Aims To explore teachers' perspectives on incorporating oral health education within schools, since its statutory introduction in the National Curriculum in England in 2020.Methods An online survey including open and closed questions was designed and distributed via postal invitations to selected schools in North West England, and via social media between September 2024 and January 2025. Quantitative data were analysed descriptively, while directed content analysis was used for free-text responses.Results Fifty-four responses were received. Oral health education was reported as inconsistent with different methods and frequencies of delivery reported; 21% (n = 11) teachers taught the topic less than once a year. Challenges included lack of curriculum time (44%, n = 23), and resources to help deliver (27%, n = 14) or plan the teaching (19%, n = 10). Most teachers (93%, n = 50) expressed confidence in their oral health knowledge, but free-text responses highlighted that teachers valued dental professionals' involvement with schools, and parental engagement to reinforce oral health at home.Conclusions Oral health education in schools remains inconsistent despite statutory requirements. Sustainable resources and multi-agency partnerships can help embed oral health promotion within a whole-school framework.
This study describes the development and feasibility testing of a digital health guide (DHG) to streamline genetic education, reduce barriers, and promote informed genetic testing (GT) decisions among cancer survivors. This study reports on the DHG's development, usability testing, acceptability, feasibility, and preliminary efficacy in improving genetic counseling (GC) and GT access for cancer survivors. Guided by the Ottawa Decision Support Framework, the DHG prototype was developed following community engagement with cancer patients and at-risk relatives from diverse sociodemographically backgrounds. It was refined through user (content-focused) and usability (functionality-focused) testing. Pilot trial participants provided data through semi-structured interviews and usability assessments. Qualitative data were analyzed using the Framework Method. The preliminary impact of the DHG on GC and GT uptake, and informed decision-making, was assessed in a feasibility and accessibility trial. The Chatbot Usability Questionnaire score for the DHG was 70.3 (IQR = 12.5), indicating good acceptability. The DHG also facilitated GT uptake (73.3%) compared to enhanced usual care (EUC; 7.7%). Pretest GC was requested by 1 of 13 patients in the EUC arm, while no request (0 of 15 patients) was made in the DHG arm. Users' feedback led to clearer language, improved navigation, and stronger messaging regarding data security. DHG participants had lower decisional conflict (33.37 ± 21.09) and decision regret (17.5 ± 16.50) than those in the EUC arm (53.25 ± 22.66 and 37.08 ± 17.38, respectively). The digital intervention is feasible, acceptable, and a promising strategy for expanding GT access and promoting informed decision-making. Further testing in a definitive randomized controlled trial is warranted. Clinical trial registration. This study was preregistered at the NIH clinical trial registry ( https://clinicaltrials.gov/study/NCT06184867 ).
To address the dilemma of homogeneous talent training and the efficiency bottleneck of human resource management in universities, this study proposes an innovative personalized training framework integrating artificial intelligence, big data, and deep learning. Based on the 18-dimensional full-cycle behavior dataset of 5,000 students and OULAD dataset, a multimodal heterogeneous data fusion pipeline is constructed. This study adopts Generative Adversarial Network (GAN) for data imputation and bias optimization, designs Hierarchical Attention Graph Neural Network (HA-GNN) to capture hierarchical correlations among features, and uses Long Short-Term Memory (LSTM) to model temporal behavior patterns. The experimental results demonstrate that, under 10 independent repeated runs with random seed variation, the Hierarchical Attention Graph Neural Network-Long Short-Term Memory (HA-GNN-LSTM) model achieves lower prediction error on the academic performance prediction task, with a Mean Absolute Error (MAE) of 4.2 ± 0.3. Compared with the Temporal Fusion Transformer (TFT) baseline model, MAE is reduced by 31.1%. Welch's two-tailed t-tests based on independent run results remain statistically significant after Holm-Bonferroni multiple comparison correction [Formula: see text]. The Normalized Discontinued Cumulative Gain at Top 5 (NDCG @ 5) index of personalized recommendation system reaches 0.90, which verifies the effectiveness of spatio-temporal feature modeling. At the management application level, the improvements in advisor allocation response time and resource idle rate are derived from simulation experiments based on historical data replay, rather than online deployment in real campus management systems. The simulation results demonstrate that, under established constraints and historical sample distributions, advisor allocation response time could be reduced by 60% and resource idle rate could be decreased by 63.4%. These findings indicate the framework's potential for optimizing educational resource allocation. However, its managerial benefits require further validation through subsequent real-world deployment and long-term follow-up studies.
Policy Points Chronic absence should be recognized as a public health indicator and early warning sign that systems are failing to meet the developmental, social, and health needs of students. Improving student attendance requires cross-sector policy action across education, health, and public health to address the structural and social determinants of chronic absence. A prevention-oriented public health approach is essential, focusing on root causes that schools cannot address alone such as poor health, housing instability, and unreliable transportation. Chronic absence, defined as missing more than 10% of time in school, has risen sharply in the United States following the COVID-19 pandemic and now affects more than one in four students. It reflects unmet health and social needs and is patterned by deep structural inequalities. Both short- and long-term consequences include adverse impacts on educational attainment, health, and social outcomes. Despite this, chronic absence remains largely framed and addressed as an education-sector problem, limiting the scope and effectiveness of current responses. This perspective synthesizes interdisciplinary evidence from education, public health, and child development literature, drawing on ecological and life course frameworks to reconceptualize chronic absence as a public health issue. We develop a conceptual model integrating multilevel determinants of attendance across individual, family, school, community, and structural domains, and identify implications for policy and cross-sector action. Viewing chronic absence through a public health lens reframes it from a purely educational outcome to a signal of unmet need and a multidimensional indicator of system performance. Attendance patterns reflect the interaction of health, social, and structural factors that lie largely outside of the control of schools. Current approaches often emphasize individual responsibility, while overlooking the broader conditions that shape attendance. Reframing chronic absence in this way underscores the need for coordinated cross-sector interventions that address underlying determinants. Positioning chronic absence as a public health priority enables a more coherent response. We propose three principles to guide action: (1) use school attendance data as a vital sign of student and system well-being; (2) develop strategic partnerships to align goals and drive progress; and (3) develop strengths-based policies and programs to prevent chronic absence. Without this shift, efforts to reduce chronic absence are likely to remain fragmented and insufficient to achieve equitable improvements in child health and educational outcomes.
Medication adherence in systemic lupus erythematosus (SLE) remains suboptimal worldwide due to complex psychosocial and structural barriers. However, few studies have explored clinical experts' perspectives on facilitators and barriers to adherence, particularly within the Chinese healthcare context. This study aimed to explore clinical experts' perspectives on medication adherence among patients with SLE and to identify multidimensional factors influencing adherence. Semi-structured interviews were conducted with clinical experts and transcribed verbatim. Data were analysed using thematic analysis with NVivo 12. Themes were organized into five predefined dimensions: patient-related, disease-related, treatment-related, medical care-related, and socioeconomic-related factors. Each theme was classified as either a facilitator or a barrier. Visual analytic methods, including heatmaps, word clouds, frequency matrices, Sankey diagrams, and bipartite network graphs, were used to illustrate expert consensus and thematic relationships. A total of 38 themes were identified, including 18 facilitators and 20 barriers. All 16 experts identified symptom relief or mild disease, complex treatment regimens, distressing medication side effects, and high medication costs among the most frequently reported barriers. The most commonly reported facilitators included effectively managed side effects and personalized health education. Themes were unevenly distributed across dimensions, with a higher concentration of facilitators in medical care-related factors and a predominance of barriers within treatment-related factors. Variations in thematic emphasis may reflect differences in clinical roles and levels of patient engagement among experts. Medication adherence in patients with SLE is influenced by multiple interrelated factors. Improving adherence may benefit from comprehensive, multi-level strategies, including patient education, simplified treatment regimens, continuity of care, and supportive health policies.
Accurately predicting student employment outcomes remains a significant challenge in educational data mining, particularly given the increasing diversity of student backgrounds and the dynamic nature of labor market demands. This study proposes an explainable hybrid deep learning framework that integrates multi-source heterogeneous data, including academic records, demographic profiles, financial attributes, and engagement in scientific or organizational activities, to perform multi-class employment prediction. The framework employs recursive feature elimination for precise feature selection, a bi-directional long short-term memory network with attention mechanisms to capture temporal academic patterns, and a tree-structured Parzen estimator-optimized XGBoost classifier to model complex feature interactions. To enhance model interpretability, SHAP values are utilized to quantify the contribution of each feature to the final prediction. Extensive experiments on a real-world vocational college dataset demonstrate that the proposed model consistently surpasses competitive baselines across multiple evaluation metrics, including accuracy, macro-F1, AUC, and Cohen's kappa. The results confirm the framework's capability to effectively leverage both longitudinal educational trajectories and static characteristics for accurate and interpretable prediction of employment outcomes.
Sexual and gender minority youth (SGMY) experience a heightened risk of HIV acquisition due to barriers to HIV prevention, specifically connected to a lack of comfort in discussing sexual identity and practices with healthcare providers (HCPs). Decision-aid tools that support communication and shared decision-making may improve both access to and uptake of numerous HIV prevention modalities among SGMY. The study aimed to inform the decision-making process of HIV prevention modalities for HCPs and SGMY, providing key information about HIV prevention modalities with PrEPChoices, a web-based decision aid tool. Our study recruited two participant groups, HCPs (N = 15) and SGMY (N = 18). Eligible HCPs held a Doctor of Medicine (MD), Doctor of Osteopathic Medicine (DO), Nurse Practitioner (NP), or Physician Assistant/Associate (PA) degree, were licensed to prescribe medications in at least one United States state, had provided care to patients aged 15 to 24 within the past month, and were 18 years of age or older. Eligible SGMY were assigned male sex at birth, reported sexual attraction to and/or sexual behavior with cisgender men or transgender women in the past six months, and were 15 to 24 years of age. Participants completed a semi-structured Webex interview focused on the clarity and relevance, presentation and usability, and application of PrEPChoices, a web-based decision aid tool to support HIV prevention modalities selection among HCPs and SGMY. Interview transcripts were coded using an iteratively developed codebook. Findings were thematically analyzed within a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis framework. Thirty-three participants enrolled (N = 33), including 15 HCPs and 18 SGMY. Strengths included: (1) support for HIV prevention-based decision making; (2) utilization of the filtering tool to select preferred HIV prevention modalities; and (3) intuitive website design. Weaknesses included: (1) gaps in needs and literacy levels between HCPs and SGMY; (2) limited visual design appeal and inclusive representation; and (3) limited in-tool features for comparing HIV prevention modalities. Opportunities included: (1) potential for multimodal dissemination; and (2) expanding external resources provided. Threats included: (1) challenge of integrating PrEPChoices into clinical practices; and (2) ability to stand out compared to other online HIV prevention education resources. HCPs and SGMY highlight the value of PrEPChoices as a web-based decision aid tool to enhance HIV prevention modality selection among SGMY. Our results emphasize the critical role PrEPChoices can play in reducing barriers to sexual health education among SGMY, improving the patient-provider relationship with the PrEP landscape, and strengthening HIV prevention among SGMY.
With the rapid integration of AI into higher education, teachers' psychological responses are critical for technology adoption. This study examines AI self-efficacy and AI anxiety among university teachers in a Chinese university. It investigates the relationship between these two constructs and explores differences based on gender, age and academic major. A quantitative survey was administered to 350 teachers selected through stratified random sampling based on major. Results showed that both AI self-efficacy and AI anxiety were significantly above the neutral midpoint (M = 4.48, SD = 0.76; M = 4.35, SD = 0.85). AI self-efficacy was strongly and negatively associated with AI anxiety (r = - 0.59, p <0.01) and remained a significant negative predictor after controlling for gender, age, and major (β = - 0.112, p = .026). Female teachers reported higher anxiety and lower self-efficacy than male teachers, whereas computer science teachers reported the highest self-efficacy and the lowest anxiety. These findings suggest that university teachers may feel simultaneously capable of using AI and apprehensive about its broader implications. The study provides evidence from Chinese higher education and highlights the value of differentiated institutional support that addresses both teachers' confidence in using AI and their professional concerns.
Hepatitis C virus self-testing (HCVST) has emerged as a potential strategy to expand testing among key populations. We assessed the feasibility of HCVST in cisgender men-who-have-sex-with-men (cis-MSM) and transgender women (TGW) on pre-exposure prophylaxis (PrEP). This cross-sectional study included cis-MSM or TGW attending a PrEP consultation in Rio de Janeiro (Brazil). Participants performed HCVST using blood-based and oral-fluid kits on the same day under observation. Difficulties, errors and assistance during HCVST were recorded. Re-reading and re-testing concordance [Kappa(k)] and values/preferences were assessed. A total of 250 participants (88% cis-MSM, age = 34 [IQR,28-41] years, 42% with high education level) were included. The main steps where participants requested assistance (95%CI) for blood-based HCVST were to add buffer [35.6%(29.9-41.8)] and to collect blood sample with the dropper [34.0%(28.4-40.1)]. The main error during oral fluid HCVST was incorrect collection of oral fluid [29.6% (95%CI,24.2-35.6)]. A total of 62.4% (95%CI,56.2-68.2) and 28.8% (95%CI,23.5-34.8) participants needed assistance in at least one step of blood-based and oral fluid HCVST, respectively. Lower education level was associated with higher odds of needing assistance for blood-based HCVST [aOR = 2.07 (95%CI,1.99-3.59),p = 0.009]. Re-reading and re-testing k-indexes were 0.92 and 0.89 for blood-based, and 1.00 and 0.75 for oral fluid HCVST, respectively. More than 95% of people felt safe; would repeat or would recommend HCVST. A total of 46.4% (95%CI,40.3-52.6) preferred oral fluid versus 36.4% (95%CI,30.6-42.6) who preferred blood-based. A relatively high proportion of participants needed assistance, especially for blood-based HCVST. Despite these challenges, high re-reading and re-testing agreements were observed and HCVST was well-accepted.
This study is the first to examine awareness, knowledge, and attitudes towards dyslexia among the general public in Mainland China. Using an online survey, we collected data on (a) demographics, (b) awareness of dyslexia and other common neurodevelopmental conditions, (c) knowledge of dyslexia causes, symptoms, functional impact, and assessment/intervention, and (d) attitudes towards dyslexia. A total of 1,008 adults from across all major regions of China completed the suvey. Around 70% reported having heard of dyslexia, lower than awareness of autism and ADHD, but higher than that of developmental language disorder. Respondents answered 49% of knowledge items correctly and demonstrated greater knowledge of dyslexia symptoms, followed by functional impact and causes, with weaker knowledge of dyslexia assessment and intervention. Dyslexia awareness and knowledge were higher among younger adults, females, urban residents, non-parents, and those with higher education and income, with some variation across regions. Attitudes towards dyslexia were generally positive, following similar demographic patterns. Although greater awareness was associated with higher levels of knowledge, only dyslexia knowledge uniquely predicted attitudes towards dyslexia after controlling for demographic factors. These indings highlight the need for culturally relevant, awareness-raising campaigns that promote a more accurate understanding of dyslexia. The findings should be interpreted in light of the limitations linked to sampling bias and methods of data collection. Future studies should include the voices of individuals with dyslexia to better understand how social and cultural factors in China influence their lived experiences across development.
The COVID-19 pandemic created major challenges to students' mental health. Research that university students continue to experience mental health challenges due to the changes in higher education and the increased technology integration in the post-COVID-19 era. The objective of this study was to examine association between academic stress related to technology integration and students' mental health problems (MHPs), with digital fatigue as a mediating variable and loneliness as a moderating variable. The study's research design was a cross-sectional survey. This study was guided by Job-Demands Resources (JD-R) Theory. The data were collected online from 388 university students through the post-COVID-19 University Students' Well-being Scale (PC-USWS). Academic stress was significantly associated with mental health problems (β = .639, SE = .04, 95% CI [.566, .713], t = 17.12, p < .001, R2 = .43). Digital fatigue partially explained the association between academic stress and mental health problems (ab = .18, SE = .04, 95% CI [.107, .261], Z = 4.64, p < .001, R2 = .51). The interaction between academic stress and loneliness was also statistically significant (βAS*Lo = .11, SE = .05, 95% CI [.008, .215], Z = 2.16, p = .03). The study suggests a more effective use of digital tools and greater socialization among students to improve their mental health. This study had several practical, policy, and research implications.
Authenticity-related strain has been increasingly recognized as a factor associated with psychological distress; however, the processes underlying this association remain insufficiently understood among university students. This study examined the direct and indirect associations between authenticity-related strain and psychological distress among Chinese university students, with self-concept clarity and emotional suppression evaluated as potential indirect pathways. A descriptive cross-sectional design was employed to collect self-reported data from full-time undergraduate and postgraduate students (N = 5,202) enrolled in accredited public and private universities across Henan Province, China. A stratified recruitment approach was used to enhance sample diversity. Data were collected through a secure online survey administered between March and April 2025. Validated scales assessed authenticity-related strain (self-alienation subscale), self-concept clarity, emotional suppression, and psychological distress (DASS-21). Structural equation modeling with bootstrapping (5,000 resamples) was used to estimate direct and indirect associations, adjusting for age and gender. Authenticity-related strain was positively associated with psychological distress (β = 0.213, p < 0.001). Both self-concept clarity (β = 0.142, 95% CI [0.091, 0.231]) and emotional suppression (β = 0.152, 95% CI [0.102, 0.243]) demonstrated significant indirect associations. A sequential pattern of indirect associations was also observed, such that authenticity-related strain was associated with lower self-concept clarity, which was associated with higher emotional suppression and greater psychological distress (β = 0.058, 95% CI [0.009, 0.111]). These indirect associations remained robust in sensitivity analyses, requiring substantial unmeasured confounding to attenuate the observed associations. The final model explained 41% of the variance in psychological distress (R2 = 0.41). This study extends current understanding of authenticity-related challenges by identifying self-concept clarity and emotional suppression as key variables associated with psychological distress among university students. The findings highlight the potential relevance of identity-related and emotion-regulation processes to student mental health and may inform future research and context-sensitive support strategies in higher education settings.
Activities of daily living (ADL) are associated with declines in physical fitness and subjective health. However, it remains unclear as to whether ADL impairments are related to specific components of physical fitness and health variables. Therefore, we examined differences between community-dwelling older persons with versus without ADL impairments with regard to various physical fitness components, physical complaints as well as subjective and objective health outcomes. Cross-sectional study among 254 participants aged ≥ 55 years [51% female; 84 with ADL impairments; mean (SD) age 62.1 (6.6) years] enrolled in the population-based "Gesundheit zum Mitmachen" study in Southwestern Germany. ADL, physical complaints and subjective health status were assessed using a self-report questionnaire, physical fitness (cardiorespiratory fitness, strength, gross motor coordination, flexibility, and functional mobility) was assessed using a fitness test battery, and objective health status was derived from health exam performed by a physician. We ran analyses of covariance, adjusted for age, sex, body mass index and education. Participants with ADL impairments had statistically significantly worse subjective (p < 0.001) and objective (p < 0.001) health and reported more physical complaints (p < 0.001) compared to those without ADL impairments. Regarding physical fitness, ADL-impaired participants performed worse in 10 out of 12 variables. The findings provide additional evidence that ADL impairments are related to decreased objective and subjective health and physical fitness in older community-dwelling adults. Future studies employing more comprehensive, preferably objective, ADL assessments and considering cognitive impairments, which may also impact ADL performance, are warranted.
Tobacco 21 (T21) laws effectively reduce youth tobacco use by preventing initiation. This study examines their impact on body weight among young adults aged 18-20. Using 2009-2019 Behavioral Risk Factor Surveillance System data and a two-way fixed-effects difference-in-differences (DID) design, we find limited evidence of broad weight changes in either direction across the BMI distribution. Obesity declines due to modest weight reductions concentrated near the upper BMI threshold, with no significant changes in overweight status or average BMI. Event study shows that the obesity decline emerges in the first post-T21 year and attenuates afterward. Results are robust to alternative specifications, including an imputation DID approach addressing staggered adoption. Effects are driven by "never smokers", consistent with a prevention-based pathway, and are more pronounced among males and non-White individuals, with heterogeneity observed across education levels in the upper BMI tail. Supplemental analyses using Youth Risk Behavior Survey data show reduced adverse weight outcomes among high schoolers aged 18+. T21 laws increase exercise, improve diets, and reduce sedentary behavior, underage drinking, marijuana use, and mental distress. Overall, T21 laws avoid the typical cessation-related weight gain and modestly improve weight outcomes among at-risk young adults, suggesting broader public health benefits beyond tobacco prevention.
Brain health disorders (BHDs) remain a concern for people with HIV (PWH) despite antiretroviral therapy access and viral suppression. The contribution of HIV to brain health is often obscured by comorbidities in high-income settings which are less prevalent in sub-Saharan Africa. Neurofilament light chain (NfL), a biomarker of axonal injury, may offer insight into underlying mechanisms. 338 virally-suppressed PWH and 250 people without HIV (PWoH) completed a Research Domain Criteria-informed battery assessing cognitive, sensorimotor, and social processing systems. Demographically-adjusted norms were derived from PWoH. Serostatus differences in impairment (≥ 1SD below the mean) were examined using multivariable logistic regression. Additional models examined associations between NfL (plasma, cerebrospinal fluid [CSF]) and task performance. PWH were similar to PWoH in age (43.9 vs. 43.5yrs), sex (female, 54 vs. 46%), and education (6.1 vs. 5.8yrs). PWH had higher odds of impairment in the cognitive control and attention (Color Trails, Symbol Digit) and sensorimotor (Grooved Pegboard) domains. Plasma NfL was associated with sensorimotor impairment in both groups. Similar trends held in CSF NfL but did not reach statistical significance, likely due to sample size (n = 85). Cognitive and sensorimotor difficulties are common in PWH in Rakai, independent of typical Western confounders. The profile of impairment differs from reports in high-income settings where declarative memory deficits are often observed. NfL was associated with sensorimotor impairment, suggesting that NfL may capture ongoing axonal injury and motor system vulnerability in PWH and PWoH. These findings suggest NfL's potential as a biomarker of sensorimotor impairment in sub-Saharan Africa.
This study advances current discourse by introducing a novel analytic framework-the global quest for genital beauty-to clarify how sociocultural ideals of vulva appearance shape motivations, meanings, and policy responses surrounding female genital cutting (FGC) and female genital cosmetic surgery (FGCS). Using a theory‑informed narrative synthesis of literature published between 2015 and August 2025 across PubMed, Scopus, and Web of Science, supplemented by seminal theoretical works and agency reports. The analysis demonstrates that although FGC and FGCS arise within distinct sociocultural systems, both are influenced by gendered expectations about bodily propriety and aesthetic norms. The review identifies substantial variations within each category: FGC encompasses procedures with differing degrees of tissue alteration and risk, while FGCS includes a growing array of elective cosmetic interventions shaped by media, pornography, and clinical marketing. An estimated 230 million women and girls in countries with available survey data have undergone FGC, requiring a 27‑fold acceleration in progress to meet the 2030 Sustainable Development Goal target, while clinical organizations report rising demands for FGCS despite limited evidence of benefit and acknowledged risks. By applying the genital beauty framework, this study reframes the comparison between FGC and FGCS, highlighting both their divergences and their shared entanglement with globalized beauty norms. This perspective supports more precise ethical, clinical, and policy guidance, including rights‑based strategies to accelerate abandonment of FGC and reduce non‑therapeutic demand for FGCS through regulation, norm‑change interventions, and education about genital diversity.
This paper examines sustainable development and tribological performance of fly ash reinforced AA8011 aluminium matrix composites by stir casting. Composites with 0, 4, 8 and 12 wt.% fly ash content were made and tested under dry sliding conditions using pin on disc apparatus. The Taguchi L16 orthogonal array with the aid of the multi-response optimization through the use of the Grey Relational Analysis (GRA) was used to analyze wear rate, frictional force, and coefficient of friction (COF). The experimental outcomes indicate that the wear rate was 0.00447-0.00813 mm3/m, 4.84-9.89 N frictional force, 0.228-0.711 Coefficient of Friction (COF). The best parameters were found to be 8 wt.% fly ash, 30 N load, 3 m/s sliding velocity and 3200 m sliding distance which gave a maximum Grey Relational Grade (GRG) of 0.831. The results of ANOVA showed that applied load had the greatest contribution (48.95%), and then followed by the sliding distance (23.57%), but fly ash content had a smaller but significant contribution (3.12%). The confirmatory test showed that there was a small error of 2.6% in the value of predicted and experimental GRG. SEM analysis revealed the shift towards the severe abrasive wear in the base alloy to the mild oxidative wear with the stable mechanically mixed layer in higher reinforcement level. The research confirms that the wear resistance with the use of fly ash is enhanced, and sustainable development of materials through the effective use of industrial waste is improved.
Color difference detection remains a critical challenge in textile manufacturing, where traditional visual inspection and offline measurement methods suffer from subjectivity, low efficiency, and delayed feedback. This study emphasizes engineering integration for online industrial fabric inspection rather than proposing a single new color-difference algorithm. The proposed system integrates a custom-designed optical acquisition platform with a lightweight color analysis pipeline, including bilateral filtering for noise suppression, K-means clustering for representative color extraction, RGB-to-CIELab color space conversion, and perceptually weighted [Formula: see text] computation. The system was deployed on an actual textile production line and evaluated using ten fabric rolls with different colors and materials. Experimental results show roll-level agreement with manual inspection in the tested samples and indicate the feasibility of continuous monitoring of chromatic variations along the fabric length. The proposed system provides a practical engineering solution for automated textile color quality control and may support production-line decision making while reducing dependence on subjective visual inspection in industrial environments.
In the context of mating, individuals of the same sex often act as rivals in the pursuit, attraction, and retention of desirable partners. This study explored the relationships between intrasexual competition and various aspects of human mating psychology across three countries: Canada, Hungary, and Indonesia. A total of 661 adults (including women, men, non-binary, and gender-unspecified individuals) completed an online questionnaire assessing sensation seeking, aggression, beauty-enhancing behavior, openness to cosmetic surgery, sexual motivation, and sociosexuality. Hypotheses were tested via Bayesian multilevel modeling. Measurement invariance testing and alignment procedures were conducted to address potential cross-cultural non-invariance. Results indicated that the superiority enjoyment component of intrasexual competition showed consistent positive associations with the examined psychological variables. Associations involving inferiority frustration were generally weaker and less consistent. The findings for openness to cosmetic surgery, sociosexuality, and aggression replicate prior research, whereas the links with sensation seeking, beauty-enhancing behavior, and sexual motivation extend the literature. Cross-national comparisons revealed no significant country differences in superiority enjoyment, whereas Canadian participants scored significantly lower than Hungarian and Indonesian participants in inferiority frustration, with no significant difference between the latter two groups. Overall, the findings suggest that intrasexual competition-particularly its superiority enjoyment component-shows consistent associations with mating-relevant psychological traits across cultural contexts, even when mean levels differ between societies.
Accurate MRI-based quantification of abdominal adipose tissue is critical for metabolic risk assessment but is limited by labor-intensive manual segmentation and the extensive labeled-data dependency of deep learning models. We introduce Dynamic Fuzzy-Gaussian Modeling (DynFGM), a fully automated, unsupervised framework for adipose tissue segmentation designed to operate without requiring training data, expert annotations, or anatomical priors. DynFGM was developed and validated on 776 abdominal MRI scans, using a benchmark cohort (n = 20) with expert ground truth segmentations and a large validation cohort (n = 756). The pipeline dynamically adapts its complexity for each MRI slice by using image intensity kurtosis to select the optimal number of tissue clusters. A fuzzy C-means (FCM) algorithm then initializes a Gaussian mixture model (GMM) for segmentation, providing a mathematically interpretable alternative to black-box neural networks. Finally, a radial distance transform with an adaptive cutoff differentiates subcutaneous (SAT) from visceral adipose tissue (VAT). Performance was evaluated against the ground truth using dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). DynFGM achieved strong spatial agreement with expert annotations (mean DSC: 0.94) and high volumetric reliability (ICC: 0.82-0.97), comparable to reported inter-expert variability. The framework reduced mean absolute volumetric error by 92.6% compared to standard FCM (482.2 cm3 vs. 6547.5 cm3). On the large validation cohort (n = 756), the method demonstrated operational stability, producing physiologically plausible adipose distributions with a low technical failure rate (3.0%). Furthermore, the computational throughput averaged 13.6 s per participant on standard CPU (Intel® Core™ i9, 3.0 GHz) hardware. DynFGM provides an interpretable and data-efficient approach for abdominal adipose tissue phenotyping, offering an alternative to supervised deep learning in settings where labeled data are limited or unavailable. By bridging the gap between manual segmentation and labeled-data-dependent AI, this unsupervised framework offers a scalable tool for population-level research and may serve as an automated labeling tool to facilitate future model development.