Search Results (92)

Background: Nutrition literacy (NL) plays a crucial role in shaping long-term health behaviors among college students, particularly during the transformative final phase of their school education. This study investigated the level of NL among college students in Beijing and examined its association with these behaviors. Methods: A cross-sectional online survey was conducted among 765 students from 12 universities in Beijing. The questionnaire comprised three sections: demographic characteristics, lifestyle factors, and a nutrition literacy scale (Cronbach’s α = 0.893; χ²/DF = 4.750; RMSEA = 0.048; GFI = 0.891; AGFI = 0.876). The NL scale was divided into two domains: cognition and skills. Descriptive statistics were used to summarize NL scores and their distributions across dimensions and subgroups. Group differences for categorical variables were examined using chi-square or Fisher’s exact tests. Logistic regression analyses was employed to identify factors associated with NL. Mediation effects were tested using the Baron and Kenny approach. Results: The mean NL score was 67.74 ± 9.07, with only 7.6% of participants achieving an excellent NL level. Several lifestyle factors were significantly associated with excellent NL. Students with monthly living expenses of 2000–3000 CNY (OR = 2.35, p = 0.019) and >3000 CNY (OR = 3.22, p = 0.023) had higher odds of excellent NL compared to those spending <2000 CNY. Occasional exercise (OR = 2.36, p = 0.026) and daily breakfast consumption (OR = 2.76, p = 0.027) were also positively associated with excellent NL. In contrast, frequent midnight snacking significantly reduced the likelihood of excellent NL (OR = 0.20, p = 0.031). Better self-rated health status was strongly correlated with higher NL (OR = 2.82, p = 0.012). Moreover, NL mediated the relationship between lifestyle factors and healthy eating behaviors, underscoring a gap between nutritional knowledge and practical food skills. Conclusions: Current findings indicated suboptimal nutrition literacy among college students in Beijing, particularly in food selection skills. Excellent NL rates were associated with demographic and lifestyle factors, with NL serving as a mediator between lifestyle and health-related behaviors. These results emphasize the need for targeted nutrition education programs to enhance both knowledge and practical skills among university students. Full article

Workplace bullying is a critical concern in high-risk sectors such as construction and manufacturing, where high-pressure environments, strict deadlines, and hierarchical structures may intensify the problem. Despite its serious impact on workers’ well-being and productivity, research in these sectors, particularly in Indonesia, is limited. This study examined the prevalence of workplace bullying, contributing factors, and its effects on mental health among construction and manufacturing workers. It also explored barriers to prevention and potential strategies for mitigation. A mixed-methods design was applied, involving 1029 workers (620 manufacturing, 409 construction). Quantitative data were collected using the Negative Acts Questionnaire—Revised (NAQ-R), while qualitative insights were obtained through Focus Group Discussions (FGDs). Analyses included chi-square tests, logistic regression, and thematic analysis. Bullying was more prevalent in construction, especially among younger and less experienced workers. Risk factors included work-related stress, role ambiguity, and gender dynamics. FGDs revealed underreporting due to absent policies, weak leadership, and workplace cultures that normalized aggression. Workplace bullying remains a significant issue in both sectors in Indonesia. Strong anti-bullying policies, effective leadership, and comprehensive training are essential. Transforming organizational culture toward inclusivity and support is critical to addressing this challenge. Full article

International postgraduate students enrich higher education institutions and host societies, contributing economically, socially, and culturally. However, much less is known about how to improve their academic outcomes compared with their undergraduate counterparts. This study explores the impact of a non-traditional form of learning, a six-week immersive block model underpinned by guided, active learning pedagogy, on the academic success, satisfaction, and experiences of international postgraduate students at an Australian university. A convergent mix-methods design was used. Chi square tests and generalised estimating equations were used to compare the students’ success rates (N = 14,340) and unit satisfaction (N = 4903) in traditional semester and immersive block learning over five years. Qualitative insights were gathered via student focus groups (N = 9). Significant positive changes in success were observed after controlling for gender, age, discipline, and home region, with particularly strong positive effects for male and information technology students. Despite some challenges with depth of learning and placement organisation, focus group participants valued the clear timelines and flexible delivery, reporting that this supported effective time management and study-work–life-balance. Immersive block learning appears to be an effective strategy for transforming the experiences and outcomes of international postgraduate students in higher education. Full article

This study pioneers a fully remote sensing-based framework for mapping heatwave susceptibility, integrating the TabTransformer deep learning model with Particle Swarm Optimization (PSO) for robust hyperparameter tuning. The central question addressed is whether a fully remote sensing-driven, PSO-optimized TabTransformer can achieve accurate, scalable, and spatially detailed heatwave susceptibility mapping in data-scarce regions such as Central Asia. Utilizing ERA5-derived heatwave evidence and thirteen environmental and socio-economic predictors, the workflow produces high-resolution susceptibility maps spanning five Central Asian countries. Comparative analysis evidences that the PSO-optimized TabTransformer model outperforms the baseline across multiple metrics. On the test set, the optimized model achieved an RMSE of 0.123, MAE of 0.034, and R² of 0.938, outperforming the standalone TabTransformer (RMSE = 0.132, MAE = 0.038, R² = 0.93). Discriminative capacity also improved, with AUROC increasing from 0.933 to 0.940. The PSO-tuned model delivered faster convergence, lower final loss, and more stable accuracy during training and validation. Spatial outputs reveal heightened susceptibility in southern and southwestern sectors—Turkmenistan, Uzbekistan, southern Kazakhstan, and adjacent lowlands—with statistically significant improvements in spatial precision and class delineation confirmed by Chi-squared, Friedman, and Wilcoxon tests, all with congruent p-values of <0.0001. Feature importance analysis consistently identifies maximum temperature, frequency of hot days, and rainfall as dominant predictors. These advancements validate the potential of data-driven, deep learning approaches for reliable, scalable environmental hazard assessment, crucial for climate adaptation planning in vulnerable regions. Full article

The Okapi Wildlife Reserve (OWR) in northeastern Democratic Republic of the Congo represents both a biodiversity hotspot and the ancestral homeland of the Indigenous Mbuti and Efe peoples, whose livelihoods and knowledge systems are closely tied to forest resources. This study investigates how Indigenous knowledge and practices contribute to sustainable resource management under conditions of rapid socio-cultural transformation. A mixed-methods approach was applied, combining socio-demographic surveys (n = 80), focus group discussions, floristic inventories, and statistical analyses (ANOVA, logistic regressions, chi-square, MCA). Results show that hunting, fishing, gathering, and honey harvesting remain central livelihood activities, governed by customary taboos and restrictions that act as de facto ecological regulations. Agriculture, recently introduced through intercultural exchange with neighboring Bantu populations, complements rather than replaces traditional practices and demonstrates emerging agroecological hybridization. Nevertheless, evidence of biodiversity decline (including local disappearance of species such as Dioscorea spp.), erosion of intergenerational knowledge transmission, and increased reliance on monetary income indicate vulnerabilities. Multiple Correspondence Analysis revealed a highly structured socio-ecological gradient (98.5% variance explained; Cronbach’s α = 0.977), indicating that perceptions of environmental change are strongly coupled with demographic identity and livelihood strategies. Floristic inventories confirmed significant differences in species abundance across camps (ANOVA, p < 0.001), highlighting site-specific pressures and the protective effect of persistent customary norms. The findings underscore the resilience and adaptability of Indigenous Peoples but also their exposure to ecological and cultural disruptions. We conclude that formal recognition of Indigenous institutions and integration of their knowledge systems into co-management frameworks are essential to strengthen ecological resilience, secure Indigenous rights, and align conservation policies with global biodiversity and climate agendas. Full article

Many studies have examined how species are shifting their ranges poleward in response to climate change, using statistical approaches such as graphical analyses, t-tests, correlation analyses, and circular data methods. However, these methods are often constrained by assumptions of linearity or reliance on a single explanatory variable, which limits their ecological applicability. This study introduces a new statistical methodology to evaluate the significance of poleward range expansion, aiming to overcome these limitations and improve the robustness of ecological inference. We developed four parameterized nonlinear models—simple, multivariable, fixed, and transformed—to characterize the relationship between latitude and species richness across 1253 islands. Model parameters were estimated using the Gauss–Newton algorithm, and residuals were calculated as the difference between observed and predicted values. To test for distributional shifts, likelihood ratio tests were applied to the residuals, with statistical significance assessed using chi-square statistics and p-values derived from the −2 log-likelihood ratio. Finally, an intuitive indicator based on the fitted models was introduced to evaluate the direction of range shifts, thereby providing a direct means of identifying northward expansion trends under climate change. Applying this framework revealed significant poleward shifts of warm temperate and subtropical species (χ² = 52.4–61.3; p < 0.001). Among the four models, the multivariable model incorporating island area provided the best fit (AIC, BIC), reflecting its ability to account for collinearity. Taken together, these results underscore the robustness and ecological relevance of the methodology, demonstrating its utility for detecting species-specific range shifts and comparing alternative models under climate change. Full article

Early-onset colorectal cancer (EOCRC; <50 years) incidence is increasing most rapidly among Hispanic/Latino (H/L) populations. While the transforming growth factor–beta (TGF-β) pathway influences colorectal cancer (CRC) progression, its prognostic role in FOLFOX-treated EOCRC, particularly in H/L patients, is unclear. We analyzed 2515 CRC cases (H/L = 266; NHW = 2249) stratified by ancestry, age at onset, and FOLFOX treatment using Fisher’s exact, chi-square, and Kaplan–Meier analyses. We then applied AI-HOPE and AI-HOPE-TGFβ, conversational artificial intelligence (AI) platforms that integrate clinical, genomic, and treatment data, to perform complex, natural language-driven queries requiring multi-parameter integration. TGF-β pathway alterations occurred in 28–39% of H/L and 23–31% of NHW patients, with SMAD4 being the predominant driver. BMPR1A mutations were enriched in FOLFOX-treated EO H/L patients (5.5% vs. 1.1% EO NHW; p = 0.0272), while late-onset NHW non-FOLFOX cases had higher SMAD2/TGFBR2 mutation rates. In FOLFOX-treated EO H/L patients, TGF-β pathway alterations predicted poorer survival (p = 0.029); no survival impact was seen in other groups. SMAD4 mutations were less frequent in EO H/L than in EO NHW receiving FOLFOX (2.74% vs. 13.87%; p = 0.013). TGF-β pathway alterations may serve as ancestry- and treatment-specific biomarkers of poor prognosis in FOLFOX-treated EO H/L CRC. AI-enabled integration accelerated biomarker discovery, supporting precision medicine. Full article

Background and Objectives: Long COVID has been linked with persistent neurological symptoms, but data on its effects on acute stroke presentation, management, and outcomes remain unclear. This study aimed to compare the clinical profile, management, and short-term outcome of acute ischemic stroke patients with and without Long COVID. Materials and Methods: A retrospective cohort study was conducted on 132 patients who presented at admission with code stroke alert in our Emergency Department (ED). Out of those, 26 were identified to have the Long COVID condition and assigned to the Long COVID group, and 106 were without the Long COVID condition and assigned to the No Long COVID group. Baseline demographics, stroke severity by NIHSS (National Institutes of Health Stroke Scale), risk factors, admission symptoms, laboratory findings, Emergency department time targets, reperfusion treatments received, and outcomes between the two groups were compared. Results: There were no significant differences between the two groups in age, gender, baseline NIHSS scores, ED time targets, or laboratory values. The proportion of patients with Long COVID significantly increased among non-smokers (Fisher’s Exact Test chi-squared, p = 0.027). Also, patients suffering from Long COVID exhibited higher incidences of headache (19.2% compared to 5.7%, OR = 3.97, p = 0.040) and facial drooping (42.3% compared to 19.8%, OR = 2.97, p = 0.022). The mechanical thrombectomy was more frequent among the group with Long COVID (30.8% vs. 16.0%), but this difference was not statistically significant. More hemorrhagic transformations happened in the Long COVID group (26.9% vs. 14.2%, p = 0.143). Discharge rates and hospital length of stay in days were similar between groups. Conclusions: Long COVID patients did not present notable differences in emergency department time targets, baseline stroke severity, or short-term outcomes when presenting with code stroke alert. Nevertheless, specific clinical characteristics—such as elevated occurrences of headache and facial drooping—were more frequently observed in patients with Long COVID, alongside non-significant trends indicating a greater utilization of mechanical thrombectomy and increased rates of hemorrhagic transformation. These results imply that Long COVID may have a subtle impact on stroke presentation and potentially on underlying cerebrovascular susceptibility. Further prospective studies with larger sample sizes are necessary to investigate Long COVID’s long-term neurological and vascular consequences. Full article

Chaotic systems, governed by deterministic nonlinear equations yet exhibiting highly complex and unpredictable behaviors, have emerged as valuable tools at the intersection of mathematics, engineering, and information security. This paper presents a comparative study of the Lorenz and Rössler systems, focusing on their dynamic complexity and statistical independence—two critical properties for applications in chaos-based cryptography. By integrating techniques from nonlinear dynamics (e.g., Lyapunov exponents, KS entropy, Kaplan–Yorke dimension) and statistical testing (e.g., chi-square and Gaussian transformation-based independence tests), we provide a quantitative framework to evaluate the pseudo-randomness potential of chaotic trajectories. Our results show that the Lorenz system offers faster convergence to chaos and superior statistical independence over time, making it more suitable for rapid encryption schemes. In contrast, the Rössler system provides complementary insights due to its simpler attractor and longer memory. These findings contribute to a multidisciplinary methodology for selecting and optimizing chaotic systems in secure communication and signal processing contexts. Full article

Near-death experiences (NDEs) encompass transformative existential experiences that lead to religious change. Although most previous research focused primarily on Western contexts, there remains less understanding of individuals’ interpretations of NDEs in pluralistic societies such as the Philippines. Using a mixed-methods approach, this study examined the relationship between NDEs and spiritual transformation in a sample of 683 Filipino adults who acknowledged having NDEs. Quantitative data were assessed in terms of levels of religiosity, NDE occurrence, and perceived spiritual change according to different demographics. Statistical analyses included descriptive statistics, Chi-square tests, and logistic regression. For the qualitative analysis, narrative responses on the reason why their spirituality increased, stayed the same, or decreased were thematically classified. Six focused phenomenological narratives are noted: altruism and helping others, challenges to spirituality, increased and strengthened religious practices, no changes or decreased faith, reflection and growth, and validation of divine presence. In addition, while the majority reported increased or unchanged spirituality following their NDE, only age emerged as a significant predictor of perceived spiritual change. Overall, findings highlight how personal experience, identity, and cultural beliefs shape religious meaning-making after NDEs. This study offers a culturally grounded understanding of spiritual change and highlights the value of a mixed-methods approach in religious studies. Full article

Objectives: The present study mainly aimed to identify whether the envelope and triangular flaps affected wound healing and patient quality of life differently. Secondarily, the study aimed to investigate whether some anatomical and operational variables may also affect healing. Study design: A prospective randomized study was conducted with 56 fully impacted lower third molars, randomly divided into two groups, one treated with the envelope flap and the other with the bayonet flap. Qualitative variables were transformed into quantitative ones and then analyzed using independent samples t-tests or analysis of variance. An analysis of bivariate correlations with Pearson’s coefficient was also used. The chi-square test was used to verify the association between each flap and the categorical variables considered. Results: No statistically significant associations were found between flap types and dehiscence, although the mean dehiscence diameter was consistently greater in the envelope flap group. The maximum diameter of the dehiscence at 14 days was found to be significantly and negatively related to the 14-day wound healing indices. Analyses relating to the quality of life did not show significant associations. Conclusions: Despite some significant healing differences between the two considered flaps exist, they do not have relevant effects on the patient’s post-operative quality of life. Full article

Accurate prediction of oil and gas reservoirs requires precise river morphology. However, geological sedimentary images are often degraded by scattered non-structural noise from data errors or printing, which distorts river structures and complicates reservoir interpretation. To address this challenge, we propose GD-PND, a generative framework that leverages pseudo-labeled non-matching datasets to enable geological denoising via information transfer. We first construct a non-matching dataset by deriving pseudo-noiseless images via automated contour delineation and region filling on geological images of varying morphologies, thereby reducing reliance on manual annotation. The proposed style transfer-based generative model for noiseless images employs cyclic training with dual generators and discriminators to transform geological images into outputs with well-preserved river structures. Within the generator, the excitation networks of global features integrated with multi-attention mechanisms can enhance the representation of overall river morphology, enabling preliminary denoising. Furthermore, we develop an iterative denoising enhancement module that performs comprehensive refinement through recursive multi-step pixel transformations and associated post-processing, operating independently of the model. Extensive visualizations confirm intact river courses, while quantitative evaluations show that GD-PND achieves slight improvements, with the chi-squared mean increasing by up to 466.0 (approximately 1.93%), significantly enhancing computational efficiency and demonstrating its superiority. Full article

Power transformers are critical assets in electrical power systems, yet their fault diagnosis often relies on conventional dissolved gas analysis (DGA) methods such as the Duval Pentagon and Triangle, Key Gas, and Rogers Ratio methods. Even though these methods are commonly used, they present limitations in classification accuracy, concurrent fault identification, and manual sample handling. In this study, a framework of optimized machine learning algorithms that integrates Chi-squared statistical feature selection with Random Search hyperparameter optimization algorithms was developed to enhance transformer fault classification accuracy using DGA data, thereby addressing the limitations of conventional methods and improving diagnostic precision. Utilizing the R2024b MATLAB Classification Learner App, five optimized machine learning algorithms were trained and tested using 282 transformer oil samples with varying DGA gas concentrations obtained from industrial transformers, the IEC TC10 database, and the literature. The optimized and assessed models are Linear Discriminant, Naïve Bayes, Decision Trees, Support Vector Machine, Neural Networks, k-Nearest Neighbor, and the Ensemble Algorithm. From the proposed models, the best performing algorithm, Optimized k-Nearest Neighbor, achieved an overall performance accuracy of 92.478%, followed by the Optimized Neural Network at 89.823%. To assess their performance against the conventional methods, the same dataset used for the optimized machine learning algorithms was used to evaluate the performance of the Duval Triangle and Duval Pentagon methods using VAISALA DGA software version 1.1.0; the proposed models outperformed the conventional methods, which could only achieve a classification accuracy of 35.757% and 30.818%, respectively. This study concludes that the application of the proposed optimized machine learning algorithms can enhance the classification accuracy of DGA-based faults in power transformers, supporting more reliable diagnostics and proactive maintenance strategies. Full article

With the rapid digital transformation of healthcare services in India, this study investigates the factors influencing the behavioral shift from traditional hospital visits to e-health consultations. The primary objective was to analyze patient attitudes, satisfaction, and perceived barriers to adopting virtual healthcare, especially in urban and semi-urban settings. Methods: The methodology adopted in the study was descriptive, and a convenience sampling technique was used for data collection because the feasible times of the patients’ availabilities were taken into consideration for data collection. Both primary and secondary data were collected using questionnaires and literature. A sample size of 385 participants was used in this study. Various statistical tools, such as frequency, ANOVA, and Chi-square tests, were used to test the hypotheses. Results: It was observed from ANOVA and Chi-square tests that the factors for switching from traditional consultation to e-health services have a positive association. It was found that integrating data through influencing factors significantly (p < 0.001) improved decisions on e-health services. Conclusion: This study highlights the shift from in-person to e-health consultations driven by convenience, flexibility, and pandemic-related needs while acknowledging barriers such as digital literacy, infrastructure gaps, and trust issues. It recommends strategies, such as secure platforms, training, and integrated care models, for a more inclusive digital health future. Full article

Background and Aims: An appropriate surveillance system must be established to efficiently identify cases of intraductal papillary mucinous neoplasm (IPMN)-related malignant transformation. We analyzed the initial clinical background that affects long-term prognosis and narrowed the population for whom continued evaluation is inevitable. Methods: We included 1645 patients with IPMN treated at our hospital since 2010. We examined the types and timing of malignant transformation in terms of the worrisome features (WFs). The chi-squared test, log-rank test, and Cox proportional hazards model were used for the analysis (statistical significance at α = 0.05). Results: In total, 123 (7.5%) and 41 patients (2.5%) had IPMN-derived carcinoma (IPMN-DC) and concomitant pancreatic ductal adenocarcinoma (c-PDAC), respectively. Compared with IPMN-DC, a significantly higher proportion of c-PDAC patients were diagnosed with an advanced disease stage that developed earlier. The factors with significantly shorter time for IPMN-DC development were maximum cyst diameter (MCD) ≥ 30 mm, nonbranched type, main pancreatic duct (MPD) diameter ≥ 5 mm, and septal nodal structure (SNS) for IPMN-DC, and MCD ≥ 30 mm, main duct type, MPD ≥ 5 mm, SNS, cyst enlargement (≥2.5 mm/year), and abnormal CA19-9 levels for c-PDAC. Both groups could be significantly stratified by the number of WFs. A relative risk analysis revealed that SNS, MCD ≥ 30 mm, and MPD ≥ 5 mm were significant factors for IPMN-DC, whereas abnormal CA19-9 and SNS were significant for c-PDAC. Conversely, significantly more patients exhibiting these factors initially later developed IPMN-DC or c-PDAC. Conclusions: Ten percent of IPMN cases will develop IPMN-DC or c-PDAC, thereby requiring careful follow-up, especially in cases with SNS, abnormal CA19-9, and MCD ≥ 30 mm. Full article