Search Results (3,557)

Background/Objectives: Rheumatic fever is preventable and can be treated successfully; however, a lack of understanding of the disease and barriers to timely healthcare can impact outcomes. Pacific people in Aotearoa New Zealand experience inequitable burdens, and a Pacific community group and health professional network are working together to co-design education initiatives. This descriptive, mixed-methods study aimed to (1) explore Pacific people’s awareness and understanding of rheumatic fever, (2) describe where Pacific people access health information. Methods: An online survey co-developed with Pacific community members was run from December 2024 to February 2025. Questions related to sore throats, rheumatic fever, medication adherence, long-term outcomes and where Pacific people access health information. Quantitative data was analyzed descriptively using SPSS version 28 and open-ended qualitative responses were analyzed using an inductive content analysis approach. Results: A total of 400 Pacific respondents were included: 34% were aged 16–24 years and 66% were female. Based on the analysis, 71% of Pacific participants knew that a sore throat should always be checked by a health professional and 65.3% had heard of rheumatic fever. Fever and sore throats were commonly identified as symptoms of rheumatic fever, with joint pain, body aches and chest pain mentioned less. Barriers to health services such as cost, long waiting times and cultural factors were discussed as reasons why many Pacific people often utilize social media and online forums for health information. Conclusions: This study highlights gaps in rheumatic fever knowledge and thus opportunities for health education initiatives for Pacific communities, potentially utilizing social media and online platforms. Full article

Background/objective: Online platforms for sharing prescription drug experiences are becoming increasingly available, yet their validity as measures of patient satisfaction remains unclear. This study aimed to evaluate the potential of an online drug review system, WePharm, as a proxy for treatment satisfaction among older adults taking antihypertensive medications. Methods: A cross-sectional survey using a convenience sample was conducted from February to July 2018 among patients aged 50–80 years recruited from four senior welfare centers and one community pharmacy in Seoul. Participants completed both an online review via WePharm and a paper-based Treatment Satisfaction Questionnaire for Medication (TSQM). Satisfaction attributes included drug efficacy, side effects, convenience, affordability, and willingness to recommend. Pearson correlation coefficients and ANOVA were used to examine concordance and associated factors. Results: A total of 313 participants were included. Online review scores were significantly correlated with TSQM scores across all domains as follows: effectiveness (r = 0.451), side effects (r = 0.363), convenience (r = 0.285), and overall satisfaction (r = 0.256), all p < 0.0001. Key factors associated with satisfaction included region, stage of hypertension, income, duration of antihypertensive use, and comorbidity count. Conclusions: Online patient medication reviews, as implemented in WePharm, demonstrated moderate correlation with validated treatment satisfaction measures. These findings support the potential utility of online drug review systems as complementary tools for capturing real-world patient experience and informing shared decision-making in clinical practice, and as these findings were from a convenience sample, further research is expected with the aim of improving generalizability. Full article

Text-based CAPTCHAs remain a widely deployed mechanism for mitigating automated attacks across web platforms. However, the increasing effectiveness of convolutional neural networks (CNNs) and advanced computer vision models poses significant challenges to their reliability as a security measure. This study presents a comprehensive forensic and security-oriented analysis of text-based CAPTCHA systems, focusing on how individual and combined visual distortion features affect human usability and machine solvability. A real-world dataset comprising 45,166 CAPTCHA samples was generated under controlled conditions, integrating diverse anti-recognition, anti-segmentation, and anti-classification features. Recognition performance was systematically evaluated using both a CNN-based solver and actual human interaction data collected through an online exam platform. Results reveal that while traditional features such as warping and distortion still degrade machine accuracy to some extent, newer features like the hollow scheme and multi-layer structures offer better resistance against CNN-based attacks while maintaining human readability. Correlation and SHAP-based analyses were employed to quantify feature influence and identify configurations that optimize human–machine separability. This work contributes a publicly available dataset and a feature-impact framework, enabling deeper investigations into adversarial robustness, CAPTCHA resistance modeling, and security-aware human interaction systems. The findings underscore the need for adaptive CAPTCHA mechanisms that are both human-centric and resilient against evolving AI-based attacks. Full article

A core challenge in urban green space (UGS) management lies in precisely identifying public demand heterogeneity toward landscape elements. Grounded in Expectation Confirmation Theory (ECT), this study aims to systematically identify the key landscape elements shaping public satisfaction and elucidate their driving mechanisms to inform UGS planning. Using 107 UGS in central Beijing as case studies, this study first retrieved 712,969 social media data (SMD) from multiple online platforms. A landscape element lexicon derived from these data was then integrated with the Bidirectional Encoder Representations from Transformers (BERT) model to assess public attention and satisfaction toward the natural, cultural, and artificial attributes of UGS, achieving an accuracy of 84.4%. Finally, spatial variations and the effects of different landscape elements on public satisfaction were analyzed using GIS-based visualization, K-means clustering, and multiple linear regression. Key findings reveal the following: (1) satisfaction follows a “core-periphery” gradient, peaking in heritage-rich City Wall Parks (>0.63) and plunging in green belts due to imbalanced element configurations (~0.04); (2) naturally dominant green spaces contribute most to satisfaction, while a nonlinear relationship exists between element dominance and satisfaction: strong features enhance perception, balanced patterns mask issues; (3) regression analysis confirms natural elements (vegetation β = 0.280, water β = 0.173) as core satisfaction drivers, whereas artificial facilities (e.g., service infrastructure β = 0.112, p > 0.05) exhibit a high frequency but low satisfaction paradox. These insights culminate in a practical implementation framework for policymakers: first, establish a data-driven monitoring system to flag high-frequency, low-satisfaction facilities; second, prioritize budgeting for enhancing natural elements and contextualizing cultural elements; and finally, implement site-specific optimization based on primary UGS functions to counteract green space homogenization in high-density cities. Full article

This article presents the development and implementation of an IoT-enabled, off-grid solar power supply prototype designed to power a range of electrical devices. The developed system comprises a Photovoltaic panel, a Maximum Power Point Tracking (MPPT) charger, a 2.5 kWh/24 V high-performance LiFePO4 battery bank with a Battery Management System, an embedded controller with IoT connectivity, and DC/DC and DC/AC converters. The PV panel serves as the primary energy source, with the MPPT controller optimizing battery charging, while the DC/DC and DC/AC converters supply power to the connected electrical devices. The article includes a case study of a developed platform for powering an information and advertising system. The system features a predictive energy management algorithm, which optimizes the appliance operation based on daily solar irradiance forecasts and real-time battery State-of-Charge monitoring. The IoT-enabled controller obtains solar irradiance forecasts from an online meteorological service via API calls and uses these data to estimate energy availability for the next day. Using this prediction, the system schedules and prioritizes the operations of connected electrical devices dynamically to optimize the performance and prevent critical battery discharge. The IoT-based controller is equipped with both Wi-Fi and an LTE modem, enabling communication with online services via wireless or cellular networks. Full article

To ensure the reliable operation of power converters and prevent catastrophic failures, this paper proposes a novel online fault diagnosis strategy for a four-level converter. The core of this strategy is an optimized multi-kernel extreme learning machine model. Specifically, the model extracts multi-scale features from three-phase output currents by combining Gaussian and polynomial kernels and employs particle swarm optimization to determine the optimal kernel fusion scheme. Experimental validation was performed on an online diagnosis platform for a four-level converter. The results show that the proposed method achieves a high diagnostic accuracy of 99.35% for open-circuit faults. Compared to conventional methods, this strategy significantly enhances diagnostic speed and accuracy through its optimized multi-kernel mechanism. Full article

Background: Social media is increasingly shaping patient decision-making about the choice of healthcare providers. However, its role in the Saudi context remains underexplored. This study aimed to examine the association between social media use and patient decision-making regarding the choice of healthcare providers in Saudi Arabia. Methods: This cross-sectional study used a validated online questionnaire. The study was conducted between December 2023 and May 2024 to assess demographics, social media usage, and decision-making factors. Results: 1242 participants completed the survey; most participants (96.2%) had personal social media accounts. Instagram (41.3%) and X (37.6%) were the preferred platforms to look up or follow doctors. The most influential factors in choosing a healthcare provider included physician qualifications, online reviews, and patient testimonials. While 81.3% believed medical practices should maintain a social media presence, traditional factors remained more decisive than promotional content. Younger participants, females, and those in the health field were significantly more likely to follow doctors online (p < 0.001). Conclusions: Social media plays a notable role in patient decision-making about healthcare provider selection in Saudi Arabia, particularly among younger individuals and those working in the health sector. Nevertheless, clinical qualifications and trust indicators remain paramount. These findings support the need for healthcare professionals to maintain a credible, ethical, and informative digital presence to enhance patient engagement and informed decision-making. Full article

Climate change is one of the most pressing environmental challenges of our time, with consequences that extend far beyond temperature rise. Its impacts include extreme weather events, sea level rise, biodiversity loss, and disruptions to food and water systems, all of which threaten ecosystems and human well-being. Addressing this crisis requires a broad understanding and engagement from society. However, climate change denial persists, often amplified through online platforms, slowing down effective action. Universities can play a critical role in this context, not only as spaces where scientific knowledge is produced and shared, but also as institutions that train future leaders to respond to environmental crises. In this study, we examined the prevalence of climate change denial among members of a Portuguese public university and explored its relationship with gender, religion, and political orientation. We collected 89 responses and analyzed the data. The findings indicate that individuals with right-leaning political views, certain religious affiliations, and male respondents were more likely to deny climate change. These results highlight the need for targeted educational approaches that address specific audiences, fostering a better understanding of the scientific and environmental realities of climate change, and ultimately promoting informed action toward sustainability. Full article

Addressing climate change necessitates a shift in everyday social practices, which are significant contributors to greenhouse gas emissions. While social media platforms are crucial for the emergence and dissemination of such practices, their role in the Russian context remains underexplored. This study employs a big data analysis to identify and systematize climate-related social practices discussed within Russian environmental online communities on the VKontakte platform. Using BERTopic topic modeling on a corpus of nearly 190,000 posts from 103 communities, followed by expert evaluation, we identified 80 distinct social practices. These practices were systematized according to actors, objects, and types of activities, revealing a rich ecosystem of grassroots initiatives focusing on waste management, sustainable consumption, education, and digital mapping. The findings demonstrate that online communities compensate for the lack of institutionalized citizen participation in Russian climate policy by fostering shared meanings and practical knowledge. The study concludes that these bottom-up practices represent a significant resource for climate policy, offering ready-made algorithms for behavioral change. We propose that public authorities leverage these communities to enhance public support and effectively implement climate mitigation and adaptation measures. Full article

Background: Social media has emerged as a powerful communication tool for healthcare professionals, including nutritionists and dietitians, particularly since the COVID-19 pandemic. Evidence suggests that their online presence can enhance nutritional literacy and play a crucial role in countering misinformation. Objective: This systematic review aims to investigate how and why Registered Nutritionists and Dietitians (RNDs) use social media in their professional practice, focusing on benefits, challenges, and impact. Methods: A systematic literature search was conducted between 1 January 2019 and 28 February 2024, in PubMed, Scopus, Scholar, and SciELO databases using terms such as ‘nutritionist’, ‘dietitian’, and ‘social media’. Quality was assessed using the MMAT tool. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The included studies were analysed with respect to their content, professional practices, and patterns of social media use. Results: Of the 359 articles identified through the systematic search, 10 cross-sectional studies conducted using questionnaires were included in this review. Sample sizes ranged from 10 to 2542 participants across nine countries. Instagram and Twitter were the most frequently used platforms among RDNs, primarily for sharing evidence-based nutritional information, counselling content, and professional promotion. Reported usage ranged from 37.5% to 100%, with a marked increase during the COVID-19 pandemic, especially among younger professionals. Key enablers included enhanced communication, professional visibility, and cost-effective outreach, while main challenges involved limited digital literacy and difficulties replicating face-to-face counselling online. Although ethical concerns were reported, many RNDs maintained compliance with professional standards, particularly in regions with strict marketing regulations. Conclusions: This systematic review provides evidence that social media is a valuable tool for RNDs, particularly in the context of food and/or nutritional education. RNDs would benefit from training in content creation, knowledge dissemination and ethical digital communication. However, clearer guidelines from professional organisations are also recommended. Full article

This study theoretically explores the mechanism of action of Chicoric acid against influenza virus based on network pharmacology, molecular docking, and molecular dynamics simulation techniques, aiming to provide insights for the development of new veterinary drugs for influenza. Potential targets for influenza virus action were identified using the PharmMapper (i.e. Version 2017) server and disease databases including GeneCards and OMIM. The STRING online analysis platform and Cytoscape 3.9.1 software were employed to construct a protein–protein interaction (PPI) network of the target proteins, followed by topological analysis to screen for key targets. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the intersecting targets using the DAVID database. A “drug–target–pathway” network diagram was constructed using Cytoscape 3.9.1 software. Molecular docking was carried out with AutoDock 1.5.6 and PyMOL 2.5 software to identify dominant binding targets, followed by molecular dynamics simulation analysis. The results of network analysis showed that there were 31 potential targets of Chicoric acid; the protein interaction network suggested that UBC, UBA52, RPS27A, HCK, and CDKN1B may be the core targets of Chicoric acid; 55 cell biological processes were obtained by GO enrichment analysis, and 15 related signaling pathways were obtained by KEGG pathway enrichment analysis; molecular docking showed that UBC and UBA52 had a good affinity to Chicoric acid and may be the dominant target of Chicoric acid exerting its effect. Chicoric acid may play a role in antiviral activity by acting on the dominant protein of UBC and UBA52, thus achieving an anti-influenza virus effect. Full article

Adaptive forest and fire management in parks and protected areas is becoming increasingly complex as climate change alters the frequency and intensity of disturbances (wildfires, pest and disease outbreaks, etc.), while park visitation and the number of people living adjacent to publicly managed lands continues to increase. Evidence-based, climate-adaptive forest and fire management practices are critical for the responsible stewardship of public resources and require the continued availability of long-term ecological monitoring data. The US National Park Service has been collecting long-term fire monitoring plot data since 1998, and has continued to add monitoring plots, but these data are housed in databases with limited access and minimal analytic capabilities. To improve the availability and decision support capabilities of this monitoring dataset, we created the Trends in Forest Fuels Dashboard (TFFD), which provides an implementation framework from data collection to web visualization. This easy-to-use and updatable tool incorporates data from multiple years, plot types, and locations. We demonstrate our approach at Rocky Mountain National Park using the ArcGIS Online (AGOL) software platform, which hosts TFFD and allows for efficient data visualizations and analyses customized for the end user. Adopting interactive, web-hosted tools such as TFFD allows the National Park Service to more readily leverage insights from long-term forest monitoring data to support decision making and resource allocation in the context of environmental change. Our approach translates to other data-to-decision workflows where customized visualizations are often the final steps in a pipeline designed to increase the utility and value of collected data and allow easier integration into reporting and decision making. This work provides a template for similar efforts by offering a roadmap for addressing data availability, cleaning, storage, and interactivity that may be adapted or scaled to meet a variety of organizational and management use cases. Full article

Within the context of Industry 4.0, the integration of heterogeneous industrial devices into unified control and supervision systems remains a fundamental challenge due to diversified communication protocols and interfaces. Conventional industrial gateways relying on customized driver development encounter issues such as high protocol extension costs, lengthy development cycles, and limited compatibility, restricting the agility and scalability of modern industrial embedded control systems. This paper proposes a novel paradigm for industrial interoperability gateways based on a software-defined architecture and graphical modeling. Through layered decoupling of software functions, protocol parsing and data conversion functionalities are encapsulated into draggable graphical components, enabling flexible adaptation and efficient debugging of heterogeneous protocols. The gateway middleware GMGbox architecture was designed and validated through an experimental platform. Results demonstrate that the gateway can accurately and concurrently parse multiple heterogeneous protocols via graphical configuration, stably acquire over 3000 data points, support online visual debugging and flexible deployment of protocol logic, and seamlessly integrate final data with upper-layer systems through standard protocols. Case studies of a level control system and a wastewater treatment plant Supervisory Control and Data Acquisition (SCADA) further validate the effectiveness and practical utility of this paradigm in real-world industrial scenarios. The proposed solution provides a novel architectural paradigm for building reconfigurable and maintainable systems in the field of industrial automation. Full article

Background/Objectives: Genicular artery embolization (GAE) is an emerging, minimally invasive treatment for symptomatic osteoarthritis. This study assesses global online search trends for GAE compared with traditional knee replacement using Google Trends data. Methods: This retrospective observational study analyzed global search trends for osteoarthritis treatments using the publicly accessible Google Trends platform. Monthly relative search volumes (RSV; scaled 0–100) for English-language queries were retrieved from January 2018 to December 2024. Three emerging minimally invasive terms (“genicular artery embolization”, “knee embolization”, “interventional radiology”), and three traditional surgical terms (“knee arthroplasty”, “knee replacement”, “total knee replacement”) were included. Temporal trends were evaluated using linear and non-linear regression models, with model fit evaluated using coefficients of determination (R²), ANOVA, and accuracy measures. Statistical significance was defined as p < 0.05. Results: GAE-related terms demonstrated significant growth over time. “Genicular artery embolization” increased by 0.9% per month (R² = 0.795, p < 0.001), and “knee embolization” increased at 0.9% per month (R² = 0.627, p < 0.001) in a linear model. Traditional terms showed slower growth rates of 0.13–0.23% per month (R² = 0.159–0.271). Exponential and quadratic models confirmed these patterns. Mean RSV values over the study period were higher for traditional procedures (e.g., “total knee replacement”: 83.13) than for GAE-related terms (e.g., “genicular artery embolization”: 22.5). Conclusions: Global online interest in GAE-related terms is increasing at a substantially faster rate than interest in traditional knee replacement. Rapidly growing search interest highlights the need for accurate and accessible online patient education regarding emerging treatments. Full article

To address the challenges posed by extensive sample pretreatment and significant matrix interference in conventional metal quantification methods for cyanobacterial culture media, an automated online metal capture and enrichment system was developed and integrated with inductively coupled plasma mass spectrometry (ICP-MS). This system enabled the simultaneous determination of nine metal elements—Cd, Pb, V, Mn, Fe, Co, Ni, Cu, and Zn—within the culture medium. Through systematic optimization and validation, the method demonstrated exceptional analytical performance: calibration curves for all analytes exhibited correlation coefficients (r) exceeding 0.999; repeatability tests yielded relative standard deviations (RSD) below 3% (n = 6); and recoveries at low, medium, and high spike levels ranged from 93.98% to 108.70%. The procedure is characterized by simplicity, high automation, low detection limits, and robust accuracy, making it an effective platform for multi-element contamination monitoring and metal metabolic studies in cyanobacterial cultivation. This approach holds significant potential for applications in algal resource utilization and environmental restoration. Full article