Search Results (24,969)

Against the background of widespread digital technology use and the increasing prevalence of smartphone addiction among adolescents, upward social comparison has been identified as an important trigger of addictive behaviors. However, the moderating role of sports participation and potential differences across types of sports remain unclear. This cross-sectional study aimed to examine the effect of upward social comparison on smartphone addiction among adolescents and to test the moderating role of football participation. A questionnaire survey was conducted among 2451 primary and secondary school students from 162 schools across 13 provinces in China. The survey included the Upward Social Comparison Scale, the Smartphone Addiction Scale—Short Version, and the Sports Participation Scale. SPSS 27.0 was used to perform descriptive statistics and correlation analyses. Moderation analyses and multi-group comparisons were conducted using PROCESS 4.2. The results showed that upward social comparison positively predicted smartphone addiction among adolescents. Sports participation significantly buffered the association between upward social comparison and smartphone addiction (β = −0.055, p < 0.001). Football participation showed a significant moderating effect (β = −0.062, p < 0.05). Higher levels of football participation were associated with a stronger buffering effect. In contrast, individual sports did not show a significant moderating effect (β = −0.029, p = 0.394). These findings suggest that upward social comparison is a risk factor for smartphone addiction among adolescents. Football participation may effectively reduce this risk, but individual sports did not exhibit a comparable moderating effect. This study provides empirical evidence to support the development of targeted intervention strategies for adolescent smartphone addiction. Full article

The Sociodemographic Index (SDI) captures a country’s or region’s relative socioeconomic development and has been linked to age-related disease burden and life expectancy. Frailty is a multidimensional geriatric syndrome associated with adverse health outcomes and mortality. This study examined the relationship between country-level SDI, frailty prevalence, and mortality across Europe. We conducted a secondary analysis of community-dwelling adults aged 50 years and older from 12 countries participating in the Survey of Health, Ageing and Retirement in Europe (SHARE). Frailty status and SDI were assessed at Wave 2 (2007), with mortality follow-up at Wave 4 (2011). Countries were categorised into lower- and higher-SDI groups using the median as a cut-off. Frailty was measured using a 70-item frailty index (FI ≥ 0.25) and a modified Fried frailty phenotype (FP ≥ 3 criteria). Frailty prevalence varied substantially by country and assessment method, ranging from 7 to 40% using the FI and 4–21% using the FP. Prevalence was lowest in Switzerland and highest in Poland and was strongly correlated with national SDI scores (r ≥ 0.8). After adjustment for age and sex, lower SDI was independently associated with higher odds of frailty using both frailty measures. Although mortality was lower in higher-SDI countries, this association was not statistically significant after adjusting for age, sex, and frailty. Lower social development was strongly associated with frailty prevalence but did not independently predict mortality, highlighting frailty as a potential pathway linking social context to later-life health outcomes in Europe. Full article

Background/Objectives: The rates of adverse maternal and neonatal outcomes—including preterm birth < 37 weeks’ gestation (PTB), hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), small for gestational age (SGA), and large for gestational age (LGA)—remain elevated in the United States. Preventive strategies beyond the current standard of care (SOC) may be needed, particularly in diverse and socioeconomically vulnerable populations. The study evaluated a targeted diet and lifestyle intervention incorporating selected nutrient and gene variant analysis with personalized trimester-based counseling and supplementation (Standard of Care Plus, PLUS). Methods: The prospective observational study compared outcomes among participants receiving PLUS in addition to SOC with regional SOC data. A Nevada PLUS cohort (n = 15), consisting of high-risk participants with 100% Medicaid coverage, received the intervention virtually. An Oregon PLUS cohort (n = 387), consisting of moderate-risk participants with approximately 50% Medicaid coverage, received PLUS through in-person group sessions. Outcomes were compared with regional SOC rates and between PLUS cohorts. Cochran–Mantel–Haenszel (CMH) analyses were performed to account for site-level differences in pooled analyses. Primary outcome was PTB < 37 weeks’ gestation; secondary outcomes included HDP, GDM, SGA, and LGA. Results: The Nevada PLUS application was associated with lower adverse outcome rates compared with regional SOC; however, statistical significance was not observed, likely reflecting limited sample size. The Oregon PLUS cohort experienced statistically significant association with reductions across all five outcomes (all p < 0.001) compared to regional SOC. No statistically significant differences were observed between the Nevada (virtual) and Oregon (in-person) PLUS cohorts. In pooled analyses (n = 402), significant reductions compared with SOC were observed for PTB (RR = 0.23), HDP (RR = 0.11), GDM (RR = 0.06), SGA (RR = 0.25), and LGA (RR = 0.35) (all p < 0.001). Conclusions: The implementation of selected nutrient and gene variant analysis combined with targeted nutritional and lifestyle interventions, delivered in collaboration with standard obstetric care, was associated with reduced adverse maternal and neonatal outcomes. Interpretation of virtual delivery remains limited by small sample size. Full article

In public safety scenarios, the precise detection and positioning of prohibited weapons such as firearms and knives along with the involved personnel are the core pre-requisite technologies for violent risk warning and emergency response. However, in security surveillance scenarios, there are common problems such as object occlusion, difficulty in capturing small-sized weapons, and complex background interference, which lead to the shortcomings of existing general object detection models in the tasks of detecting and locating security-related objects, including poor adaptability, low detection accuracy, and insufficient robustness in complex scenarios. Therefore, this paper proposes a threat object detection framework for security scenarios (PDGV-DETR) based on adaptive dynamic convolution and cross-scale semantic fusion, specifically optimized for the detection and positioning tasks of weapons and personnel objects in static security surveillance images. This research focuses on category recognition at the object level and pixel-level spatial positioning, and does not involve the classification and identification of violent behaviors based on temporal information. There are clear technical boundaries and scene limitations between the two. This framework is optimized through three core modules: designing a dynamic hierarchical channel interaction convolution module to reduce computational complexity while enhancing the ability to detect occluded and incomplete objects; constructing an improved bidirectional hybrid feature pyramid network, combining the cross-scale fusion module to strengthen multi-scale feature expression, and adapting to the simultaneous detection requirements of small weapon objects and large personnel objects; and introducing a global semantic weaving and elastic feature alignment network to solve the problem of low discrimination between objects and complex backgrounds. Under the same experimental configuration, the proposed model is verified against current mainstream models on typical datasets: on a dataset of 2421 conflict scene personnel violent images, the peak average precision mAP50 of PDGV-DETR reached 85.9%. Through statistical verification, compared with the baseline model RT-DETR with an average value ± standard deviation of 0.840 ± 0.007, the average value ± standard deviation of PDGV-DETR reached 0.858 ± 0.004, demonstrating statistically significant performance improvement, with a p-value less than 0.01. This model can accurately complete the task of locating the object area of personnel, and compared with the deformable DETR, the accuracy improvement rate reached 15.1%.; on the weapon-specific dataset OD-WeaponDetection, the mAP for gun and knife detection reached 93.0%, improving by 2.2% compared to RT-DETR. Compared to the performance fluctuations of other general object detection models in complex security scenarios, PDGV-DETR not only has better detection and positioning accuracy for security-related objects, but also significantly improves the generalization and stability of the model. The results show that PDGV-DETR effectively balances the accuracy of positioning, detection, and computational efficiency, accurately completing end-to-end detection and positioning of weapon and personnel objects in static security surveillance images, demonstrating highly competitive performance in the detection and positioning of security-related objects in security scenes, providing core object-level pre-processing technology support for scenarios such as public area monitoring, intelligent video monitoring, and early warning of violent risks, and providing basic data for subsequent violent behavior recognition based on temporal data. Full article

The implant–abutment connection (IAC) is a critical determinant of the mechanical and biological performance of dental implants. Connection design and insertion torque may influence fatigue resistance, micromovement, and microgap formation, thereby affecting long-term implant success. This in vitro study evaluated a novel conical implant–abutment connection under controlled mechanical loading conditions. Methods: A sequential in vitro protocol was applied. Mechanical testing was conducted according to ISO 14801:2016 and included static and cyclic loading tests of the KS implant system inserted at two different torque values (35 Ncm and 70 Ncm). High-resolution micro-computed tomography (micro-CT) was performed after mechanical loading to evaluate implant–abutment interface integrity, microstructural alterations, and microgap behavior. Results: Static and cyclic loading tests revealed no observable differences between implants inserted at 35 Ncm and 70 Ncm, with all specimens completing the loading protocols without mechanical failure. Micro-CT analysis showed no evidence of microfractures, permanent deformation, or clinically relevant alterations at the implant–abutment interface. A stable and well-sealed connection was observed for both torque values following mechanical loading. Conclusions: Within the limitations of this in vitro study, the investigated conical implant–abutment connection demonstrated stable mechanical performance and preserved interface integrity after static and cyclic loading, regardless of whether implants were placed at 35 Ncm or 70 Ncm. These findings indicate that, under the present experimental conditions, both torque levels were associated with comparable structural integrity and mechanical stability of the investigated implant–abutment connection. This study should be interpreted as a preliminary experimental investigation, designed to provide descriptive and mechanistic insights rather than statistically powered comparative conclusions. Further long-term clinical trials are required to confirm these preliminary results. Full article

In this study, we develop a regression analysis method, namely, the Semiparametric Binary Logistic Regression (SBLR), by extending the classical logistic regression that integrates both parametric and nonparametric components, which allows it to simultaneously model linear and non-linear relationships. Here, to obtain the estimation of a nonparametric component in the form of a non-linear curve (sigmoid curve), we use the penalized spline, which is a smoothing technique used in the nonparametric approach due to its ability to produce smooth and adaptive curves for fluctuating data. In this smoothing technique, selecting the optimal smoothing parameters plays an important role in fitting the model. Commonly, this selection is based on the minimum value of ordinary Cross-Validation (CV) or Generalized Cross-Validation (GCV). However, these CV and GCV criteria cannot be used when the CV and GCV curves continuously decline and never rise; the minimum CV and GCV values would not be achieved because they are not directly applicable due to the non-quadratic nature of the log-likelihood function. Therefore, a Generalized Approximate Cross-Validation (GACV) criterion is used to address such cases. This distinguishes it from previous studies that used the CV or GCV criterion. In the application to real data, we define an SBLR model of Coronary Heart Disease (CHD) risk factors that can be used for prediction and interpretation purposes. The results of the study successfully demonstrate the efficacy of the proposed method in identifying critical non-linear thresholds for CHD risk factors, and it is statistically valid and highly effective for CHD risk prediction. In the future, we can use the results of this research as a basis of an early warning system, specifically alerting individuals with moderate stress levels and dietary habits exceeding the identified thresholds to be aware of the heightened probability of developing CHD. In addition, this research aligns with point three of the Sustainable Development Goals (SDGs), namely, premature mortality reduction from non-communicable diseases by 2030. Full article

Background: Human papillomavirus (HPV) vaccination is a major concern and highly effective strategy for preventing cervical cancer and other related diseases. Despite the inclusion of the HPV vaccine in the Saudi national immunization program and recent school-based initiatives, vaccine uptake remains suboptimal, mainly due to the hesitancy of parents. Evidence regarding HPV vaccine hesitancy in the Aseer region of Saudi Arabia is limited. Objectives: This study aimed to assess the level of the HPV vaccine hesitancy, knowledge, attitude and barriers among parents in the Aseer region of Saudi Arabia using the World Health Organization (WHO) HPV Vaccine Hesitancy Scale (VAS). Methods: A cross-sectional study was conducted among parents residing in the Aseer region, Saudi Arabia. Data were collected using a structured questionnaire that included sociodemographic characteristics, knowledge, barriers and the validated nine-item WHO HPV Vaccine Hesitancy Scale. Data was expressed in numbers (%) for categorical variables and mean ± SD for continuous variables. Independent t-tests and one-way ANOVA were used for inferential statistics. Results: A total of 379 parents participated in the study. Overall, 49% of parents exhibited high HPV vaccine hesitancy. The most frequently reported barriers were safety concerns (82.6%), insufficient information (80.3%) and fear of side effects (79.4). Lower hesitancy scores were observed among parents aged ≥ 46 years than among those aged 18–25 years (p = 0.022), and respondents with postgraduate education were less hesitant than those with a high school education or less (p = 0.030). Parents whose children were fully vaccinated exhibited significantly lower hesitancy scores compared with those whose children were unvaccinated (p = 0.004). Conclusions: The HPV vaccine hesitancy among parents in the Aseer region is greatly influenced by the age of the parents, their educational level, and the child’s vaccination history. Higher hesitancy among younger parents, those with lower educational levels, and parents of unvaccinated children highlights priority groups for targeted interventions. Strengthening healthcare-provider recommendations and implementing culturally tailored, evidence-based communication strategies may improve HPV vaccine acceptance and support national cervical cancer prevention efforts in Saudi Arabia. Full article

Introduction: Thyroid disorders are among the most prevalent endocrine diseases worldwide, with rising incidence linked to aging, lifestyle, and environmental factors. Early identification of both functional and structural abnormalities is essential to prevent complications. This study aimed to investigate the coherence between thyroid function as measured by thyroid-stimulating hormone (TSH) and free thyroxine (FT4) tests and ultrasound-based thyroid classification according to the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). Methods: This retrospective cross-sectional study included patients in Tabuk, Saudi Arabia, who underwent thyroid ultrasound alongside TSH and FT4 tests within two weeks. Thyroid nodules were classified using TI-RADS. Demographic, clinical, and laboratory data were extracted from electronic records, and statistical analyses examined associations between hormone levels, ultrasound findings, and clinical variables (p < 0.05). Results: A total of 102 patient records were included in the study. Most participants were female and overweight, with a mean body mass index (BMI) of 30.2 ± 4.6 kg/m². The majority were euthyroid (58.3%) or subclinical hypothyroid (27.8%); most nodules were benign (TI-RADS 2–3). BMI showed a moderate positive correlation with TSH (ρ = 0.20, p = 0.041) and a negative correlation with FT4 (ρ = –0.20, p = 0.040). No significant relationship was observed between TI-RADS classification and thyroid hormone levels (p > 0.05). Conclusions: Structural thyroid changes identified by ultrasound appeared largely independent of hormonal status. Meanwhile, BMI demonstrated a modest physiological association with thyroid function reflected in TSH and/or FT4 levels. These findings emphasize the need for integrated biochemical and imaging evaluation to enhance diagnostic precision in the assessment of thyroid disease. Full article

Declining fertility rates are decreasing college-aged populations and placing sustained pressure on higher education systems worldwide. This study examines how long-term demographic contraction brings recurring system-level challenges, using Germany, the United States, South Korea, and Japan as illustrative examples. Drawing on secondary data from official demographic and higher education statistics, the study applies a data-informed analytical approach and a structured analytical framework to identify key pressures related to financial and operational viability, educational quality assurance, structural and regional imbalances, and governance constraints. The analysis indicates that higher education systems have responded with strategic and operational measures, including international student recruitment, lifelong and recurrent education, institutional consolidation, and enrollment and retention management. While these responses can partially mitigate demographic pressures in the short to medium term, they face structural limits under sustained population decline. Long-term sustainability, therefore, requires coordinated system-level adaptation through diversified learner populations, institutional differentiation, and policy frameworks aligned with evolving labor market and societal demands, in order to maintain educational quality and equitable access. Full article

Long COVID is a complex condition characterized by persistent symptoms following SARS-CoV-2 infection. Understanding its biochemical mechanisms is essential for effective management and treatment strategies. Objective: This study investigated biochemical alterations associated with long COVID in unvaccinated individuals presenting symptoms persisting for more than six months, highlighting the prolonged nature of the condition and its systemic and neurological manifestations. A cross-sectional study was conducted with 60 unvaccinated patients at least six months post-COVID-19 infection. Serum biomarkers, including C-reactive protein (CRP), interleukin-6 (IL-6), N-terminal pro-brain natriuretic peptide (NT-proBNP), and irisin, were analyzed. Correlations between these biomarkers and persistent symptoms were assessed using statistical regression models. Elevated CRP levels were significantly associated with persistent respiratory and musculoskeletal symptoms, suggesting ongoing inflammation. Increased IL-6 levels correlated with fatigue and musculoskeletal complaints. NT-proBNP elevations were linked to cardiovascular manifestations, including dyspnea and chest pain. A positive correlation between irisin and persistent sensory impairments, such as anosmia and dysgeusia, indicates potential neuroinflammatory mechanisms. This study highlights that persistent inflammation plays a critical role in long-term (>6 months) post-COVID manifestations. Monitoring biomarkers such as CRP, IL-6, NT-proBNP, and irisin may enhance understanding and management of prolonged post-COVID conditions. Full article

Backdoor attacks pose a critical security risk to pre-trained language models (PLMs) by utilizing concealed triggers to manipulate model outputs. Existing defense strategies largely depend on statistical thresholds, which often struggle to identify sophisticated backdoor samples that exhibit high cognitive similarity to benign data. Such similarities make precise threshold calibration difficult, frequently leading to unreliable or failed detection. To overcome these limitations, we propose a backdoor detection method based on consensus deviation, shifting the defensive paradigm from surface-level statistical metrics to deep cognitive consensus verification. This approach obviates the reliance on fixed thresholds, enabling the more robust identification of covert triggers. Extensive experiments on the SST-2, HSOL, and AG‘s News datasets revealed that our method achieved significantly lower attack success rates (ASRs) and enhanced robustness compared with the current baselines across word-, sentence-, and structural-level attack scenarios. Full article

Background/Objectives: Choosing the best prosthetic foot for a patient is complicated by the many available options and limited evidence to distinguish them. This work aimed to clarify performance differences in the level-ground walking of K3-functional-level persons with amputations across a variety of prosthetic feet within the energy storage and return class. Methods: This clinical trial assessed 10 subjects fitted with the Ossur ProFlex foot (LP and XC) compared to their original foot after a 30-day adaptation period and careful prosthetic alignment matching. Multivariate data (walking performance, noise/play, balance and satisfaction) were collected in the gait laboratory. Results: Results were mixed across the cohort. MCID and statistical analysis were used to assess the magnitude and importance of the changes observed. Overall, the changes were small and not statistically significant. Conclusions: Our findings support that performance across a variety of measures for K3-level amputees walking over level ground is relatively insensitive to prosthetic foot componentry within the energy storage and return class. While functional performance is not the only metric that contributes to foot choice, it is an important one. This study helps to circumscribe its role in the larger decision-making framework for this class of componentry in persons with transtibial amputation. Full article

Coastal societies face increasing health risks from climate change, such as weather-related extreme conditions, environmental destruction, and the occurrence of epidemics, posing significant challenges to sustainable development. There is a need to accurately measure the risks in place through integrating the climate variability with socio-economic exposure and health components to support long-term resilience and sustainable adaptation. This study conceptualized and validated a composite index-based method to assess climate–health risks across three Indonesian coastal cities: Banda Aceh, Mataram, and Ambon. This validation process was conducted by checking for face validity and consistency between sub-indices, as well as conformity to existing frameworks in the literature. Using satellite-derived climate data, national socio-economic statistics, and public health records, we identified the key parameters (hazard, sensitivity, exposure, and adaptive capacity) and quantified the risk levels for 190 villages. The results show that over 92% of villages fall into the high or very high risk categories, with universal high sensitivity and low adaptive capacity (78.95%). This points towards structural inequalities that hinder sustainable development. Spatial and quadrant analyses revealed region-specific vulnerabilities where Ambon showed higher hazard exposure (56% high and 42% very high). The findings provide policymakers and stakeholders with priority areas for targeted interventions and actionable suggestions to support public health planning, equitable resource allocation, and long-term sustainable coastal development. Full article

Metaheuristic methods for three-dimensional (3D) unmanned aerial vehicle (UAV) path planning often suffer from premature convergence and reduced accuracy in complex high-dimensional spaces, in which waypoint-based decision variables exhibit structured dependencies and segment-level regularities. In a symmetry-inspired operational sense, these regularities can be interpreted as exploitable dependency patterns across path segments and permutation invariance among homogeneous UAVs, which are often overlooked by standard algorithms. The paper proposes an enhanced dung beetle optimizer (LEDBO) that integrates interaction-aware variable handling, adaptive role regulation, and a fitness-state-driven hybrid search mechanism. Correlation-based variable grouping clusters dependent waypoints into segments to exploit statistical dependency patterns among waypoint-coordinate variables and enhance local refinement. A three-level adaptive role-regulation scheme adjusts search behaviors according to convergence status and population diversity, thereby mitigating stagnation. Meanwhile, a fitness-state-driven hybrid engine combines Nelder–Mead local refinement with Lévy-flight global exploration to balance exploitation and exploration across stages. Experiments on the CEC2017 benchmark suite and complex 3D UAV path-planning simulations demonstrate that LEDBO achieves better solution quality, convergence behavior, and robustness than representative metaheuristics, producing smoother, shorter, and safer trajectories. The results suggest that incorporating interaction-aware variable grouping and adaptive search regulation can improve UAV path planning and related high-dimensional continuous optimization tasks. Full article

This study presents the Multi-Method Convergence Protocol (MMCP), a decision-making framework designed to overcome the mono-objective limitations of HOMER Pro (Hybrid Optimization Model for Electric Renewables) and the instability commonly observed among traditional MCDM approaches. Applied to a hybrid PV–wind–grid Smart Grid (Intelligent Electrical Power Grid) in the Provence-Alpes-Côte d’Azur region (France), the protocol transforms techno-economic simulation outputs into robust and explainable multi-criteria decisions. MMCP integrates five sequential stages—normalization, AHP-based (Analytic Hierarchy Process) weighting, multi-method ranking (TOPSIS, PROMETHEE II, ELECTRE II (Elimination and Choice Expressing Reality II), and VIKOR), Borda–Copeland (Borda Count Ranking Method–Copeland Pairwise Aggregation Method) co-aggregation, and statistical validation—using Kendall’s τb (Kendall’s Rank Correlation Coefficient) and Spearman’s ρ (Spearman’s Rank Correlation Coefficient). Results reveal strong convergence between compensatory and non-compensatory models (τb ≥ 0.75; ρ ≥ 0.90), confirming the internal coherence and structural stability of the rankings. Scenario 17 emerges as the optimal configuration, combining low LCOE (Levelized Cost of Energy) with reduced emissions and balanced renewable penetration. The near-linear alignment between aggregation methods validates the protocol’s reliability and methodological transparency. Overall, MMCP provides a scalable and traceable foundation for sustainable Smart Grid planning and evidence-based energy governance. Full article