Search Results (11,059)

Background: Visual impairment (VI) continues to be a significant global public health concern, especially in underserved rural communities. Objectives: This study aims to assess the prevalence of VI and refractive errors, as well as to identify the causes and risk factors associated with VI in Duhknah, a rural area in Qassim Province, Saudi Arabia. Methods: This cross-sectional study, conducted in May 2024, included 929 participants aged 6–90 years from Duhknah, a rural area in Qassim Province, Saudi Arabia. Refractive errors (REs) were measured using a non-cycloplegic autorefractometer. Anterior and posterior eye examinations were performed using slit lamp biomicroscopy, direct ophthalmoscopy, and 90 D fundus biomicroscopy. VI was classified based on the International Classification of Diseases 11th revision (ICD-11), 2018. Results: The findings revealed that 671 (72.2%) participants had never undergone an eye examination. The overall prevalence of presenting VI was 370 (39.8%), comprising 21.6% with mild VI, 11.0% moderate, 4.1% severe, and 3.1% classified as blind. The prevalence of hyperopia, myopia, and astigmatism was 20.6%, 36.9%, and 13.2%, respectively. Uncorrected REs were the most common cause of VI (81.4%), followed by amblyopia (13.5%) and cataracts (3.2%). Regression analysis showed that women had 1.58 times higher odds of VI (p = 0.001). Participants with eye examinations for one year or more had 3.64 times higher odds (p < 0.001). Additionally, the risk of VI was significantly lower among older participants (ages 18–90) compared to younger ones (ages 6–17), (p < 0.001). Conclusions: This study found most participants had never had an eye exam, and VI was highly prevalent in the rural community. These findings underscore the need to strengthen primary eye care in rural Saudi Arabia. Regular vision screening, particularly for children, and better access to refractive services could significantly reduce VI and support the goals of Saudi Vision 2030. Full article

Background/Objectives: Dental caries is the most prevalent oral condition worldwide. Consequences of untreated dental caries (CUDC) can range from pulp damage and soft tissue ulceration due to root debris to more severe issues, such as fistulas and abscesses. Rural communities might be more vulnerable to CUDC because of lower socioeconomic status, poorer access to healthcare, and lower education levels. The objective of this study was to evaluate and compare the prevalence of CUDC in rural and urban areas in schoolchildren aged 8 to 12 years in the State of Mexico. Methods: A cross-sectional study was conducted using the PUFA index, considering the presence of pulp involvement (P), soft tissue ulcerations due to root remnants (U), fistulas (F), and abscesses (A). The independent variable was the geographic area (rural or urban), and the covariates were nutritional status, hyposalivation, having one’s own toothbrush, and having received topical fluoride in the last year. Logistic regression models were fitted, calculating odds ratios (ORs) and 95% confidence intervals (CIs). Results: The prevalence of CUDC (PUFA > 0) was 42.9% in rural areas and 25.9% in urban areas. Residing in a rural area (OR: 2.15, 95% CI 1.38–3.34, p = 0.001), hyposalivation (OR: 1.93, 95% CI 1.11–3.37, p = 0.020), and professional fluoride application (OR: 0.15, 95% CI 0.07–0.32, p < 0.001) were associated with the prevalence of CUDC. Conclusions: To prevent caries and its clinical consequences due to the lack of treatment, it is important to promote timely care seeking and access to dental care services, considering the conditions of each geographic area. Full article

Since its first appearance as a human pathogen in 1976, Legionella pneumophila has been identified as a causative agent of community-acquired pneumonia (CAP). It survives in rivers, bays, lakes, and water reservoirs, and it is categorized as the fourth most common causative agent of CAP leading to hospitalization. We aimed to investigate patterns in which environmental, seasonal and regional factors may affect the prevalence of Legionnaires’ disease in Crete during the last two decades (2000–2022).The data used originated from the national surveillance database and included any person reported with travel-associated Legionnaires’ disease (TALD) between January 2000 and December 2022. Meteorological data were collected from the National Weather Service. The meteorological variables included (max) temperature (in °C), cloudiness (in octas), wind speed (in knots), and relative humidity (RH) (%). The statistical analysis was based on a case-crossover design with 1:1 matching characteristic. We revealed both seasonal and regional effects on the incidence of Legionnaires’ disease. Cases are significantly more frequent in autumn, in comparison to the other three seasons, while Rethymnon is the prefecture with fewer cases in comparison to Chania or Heraklion. In addition, our research showed that the majority of cases occurred during the years 2017–2018. TALD in Crete is significantly associated with temperature in °C and wind speed in knots. Our research suggests that temporal and spatial factors significantly influence disease cases. These results are in line with studies from foreign countries. The study results aspire to expand our knowledge regarding the epidemiological characteristics of Legionnaires’ disease in relation to local, geographical and meteorological factors on the island of Crete. Full article

Water-related ecosystem services provide a broad range of benefits, including the mitigation of extreme hydrometeorological events, the provision of water for various uses, the support of tourism, and the provision of cultural services. This study assesses the perceptions and accessibility of these services among communities located near the Alafiarou and Okpara dams in Benin and the Bagré dam in Burkina Faso. The methodology involved designing and implementing a questionnaire in KoboCollect, with trained agents deployed to conduct data collection at each of the three sites. Data analysis indicates that respondents identified biodiversity conservation and the provision of drinking water as the most crucial ecosystem services. Over two-thirds of participants reported observing both positive and negative changes in the services provided by rivers and in socio-economic activities since the construction of the dams. While the majority noted improvements in agriculture, irrigation, water quality, fisheries, and flow rates, other changes included biodiversity loss, a decrease in vegetation cover (notably trees and shrubs), an increase in the population of mosquitoes and other insects, and a decline in fishery resources downstream. Despite these challenges, local communities were strongly willing to participate in initiatives aimed at protecting and restoring river ecosystems and their related services. Full article

The global Black Lives Matter movement and COVID-19 pandemic drew attention to the urgency of addressing entrenched structural dynamics such as racialization, gender, and colonization shaping health inequities for diverse racialized people. Canadian community-based research with racialized immigrant women recognized the need to enhance service provider capacity using a strengths-based activism approach to support client health and wellbeing. In this study, we aimed to understand the impacts of this mental health promotion practice on service providers and strategies to support them. Through purposeful convenience sampling, three focus groups were completed with 19 service providers working in settlement and mental health services in Toronto, Canada. Participants represented varied ethnicities and work experiences; most self-identified as female and racialized, with experiences living as immigrant women in Canada. Postcolonial feminist and critical mental health promotion analysis illuminated organizational and structural dynamics contributing to burnout and vicarious trauma that necessitate a focus on trauma- and violence-informed care. Transformative narratives reflected service provider resilience and activism, which aligned with and challenged mainstream biomedical approaches to mental health promotion. Implications include employing a postcolonial feminist lens to identify meaningful and comprehensive anti-oppression strategies that take colonialism, racialization, gender, and ableism and their intersections into account to decolonize nursing practices. Promoting health equity for diverse racialized women necessitates focused attention and multilevel anti-oppression strategies aligned with critical mental health promotion practices. Full article

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

The rapid proliferation of Internet of Things (IoT) devices has significantly increased vulnerability to Distributed Denial of Service (DDoS) attacks, which can severely disrupt network operations. DDoS attacks in IoT networks disrupt communication and compromise service availability, causing severe operational and economic losses. In this paper, we present a Deep Learning (DL)-based Intrusion Detection System (IDS) tailored for IoT environments. Our system employs three architectures—Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Transformer-based models—to perform binary, three-class, and 12-class classification tasks on the CiC IoT 2023 dataset. Data preprocessing includes log normalization to stabilize feature distributions and SMOTE-based oversampling to mitigate class imbalance. Experiments on the CIC-IoT 2023 dataset show that, in the binary classification task, the DNN achieved 99.2% accuracy, the CNN 99.0%, and the Transformer 98.8%. In three-class classification (benign, DDoS, and non-DDoS), all models attained near-perfect performance (approximately 99.9–100%). In the 12-class scenario (benign plus 12 attack types), the DNN, CNN, and Transformer reached 93.0%, 92.7%, and 92.5% accuracy, respectively. The high precision, recall, and ROC-AUC values corroborate the efficacy and generalizability of our approach for IoT DDoS detection. Comparative analysis indicates that our proposed IDS outperforms state-of-the-art methods in terms of detection accuracy and efficiency. These results underscore the potential of integrating advanced DL models into IDS frameworks, thereby providing a scalable and effective solution to secure IoT networks against evolving DDoS threats. Future work will explore further enhancements, including the use of deeper Transformer architectures and cross-dataset validation, to ensure robustness in real-world deployments. Full article

Urban public transportation systems face increasing pressure from shifting travel patterns, rising peak-hour demand, and the need for equitable and resilient service delivery. While complex network theory has been widely applied to analyze transit systems, limited attention has been paid to behavioral segmentation within such networks. This study introduces a frequency-based framework that differentiates high-frequency (HF) and low-frequency (LF) passengers to examine how distinct user groups shape network structure, congestion vulnerability, and robustness. Using over 20 million smart-card records from Beijing’s multimodal transit system, we construct and analyze directed weighted networks for HF and LF users, integrating topological metrics, temporal comparisons, and community detection. Results reveal that HF networks are densely connected but structurally fragile, exhibiting lower modularity and significantly greater efficiency loss during peak periods. In contrast, LF networks are more spatially dispersed yet resilient, maintaining stronger intracommunity stability. Peak-hour simulation shows a 70% drop in efficiency and a 99% decrease in clustering, with HF networks experiencing higher vulnerability. Based on these findings, we propose differentiated policy strategies for each user group and outline a future optimization framework constrained by budget and equity considerations. This study contributes a scalable, data-driven approach to integrating passenger behavior with network science, offering actionable insights for resilient and inclusive transit planning. Full article

Background/Objectives: Pressure injuries are a significant concern among older adults, particularly in community-based long-term care settings where prolonged immobility is prevalent. This study aimed to identify factors influencing sacral interface pressure in community-dwelling older adults, with an emphasis on support surface usage and clinical risk indicators. Methods: A total of 210 participants aged 65 years and older, all receiving long-term care services in South Korea, were enrolled in this study. Sacral interface pressure was measured in the supine position using a portable pressure mapping device (Palm Q7). General characteristics, Braden Scale scores, Huhn Scale scores, and mattress usage were assessed. Data were analyzed using descriptive statistics, t-tests, chi-square tests, and logistic regression. Results: Mattress non-use was identified as the strongest predictor of elevated sacral interface pressure (OR = 6.71, p < 0.001), followed by Braden Scale scores indicating moderate risk (OR = 4.8, p = 0.006). Huhn Scale scores were not significantly associated with interface pressure. These results suggest that support surface quality and skin condition have a stronger impact on interface pressure than mobility-related risk factors. Conclusions: The findings highlight the importance of providing high-quality pressure-relieving mattresses and implementing standardized nursing assessments to reduce the risk of pressure injuries. Integrating smart technologies and expanding access to advanced support surfaces may aid in developing tailored preventive strategies for vulnerable older adults. Full article

Static Priority-based Multiple Access (SPMA) is an emerging and promising wireless MAC protocol which is widely used in Unmanned Aerial Vehicle (UAV) networks. UAV (Unmanned Aerial Vehicle) networks, also known as drone networks, refer to a system of interconnected UAVs that communicate and collaborate to perform tasks autonomously or semi-autonomously. These networks leverage wireless communication technologies to share data, coordinate movements, and optimize mission execution. In SPMA, traffic arriving at the UAV network node can be divided into multiple priorities according to the information timeliness, and the packets of each priority are stored in the corresponding queues with different thresholds to transmit packet, thus guaranteeing the high success rate and low latency for the highest-priority traffic. Unfortunately, the multi-priority queue scheduling of SPMA deprives the packet transmitting opportunity of low-priority traffic, which results in unfair conditions among different-priority traffic. To address this problem, in this paper we propose the method of Adaptive Credit-Based Shaper with Reinforcement Learning (abbreviated as ACBS-RL) to balance the performance of all-priority traffic. In ACBS-RL, the Credit-Based Shaper (CBS) is introduced to SPMA to provide relatively fair packet transmission opportunity among multiple traffic queues by limiting the transmission rate. Due to the dynamic situations of the wireless environment, the Q-learning-based reinforcement learning method is leveraged to adaptively adjust the parameters of CBS (i.e., idleslope and sendslope) to achieve better performance among all priority queues. The extensive simulation results show that compared with traditional SPMA protocol, the proposed ACBS-RL can increase UAV network throughput while guaranteeing Quality of Service (QoS) requirements of all priority traffic. Full article

Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81–96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88–99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework’s decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services. Full article

Background: Access to safe and reliable water and sanitation remains a critical public health and development challenge, with rural and low-income communities being disproportionately affected by inadequate services and heightened exposure to waterborne diseases. Despite global efforts and infrastructure-based progress indicators, significant disparities persist, and these often overlook users’ perceptions of water quality, reliability, and safety. This study explores the determinants of household satisfaction with drinking water in rural areas, comparing subjective user feedback with official access data to reveal gaps in current monitoring approaches and support more equitable, user-centered water governance. Methods: This study was conducted in Kazakhstan’s Atyrau Region, where 1361 residents from 86 rural villages participated in a structured survey assessing household access to drinking water and perceptions of its quality. Data were analyzed using descriptive statistics and multinomial logistic regression to identify key predictors of user satisfaction, with results compared against official records to evaluate discrepancies between reported experiences and administrative data. Results: The field survey results revealed substantial discrepancies between official statistics and residents’ reports, with only 58.1% of respondents having in-house tap water access despite claims of universal coverage. Multinomial logistic regression analysis identified key predictors of user satisfaction, showing that uninterrupted supply and the absence of complaints about turbidity, odor, or taste significantly increased the likelihood of higher satisfaction levels with drinking water quality. Conclusions: This study underscores the critical need to align official water access statistics with household-level experiences, revealing that user satisfaction—strongly influenced by supply reliability and sensory water quality—is essential for achieving equitable and effective rural water governance. Full article

(1) Background: People living with spinal cord injury (SCI) face numerous challenges in their lives in terms of health conditions, everyday activity, and participation in society, which are not fully recognized. To address such issues, a community survey with 125 questions for people living with SCI was conducted and the response rates, population characteristics, health and functioning problems are reported. (2) Methods: The survey questionnaire comprised 125 questions on SCI characteristics, health conditions, activities, participation, and environmental and personal factors. The survey response rates were calculated, and demographics and health and functioning characteristics were analyzed. (3) Results: A total of 890 individuals responded to the survey. The median age of the participants was 48 years (interquartile range (IQR), 39–56), and 76% of the population were males. Paraplegia (60%) and complete injury (58%) were the most common injury type, and the cause was mostly traumatic (92%). More health problems and lower quality of life were more frequent with older age and in patients without paid work. (4) Conclusions: The Ko-InSCI study provides valuable information in terms of health needs and service gaps for people with SCI in the community. Full article

Teacher education faces persistent challenges, including limited access to authentic field experiences and a disconnect between theoretical instruction and classroom practice. While virtual reality (VR) simulations offer an alternative, most are constrained by inflexible design and lack scalability, failing to mirror the complexity of real teaching environments. This study introduces TeacherGen@i, a generative AI (GenAI)-enhanced VR simulation designed to provide pre-service teachers with immersive, adaptive teaching practice through realistic GenAI agents. Using an explanatory case study with a mixed-methods approach, the study examines the simulation’s usability, design challenges, and instructional utility within a university-based teacher preparation course. Data sources included usability surveys and reflective journals, analyzed through thematic coding and computational linguistic analysis using LIWC. Findings suggest that TeacherGen@i facilitates meaningful development of teaching competencies such as instructional decision-making, classroom communication, and student engagement, while also identifying notable design limitations related to cognitive load, user interface design, and instructional scaffolding. This exploratory research offers preliminary insights into the integration of generative AI in teacher simulations and its potential to support responsive and scalable simulation-based learning environments. Full article

Soil microbial communities are crucial for ecosystem services, soil fertility, and the resilience of agroecosystems. This study investigated how long-term (31 years) agronomic practices—tillage, NPK fertilization, and cropping system—along with measured environmental variables influence the microbial biomass and its community composition in Chernozem soil under corn cultivation. The polyfactorial field experiment included three tillage treatments ((moldboard (MT), ripped (RT), strip (ST)), two fertilization regimes (NPK (N: 160; P: 26; K: 74 kg/ha), and unfertilized control) and two cropping systems (corn monoculture and corn–wheat biculture). The soil samples (0–30 cm) were collected in June and September 2023. Microbial biomass and community structure were quantified using phospholipid fatty acid (PLFA) analysis, which allowed the estimation of total microbial biomass and community composition (arbuscular mycorrhizal (AM) fungi, fungi, Gram-negative (GN) and Gram-positive (GP) bacteria, actinomycetes). Our results showed that microbial biomass increased from June to September, rising by 270% in unfertilized plots and by 135% in NPK-fertilized plots, due to higher soil moisture. Reduced tillage, especially ST, promoted significantly higher microbial biomass, with biomass reaching 290% and 182% of that in MT plots in June and September, respectively. MT had a higher ratio of bacteria-to-fungi compared to RT and ST, indicating a greater sensitivity of fungi to disturbance. NPK fertilization lowered soil pH by about one unit (to 4.1–4.8) and reduced microbial biomass—by 2% in June and 48% in September—compared to the control, with the particular suppression of AM fungi. The cropping system had a smaller overall effect on microbial biomass. Full article