Search Results (3,618)

With the rapid advancement of deepfake generation technologies, forged videos have become increasingly realistic in visual quality and temporal consistency, posing serious threats to multimedia security. Existing detection methods often struggle to effectively model temporal dynamics and capture subtle inter-frame anomalies. To address these challenges, we propose a Stochastic Differential Equation and Quantum Uncertainty Network (SDEQ-Net), a novel deepfake video anomaly detection framework that integrates continuous time stochastic modeling with quantum uncertainty mechanisms. First, a Continuous Time Neural Stochastic Differential Filtering Module (CNSDFM) is introduced to characterize the continuous evolution of latent inter-frame states using neural stochastic differential equations, enabling robust temporal filtering and uncertainty estimation. Second, a Quantum Uncertainty Aware Fusion Module (QUAFM) incorporates Hermitian-symmetric density matrix representations and von Neumann entropy to enhance feature fusion under uncertainty, leveraging the mathematical symmetry properties of quantum state representations for principled uncertainty quantification. Third, a Fractional Order Temporal Anomaly Detection Module (FOTADM) is proposed to generate fine grained temporal anomaly scores based on fractional order residuals, which are used as dynamic weights to guide attention toward anomalous frames. Extensive experiments on three benchmark datasets, including FaceForensics++, Celeb-DF, and DFDC, demonstrate the effectiveness of the proposed method. SDEQ-Net achieves AUC scores of 99.81% on FF++ (c23) and 97.91% on FF++ (c40). In cross dataset evaluations, it obtains 89.55% AUC on Celeb-DF and 86.21% AUC on DFDC, consistently outperforming existing state-of-the-art methods in both detection accuracy and generalization capability. Full article

Aquaculture-derived bio-based fertilizers (BBFs) represent a promising alternative to inorganic P in Andisols for sustainable alfalfa (Medicago sativa L.) cultivation. However, their agronomic performance and physiological impacts on alfalfa remain poorly understood. This study evaluated three BBFs, consisting of composted fish sludge (CFS), dried fish sludge (DFS), and fish bone meal (FBM), in comparison with inorganic P (InoP) and a zero-P control (NoP). Forage yield (FY), P use efficiency (PUE), spectral canopy indices, and leaf gas-exchange parameters were assessed across five harvests in a Mediterranean environment. Results showed significant differences among fertilizer types driven by their distinct P release dynamics. DFS consistently maintained stable leaf P concentrations, enhanced PUE and P uptake, and lead to higher FY, improved photosynthesis, and water use efficiency (WUE). It performs statistically similarly to the inorganic P. In contrast, CFS released P too slowly, which in turn lowered leaf P concentration, P uptake, and PUE, resulting in the lowest photosynthesis and WUE. FBM produced intermediate responses but maintained WUE comparable to inorganic fertilizer. Gas-exchange measurements demonstrated that photosynthesis ranged from 9.01 to 16.7 μmol m⁻² s⁻¹, with no significant difference between DFS and inorganic P. Transpiration remained stable across BBF treatments (mean 3.2 mmol m⁻² s⁻¹). The canopy reflectance indices such as RARS, Gite2, and PSSR proved to be strong predictors of both P concentration and PUE in alfalfa. In conclusion, DFS emerged as the most efficient BBF that matched inorganic fertilizer to enhance P nutrition, plant physiology, and FY. These findings highlight the potential of aquaculture-derived BBFs, particularly DFS as sustainable P sources for improving alfalfa productivity while reducing reliance on synthetic fertilizers. Full article

Background: Research has shown that disordered eating behaviors—including binge eating, night eating syndrome, and food addiction—contribute to the heterogeneity of obesity and assist in phenotyping patients for more tailored interventions. The Eating Behavior Assessment for Obesity (EBA-O) is a recently developed 18-item questionnaire that assesses five pathological eating-behavior domains among individuals with obesity (night eating, food addiction, sweet eating, hyperphagia, and binge eating). The present study aimed to translate, culturally adapt, and validate the Persian (Farsi) version of the EBA-O. Methods: The original English EBA-O was translated into Persian following a standardized forward–backward translation procedure, with cultural adaptations implemented to ensure linguistic accuracy and conceptual clarity. A cross-sectional sample of 278 Iranian adults with overweight or obesity (body mass index [BMI] ≥ 25 kg/m²) completed the Persian EBA-O. Confirmatory factor analysis (CFA) was conducted to verify the five-factor model in the Persian sample. Internal consistency was evaluated using Cronbach’s alpha and composite reliability (CR). Convergent validity was assessed using the average variance extracted (AVE), and discriminant validity was examined with the Heterotrait–Monotrait ratio (HTMT). Model fit indices, including the Comparative Fit Index [CFI], Tucker–Lewis Index [TLI], Normed Fit Index [NFI], Goodness-of-Fit Index [GFI], the Standardized Root Mean Square Residual [SRMR] and relative chi-square value [χ²/df] were used to determine the adequacy of the factor structure. Results: The Persian EBA-O demonstrated a clear and stable five-factor structure consistent with the original instrument. CFA indicated good model fit (CFI = 0.95, TLI = 0.94, NFI = 0.91, GFI = 0.92, SRMR = 0.05, χ²/df = 1.94), confirming the presence of the intended domains. Internal consistency was acceptable to high across all subscales (Cronbach’s α = 0.78–0.86; CR > 0.70), and the total scale showed strong reliability. Three of the five factors demonstrated acceptable convergent validity (AVE = 0.54–0.68), while Food Addiction (AVE = 0.46) and Night Eating (AVE = 0.43) fell slightly below the 0.50 threshold; however, their adequate CR and α values indicate that these constructs remain coherent and psychometrically sound. All inter-factor correlations satisfied discriminant validity criteria (HTMT < 0.90), with the highest association observed between the Binge Eating and Hyperphagia factors. Overall, the psychometric properties of the Persian EBA-O were comparable to those reported in the original validation and subsequent translations. Conclusions: The Persian version of the EBA-O is a valid and reliable instrument for assessing pathological eating behaviors among individuals with obesity. It preserves the original questionnaire’s five-factor structure and demonstrates acceptable internal consistency and construct validity in a Persian-speaking population. This validated tool will support both clinical assessment and research on eating-behavior phenotypes and may contribute to the development of more personalized and effective obesity-management strategies among Persian-speaking individuals. Full article

Background: Head and neck squamous cell carcinoma (HNSCC) remains a major global health concern, with cervical lymph node metastasis being one of the most important determinants of prognosis. Level II (2A/2B) lymph nodes, in particular, play a key role in disease spread and survival outcomes. This study aimed to assess the prognostic impact of level II lymph node metastasis and evaluate the concordance between clinical and pathological staging in patients undergoing neck dissection. Methods: We retrospectively analyzed 138 non-metastatic HNSCC patients treated between 2007 and 2015. Clinical staging was standardized according to the AJCC 8th edition. Level II lymph nodes were dissected and evaluated separately by two independent pathologists. Survival outcomes were assessed using Kaplan–Meier analysis and Cox proportional hazards models. Correlation between clinical and pathological staging was examined using Pearson correlation analysis. Results: The median follow-up was 55.6 months. The 5-year overall survival (OS) and disease-free survival (DFS) rates were 62.4% and 60.1%, respectively. There was a strong correlation between clinical and pathological staging (r = 0.871, p < 0.001). Patients with level II metastasis had significantly worse outcomes, with median OS of 27 months versus an estimated 128 months among those without involvement (p = 0.008), and median DFS of 17.3 versus 114 months (p = 0.004). Age was identified as an independent predictor of mortality in multivariate analysis. Conclusions: Metastasis to level II lymph nodes is a strong adverse prognostic factor in HNSCC. These findings highlight the importance of detailed nodal evaluation in guiding neck dissection strategy, risk stratification, and subsequent adjuvant treatment decisions. Full article

Industrialised construction (IC) represents a foundational strategy for overcoming entrenched productivity constraints and supply shortfalls in the housing sector. By enabling the mass production and mass customisation of advanced kit-of-parts systems, IC supports more efficient, predictable, scalable, and sustainable building delivery through integrated, standardised, and digitally enabled processes. However, adoption remains uneven due to fragmentation across regulatory, organisational, and technological systems. This paper presents a systematic literature review and thematic synthesis of the literature published between 2000 and 2025 to examine performance outcomes, adoption trends, digital integration maturity, and emerging platform-based design for manufacture and assembly (P-DfMA) approaches, and the main drivers. The review shows that significant performance gains are achievable, including notable reductions in construction time and cost variability, along with substantial reductions in material waste, together with measurable improvements in quality, safety, and delivery predictability. However, widespread uptake of IC remains constrained. This is largely driven by regulatory misalignment, rigid and bespoke procurement and delivery models, inconsistent and unstable supply chain capacity, and the lack of standardised components and integrated digital workflows. Building on these insights, this paper examines the key enablers required for sector-wide transformation toward an ecosystem that supports standardised kit-of-parts solutions, digitally driven design-to-production workflows, and aligned policy and procurement frameworks that are capable of delivering scalable and repeatable industrialised housing. The findings provide a consolidated evidence base and identify the key enablers for policymakers, industry stakeholders, and researchers working to move from project-centred delivery models to platform-based, digitally integrated, and industrialised construction systems. We searched Scopus, Web of Science, ScienceDirect, and Google Scholar, complemented by targeted industry and policy repositories; the searches were last updated on 1 December 2025. After screening, 117 sources were included. The review was not registered, and no review protocol was prepared. Full article

Climate change reshapes crop–weed interactions and challenges the cultivation of oilseed rape (Brassica napus L.). Common ragweed (Ambrosia artemisiifolia L.) strongly suppresses early crop development, increases stress sensitivity and leads to yield loss. The stress–physiological responses of oilseed rape to ragweed competition were investigated using a combination of conventional and non-invasive methods. A pot experiment was conducted with increasing ragweed densities (0, 1, 3, 5 and 10 plants). Plant height and biomass were evaluated via non-destructive indicators (SPAD, NDVI) and different stages (1–15 and 16–30 min) of delayed fluorescence (DF) alongside ferric reducing antioxidant power (FRAP). Increasing ragweed density caused changes in growth, altered DF magnitude and decay kinetics, indicating photosynthetic imbalance. Moderate weed competition (1–5) induced an adaptive, eustress-like response characterised by enhanced non-enzymatic antioxidant capacity, whereas higher ragweed densities overwhelmed this compensatory mechanism, resulting in oxidative stress-like responses. Among all measured traits, DF₁–₁₅ proved to be the earliest and most sensitive indicator of the transition from adaptive to disruptive stress: T1: 0 ragweed: 213.07 ± 10.36 cps/mm² and 92.66 ± 6.67 cps/mm². These results demonstrate that delayed fluorescence, combined with conventional physiological and antioxidant-based parameters, enables the early detection of competitive stress in oilseed rape well before visible symptoms appear. Full article

Young adults often underuse ultraviolet radiation (UVR) protective strategies and engage in indoor tanning, heightening lifetime skin cancer risk. The national Skin Smart Campus (SSC) initiative encourages universities to adopt policies that reduce UVR exposure. We evaluated a 6-month SSC university campaign using an independent-samples pre-post design with surveys before (N = 230) and after (N = 267) implementation. The campaign included SSC designation, an educational webpage, targeted Instagram content, small media, and sunscreen dispensers. Following campaign launch, knowledge increased over time (t = 5.02, df = 493, p < 0.001), as did dispenser use (21.9% to 57.7%; χ² = 64.4, p < 0.001). The sun safety behavior composite showed an upward trend (13.5 to 14.2, t = 1.71, df = 490, p = 0.09). Variance models indicated a significant time effect (F [1, 482] = 4.55, p = 0.03, η²p = 0.01; small effect), with higher sun safety associated with greater knowledge (F = 8.29, p = 0.004, η²p = 0.02; small effect) and SSC campaign awareness (F = 56.88, p < 0.001, η²p = 0.10; large effect). In multivariable regression, campaign engagement predicted higher odds of dispenser use (Odds Ratio = 3.01, 95% CI: 1.82–4.98, p < 0.001). Implementing SSC with environmental supports and tailored education increased knowledge, sun safety, and dispenser use, highlighting the strong influence of SSC visibility and multimodal campus-wide prevention strategies. Full article

With the expansion of interconnection in power systems and the extensive adoption of phasor measurement units (PMUs), the secure operation of power systems has been increasingly covered in research. In this article, a unified online framework for pre-fault and post-fault dynamic security assessment (DSA) is proposed. First, maximum mutual information (MIC) and the random subspace method (RSM) are employed to select the key variables and enhance the diversity of input data, serving as feature engineering. Then, a deep forest (DF) regressor and classifier are utilized respectively to predict security margin (SM) and security state (SS) during online pre-fault and post-fault DSA based on the selected variables. In pre-fault DSA, scenarios with high SM are identified as stable, while those with low SM are forwarded to post-fault DSA. In addition, a time self-adaptive scheme is employed to balance low response time and high prediction accuracy. This approach prevents the misclassification of unstable scenarios as stable by either outputting high-credibility predictions of unstable SS or deferring decisions on SS until the end of the decision-making period. The unified framework, tested on an IEEE 39-bus system and a practical 1648-bus system provided by the PSS/E version 35 software, demonstrates significantly improved assessment accuracy and response times. Specifically, it achieves an average response time (ART) of 2.66 cycles for the IEEE 39-bus system and 3.13 cycles for the 1648-bus system while maintaining an accuracy exceeding 98%, surpassing the performance of currently widely used deep learning models. Full article

Background/Objectives: The aim of this study was to translate the Dietary Fat and Free Sugar-Short Questionnaire (DFS) into Turkish (DFS-TR) and to establish its construct validity and reliability. Methods: Quota sampling was used to ensure demographic homogeneity across gender and age groups. Participant distribution by age categories was proportionally aligned with the demographic statistics of the adult Turkish population. The study comprised 314 participants aged 19–64 years (38.78 ± 12.10), of which 54.5% were female. The data collection form consisted of demographic characteristics, anthropometric measurements, information on eating habits, DFS-TR, the Food Frequency Questionnaire (FFQ), and the Power of Food Scale (PFS). Results: Test–retest reliability was confirmed in the 314 participants with a 4-week interval (r = 0.997, p < 0.01). The Cronbach α reliability coefficient was α = 0.777. Using the split-half method, the correlation coefficient between the two halves was 0.681, the Spearman–Brown coefficient was 0.811, and the Guttman coefficient was 0.809. Participants’ total DFS-TR scores and sub-dimension scores varied according to age, education level, income level, anthropometric characteristics, physical activity, and dietary habits, but did not vary according to gender or marital status. To investigate convergent validity, participants’ DFS-TR scores were correlated with the FFQ and PFS. DFS-TR scores showed a significant correlation with the percentage of energy from saturated fat and free sugar assessed by the FFQ. Positive relationships were found between DFS-TR scores and the total PFS score, particularly with the sub-dimension scores for food available and food present. Conclusions: The DFS-TR can be used as a reliable and valid measurement tool for estimating saturated fat and free sugar intake among Turkish adults. Full article

Introduction: Ethical climate theory traditionally conceptualizes organizational ethics as a set of distinct normative dimensions. However, recent evidence suggests that ethical perceptions may converge into a unified climate in culturally cohesive and institutionally regulated contexts. This study aims to validate the Emirati Higher Education Institutions Ethical Climate (EHEC) scale and examine whether the ethical climate operates as a unidimensional construct within Emirati higher education institutions. Methods: A quantitative validation design was employed using survey data from 200 academic and administrative staff across three Emirati universities. Data were analyzed via exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), alongside reliability and validity assessments, using IBM SPSS (Version 27) and AMOS (Version 24). Principal axis factoring without rotation was applied to examine the latent structure, followed by CFA for model fit testing and to compare alternative structures. Results: EFA revealed a single dominant factor with an eigenvalue of 11.8, explaining 47.1% of the total variance, and factor loadings ranging from 0.46 to 0.79. CFA confirmed the adequacy of the one-factor model (χ²/df = 2.31; CFI = 0.93; TLI = 0.91; RMSEA = 0.06; SRMR = 0.05). The scale demonstrated excellent reliability (Cronbach’s α = 0.93; CR = 0.95) and acceptable convergent validity (AVE = 0.48). Comparative analysis showed that the unidimensional model substantially outperformed the traditional five-factor structure. Discussion: These findings indicate that the ethical climate in Emirati higher education institutions is perceived as a single, shared institutional environment rather than as separate ethical dimensions. The validated EHEC scale provides a parsimonious, reliable, and context-sensitive instrument for assessing the ethical climate, suggesting that ethical climate theory may require contextual adaptation in institutionally cohesive and collectivist settings. Full article

Background: Intimate partner violence (IPV) represents a major public health problem in Europe, with significant physical, psychological, and social consequences. Nurses are often the first professionals capable of detecting early signs of IPV, yet they lack validated instruments to assess their clinical competency in detection, evaluation, documentation, and intervention. This study aimed to develop and validate the Intimate Partner Violence Nursing Competency Scale (IPVNCS), aligned with the Nursing Intervention Classification (NIC 6403). Methods: A cross-sectional psychometric study was conducted among registered nurses in the Community of Madrid. A 30-item Likert-type self-administered instrument (1–5 scale) was developed based on NANDA, NIC 6403, and NOC frameworks. A total of 202 nurses participated. Reliability was assessed through Cronbach’s alpha. Construct validity was examined using exploratory factor analysis (EFA) with Promax rotation and confirmatory factor analysis (CFA) using AMOS 26. Ethical approval was obtained (CEU San Pablo, code 843/24/104). Results: After item refinement, 26 items remained across four dimensions: (1) Intervention and Referral, (2) Detection and Assessment, (3) Documentation and Recording-keeping, (4) Psychosocial Support. The instrument showed excellent reliability ($\alpha$ = 0.97). KMO was 0.947 and Bartlett’s test was significant (p < 0.001). CFA demonstrated satisfactory fit: ${\chi }^2$/df = 2.066, RMSEA = 0.073, CFI = 0.92, TLI = 0.91, NFI = 0.86. The final model adequately represented the latent structure. After debugging, its psychometric properties were significantly improved. Four redundant items were eliminated, achieving internal consistency ($\alpha$ = 0.97), a KMO value of 0.947 and a significant Bartlett’s test of sphericity. It showed a better fit, according to ${\chi }^2$/df = (2.066); Parsimony = (720.736); RMR (0.0529; RMSEA (0.073); NFI (0.860); TLI (0.910) and CFI (0.920). The final model provides an adequate representation of the latent structure of the data. This study provides initial evidence of construct validity and internal consistency reliability of the IPVNCS. Conclusions: The IPVNCS is a valid and reliable tool to assess nursing competencies for clinical management of IPV. It supports structured evaluation across four core nursing domains, enabling improved educational planning, clinical decision-making, and quality of care for victims. The scale fills a gap in clinical nursing assessment tools and can support protocol development in emergency, primary care, and hospital settings. Full article

Objectives: Mindfulness has been proposed as an important health outcome and an indicator of mental well-being. This study aimed to evaluate the psychometric properties of the Five Facet Mindfulness Questionnaire (FFMQ-15) in two samples of mental health service users with diverse cultural and linguistic backgrounds (English- and Chinese-speaking). The study addresses the conceptual gap regarding the limited validation of the FFMQ-15 in Chinese-speaking clinical populations and examines the implications of measurement invariance. This study aimed at (1) confirming the reliability and validity of the FFMQ-15 in mental health service users; (2) assessing the validity of the FFMQ-15 in Chinese-speaking populations, where evidence is limited; and (3) examining measurement invariance across English- and Chinese-speaking groups to ensure cross-cultural applicability and comparable score interpretation. Methods: Participants were recruited using snowball sampling and social media advertising, targeting adults aged 18 or older who could read and write English or Chinese and had received mental health services. The English-speaking sample comprised 115 adults, and the Chinese-speaking sample included 118 adults. Exploratory factor analysis was used to identify structural dimensions, while confirmatory factor analysis was conducted for both samples to evaluate the five-factor structure of the FFMQ-15. Results: The EFA showed literature-aligned results supporting the 5-factor structure model, while the CFA model demonstrated acceptable fit: χ²/df = 159.50/80 = 1.99, p < 0.001; CFI = 0.927; TLI = 0.904; RMSEA = 0.065 (90% CI [0.050, 0.080]); SRMR = 0.060, BIC = 10,843.067, meeting established thresholds, and the non-significant measurement variance indicated the measurement model’s consistency among clinical patients and across different cultural contexts. Conclusions: The FFMQ-15 shows strong psychometric properties for measuring mindfulness in English- and Chinese-speaking mental health service users, supporting its value in clinical research and practice. Full article

The primary objective of this study is to bridge the gap between descriptive geometry and mechanistic design by establishing a dual-domain fractal framework to analyze the internal architecture of asphalt mixtures. This research quantitatively assesses the sensitivity of volumetric indicators—namely air voids (VV), voids in mineral aggregate (VMA), and voids filled with asphalt (VFA)—by employing the coarse aggregate fractal dimension (D_c), the fine aggregate fractal dimension (D_f), and the coarse-to-fine ratio (k) through Grey Relational Analysis (GRA). The findings demonstrate that whereas D_f and k substantially influence macro-volumetric parameters, the mesoscopic void fractal dimension (D_V) remains structurally unchanged, indicating that gradation predominantly dictates void volume rather than geometric intricacy. Sensitivity rankings create a prevailing hierarchy: Process Control (Compaction) > Skeleton Regulation (D_c) > Phase Filling (Pb) > Gradation Adjustment (k, D_f). D_c is recognized as the principal regulator of VMA, while binder content (Pb) governs VFA. A “Robust Design” methodology is suggested, emphasizing D_c to stabilize the mineral framework and reduce sensitivity to construction variations. A comparative investigation reveals that the optimized gradation (OG) achieves a more stable volumetric condition and enhanced mechanical performance relative to conventional empirical gradations. Specifically, the OG group demonstrated a substantial 112% enhancement in dynamic stability (2617 times/mm compared to 1230 times/mm) and a 75% increase in average film thickness (AFT), while ensuring consistent moisture and low-temperature resistance. In conclusion, this study transforms asphalt mixture design from empirical trial-and-error to a precision-engineered methodology, providing a robust instrument for optimizing the long-term durability of pavements in extreme cold and arid environments. Full article

Prevention of complications related to diabetic foot (DF) can now be performed using smartphone-connected thermal cameras. However, the absolute error associated with these devices remains particularly high, compromising measurement reliability, especially under variable environmental conditions. To address this, we introduce a physiologically motivated two-region segmentation task (forehead + plantar foot) to enable stable temperature correction. First, we developed a fully automated joint method for this task, building upon a new multimodal thermal–RGB dataset constructed with detailed annotation procedures. Five deep learning methods (U-Net, U-Net++, SegNet, DE-ResUnet, and DE-ResUnet++) were evaluated and compared to traditional baselines (Adaptive Thresholding and Region Growing), demonstrating the clear advantage of data-driven approaches. The best performance was achieved by the DE-ResUnet++ architecture (Dice score: 98.46%). Second, we validated the correction approach through a clinical study. Results showed that the variance of corrected temperatures was reduced by half compared to absolute values (p < 0.01), highlighting the effectiveness of the correction approach. Furthermore, corrected temperatures successfully distinguished DF patients from healthy controls (p < 0.01), unlike absolute temperatures. These findings suggest that our approach could enhance the performance of smartphone-connected thermal devices and contribute to the early prevention of DF complications. Full article

Methanol is a promising alternative marine fuel due to its favorable combustion characteristics and potential to reduce exhaust emissions under increasingly stringent International Maritime Organization (IMO) regulations. This study presents a three-dimensional computational fluid dynamics (CFD) analysis of a four-stroke, medium-speed marine engine operating in methanol–diesel dual-fuel (DF) mode. Simulations were performed using AVL FIRE for a MAN B&W 6H35DF engine, covering the in-cylinder process from intake valve closing to exhaust valve opening. Nine operating cases were investigated, including seven methanol–diesel DF cases with equivalence ratios (Φ) from 0.18 to 0.30, one methane–diesel DF case (Φ = 0.22), and one pure diesel baseline. A power-matched condition (IMEP ≈ 20 bar) enabled consistent comparison among fueling strategies. The results show that methanol–diesel DF operation reduces peak in-cylinder pressure, heat-release rate, turbulent kinetic energy, and wall heat losses compared with diesel operation. At low to moderate Φ, methanol DF combustion significantly suppresses nitric oxide (NO), soot, and carbon monoxide (CO emissions), while carbon dioxide (CO₂) emissions increase with Φ and approach diesel levels under power-matched conditions. These results highlight methanol’s potential as a viable low-carbon fuel for marine engines. Full article