Search Results (639)

Background/Objectives: Complementary medicines (CMs) are widely used in Australia, yet consumer beliefs about their safety and effectiveness often diverge from the scientific evidence. Contemporary statewide data from Victoria, particularly about these perceptions and underlying perception profiles, are limited. We therefore aimed to characterise CM use patterns and perceptions of it among Victorian adults and identify the demographic and use-related belief patterns. Methods: A cross-sectional survey was conducted in metropolitan and regional Victoria (November 2024–August 2025) among adults (≥18 years) who had used complementary medicines in the previous 12 months (N = 447). The questionnaire assessed CM use patterns, perceived effectiveness, safety, quality, perceived risk relative to prescription medicines, adverse events, and demographics. The analyses included descriptive statistics, χ² tests with multiple-comparison control, Spearman correlations, and a multivariable regression. An exploratory factor analysis (EFA) and latent class analysis (LCA) were used to identify the perception dimensions and distinct consumer profiles. Results: CM use was frequent (62.2% daily; 19.2% weekly) and often long term (>1 year, 55.0%). The most commonly used products were vitamin D (53.0%), multivitamins (39.8%), magnesium (34.5%), iron (33.8%), and vitamin C (30.0%). The perceptions were favourable: 77.3% rated CMs as effective, 90.4% as safe, and 60.3% as high quality; 78.5% perceived CMs to have lower side-effect risks than prescription medicines. Adverse events were reported by 12.3%. In the adjusted models, adults ≥ 65 years and monthly/occasional users were less likely to endorse “lower risk than prescription medicines” (aOR: 0.18; 95% CI: 0.06–0.51; aOR: 0.36, 0.18–0.72). East Asian respondents had lower odds of endorsing CM effectiveness than Caucasian/White respondents (aOR: 0.28, 0.11–0.72). Their perceived quality was higher among men (aOR: 1.73, 1.09–2.74) and adults aged 55–65 years (aOR: 3.81, 1.39–10.48). Conclusions: In this contemporary statewide Victorian sample, CM use was common and generally viewed positively, yet the comparative risk may be underestimated. Profiling perception patterns and identifying belief patterns by age, culture, and use intensity provides actionable targets for clinician/pharmacist counselling and culturally tailored education to support safer, evidence-aligned CM use. Full article

Chryseobacterium indologenes MA9 is a causative agent of root rot disease in Panax notoginseng (P. notoginseng), with its high incidence being a major manifestation of continuous cropping barriers, severely hindering the sustainable development of the P. notoginseng industry. In this study, a novel lytic bacteriophage, MA9V-3, was isolated from wastewater, targeting C. indologenes MA9. The phage produced clear plaques, ranging from 1 to 3 mm in diameter, with a surrounding halo. Phage MA9V-3 achieved an adsorption rate of up to 80% after 30 min of contact with C. indologenes MA9, a latent period of approximately 40 min, and an average burst-size if 160 PFU/cell. Transmission electron microscopy revealed that phage MA9V-3 possesses an icosahedral head and a contractile tail, exhibiting a typical myovirus-like morphology. According to the latest ICTV taxonomy, MA9V-3 belongs to the class Caudoviricetes, and the phage’s biocontrol efficacy and inhibitory capacity were evaluated at different multiplicity of infection (MOI s). The results showed that the highest titer recorded at 1.6 × 10¹⁰ PFU/mL. Whole-genome sequencing revealed that MA9V-3 is a double-stranded circular DNA virus, with a genome length of 103,203 bp, GC content of 34.29%, and 150 open reading frames (ORFs), one of which is related to tRNA. Only 13 of these ORFs encode known functional sequences, likely due to the limited available gene data for such phages in the database, with additional details on hypothetical proteins yet to be uncovered. Comparative database analysis confirmed that the phage genome contains no antibiotic resistance or toxin-related genes. Phage therapy experiments were performed using MA9V-3 and two other phages screened in our laboratory. The experimental results showed that phage MA9V-3 may be a potential candidate for effectively controlling the infection of Panax notoginseng by C. indologenes MA9, and offering valuable insights into the potential application of phage therapy for managing bacterial plant diseases. Full article

This study evaluates the ability of three classification procedures to distinguish areas with different levels of atmospheric pollution, based on biomonitoring carried out by analyzing the color and spectral characteristics of mulberry (Morus L.) and linden (Tilia L.) leaves. Sampling was carried out in areas that were grouped into four classes according to the concentrations of fine particulate matter (PM2.5, PM10) and gaseous pollutants (TVOC, NOx, SOx, CO, and eCO₂), measured using a specialized multisensor device. A total of 57 informative features were analyzed, representing indices obtained from two color models (RGB and Lab), as well as from VIS and NIR spectral characteristics measured for the adaxial and abaxial leaf surfaces. The data processing methodology includes feature selection using the ReliefF method and a comparative analysis between two approaches to dimensionality reduction—principal components (PC) and latent variables (LV). The results indicate that data reduction using PC provides significantly higher accuracy and better class separability, regardless of the classifier used, compared to LV, where errors exceed 40%. The comparison between classifiers shows a clear superiority of nonlinear models. While linear discriminant analysis demonstrates low efficiency, quadratic discriminant analysis (Q and DQ) and SVM with radial basis function (RBF) achieve high accuracy of class separability, reaching 100% in the SVM-RBF model for both tree species. The study also reveals functional asymmetry: the adaxial side of the leaves is more informative for spectral indices, while the abaxial side is more sensitive to color changes. The results confirm that the combined optical characteristics obtained from the leaf surface of bioindicators form a reliable method for ecological monitoring of air quality in urban areas. Full article

Global progress towards the 2030 Sustainable Development Goals (SDGs) remains critically off track, with current trends indicating that only 17% of targets will be met by the deadline. As sustainability transitions increasingly depend on regional and institutional capacity, understanding heterogeneous transition pathways and resilience across territorial contexts is essential. This study investigates whether observed divergence in SDG performance reflects temporary setbacks or persistent structural regimes characterised by distinct institutional and technological configurations. Using panel data from over 160 countries (2019–2024), we employ annual latent class analysis to identify hidden structures in SDG performance across 15 goals, introducing intertemporal volatility as a dimension of development dynamics. We complement this with ordered logistic regression to examine structural determinants of regime membership, including governance quality, digital infrastructure, health investment, and macroeconomic indicators. Our analysis identifies three temporally stable development regimes—lagging, transitional, and leading—with fewer than 15% of countries transitioning between classes over the observation period. ANOVA results reveal that internet access and government effectiveness exhibit the most substantial between-regime differences. Ordered logit models indicate that governance quality and digital connectivity are the strongest correlates of regime membership (government effectiveness: β = 0.943, p < 0.001; internet penetration: β = 0.049, p < 0.001), whereas short-term GDP growth exerts negligible influence (p > 0.10). These findings challenge assumptions of linear convergence in sustainable development and provide a data-driven framework for evaluating transition dynamics across diverse territorial contexts. The results suggest that achieving the SDGs requires that deep structural constraints be addressed—particularly digital divides and institutional quality—through regionally targeted policy design rather than relying solely on incremental adjustments or economic growth. The identified regimes provide a basis for place-based targeting by distinguishing contexts where governance and digital capacity constraints are binding. Full article

Background/Objectives: Sleep is an important biobehavioral process that supports child and adolescent health and development. However, many prior studies examining sleep and mental health have relied on total sleep scores, which may mask the heterogeneity of sleep disturbances. Youth exposed to childhood adversity are at increased risk for sleep disturbances and poor mental health, and thus it is important to examine the links between sleep and mental health within adversity-exposed samples, such as those involved with the child welfare system. Methods: This study used latent class analysis to identify underlying patterns of sleep disturbances and examine differences in mental health symptoms (assessed at baseline and at an 18-month follow-up) across the identified subgroups in a sample of child welfare-involved adolescents (N = 1041, M_age = 13.63 years, SD = 1.86). Our sample was derived from the second cohort of the National Survey on Child and Adolescent Well-Being (NSCAW) study. Results: We identified three subgroups of sleep disturbances: no sleep disturbances (38%), sleeping more than peers and overtired (16%), and trouble maintaining sleep (47%). We found significant mean differences in mental health symptoms across subgroups. Across internalizing, externalizing, and post-traumatic stress disorder (PTSD) symptoms at baseline and at an 18-month follow-up, those in the no sleep disturbances subgroup had overall lower levels of symptoms compared to those in the trouble maintaining sleep subgroup, which had higher levels of symptoms. Compared to those in the sleeping more than peers and overtired subgroup, the trouble maintaining sleep subgroup had higher levels of PTSD symptoms at baseline, and higher levels of externalizing and PTSD symptoms at the follow-up. Those in the sleeping more than peers and overtired subgroup had significantly higher levels of internalizing, externalizing, and PTSD symptoms at baseline compared to the no sleep disturbances subgroup, but there were no significant differences at the 18-month follow-up. Conclusions: The current study highlights the importance of considering the heterogeneity of sleep disturbances to identify child welfare-involved youth who may be more at risk for sleep disturbances and poor mental health and to inform more targeted sleep interventions for this population. Full article

Deep learning models for brain tumor classification from magnetic resonance imaging (MRI) often achieve high in-dataset accuracy but exhibit substantial performance degradation when evaluated on unseen clinical data due to domain shift arising from variations in imaging protocols and intensity distributions. Existing approaches largely rely on architectural scaling or parameter-level regularization, which do not explicitly constrain the stability of learned feature representations. This manuscript proposes Feature Space Stability Regularization (FSSR), a lightweight and model-agnostic training framework that enforces consistency in latent feature representations under realistic, MRI-safe-intensity perturbations. FSSR introduces an auxiliary feature space loss that minimizes the ${\ell }_2$ distance between normalized embeddings extracted from the input MRI images and their intensity-perturbed counterparts, alongside standard cross-entropy supervision. This manuscript evaluated FSSR across three convolutional backbones, ResNet-18, ResNet-34, and DenseNet-121, trained exclusively on the Kaggle Brain MRI dataset. Feature space analysis demonstrates that FSSR consistently reduces mean feature deviation and variance across architectures, indicating more stable internal representations. Generalization is assessed via zero-shot evaluation on the fully unseen BRISC-2025 dataset without retraining or fine-tuning. On the source domain, the best-performing configuration achieves 97.71% accuracy and 97.55% macro-F1. Under domain shift, FSSR improves external accuracy by up to 8.20 percentage points and the macro-F1 by up to 12.50 percentage points, with DenseNet-121 achieving a 96.70% accuracy and 96.87% macro-F1 at a domain gap of only 0.94%. Confusion matrix analysis further reveals the reduced class confusion and more stable recall across challenging tumor categories, demonstrating that feature-level stability is a key factor for robust brain MRI classification under domain shift. Full article

Open Educational Resources (OER) have emerged as a cost-effective alternative to traditional commercial textbooks in higher education, towards the goal of alleviating college students’ financial burden of educational expenses. However, mixed findings about the influences of the integration of OER on student learning are present. To address the gap, this study investigated whether student motivation in OER served as a latent factor that impacts their academic achievement in online asynchronous courses offered in public universities. Particularly, this study (N = 247) implemented an advanced person-centered approach—stepwise latent class analysis—to profile student achievement goals in an OER-based course and examined their relationships with academic achievement. The 7-point Likert responses were collapsed into three categories to address sparse response distributions. The analysis identified four latent classes based on students’ responses to a validated survey aligned with the 2 × 2 achievement goal theory framework, including highly ambitious, cautious, strategic, and low-goal learners. Subsequent analysis revealed that these four latent classes showed differences in academic achievement as well as task value and expectancy beliefs. The implications of these results for researchers and college instructors and future research directions are discussed. Full article

Deep learning approaches for egocentric video understanding often lack a principled theoretical treatment of stability, particularly when dealing with the sparse, noisy, and temporally ambiguous observations characteristic of first-person imaging. In this work, we frame egocentric video question answering not merely as a classification task, but as an ill-posed inverse problem aimed at reconstructing latent semantic intent from stochastically perturbed visual signals. To address the instability inherent in standard dual-encoder architectures, we present a framework with a mathematical interpretation that incorporates gated cross-modal interaction within the transformer backbone. Formally, the video-side update analyzed in this work is defined as a learnable convex combination of unimodal feature representations and cross-modal attention residuals; the full implementation applies analogous gated cross-modal updates bidirectionally. From a regularization perspective, the gating mechanism can be interpreted as an adaptive parameter that balances data fidelity against language-conditioned structural constraints during feature reconstruction. We provide the Bounded Update Property (Lemma 1) and an analytical layer-wise sensitivity bound and empirically demonstrate that the proposed framework achieves measurable improvements in both accuracy and stability on the EgoTaskQA and MSR-VTT benchmarks. On EgoTaskQA, our model improves accuracy from 27.0% to 31.7% (+4.7 pp) and reduces the accuracy drop under 50% frame drop from 3.93 pp to 0.94 pp. On MSR-VTT, our model improves accuracy by 13.0 pp over the dual-encoder baseline. Under severe perturbation (50% frame drop) on MSR-VTT, our model retains 97.7% of its clean performance, whereas the baseline exhibits near-zero drop accompanied by majority-class behavior. These results provide empirical evidence that the proposed interaction induces stable behavior under perturbations in an ill-posed multimodal inference setting, mitigating sensitivity to sampling variability while preserving query-relevant temporal structure. Furthermore, an entropy-based analysis indicates that the gating mechanism prevents excessive diffusion of attention, promoting coherent temporal reasoning. Overall, this work offers a mathematically informed perspective on designing interaction mechanisms for stable multimodal systems, with a focus on robust reasoning under temporal ambiguity. Full article

Medical mediation (MM) is a collaborative tool for resolving ethically complex disputes in healthcare. Background/Objectives: Though widely recognized in international clinical ethics, it has only been recently introduced in Greece. The objective of this study was (i) to quantify agreement with MM across three clinical scenarios, (ii) to estimate the proportion of professionals that support mediation and institutional training, and (iii) to identify distinct attitudinal profiles using latent class analysis (LCA). Methods: A structured, cross-sectional online questionnaire was completed by 431 healthcare professionals across Greece. The survey included three clinical vignettes (on (1) end-of-life care, (2) religious refusal of treatment, and (3) medical error disclosure), Likert-scale items on attitudes toward mediation, and demographic information. LCA was used to identify patterns of response across the scenarios and differentiate between strongly supportive, moderately supportive, and cautiously positive professional profiles. Results: Participants expressed strong support for mediation across all scenarios (median scores ≥ 9), with the highest support for medical error disclosure (mean 8.67 ± 2.10 and a median of 10). Most participants (97.2%, n = 419) considered mediation at least sometimes effective, and 80.7% (n = 348) endorsed institutional training. However, only 3.0% (n = 13) reported formal training and 1.9% (n = 8) reported being very familiar with MM. LCA revealed three distinct respondent profiles: strongly supportive (73.3%, n = 316), moderately supportive (14.6%, n = 63), and cautiously positive (12.1%, n = 52). Significant trends were observed across profiles for the perceived effectiveness of mediation and support for institutional training (p < 0.01). However, formal training and familiarity with mediation among the participants were low (<5%). Conclusions: Despite limited training and formal implementation, Greek healthcare professionals show high support for MM. The demand and need for structured mediation training and integration into the Greek healthcare system is strong. The identification of distinct attitudinal profiles provides insight into potential variation in organizational readiness for implementing structured mediation training Full article

The primary objective of this study was to evaluate the relative contributions of the MMSE and nWBV in three-class cognitive stage classification, with a secondary objective of conducting a subgroup analysis to investigate latent risk within the MMSE-normal population. To achieve this, we proposed an explainable deep-learning-based analytical framework integrating the MMSE with nWBV, a structural brain atrophy indicator, and systematically assessed the relative contributions of each variable in cognitive impairment stage classification and potential risk screening. Although the MMSE is widely used in clinical practice as a cognitive screening tool, it has limited sensitivity to early or subtle cognitive decline and may not adequately reflect structural brain changes due to the ceiling effect. To address this limitation, we compared four tabular deep learning models—MLP, Tab ResNet, Tab Transformer, and FT Transformer—under identical fivefold cross-validation conditions. Age and sex were fixed as covariates, and feature ablation analysis was conducted to examine the independent and combined effects of the MMSE and nWBV. The results showed no statistically significant differences in classification performance among model architectures, indicating that predictive performance was primarily determined by the informational content of the input variables rather than model complexity. In the feature ablation analysis, the MMSE alone demonstrated strong discriminative power, whereas nWBV alone showed relatively limited performance; however, when combined with the MMSE, nWBV consistently improved classification performance. Furthermore, for interpretability analysis, both Integrated Gradients (IG) and SHAP were applied to validate variable contributions from complementary perspectives. Across both methods, the MMSE and nWBV were repeatedly identified as key contributing features, and interpretability stability was maintained throughout cross-validation folds, supporting the robustness and reliability of the explanatory results. Beyond simple model performance comparisons, this study provides evidence supporting the complementary integration of structural brain atrophy information into MMSE-centered traditional cognitive assessment by jointly considering variable contribution and interpretability stability. This approach is expected to contribute to precision risk screening and clinical decision support in the early stages of cognitive decline. Although the MMSE exhibited strong discriminative performance, nWBV provided complementary structural risk signals within the MMSE-normal subgroup, suggesting that integrating cognitive assessment with structural biomarkers may enhance early risk identification. Full article

This study aimed to translate, culturally adapt, and validate the Arabic version of the 6-Item Lubben Social Network Scale (LSNS-6). The LSNS-6 was translated, culturally adapted, and administered, alongside the Medical Outcomes Study Social Support Survey (MOS-SSS), to 327 Arabic-speaking adults aged 60 years and older. Internal consistency was examined using Cronbach’s alpha and McDonald’s omega. Confirmatory factor analysis (CFA) tested the hypothesized two-factor structure (Family and Friends), and measurement invariance was evaluated across key sociodemographic and lifestyle variables. Convergent validity was assessed through correlations with MOS-SSS domains. Item response theory (IRT) analyses examined item discrimination and threshold parameters. Latent class analysis (LCA) explored whether the LSNS-6 could identify subgroups with distinct patterns of social connectedness and perceived support. The Arabic LSNS-6 demonstrated good internal consistency (α = 0.83; ω = 0.84) and supported the expected two-factor structure with satisfactory model fit (CFI = 0.963; TLI = 0.931; SRMR = 0.03). Convergent validity was evidenced by moderate correlations with overall perceived social support (r = 0.51). IRT analyses indicated strong discrimination for most items, and LCA identified four distinct latent classes. Overall, the Arabic LSNS-6 is a reliable and valid tool for assessing social isolation among older Arabic-speaking adults. Full article

(1) Background: Proactive health behaviors are key to reducing their burden and supporting healthy aging. (2) Methods: We analyzed five waves (2011–2020) of CHARLS data from 1343 middle-aged and older adults (≥45 years) with multimorbidity. An entropy weight method was used to create a composite score for proactive health behaviors, and group-based trajectory modeling identified behavioral trajectories. Multivariate logistic regression and Shapley value decomposition assessed determinants and their relative contributions. Generalized structural equation modeling and latent class analysis were applied to estimate direct and indirect effects across the full sample and key multimorbidity subgroups. (3) Results: Two trajectories emerged: a “declining group” (91.44%) and an “improving group” (8.56%). The improving group was more likely to include younger, urban individuals with higher education, retired status, smaller family size, and lower depression levels. Education (40.67%) and depressive symptoms (31.22%) were the strongest determinants of trajectory. Path analysis showed that higher education and retirement indirectly supported sustained proactive health behaviors by reducing depression. The direct and indirect effects varied across subgroups. (4) Conclusion: The proactive health behaviors of middle-aged and elderly patients with multimorbidity exhibit a declining trend. Future health policies and interventions should prioritize mental health. Full article

Background: Children with Attention Deficit Hyperactivity Disorder (ADHD) often face behavioral challenges, which may be exacerbated through bidirectional parent–child interactions. Sex differences and cultural context may further shape this pathway. This study aims to examine these relationships among children’s ADHD symptoms, behavioral problems, and parental emotions (anxiety and depression) within China, testing whether parental emotions serve as a mediator and exploring potential differences across child sex. Methods: A path analysis was conducted among children’s ADHD symptoms, children’s behavioral problems, and parental emotional symptoms. Children’s ADHD symptoms were measured using the Swanson, Nolan, and Pelham version IV scale-parent form (SNAP-IV), while the Strengths and Difficulties Questionnaire (SDQ) assessed behavioral problems. Parental emotional symptoms were measured with the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) scales. All questionnaires are in Chinese. Results: The direct, indirect, and total associations of children’s ADHD symptoms on behavioral problems were significant in all models. In the full model, the indirect association, defined through parental emotional symptoms, was estimated at 0.206 (95% CI: 0.157–0.262). The indirect pathway constituted 27.3% of the total association. Conclusions: Parental emotional symptoms are associated with both children’s ADHD symptoms and their behavioral problems, indicating a potential pathway warranting further investigation. Child sex does not play a significant moderating role in the path, but an indirect association from ADHD symptoms to peer problems is observed in boys, not girls. Full article

The rapid deployment of Internet of Things (IoT) devices has increased exposure to a diverse array of evolving cyberattacks, motivating the need for accurate and interpretable intrusion detection systems (IDS). In this work, we develop an explainable hybrid Convolutional Neural Network–Extreme Gradient Boosting (CNN–XGBoost) framework for multi-class IoT attack classification using the CIC IoT-DIAD 2024 dataset. Network-traffic records are preprocessed and standardized using a scalable, chunk-wise workflow, after which a compact top-k subset of features is selected via Random Forest importance ranking. To reduce selection bias, a leakage-prone feature-ranking strategy is compared with a leakage-aware strategy in which features are ranked using only the training data within each split. Subsequently, a one-dimensional Convolutional Neural Network (CNN) learns a 128-dimensional representation from the selected predictors, and XGBoost performs the final multi-class classification. Under the leakage-aware protocol, the proposed model achieves 0.9324 accuracy with 0.5910 macro-F1. Results indicate that leakage-aware selection provides a more defensible estimate of generalization while maintaining competitive detection performance. Finally, SHapley Additive exPlanations (SHAP) is used to interpret the model’s decisions in the learned latent space. The analysis shows that only a small number of embedding dimensions contribute most of the decision evidence, which can aid analyst triage, although the explanations remain indirect with respect to the original traffic features. Full article

Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in rare oncological conditions, and heterogeneous data fusion constraints. To address these challenges, we present a Quantum-Inspired Classical Convolutional Neural Network (QC-CNN) inspired by quantum analogies for automated bone cancer detection in radiographic images. The proposed architecture integrates classical convolutional layers for hierarchical feature extraction with a classical variational layer motivated by high-dimensional Hilbert space analogies for enhanced pattern discrimination. A curated and annotated dataset of bone X-ray images was utilized, partitioned into training, validation, and independent test cohorts. The QC-CNN was optimized using stochastic gradient descent (SGD) with adaptive learning rate scheduling, and regularization strategies were applied to mitigate overfitting. Quantitative evaluation demonstrated superior diagnostic performance, achieving high accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results highlight the ability of classical CNN with quantum-inspired design to capture non-linear correlations and subtle radiographic biomarkers that classical CNNs may overlook. This study establishes QC-CNN as a promising framework for quantum-analogy motivated medical image analysis, providing evidence of its utility in oncology and underscoring its potential for translation into clinical decision-support systems for early bone cancer diagnosis. All computations in the present study are performed using classical algorithms, with quantum-inspired concepts serving as a conceptual framework for model design and motivating future extensions. Full article