Search Results (5,395)

Tropical cyclones pose major risks to life and property, especially as coastal populations grow and climate change increases the likelihood of intense storms, making seasonal prediction of tropical cyclones an important scientific and societal goal. This study uses HURDAT best-track records from 1950 to 2024 to quantify annual tropical cyclone, hurricane, and major hurricane counts across the Atlantic basin, Caribbean Sea, and Gulf of Mexico. These nine targets are paired with 34 monthly climate predictors from NOAA and NASA GISS—including SST and ENSO indices, Main Development Region (MDR) wind and pressure fields, and latent heat flux empirical orthogonal functions—evaluated under nine predictor-set configurations. Four forecasting approaches were developed and tested under operationally realistic conditions—Lasso regression, K-nearest neighbors (KNN), an artificial neural network (ANN), XGBoost—using a 30-year sliding-window cross-validation design and a Poisson log-likelihood skill score relative to climatology. Lasso performs reliably with concise, physically interpretable predictors, while XGBoost provides the most consistent overall skill, particularly for basin-wide total cyclone and hurricane counts. The skill of ANN is limited by small sample sizes, and KNN offers only marginal improvements. Forecast skill is the highest for basin-wide storm totals and decreases for regional major-hurricane targets due to lower event frequencies and stronger predictability limits. Full article

Background/Objectives: The COVID-19 pandemic exposed global weaknesses in healthcare preparedness and highlighted the pivotal role of the World Health Organization (WHO) in coordinating responses and issuing technical guidance. Among these, the document “Rational use of personal protective equipment (PPE) for COVID-19 and considerations during severe shortages” (December 2020) aimed to standardize PPE use amid global scarcity. This study assessed the awareness, implementation, and perceived usefulness of this WHO guidance among Polish healthcare personnel and evaluated discrepancies between the WHO expectations and workplace realities. Methods: A cross-sectional, anonymous online survey was conducted between July and September 2025 among employees of 243 randomly selected healthcare facilities in Poland (constituting 20% of all hospitals). The original 24-item questionnaire covered the demographics, awareness and implementation of the WHO PPE guidelines, and perceptions of their effectiveness during and after the pandemic. Data were analyzed descriptively. Results: A total of 542 healthcare workers participated, predominantly nurses (56.8%) and physicians (12.2%), with 86.8% being female and 59.3% having over 20 years of experience. Most respondents (76.5%) reported familiarity with the WHO PPE document, and 63.1% confirmed its implementation in their institutions. Over two-thirds (68.0%) reported that the guidelines improved their sense of safety at work. The main barriers to implementation included staff shortages (52.9%) and insufficient local guidance (20.6%). In 2025, 52.3% continue to apply the WHO recommendations, and 70.8% believe they remain relevant in current practice. However, 80.2% indicated that the WHO guidance should be more closely adapted to local conditions. Conclusions: The WHO PPE guidance was widely recognized and reported as implemented by respondents from participating healthcare facilities, contributing to improved preparedness. Nonetheless, limited institutional support and inadequate local adaptation reduced implementation effectiveness. Future WHO recommendations should better align with national healthcare contexts to enhance preparedness for future crises. Full article

Background/Objectives: Irritable Bowel Syndrome (IBS) is a widely prevalent chronic disorder of brain–gut interaction which represents a clinical challenge due to its complex underlying causes and the lack of a standardized treatment approach. This cross-sectional research collected real-world data (RWD) on the effectiveness, safety, and usage pattern of a natural substance-based medical device, Colilen IBS, indicated for the treatment of IBS. Methods: Surveys were conducted both in Italy and Germany with 6101 participants, including 4425 patients, 1014 pharmacists, and 662 physicians using a structured GxP web platform that allows voluntary participants to share their experiences with the device. The validated platform was designed to comply with post-market surveillance requirements of EU Regulation 2017/745. Statistical analyses included descriptive evaluations of responses to gauge overall effectiveness and safety of the device. Results: The effectiveness reported with the medical device was judged extreme or great by 79.2% of patients, with 89.2% of whom observed symptom improvement within one month. Both safety and tolerability were rated extreme or great by 90.7% of patients. Healthcare professionals reported a similar rate on the overall effectiveness, with 94.9% of pharmacists and 95.9% of physicians indicating it extreme or great. Similarly, the safety profile was corroborated by nearly all pharmacists (97.0%) and physicians (98.2%) reporting extreme or great satisfaction with both safety and tolerability of the medical device. Conclusions: This research provides RWD supporting the effectiveness and safety of the product for treating IBS. The strong coherence among patients, pharmacists, and physicians in positively rating the device’s performance suggests that this medical device represents a therapeutic option that effectively addresses patient needs while minimizing safety concerns. Continuous RWD collection is essential, as it offers insights into real-world practice and ensures ongoing confirmation of the product’s safety and effectiveness. Ultimately, this will advance IBS patient care by integrating real-world evidence into clinical management. Full article

We propose a novel clothing application technique designed for story visualization framework where various characters appear wearing a wide range of outfits. To achieve our goal, we extend a Virtual Try-On framework for synthetic garment fitting. Conventional Virtual Try-On methods are limited to generating images of a single person wearing a restricted set of clothes within a fixed style domain. To overcome these limitations, we apply an improved Virtual Try-On model trained with appropriately processed datasets, enabling the generation of upper and lower garments separately across diverse characters and producing images in four distinct styles: photorealistic, webtoon, animation, and watercolor. Our system collects character images and clothing images and performs accurate masking of garment regions. Our system takes a style-specific text prompt as input. Based on these inputs, garment-specific conditioning is applied to synthesize the clothing, followed by a cross-style diffusion process that generates Virtual Try-On images reflecting multiple visual styles. Our approach significantly enhances the adaptability and stylistic diversity of Virtual Try-On technology for story visualization applications. Full article

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

We report on the post-capture, on-demand deblurring technique based on a Dynamic Vision Sensor (DVS). Motion blur causes photographic defects inherently in most use cases of mobile cameras. To compensate for motion blur in mobile photography, we use a fast event-based vision sensor. However, in this paper, we found severe artifacts resulting in image quality degradation caused by color ghosts, event noises, and discrepancies between conventional image sensors and event-based sensors. To overcome these inevitable artifacts, we propose and demonstrate event-based compensation techniques such as cross-correlation optimization, contrast maximization, resolution mismatch compensation (event upsampling for alignment), and disparity matching. The results show that the deblur performance can be improved dramatically in terms of metrics such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Spatial Frequency Response (SFR). Thus, we expect that the proposed event-based image restoration technique can be widely deployed in mobile cameras. Full article

In Italy, access to public dental care is limited, and the characteristics of patients seeking hospital-based services are poorly described. A single-centre retrospective cross-sectional study was conducted, including all individuals attending their first appointment at the public Dental Clinic of Bari University Hospital (Southern Italy) between 1 January and 31 December 2023. Demographic and clinical variables, comorbidities, reasons for consultation, and travel distance from residence were retrieved from electronic records and analysed. Among 1361 patients (49% male; mean age 47.8 ± 23.3 years), most attended for oral surgery (35%) or oral pathology (17%), while other specialties accounted for the remaining visits. Many patients presented with multiple systemic conditions, particularly cardiovascular and metabolic diseases; however, a sizeable proportion were young, apparently healthy individuals who did not meet national eligibility criteria for publicly funded dental care. The dental clinic served a wide catchment area, including referrals from other regions. Documentation on education and behavioural risk factors was frequently incomplete. Overall, these findings show that complex oral medicine and oral surgery needs are concentrated in a small number of hospital clinics and support the expansion of Italian public dental services and improvements in routine data collection. Full article

Artificial intelligence (AI), particularly machine learning and deep learning architectures, has been widely applied to support thyroid cancer diagnosis, but existing evidence on its performance and limitations remains scattered across techniques, tasks, and data types. This systematic review synthesizes recent work on knowledge extraction from heterogeneous imaging and clinical data for thyroid cancer diagnosis and detection published between 2021 and 2025. We searched eight major databases, applied predefined inclusion and exclusion criteria, and assessed study quality using the Newcastle–Ottawa Scale. A total of 150 primary studies were included and analyzed with respect to AI techniques, diagnostic tasks, imaging and non-imaging modalities, model generalization, explainable AI, and recommended future directions. We found that deep learning, particularly convolutional neural networks, U-Net variants, and transformer-based models, dominated recent work, mainly for ultrasound-based benign–malignant classification, nodule detection, and segmentation, while classical machine learning, ensembles, and advanced paradigms remained important in specific structured-data settings. Ultrasound was the primary modality, complemented by cytology, histopathology, cross-sectional imaging, molecular data, and multimodal combinations. Key limitations included diagnostic ambiguity, small and imbalanced datasets, limited external validation, gaps in model generalization, and the use of largely non-interpretable black-box models with only partial use of explainable AI techniques. This review provides a structured, machine learning-oriented evidence map that highlights opportunities for more robust representation learning, workflow-ready automation, and trustworthy AI systems for thyroid oncology. Full article

Background/Objectives: Diagnosing Rotator Cuff Tears (RCTs) via Magnetic Resonance Imaging (MRI) is clinically challenging due to complex 3D anatomy and significant interobserver variability. Traditional slice-centric Convolutional Neural Networks (CNNs) often fail to capture the necessary volumetric context for accurate grading. This study aims to develop and validate the Patient-Aware Vision Transformer (Pa-ViT), an explainable deep-learning framework designed for the automated, patient-level classification of RCTs (Normal, Partial-Thickness, and Full-Thickness). Methods: A large-scale retrospective dataset comprising 2447 T2-weighted coronal shoulder MRI examinations was utilized. The proposed Pa-ViT framework employs a Vision Transformer (ViT-Base) backbone within a Weakly-Supervised Multiple Instance Learning (MIL) paradigm to aggregate slice-level semantic features into a unified patient diagnosis. The model was trained using a weighted cross-entropy loss to address class imbalance and was benchmarked against widely used CNN architectures and traditional machine-learning classifiers. Results: The Pa-ViT model achieved a high overall accuracy of 91% and a macro-averaged F1-score of 0.91, significantly outperforming the standard VGG-16 baseline (87%). Notably, the model demonstrated superior discriminative power for the challenging Partial-Thickness Tear class (ROC AUC: 0.903). Furthermore, Attention Rollout visualizations confirmed the model’s reliance on genuine anatomical features, such as the supraspinatus footprint, rather than artifacts. Conclusions: By effectively modeling long-range dependencies, the Pa-ViT framework provides a robust alternative to traditional CNNs. It offers a clinically viable, explainable decision support tool that enhances diagnostic sensitivity, particularly for subtle partial-thickness tears. Full article

Insects acting as agricultural pests or disease vectors represent some of the greatest challenges to global health, food security and economics. Diverse technologies to combat insects of economic and medical importance have been and are continually being developed. These include natural and synthetic chemical insecticides and repellents, mass-trapping approaches and, more recently, an increasingly wide range of biological as well as genetic manipulations of insect vectors/pests. The increase in biological resistance and cross-resistance to many insecticides and repellents, the rapid expansion of human populations, as well as escalating climate change have extended or shifted the active periods and habitats of many insect species, creating new hurdles for attempts to defend humans from insects. At the same time, environmental, ecological and socio-political concerns continue to impact the utility of both current interventions as well as newly emerging innovative strategies. The near exponential increase in insect-based threats highlights the importance of basic and translational studies to design and develop novel technologies to combat detrimental insect populations. This review outlines the history of these challenges and describes the evolution of chemical insect control technologies, while highlighting existing and contemporary approaches to develop and deploy chemical repellents to address this threat to human health and agriculture. Full article

The aquatic plant family Lemnaceae, commonly called duckweed or water lentil, has attracted increasing interest in the scientific literature over the past two decades. It holds extraordinary potential as a new crop due to its multiple applications: as an alternative protein source for feed and food production, as a starch producer for renewable biofuel, and for its capacity to provide valuable ecosystem services. Its high biomass productivity, ability to thrive under a wide range of environmental conditions, lack of requirement for arable land, and aptitude for nutrient recycling from wastewater align with the criteria for future sustainable crops. The Lemnaceae is a small plant family comprising a still uncertain number of species and hybrids with largely unexplored genetic diversity, owing to its taxonomic complexity. We focus on critical aspects that must be addressed to establish duckweed as a viable crop: the availability and accessibility of genomic resources to understand the genetic basis of key agronomic traits; the development of protocols for flower induction and crossing; and the establishment of effective methods for genetic transformation and plant regeneration, all aimed at enabling selection and breeding strategies. We highlight the importance of duckweed germplasm collections, including accessions from a wide geographic and ecological range, as essential resources for addressing duckweed diversity and supporting both fundamental research and agronomic applications. Full article

Avobenzone (AVO) and ethylhexyl salicylate (EHS) are widely used organic UV filters with distinct photochemical properties and reported biological effects. Experimental and predictive evidence suggests that some lipophilic UV filters may reach systemic circulation and potentially cross the blood–brain barrier (BBB), raising concerns about possible central nervous system effects, although direct evidence for AVO and EHS remains limited. This study evaluated the effects of subcytotoxic concentrations (0.01–1 µM) of AVO and EHS on differentiated SH-SY5Y human neuroblastoma cells, focusing on early stress-related molecular responses. Cell viability and reactive oxygen species production were not significantly affected at any tested concentration. Integrated analyses of microRNA, gene, and protein expression revealed modest and variable modulation of miR-200a-3p and miR-29b-3p. Western blot analysis showed increased phosphorylation of AKT and ERK, no significant changes in mTOR activation, and an increased Bax/Bcl-2 ratio. Overall, these findings indicate that AVO and EHS trigger an early stress-adaptive response involving PI3K/AKT and MAPK/ERK signaling and modulation of apoptosis-related pathways. Such responses reflect a dynamic balance between cellular adaptation and pro-apoptotic signaling, which may become relevant under prolonged or higher-intensity exposure conditions. Full article

Accessible and inclusive community environments support physical activity and health equity for people with disabilities, yet gaps in design, maintenance, and communication limit safe, independent use. This statewide cross-sectional audit assessed park accessibility and usability and playground safety in publicly accessible, non-fee-based Delaware community parks with playgrounds. Fifty stratified sites were evaluated using the Community Health Inclusion Index and the America’s Playgrounds Safety Report Card by trained raters with strong interrater reliability. Descriptive analyses summarized accessibility, usability, communication, and safety features by county, with exploratory urban-suburban/micropolitan contrasts. Most sites provided wide, smooth paths, shade, and strong playground visibility, but foundational accessibility varied. Only 30% had a nearby transit stop, fewer than 10% of crossings included auditory or visual signals. Curb-ramp completeness was inconsistent, with detectable warnings frequently absent. Restrooms commonly lacked low-force doors or operable hardware, and multi-use trails often had obstacles or lacked wayfinding supports. Playground accessibility features were present at approximately two-thirds of sites, and 62% were classified as safe, although 10% were potentially hazardous or at-risk. Higher playground accessibility scores were strongly associated with lower life-threatening injury risk. Overall, gaps in transit access, pedestrian infrastructure, amenities, and communication support limit equitable, health-supportive park environments and highlight priority improvement areas. Full article

Rapid urban growth in many Global South cities strains waste systems and slows the shift to circular economy (CE) practice. Colombo, Sri Lanka, exemplifies this challenge, where overstretched state-led services coexist with neighborhood groups, NGOs, and informal collectors driving circular activities. This study adopts a layered social network diagnostic framework to examine how community-based waste management networks operate and how they might be reshaped to enable a city-wide CE. Using survey and interview data from 185 actors, information-sharing, collaboration, and resource-exchange networks are analyzed separately and in combination. The results reveal three principal findings: (i) Social-capital forms operate largely in parallel, with limited conversion between information, collaboration, and material exchange; (ii) the network exhibits “thin bridges and thick clusters,” in which a small number of NGO hubs mediate most cross-cluster connectivity; (iii) layers operate with mismatched coordination logics, producing gaps between awareness, collective action, and resource mobilization. As a result, ideas circulate widely but rarely translate into joint projects, local teams coordinate effectively yet remain isolated, and material flows depend on a narrow and fragile logistics spine. By diagnosing these structural misalignments, this study demonstrates a key novelty: scalable circular economy adoption depends not only on technology and policy but also on the design and alignment of underlying coordination networks. Full article