landscape of advanced or metastatic urothelial carcinoma (UC) has shifted from platinum chemotherapy to precision immuno-oncology. Immune checkpoint inhibitors (ICIs)—pembrolizumab, nivolumab, and avelumab—show efficacy across platinum-refractory, maintenance, and adjuvant settings, yet benefit is limited to subsets, underscoring the need for biomarkers. Antibody–drug conjugates (ADCs), notably enfortumab vedotin(EV), and targeted agents such as FGFR inhibitors further expand options. This review synthesizes current evidence and emerging paradigms to guide combinations and sequencing. Methods: We performed a narrative synthesis of peer-reviewed trials (emphasizing pivotal phase III studies), key translational investigations, and contemporary guidelines on ICIs, ADCs, HER2-directed therapies, FGFR inhibitors, molecular subtyping, and genomic profiling in UC, integrating efficacy signals, biomarker associations, and practical implications for sequencing. Results: ICIs now occupy multiple settings, but heterogeneous benefit highlights the importance of molecularly informed selection. EV alone and with pembrolizumab has produced unprecedented first-line activity, prompting a strategic shift. Molecular subtyping and genomic profiling delineate phenotypes with variable immune responsiveness and targetable vulnerabilities, enabling rational combinations and refined sequencing. Ongoing trials are evaluating next-generation ADCs, HER2-directed approaches, and dual checkpoint blockade to achieve durable, personalized disease control. Conclusions: Management of locally advanced or metastatic UC is converging on precision immuno-oncology, wherein biomarker-driven selection, molecular subtyping, and thoughtful sequencing of ICIs, ADCs, and targeted agents are central to optimizing outcomes. Active trials and translational advances are expected to refine personalized strategies and embed molecular guidance into routine care. Full article

With the rapid development of unmanned aerial vehicle (UAV) technology, quadrotor UAVs have demonstrated extensive application potential in various fields. However, due to parameter uncertainties and strong coupling, the flight attitude of quadrotors is prone to external disturbances, posing challenges for achieving precise control and stable flight. In this paper, we address the tracking control problem under unknown command rate variations by proposing a Modified Linear Active Disturbance Rejection Control (LADRC) strategy, aiming to enhance flight stability and anti-disturbance capability in complex environments. First, based on the attitude dynamics model of quadrotors, an LADRC technique is adopted to realize three-channel decoupling-free control. By integrating a parameter estimator, the proposed method can compensate unknown disturbances in real time, thereby improving the system’s anti-disturbance ability and dynamic response performance. Second, to further enhance system robustness, a linear extended state observer (LESO) is designed to accurately estimate the tracking error rate and total disturbances. Additionally, a Levant differentiator is introduced to replace the traditional differentiation component for optimizing the response speed of command rate. Finally, a modified LADRC controller incorporating error rate estimation is constructed. Simulation results validate that the proposed scheme maintains good tracking accuracy under high-frequency disturbances, providing an effective solution for stable UAV flight in complex scenarios. Compared with traditional control methods, the modified LADRC strategy exhibits significant advantages in tracking performance, anti-disturbance capability, and dynamic response. This research not only offers a novel perspective and solution for quadrotor control problems but also holds important implications for improving UAV performance and reliability in practical applications. Full article

Urban Form (UF), as a synthesis of urban functions and socioeconomic elements, is closely associated with Anthropogenic Carbon Emissions (ACE) and has important implications for low-carbon urban planning. As a key national economic strategy region, the Yangtze River Economic Belt (YREB) exhibits pronounced heterogeneity in urban development, highlighting the urgent need to elucidate the interaction mechanisms between UF and ACE to support carbon reduction strategies. This study employs nighttime light data and carbon emission records from 2002 to 2022 in the YREB. By integrating Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT), we developed a neural network ensemble model (RSG-Net) to analyze the impacts and driving mechanisms of UF on ACE. The results indicate the following: (1) Over the past two decades, total ACE in the YREB increased by 196%, displaying a three-phase trajectory of rapid growth, deceleration, and rebound. (2) The RSG-Net model achieved superior predictive performance, with an R² of 0.93, an RMSE of 1.96 × 10⁶ t, an RPD of 3.69, and a PBIAS of 4.53%. (3) Based on Pearson correlation analysis and SHAP (Shapley Additive Explanations) feature importance, beyond economic and demographic indicators, the most influential UF indicators are ranked as Number of Urban Patches (NP), Normalized Difference Vegetation Index (NDVI), and Construction Land Concentration (CLC). These findings demonstrate that the RSG-Net model can not only predict ACE but also identify key UF factors and explain their interrelationships, thereby providing technical support for the formulation of urban carbon reduction strategies. Full article

Polycystic ovary syndrome (PCOS) represents one of the most prevalent endocrine–metabolic disorder in women of reproductive age, which includes but not restricted to reproductive disruptions, insulin resistance (IR), hyperandrogenism, and chronic low-grade inflammation. Its heterogeneous pathophysiology arises from the interplay of metabolic, endocrine, and immune factors, including dysregulated adipokine secretion, cytokine-mediated inflammation, oxidative stress (OS), and mitochondrial dysfunction. Current pharmacological therapies, such as metformin, clomiphene, and oral contraceptives, often provide partial benefits and are limited by side effects, necessitating the exploration of safer, multi-target strategies. Flavonoids, a structurally diverse class of plant-derived polyphenols, have gained attention as promising therapeutic candidates in PCOS due to their antioxidant, anti-inflammatory, insulin-sensitizing, and hormone-modulating properties. Preclinical studies in rodent PCOS models consistently demonstrate improvements in insulin sensitivity, normalization of ovarian morphology, restoration of ovulation, and reduction in hyperandrogenism. Human clinical studies, though limited in scale and heterogeneity, report favorable effects of flavonoids such as quercetin, isoflavones, and catechins on glucose metabolism, adipokine balance, inflammatory markers, and reproductive functions. This evidence-based study critically synthesizes mechanistic insights into how flavonoids modulate insulin signaling, adipokine–cytokine crosstalk, OS, and androgen excess, while highlighting translational evidence and emerging delivery systems aimed at overcoming bioavailability barriers. Collectively, flavonoids represent a promising class of nutraceuticals and adjuncts to conventional therapies, offering an integrative strategy for the management of PCOS. Full article

This study aimed to develop an industrially viable method for rapid detoxification of raw kidney beans (Phaseolus vulgaris L.) while enhancing nutritional properties. Through optimized 5 min water soaking combined with intermittent microwaving (500 W, 5 min), we achieved significant reductions in key antinutrients, namely phytic acid (43–49%), tannins (74–90%), and saponins (59–68%)—all below safety thresholds—concurrently elevating antioxidant capacity (e.g., Ferric Reducing Antioxidant Power: +66–115%) across four colored varieties. Metabolomic analysis of 412 identified metabolites revealed substantial accumulation of key antioxidants including glutathione and quercetin derivatives. Pathway analysis demonstrated dual mechanisms: (1) Detoxification via activated phenylpropanoid biosynthesis degrading tannin precursors and glutathione metabolism reducing phytate; (2) Nutrient enrichment through upregulated phenolic biosynthesis and color-specific flavonoid/betalain pathways. This integrated approach achieves comparable detoxification to 30 min boiling in just 5 min, establishing water-assisted microwave processing as an efficient strategy for industrial-scale production of safer, nutrient-enhanced legumes. Full article

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article