Search Results (17,370)

This study examines the role of fintech adoption in enhancing financial inclusion in Qatar, with a particular focus on the mediating influence of access barriers. A structured questionnaire was administered to 220 respondents, of which 200 valid responses were retained for analysis after screening for completeness and outliers. The constructs of fintech adoption (FA), financial inclusion (FI), and access barriers (AB) were measured using validated multi-item scales adapted from prior literature. Measurement reliability and validity were confirmed through Cronbach’s alpha, composite reliability, and average variance extracted (AVE), alongside confirmatory factor analysis (CFA) for construct validity. A structural equation modeling (SEM) approach was employed to test the hypothesized relationships, using maximum likelihood estimation with bootstrap standard errors and confidence intervals. Model fit indices indicated excellent fit (χ² = 48.983, df = 51, p = 0.554; CFI = 1.000; TLI = 1.003; RMSEA = 0.000; SRMR = 0.036). Factor loadings were all significant (p < 0.001), supporting convergent validity. However, the structural paths from FA to FI (β = −0.020, p = 0.827), AB to FI (β = −0.077, p = 0.394), and FA to AB (β = 0.054, p = 0.527) were not significant. The indirect mediation effect of AB was also statistically insignificant (β = −0.004, p = 0.700). Full article

Objectives: The aim of this cohort study was to assess the associations of first-trimester energy-adjusted Dietary Inflammatory Index (E-DII) and maternal educational level with anemia during the second and third trimesters, as well as their potential interactions. Methods: This study enrolled 562 eligible pregnant women. Multivariable modified Poisson regression with robust variance was used to assess the associations of first-trimester E-DII and maternal educational level with anemia during the second and third trimesters. Restricted cubic splines (RCS) explored nonlinear associations, while additive and multiplicative interaction models evaluated the interaction between first-trimester E-DII and maternal education. Results: The participants, with a mean age of 29.45 ± 4.28 years, had an anemia incidence of 14.59% during the second and third trimesters. In fully adjusted models, higher first-trimester E-DII (T3 vs. T1) significantly increased anemia risk (RR = 2.30, 95% CI: 1.36–3.90). Lower education (below bachelor’s degree) independently elevated anemia risk (RR = 2.27, 95% CI: 1.52–3.39). RCS revealed no significant nonlinear relationship between the E-DII and anemia (p > 0.05). Although no significant multiplicative interaction was observed, a positive additive interaction was identified between first-trimester E-DII and educational level on the risk of anemia after adjustment for covariates, including age, ethnicity, pre-pregnancy BMI, employment, and baseline serum iron, among others. The measures of additive interaction were statistically significant: RERI = 4.64 (95% CI: 1.51–11.34), AP = 0.68 (95% CI: 0.26–0.86), and S = 4.91 (95% CI: 1.16–20.69) (all p < 0.05). Conclusions: First-trimester pro-inflammatory diets and lower educational attainment independently predicted anemia during the second and third trimesters and demonstrated a significant positive additive interaction. Combined nutritional and educational interventions integrated into prenatal care targeting pregnant women with limited education could effectively reduce anemia in pregnancy and improve perinatal outcomes. Full article

Decorrelation stretch is an image enhancement technique that emphasizes color differences, which also applicable to multispectral datasets. It transforms an image so that its color plane result is uncorrelated, with assigned variances. The standard algorithm may suffer from numerical instability. Moreover, it is not able to manage degenerate cases, where color planes are linearly dependent. In this paper, we review the theory behind decorrelation stretch and propose some alternative algorithms that resolve the issues of the standard approach. Full article

The accuracy of genomic selection has a significant impact on the selection of superior individuals in livestock. Studies have reported that integrating GWAS information can improve the accuracy of genomic prediction. In this study, phenotypic data, systematic environmental data, and genotypic data of important economic traits (cashmere yield, cashmere diameter, body weight, and cashmere length) of Inner Mongolia cashmere goats were utilized. Based on the results of a previous genome-wide association study that considered additive and dominance effects, the top 5%, top 10%, top 15%, and top 20% of loci were extracted as prior marker information. The genomic breeding values for each trait were estimated using the GBLUP–GA method based on GWAS prior information, and the accuracy of genomic prediction was further evaluated using a five-fold cross-validation method. The results showed that the contribution of significant loci to the genetic variance of each trait gradually increased with an increase of the number of integrated loci. The genetic variance contribution rates of significant loci to cashmere yield, cashmere diameter, body weight, and cashmere length were 64–72%, 47–57%, 76–82%, and 66–80%, respectively. The additive heritability estimates for cashmere yield, cashmere diameter, body weight, and cashmere length using GWAS prior information were 0.252–0.266, 0.297–0.580, 0.305–0.330, and 0.107–0.117, respectively. These values were higher than those obtained using the traditional G matrix constructed from all loci, with increases of 0.052–0.066, 0.007–0.29, 0.134–0.159, and 0.015–0.025, respectively. The results of genomic prediction accuracy showed that when 5% of the GWAS prior information was integrated, the highest genomic prediction accuracy was achieved for cashmere yield (0.8156), body weight (0.8361), and cashmere length (0.7571). When 20% of the GWAS prior information was integrated, the genomic prediction accuracy for cashmere diameter was 0.8074, which was significantly higher than that at other levels. Additionally, it was found that the dominance heritability for cashmere diameter, body weight, and cashmere length was very small and could be ignored when integrating GWAS prior information. Therefore, when integrating prior information for genomic selection of these traits, the influence of dominance effects can be disregarded. Full article

Background. The literature suggests that deficits in mentalization, operationalized as reflective functioning, are associated with emotional and behavioral dysregulation, including emotional eating and uncontrolled eating. These eating behaviors may be intensified by food addiction, yet its moderating role within this framework has not been thoroughly investigated. This study examined whether the relationship between deficits in reflective functioning and uncontrolled eating is mediated by emotional eating, and whether food addiction diagnosis moderates this pathway. Methods. A cross-sectional survey was administered to 559 adults from the general population. Participants completed self-report measures assessing reflective functioning (RFQ-8), emotional and uncontrolled eating (TFEQ-R-18), and food addiction (YFAS 2.0). A moderated mediation model was tested using conditional process analysis with 10,000 bootstrap resamples. Results. Deficits in reflective functioning were positively associated with emotional eating (β = 0.155, p < 0.001), which in turn were associated with uncontrolled eating (β = 1.314, p < 0.001). Food addiction diagnosis significantly moderated the relationship between emotional eating and uncontrolled eating (β = 0.744, p < 0.001). Specifically, individuals with food addiction exhibited a stronger association between emotional eating and uncontrolled eating compared to those without food addiction. The indirect effect from reflective functioning to uncontrolled eating via emotional eating was significantly stronger among individuals with food addiction than those without. The overall model explained 57.3% of the variance in uncontrolled eating. Conclusions. Food addiction diagnosis amplifies the pathway from emotional eating to uncontrolled eating, originating from deficits in reflective functioning. These findings highlight the clinical importance of targeting mentalization processes and emotional eating in interventions for disordered eating behaviors, particularly among individuals with food addiction. Full article

Background: Swallowing difficulties (dysphagia) are highly prevalent among older adults and significantly contribute to malnutrition, dehydration, and poor health outcomes. Healthcare aides (HCAs), as frontline caregivers in long-term care, play a pivotal role in supporting residents’ nutritional intake. However, validated tools to evaluate their competencies in nutrition-related eating support are lacking. Methods: A cross-sectional study was conducted to develop and validate a competency-based questionnaire assessing healthcare aides’ knowledge, attitudes, and behaviors toward nutrition-focused eating support. Core domains, including oral function care, safe feeding practices, food texture modification, and nutrition safety, were identified through a systematic literature review and refined using a two-round modified Delphi process involving 26 experts. A 47-item questionnaire was then administered to 202 HCAs in Taiwan. Psychometric testing included item analysis, KR-20, Cronbach’s α, confirmatory factor analysis (CFA), composite reliability (CR), and average variance extracted (AVE). Results: The final instrument demonstrated strong content validity. The knowledge domain achieved acceptable reliability (KR-20 = 0.61), while the attitude and behavior domains showed excellent internal consistency (Cronbach’s α = 0.98). CFA confirmed good structural validity (χ²/df = 3.86, CFI = 0.93). CR and AVE values further supported construct validity. Conclusions: This nutrition-centered questionnaire is a valid and reliable tool to assess HCAs’ competencies in providing eating support. It offers a foundation for identifying training needs and designing educational programs aimed at preventing malnutrition and enhancing person-centered mealtime care in long-term care facilities. Full article

Northern corn leaf blight (NCLB), caused by the fungus Exserohilum turcicum, is one of the most significant foliar diseases in maize worldwide, with its severity being highly influenced by environmental conditions. An effective strategy used to control NCLB involves screening diverse maize germplasm for resistant sources through multi-environment inoculation assays, ultimately aiming to develop resistant varieties. This study systematically evaluated 711 maize germplasm accessions with rich genetic diversity. The evaluation was conducted under four location–year environment combinations (Shangluo, Shaanxi Province, China in 2014–2015 and Xinzhou, Shanxi Province, China in 2021–2022) using artificial inoculation with physiological race 123N (or races 1, 2, 3, N). The results showed that the estimated variances of genotype, environment, and genotype-by-environment interaction were all highly significant (p < 0.01). Significant correlations (p < 0.01) were observed among replicates within each environment, with correlation coefficients (r) ranging from 0.67 to 0.88. At the Xinzhou trial in 2021, four replicates were inoculated with four physiological races (1, 2, 3, and N), revealing highly significant correlations (r = 0.77–0.80, p < 0.01) among them. The disease severity of the tropical germplasm was significantly lower (p < 0.001) than that of the temperate germplasm. Among the temperate subgroups, the PA and PB (groups A and B germplasms derived from modern US hybrids) subgroups exhibited lower disease severity, with the PB subgroup showing the lowest, while the Iodent and Reid subgroups exhibited higher susceptibility. The disease severity responses to the four physiological races were highly positively correlated (r = 0.77–0.80, p < 0.001), and their correlations with the composite inoculation (race 123N) ranged from 0.65 to 0.83. Based on the resistance evaluations across four location–year environment combinations, the 711 maize accessions were classified into five categories: 20 were highly resistant, 236 resistant, 205 moderately resistant, 237 susceptible, and 13 highly susceptible. The findings indicate that the tropical germplasm and the temperate PB subgroup are major sources of NCLB resistance. Full article

Air pollution with particulate matter (PM), recognized by the EU and WHO as a significant factor affecting human health, is subject to standards. Exceeding these standards on a daily or annual basis poses an increased health risk. This article presents an analysis of data from 2022 to 2024 from the administrative area of Pleszew (Poland), which, in 2023, ranked second in the country in terms of annual PM₁₀ concentration [µg/m³]. The main cause of the poor air quality is identified as so-called “low emissions” resulting from residential heating using high-emission coal-fired boilers. The methods used in this analysis not only identified the main causes of pollutant emissions but also demonstrated the seasonal impact of these sources on air quality, both on an annual and daily basis. The analysis utilized statistical tools such as a mixed linear regression model and Tukey’s post hoc tests performed after analysis of variance (ANOVA). The obtained regression model of PM₁₀ concentration on the outside air temperature (defining the intensity of operation of heating devices) clearly indicates the predicted air pollution. Dividing the day into three time intervals proved to be an effective analytical tool enabling the identification of periods with the highest risk of high PM₁₀ concentrations. The highest average PM₁₀ concentration values were recorded in the autumn and winter months between 3:00 PM and 9:00 PM. The developed methods can serve as fundamental tools for local government authorities, guiding further energy policy directions for the study area to improve the identified situation. At the same time, daily and hourly air pollution analysis clearly confirmed the characteristics of inefficient heat sources, which will allow residents to protect their health by avoiding spending time outdoors during peak particulate matter concentration hours. Until the energy situation in the region changes, this will continue. Full article

Hymenaea martiana is a species native to Brazil. It has ecological value, contributes to forest restoration, and is economically important because of the use of its wood and fruits. However, it is frequently exploited. Therefore, understanding genetic diversity becomes essential for guiding conservation strategies as well as ecological restoration actions in the face of climate change and anthropogenic pressures. Thus, this study aimed to evaluate the intraspecific diversity of 160 H. martiana mother plants on the basis of morphological descriptors of fruits and seeds and physiological indicators of seed quality, identifying the most discriminating characters. Eighteen traits were analyzed and subjected to analysis of variance and the Scott–Knott test (p < 0.05), with estimates of heritability and the ratio between genetic and environmental coefficients of variation. Phenotypic divergence was obtained via the Mahalanobis distance (D²) and grouped via UPGMA, whereas the relative contribution of the traits was estimated via the Singh method. The results revealed that seed length and weight, emergence speed index, and shoot dry mass were the most effective descriptors for discriminating parent plants. Multivariate analysis revealed the formation of eleven phenotypically distinct groups, demonstrating high variability. These findings support the selection of superior genotypes and representative seed collection, as well as practical initiatives such as the formation of germplasm banks, the selection of breeding stock for forest nurseries, and reintroduction programs. Thus, the data obtained offer technical and scientific support for biodiversity conservation and ecosystem recovery in the semiarid region of Brazil. Full article

The use of recycled coarse aggregate (RCA) concrete and polypropylene fibers (PPFs) presents a sustainable alternative in concrete production. However, the non-linear and interactive effects of RCA and PPF on both fresh and hardened properties are not yet fully quantified. This study employs Response Surface Methodology (RSM) and the Random Forest (RF) algorithm with K-fold cross-validation to predict the combined effect of using recycled coarse aggregate (RCA) as a partial replacement for natural coarse aggregate and polypropylene fiber (PPF) on the engineering properties of RCA-PPF concrete, addressing the critical need for a robust, data-driven modeling framework. A dataset of 144 tested samples obtained from literature was utilized to develop and validate the prediction models. Three input variables were considered in developing the proposed prediction models, namely, RCA, PPF, and curing age (Age). The examined responses were compressive strength (CS), tensile strength (TS), ultrasonic pulse velocity (UPV), and water absorption (WA). To assess the developed models, statistical metrics were calculated, and analysis of variance (ANOVA) was employed. Afterwards, the responses were optimized using optimization in RSM. The optimal results of responses by maximizing TS, CS, and UPV and minimizing WA were achieved at a PPF of 3% by volume of concrete and an RCA of approximately 100% replacing natural coarse aggregate, highlighting optimal reuse of recycled aggregate, with an AGE of 83.6 days. The RF model demonstrated superior performance, significantly outperforming the RSM model. Feature importance analysis via SHAP values was employed to identify the most effective parameters on the predictions. The results confirm that ML techniques provide a powerful and accurate tool for optimizing sustainable concrete mixes. Full article

Background/Objectives: Professional competencies and personal mastery are key dimensions for the well-being of health professionals and the quality of care. In physiotherapy, where organizational complexity is common, job satisfaction depends on both clinical skills and resilience. While these aspects have been explored in nursing, evidence for physiotherapists is limited. This study aimed to (1) assess perceived competencies and personal mastery in Italian physiotherapists; (2) analyze their relationship with job satisfaction; and (3) examine the factorial structure of the Multidimensional Scale of Competences. Methods: A cross-sectional study was conducted with 481 physiotherapists working in various care settings. Data were collected using the 25-item Multidimensional Scale of Competences, the 7-item Personal Mastery Scale, and a single job satisfaction item, all on a 5-point Likert scale. Analyses included descriptive statistics, Pearson correlations, logistic regression, and exploratory factor analysis (Principal Component Analysis with five components). Results: Participants had a mean age of 31.1 years (SD = 8.3) and 7.3 years of professional experience (SD = 7.7); gender distribution was balanced. Most held a master’s (44.5%) or bachelor’s degree (36.8%). Job satisfaction was high, with 95% reporting moderate to very high satisfaction. Competencies showed a mean of 4.16 (SD = 0.95; α = 0.86), while Personal Mastery averaged 3.52 (SD = 1.29; α = 0.60). Competencies significantly predicted job satisfaction (OR = 8.37, p = 0.003), whereas Personal Mastery did not. Factor analysis identified five domains—technical–clinical, communicative, collaborative, ethical, and educational—explaining 50.3% of variance. Conclusions: Italian physiotherapists report high competencies and moderate personal mastery. Job satisfaction is strongly linked to competencies, highlighting their central role in professional well-being. Results support the importance of continuous professional development and organizational strategies that enhance competencies and resilience. Full article

This study examined the influence of brand presence and discrete emotions on consumer acceptance and purchase intent of commercial chicken noodle soups. A total of 324 evaluations across three soup categories (chunky, low-sodium, condensed) were conducted under blind and unblinded conditions using a 42-term emotion lexicon. Brand presence did not exert moderate-to-large effects, though subtle brand-specific differences cannot be excluded. Instead, three emotions, “satisfied,” “disgusted,” and, for condensed soups, “bored,” emerged as the strongest predictors, together explaining a substantial proportion of variance in liking and purchase intent. Many other positive emotions clustered around “satisfied,” highlighting a parsimonious set of dominant drivers. Quiet positive emotions such as contentment, peacefulness, and warmth consistently aligned with both acceptance and purchase intent. These findings extend prior research by showing that consumer responses consolidate around a limited set of emotions, underscoring that evoking subtle, self-focused positive feelings may be more effective in comfort food marketing and product development than reliance on brand identity or nostalgia. Full article

Durum wheat is a vital wheat species cultivated worldwide for human consumption, ranking second to bread wheat. The Ethiopian durum wheat allele pool shows wide gene diversity; however, limited improvement work has been done to exploit this diversity. Thus, this study aimed to assess the genetic variability, heritability, and interrelationship among different phenotypic traits in 210 recombinant inbred lines (RILs) using an alpha lattice design with two replications. The analysis of variance revealed a significant difference for all the measured traits. The phenotypic coefficient of variation (PCV) was greater than the genotypic coefficient of variation (GCV) for all the characters, which reflects that the existing range of variability within the genotypes was not only due to the varying influence of genotype but also the environment. A correlation analysis disclosed that grain yield was positively related to the traits of plant height and 1000-kernel weight, suggesting that selecting these traits could enhance yield. Path analysis revealed that days to booting, maturity, and 1000-kernel weight directly affect grain yield. Among the measured traits, early developmental traits revealed higher broad-sense heritability. The findings of this study highlight high genetic diversity among Ethiopian durum wheat genotypes, opening up opportunities to integrate these materials into future wheat-breeding programs through introgression with other germplasm sources in Ethiopia and beyond. Full article

The hospitality industry’s rapid adoption of AI service robots has revealed significant variability in consumer acceptance, highlighting the need for personality-based implementation strategies rather than one-size-fits-all approaches. This study extended the AIDUA (Artificial Intelligence Device Use Acceptance) model by integrating Big Five personality traits and robot design characteristics to understand AI service robot acceptance among South African hospitality consumers. A convergent mixed-methods design combined structural equation modeling of survey data (n = 301) with natural language processing analysis of qualitative responses to examine personality-acceptance pathways and consumer concern themes. Results demonstrated that neuroticism negatively influenced performance expectancy (β = −0.284, p < 0.001), while openness enhanced hedonic motivation and preference for humanoid robots (β = 0.347, p < 0.001). Privacy concerns partially mediated the neuroticism-rejection relationship, while transparency interventions significantly improved acceptance among high-neuroticism consumers (effect size d = 0.98). Four distinct consumer segments emerged: Tech Innovators (23.1%), Pragmatic Adopters (31.7%), Cautious Sceptics (28.4%), and Social Moderates (16.8%), each requiring tailored robot deployment strategies. The extended AIDUA framework explained 68.4% of variance in acceptance intentions, providing hospitality operators with empirically validated guidelines for matching robot types to guest personality profiles, optimizing guest satisfaction while minimizing resistance through culturally sensitive implementation strategies. Full article

This study explores the time-varying influence of Environmental, Social, and Governance (ESG) factors on asset pricing in China’s A-share market from 2017 to 2023, integrating investor heterogeneity categorized as ESG-unaware (Type-U), ESG-aware (Type-A), and ESG-motivated (Type-M). taxonomy. It adopts a linear regression model with seven control variables (including firm systematic risk, asset turnover ratio, and ownership concentration) to quantify ESG’s marginal effect on stock returns. Annual regressions (2017–2022) reveal distinct ESG coefficient shifts: insignificant negative coefficients in 2017–2018, significantly positive coefficients in 2019–2020, and significantly negative coefficients in 2021–2022. Heterogeneity analysis across five non-financial industries (Utilities, Properties, Conglomerates, Industrials, Commerce) shows industry-specific ESG effects. Portfolio performance tests using 2023 data (stocks divided into eight ESG groups) indicate that portfolios with medium ESG scores outperform high/low ESG portfolios and the traditional mean-variance model in risk-adjusted returns (Sharpe ratio) and volatility control, avoiding poor governance risks (low ESG) and excessive ESG resource allocation issues (high ESG). Overall, policy shocks and institutional maturation transformed the market from ESG indifference to ESG-motivated pricing within a decade, offering insights for stakeholders in emerging ESG markets. Full article