This study explores the development of a Maintenance Management Model to enhance Overall Equipment Effectiveness (OEE) in workshop machinery, focusing on the Training Workshops (TWs) at the College of Engineering and Technology (CoET), University of Dar es Salaam (UDSM). The research addresses challenges such as frequent equipment breakdowns and the absence of structured maintenance and stock control systems, which hinder practical training and academic performance in engineering education. The study highlights the importance of adopting effective maintenance practices to improve equipment availability, performance, and quality. A mixed-methods approach was applied, involving structured questionnaires, interviews, observations, and record reviews. Using Multiple Linear Regression (MLR), a data-driven model was developed to quantify the influence of critical variables, including time-based financial planning, technician capability, equipment availability, maintenance response time, and technology integration on OEE. The findings reveal that organisational support, proactive maintenance practices, timely intervention, and competent human resources significantly affect OEE. The developed conceptual model demonstrated high predictive validity, achieving a Mean Absolute Error (MAE) of 0.5413 and Root Mean Square Error (RMSE) of 0.5973 on a 1–10 OEE scale. These results confirm the reliability of the model in predicting OEE improvements. The study concludes that implementing a structured maintenance management system, specifically adapted to academic workshops, can substantially improve machine uptime and operational efficiency. The proposed model provides a practical framework for CoET to strengthen workshop performance and, in turn, improve the quality of engineering education. The research recommends further validation of the model in other institutions and integration of real-time monitoring technologies to broaden its applicability and effectiveness.

Traditional animal-based preclinical models, including xenografts and genetically engineered mice, have been used for assessing pharmacodynamics, toxicity, efficacy, and safety for decades. Despite their limited ability to mimic human tumor heterogeneity, immune interactions, and microenvironmental complexity, over 90% of oncology candidates that succeed in animal studies fail in clinical trials. The New Approach Methodologies (NAMs), which include patient-derived organoids, organ-on-chip platforms, and AI-driven computational models, provide human-relevant solutions that can improve predictive validity, mechanistic insight, and ethics. Through these technologies, it will be possible to replicate tumor biology specific to patients, to support co-clinical trial designs, and to facilitate biomarker discovery while reducing animal testing. Several recent regulatory reforms, including the Food and Drug Administration (FDA) Modernization Act 2.0 and the European Medicines Agency’s NAM qualification framework, have established clear pathways for the integration of validated NAMs into preclinical drug evaluation. Critically evaluating the scientific rationale, comparative performance, and regulatory context of key NAM platforms in oncology, this review highlights opportunities for synergistic integration, technical refinement, and global harmonization in order to accelerate the development of clinically effective cancer therapeutics based on preclinical findings.

Parental emotion regulation is crucial for parent-child interactions and child psychological outcomes. However, limited research has examined whether the Regulation of Emotions in Parenting Scale (REPS) functions equivalently across ethnoracial groups, raising concerns about measurement bias. This study evaluated the psychometric properties of the REPS, including measurement invariance, reliability, and differential validity, across an ethnically diverse sample of n = 1,408 parents. Using multigroup confirmatory factor analysis, we tested configural, metric, and scalar invariance. While configural and metric invariance were supported, full scalar invariance was not. A partial scalar invariance model, allowing three item intercepts to vary, showed acceptable fit. McDonald's omega coefficients indicated strong internal reliability across all subscales and racial groups. Multiple regression analyses tested differential validity and found no significant interaction effects, supporting consistent predictive validity. These findings confirm the REPS as a reliable tool for diverse populations. Future research should explore REPS applicability across sociocultural contexts.

Sulforaphane, a natural antioxidant rich in cruciferous vegetables, has emerged as a promising dietary supplement for autism spectrum disorder (ASD). However, its therapeutic efficacy remains controversial, and the pharmacological mechanisms are not fully elucidated. Eligible randomized controlled trials were retrieved from PubMed, Web of Science, Embase, and Cochrane Library databases. Review Manager 5.4 was used for meta-analysis and bias risk assessment. Network pharmacology, Mendelian randomization, GEO data analyses, molecular docking, and molecular dynamics simulation were employed to explore the mechanisms of sulforaphane in ASD. Six trials involving 333 participants were included in the meta-analysis. Pooled results demonstrated that both 4-5 weeks and 8-10 weeks of sulforaphane supplementation significantly decreased the scores on the Social Responsiveness Scale compared to placebo controls. No significant difference was observed in the incidence of adverse events. Network pharmacology identified 10 core targets of sulforaphane in ASD, including AKT1, EGFR, HSP90AA1, SRC, CASP3, STAT1, MAPK1, MMP9, MAPK8, and JAK2. These targets were implicated in the PI3K-Akt signaling pathway, MAPK signaling pathway, Chemokine signaling pathway, Chemical carcinogenesis - reactive oxygen species, TNF signaling pathway, Th17 cell differentiation, mTOR signaling pathway, and IL-17 signaling pathway. Mendelian randomization further revealed an inverse association between STAT1 levels and ASD risk. GEO transcriptomic data provided independent validation for the network pharmacology predictions. The binding energies between sulforaphane and the top 10 core targets are all ≤ -4.0kcal/mol. Molecular dynamics simulations further validated the stable interaction between MMP-9 and sulforaphane. Sulforaphane may serve as an efficacious and safe adjunctive therapy for ASD, mediated by its anti-oxidant and anti-inflammatory effects along with the modulation of autophagy. CRD42025635045.

Background/Objectives: Cochlear implantation is the most widely used treatment option for patients with severe to profound hearing loss. Despite being a relatively standardized surgical procedure, cochlear implant (CI) outcomes vary considerably among patients. Several studies have attempted to develop predictive models for CI outcomes but achieving accurate and generalizable predictions remains challenging. The present study aimed to evaluate whether simple and complex statistical and machine learning models could outperform the Null model based on various pre-CI implantation variables. Methods: We conducted a retrospective analysis of 236 ears with postlingual profound sensorineural hearing loss (SNHL) and measurable residual hearing (WRSmax > 0%) at the time of implantation. The median postoperative word recognition score with CI (WRS65(CI)) was 75% [Q1: 55%, Q3: 80%]. The dataset was divided using a 70:15:15 split into training (n = 165), validation (n = 35) and test (n = 36) cohorts. We evaluated multiple modeling approaches: different Generalized Linear Model (GLM) approaches, Elastic Net, XGBoost, Random Forest, ensemble methods, and a Null model baseline. Results: All models demonstrated similar predictive performance, with root mean squared errors ranging from 26.28 percentage points (pp) to 30.74 and mean absolute errors ranging from 20.62 pp to 23.75 pp. Coefficients of determination (R2) ranged from −0.468 to −0.073. Bland–Altman analyses revealed wide limits of agreement and consistent negative bias, while Passing–Bablok regression indicated calibration errors. Nonetheless, all models incorporating predictors significantly outperformed the Null model. Conclusions: Increasing model complexity yielded only marginal improvements in predictive accuracy compared with simpler statistical models. Pre-implantation clinical variables showed limited evidence of predictive validity for CI outcomes, although further research is needed.

Application of the 2HELPS2B score with short-duration EEG for seizure prediction in acute impaired consciousness: A multicenter study.

On Mars, the amount of ultraviolet C (UVC) radiation that reaches the surface is sufficiently deleterious for life as we know it. However, it has been predicted that some ancient lakes on Mars had high concentrations of Fe3+, an ionic species known for a high absorption of UVC radiation. Some models of UV attenuation have already been established; however, there is a lack of reliable simulations that make the connection between this radiation absorption in an aqueous medium and its impact on the viability of microorganisms. This work proposes a simple model to estimate the viability of microorganisms irradiated in solution with different concentrations of Fe3+ and constrains the lethal UVC dose in these conditions. In experimental assays, the median lethal dose of Saccharomyces boulardii increased consistently with the model's predictions, which thereby demonstrated the model's predictive validity. This ability was then used in a case study to simulate the viability of life in a Fe3+-containing lake on ancient Mars. Although the actual conditions of this kind of environment are not known, the simulations showed that lakes with small water columns that contain Fe3+ should have been able to protect growing microorganisms. This model enhances the ability to assess potentially habitable conditions on ancient Mars. Key Words: Photoprotection-UVC radiation-Fe3+ ions-Mars-Astrobiology. Astrobiology xx, xxx-xxx.

The present study focused on optimizing encapsulation parameters for Lactobacillus acidophilus through a composite formulation consisting of sodium alginate, carrageenan, omega-3 fatty acids, and phytosterol. Response Surface Methodology (RSM), integrated with a Central Composite Design (CCD), was used to evaluate interactive effects of four independent variables on two key response parameters—encapsulation efficiency and probiotic viability. The optimized formulation was identified as 0.25% sodium alginate, 0.1% carrageenan, 0.375% omega-3 fatty acids, and 0.275% phytosterol, achieving high encapsulation efficiency of 96.8% and a probiotic viability of 8.21 log CFU/g. Statistical models demonstrated strong predictive validity (R² > 0.98), indicating a high degree of reliability in the optimization framework. Overall, the results highlight how biopolymer–lipid interactions might work in concert to enhance the stability and proper distribution of probiotic systems.

The predictive validity of machine learning models depends on the training data. In some cases, training data contains historical, social, or demographic inequalities, which leads algorithms to reproduce unfair results. This paper proposes a fairness-constrained stacking meta-learning approach for reducing bias in classification by aggregating a set of classifiers through a constrained ensemble learning scheme. A set of base classifiers, including Decision Tree, Naive Bayes, Support Vector Machine (SVM), and LightGBM, are trained and evaluated on the Adult Census Income dataset using both predictive and fairness metrics. The final meta-model is constructed as an aggregation of only the fair-performing models, while models failing to meet the fairness threshold are excluded. Learned weights are then optimized to maximize the F1-score while maintaining fairness constraints. Experimental results demonstrate that the proposed method achieves predictive performance (Accuracy = 0.91, F1-score = 0.82) while substantially reducing disparity between demographic groups (EOD = 0.03 for sex and 0.04 for race). These findings indicate that fairness-aware stacking ensembles can provide a solution for mitigating algorithmic bias through an aggregation framework that balances accuracy and fairness.

Dampening of positive affect (PA) constitutes a transdiagnostic risk and maintenance factor for affective dysregulation in various psychopathologies, including depression. However, the motives underlying this PA downregulation strategy remain unclear, even though they may be highly relevant for improving traditional psychological treatments. This study examined whether avoidance of negative emotional contrasts (NECs) and diminished preference for positive emotions were predictive of dampening. The latter was operationalised as low pro- and high contra-hedonic emotion regulation (ER) goal endorsement. An adult community sample (N = 159) completed an online survey, and multiple linear regressions were conducted to examine the predictive validity of both factors, after controlling for age, gender, and repetitive negative thinking (RNT). Higher levels of NEC avoidance and higher contra-hedonic ER goal endorsement were consistently found to uniquely predict concurrent dampening levels, above and beyond age, gender and RNT. Crucially, inclusion of both factors in the same regression model still yielded evidence for the unique predictive validity of NEC avoidance. Findings support the possibility that dampening is motivated by NEC avoidance rather than solely by emotional preferences. Study limitations are noted, and implications for future research and clinical practice are discussed.

ABSTRACT The present study aimed to validate the Bergen Facebook Addiction Scale (BFAS). In study 1, personality dimensions were employed to establish criterion and predictive validity on a sample size of 120 young adults in the age group of 18–23 years who participated voluntarily. The EFA revealed a 12 items, three-factor solution (Time, Withdrawal and Mood Modification) with 65.86% of variance. To validate correlations were examined between total BFAS score, BFAS dimensions, total NEO-FFI score, NEO-FFI dimensions, BIS and BAS. Hierarchical Regression indicated that age and gender had predictive efficiency of 15% whereas in step 2 Age, Gender, Neuroticism, Extraversion, Openness, Agreeableness, Conscientiousness, BIS, BAS Drive, BAS Fun Seeking and BAS Reward Responsiveness predicted Facebook Addiction to 12.3%. In study 2, good factor solution was revealed thus, confirming the three-factor solution from study 1. The overall results of this studies indicated that a three-factor solution was more relevant for Indian adolescents.

Wastewater-based epidemiology (WBE) monitors pathogens in sewage to estimate community disease trends and prevalence, often capturing cases missed by clinical reporting. WBE models use shedding data to facilitate WBE implementation and interpretation; however, their performance is uncertain because "true" case numbers are unknown. Hence, we compared model-predicted wastewater genome loads and detection rates of pepper mild mottle virus (PMMoV) and Carjivirus with values derived from wastewater data in literature. We found that predicted and observed wastewater DNA/RNA load distributions overlapped by 86.1% for PMMoV and 83.2% for Carjivirus, and that detection probabilities are within 5% of reported values in 14/15 and 13/14 studies, respectively, supporting the model as a robust tool for predicting wastewater detection likelihoods and guiding WBE applications. However, the median observed wastewater load exceeded the predicted distribution median in over half of all studies, suggesting that available shedding data underestimate wastewater concentrations due to higher shedding by a small population subset ("supershedders") or sewage network virus accumulation─using average shedding rates and WBE data without accounting for these factors would overestimate prevalence by 8.17-fold (PMMoV) and 3.75-fold (Carjivirus). This comparative analysis can be applied to other targets to improve WBE prevalence estimates and public health utility.

Social health has been increasingly recognized as an important determinant of dementia progression and quality of life. The Social Network Index (SNI), developed by Cohen, is widely used to assess social networks as a proxy for social health. This study aimed to adapt the SNI cross-culturally and validate its psychometric properties for use among Indonesians with dementia. A cross-sectional study was conducted at the Memory Clinic of Dr. Sardjito General Hospital, Yogyakarta, Indonesia, involving 56 individuals with mild to moderate dementia. The original 12-item SNI was translated and culturally adapted according to WHO guidelines. Construct validity was examined using Confirmatory Factor Analysis (CFA). Composite Reliability (CR) and Average Variance Extracted (AVE) were used to assess internal consistency. The finalized modified Indonesian version of the SNI (m-SNI-INA), consisting of 10 items (2 excluded due to low factor loadings), demonstrated strong construct validity with factor loadings >0.5 and good model fit (GFI=0.958, AGFI=0.934, CFI=1.000, RMSEA=0.000). Reliability was high (CR=0.91; AVE=0.53). The m-SNI-INA is a valid, reliable, and culturally adapted tool for assessing social networks in people with dementia in Indonesia. Further studies should examine its predictive validity in larger populations.

This study develops and validates a multi-dimensional identity-based scale for measuring LGBTQ+-friendly destination images, grounded in the sexual orientation, gender identity, and gender expression dimensions. Through qualitative and empirical testing with 654 international tourists in Thailand, the scale demonstrates strong psychometric properties, including reliability, convergent validity, discriminant validity, and nomological validity. The overall LGBTQ+-friendly destination image significantly predicts tourists’ satisfaction, word-of-mouth, and revisit intentions, highlighting its predictive validity. Notably, LGBTQ+ friendliness has universal relevance; while friendliness toward diverse sexual orientations and gender expressions is a central determinant for all tourists, including non-LGBTQ+ travelers, friendliness toward diverse gender identities holds particular importance for LGBTQ+ travelers. The scale is designed for broad application in assessing LGBTQ+-friendly destination images in diverse tourism contexts. This validated tool offers destination marketing organizations a comprehensive framework to assess and enhance LGBTQ+ friendliness, benefiting both LGBTQ+ and non-LGBTQ+ travelers through its association with general safety and openness.

Enhancing predictive validity in the english placement test: evidence and insights from a Thai public university

Purpose In recent years, supply chain risk management capabilities (SCRMC) has become integral to enhancing resilience, mitigating disruptions and ensuring consistent operations in supply chains. SCRMC has gained significant attention from researchers, leading to different fragmented conceptualizations. This study aims to reconcile this fragmentation by proposing a theory-driven reconceptualization of the construct and a multidimensional scale that captures SCRMC’s complex nature. Design/methodology/approach A two-step mixed methodology was adopted. First, drawing on existing supply chain risk management literature and expert discussions, scale items were generated and refined using the Q-sort method. Second, the scale was validated using survey data from 301 supply chain professionals across various industries in the USA. Findings The findings reveal three distinct yet interrelated dimensions – warning capability, robustness capability and resilience capability – which exhibit strong psychometric properties. The scale also demonstrates high levels of validity and reliability and improves predictive validity compared to previously developed scales. Originality/value This study offers a comprehensive and holistic understanding of SCRMC and serves as a reliable tool for measuring SCRMC, providing a foundation and analytical consistency for both practical application and future research.

The Stress and Adversity Inventory for Adults (Adult STRAIN) systematically assesses the count, severity, timing, types of lifetime stressors, primary life domains, and core social-psychological characteristics. The study aimed to replicate findings from the original Adult STRAIN validation study with a sample of middle-aged and older African American adults. Participants from the Health among Older Adults Living in Detroit study [N = 200; M(SD) = 67.48 years old (8.53), range = 50-89; 74.50% female], completed two home visits, daily diaries, and questionnaires. Pearson correlations and regression models assessed concurrent, discriminant, predictive, and comparative predictive validity (vs. the Perceived Stress Scale-4 and Risky Family Questionnaire) of the Adult STRAIN to stress-related (e.g. subjective physical health) and expected unrelated outcomes (e.g. personality variables), with and without covariates. The present study provides evidence of the Adult STRAIN as a valid measure of cumulative lifetime stressor exposure in older African American adults.

Background: Polygenic risk scores (PRS) offer promising avenues for stratifying myocardial infarction (MI) risk and informing precision prevention. However, most PRS are derived from European-ancestry datasets, raising concerns about predictive validity and clinical equity across ancestrally diverse populations. Goals/Aims: To evaluate the predictive performance, calibration, and clinical utility of MI-related PRS across global ancestries, and to identify strategies that enhance transethnic applicability. Methods: We conducted a systematic review and meta-analysis in accordance with PRISMA guidelines. PubMed, Embase and Scopus were searched through March 2025 for studies reporting ancestry-stratified PRS performance for MI. Primary outcomes included area under the curve (AUC), odds ratio (OR) per standard deviation of PRS, observed-to-expected event ratios (O/E), and reclassification metrics (net reclassification improvement [NRI], integrated discrimination improvement [IDI]). Random-effects models were used for pooled estimates, with subgroup analyses by ancestry (European, African, South Asian, East Asian, Hispanic/Latino, admixed) and PRS construction method. Meta-regression, heterogeneity (I2), and risk-of-bias assessments were applied. Publication bias was evaluated via funnel plots and Egger’s test. Results: Forty-six studies encompassing 1.32 million individuals across six ancestral groups were included. In European cohorts, pooled PRS AUC was 0.74 (95% CI: 0.72–0.76), compared to 0.63 (95% CI: 0.60–0.66) in African and 0.66 (95% CI: 0.64–0.68) in South Asian populations (p < 0.001 for heterogeneity). Calibration was poorer in non-European groups (O/E >1.4), indicating systemic overestimation of risk. While PRS improved net reclassification in European cohorts (NRI: +12.1%), clinical utility was limited in African ancestry (NRI: +2.3%). Meta-regression revealed that ancestry-specific allele frequency adjustment and inclusion of multi-ancestry training datasets significantly improved PRS performance (p < 0.01). Conclusion: Current PRS for MI demonstrate reduced accuracy and suboptimal calibration in non-European populations, undermining clinical utility and exacerbating genomic health disparities. These findings highlight the urgent need for globally inclusive genomic data and ancestry-aware PRS optimization. Implementation of strategies is critical for equitable risk prediction tools and for aligning precision cardiology with global clinical practice.

Responsible drinking is a common term used by a variety of stakeholders. Although many people and organizations discuss responsible drinking, its meaning remains unclear. Researchers have begun to scrutinize the concept, critically questioning its utility, definition, and distinction from other alcohol-related constructs; however, its measurement has remained limited. Accordingly, we present a series of studies describing the development of the Responsible Drinking Inventory (RDI), a new 18-item self-administered measure of responsible drinking beliefs and behaviors. We report upon the creation and the psychometric properties of the RDI across six primary studies. Examinations of the RDI indicated appropriate reliability and validity, including convergent and divergent validity, as well as known groups and predictive validity. The RDI appears to provide information that is consistent with alcohol safety-oriented measures, such as the Protective Behavioral Strategies Scale, and distinct from alcohol harm measures, such as the Alcohol Dependence Scale. The RDI predicts acute consequences of drinking behavior 3 months in the future. This new measure provides unique insights into the nature of responsible drinking and a concise, yet comprehensive way to assess this concept. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The genetic architecture of essential tremor (ET) remains incompletely understood. A key challenge is translating genome-wide association study (GWAS) loci into specific effector genes to elucidate disease mechanisms and develop targeted therapies. To implement a multistage computational framework to prioritize high-confidence candidate genes for ET and to assess these predictions against publicly available, patient-derived transcriptomic data. We employed a convergent evidence strategy to prioritize genes, integrating cross-tissue (UTMOST) and tissue-specific (FUSION) transcriptome-wide association studies (TWAS) with gene-based association tests (MAGMA). Prioritized genes were subjected to causal inference analyses (summary-data-based Mendelian randomization [SMR] and colocalization), co-expression network analysis (GeneMANIA), and pharmacogenomic analysis (DGIdb). We leveraged spatial transcriptomics to characterize gene expression patterns across cortical layers and cell types. Finally, we validated computational predictions using two independent post-mortem brain datasets from ET patients and controls. Our prioritization pipeline identified 12 high-confidence candidate genes. Co-expression network analysis revealed 83.3% of candidates exhibit functional relationships, forming three modules centered on RNA processing (NRBP1), metabolic regulation (SLC5A6), and nucleotide synthesis (CAD). Pharmacogenomic analysis demonstrated 66.7% of candidates possess therapeutic target potential. Spatial transcriptomics revealed preferential expression in cortical Layer 5 pyramidal neurons. However, validation in post-mortem cerebellar tissue showed no significant differential expression. Our study provides a robust pipeline for ET gene prioritization and puts forward a novel cortical hypothesis for the disease. The discordance between strong computational predictions and their lack of validation in available patient tissue highlights a critical gap in the field. © 2025 International Parkinson and Movement Disorder Society.