Search Results (9,723)

This study developed and validated an integrated theoretical framework combining the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to investigate how driving assistance technologies (DATs) influence risky driving behaviors among young urban Chinese drivers. Based on this framework, we proposed and tested several hypotheses regarding the effects of psychological and technological factors on risky driving intentions and behaviors. A survey was conducted with 495 young drivers in Shaoguan, Guangdong Province, examining psychological factors, technology acceptance, and their influence on risky driving behaviors. Structural equation modeling revealed that the integrated TPB-TAM explained 58.3% of the variance in behavioral intentions and 42.6% of the variance in actual risky driving behaviors, significantly outperforming single-theory models. Attitudes toward risky driving (β = 0.287) emerged as the strongest TPB predictor of behavioral intentions, while perceived usefulness (β = −0.172) and perceived ease of use (β = −0.113) of driving assistance technologies negatively influenced risky driving intentions. Multi-group analysis identified significant gender and driving experience differences. Logistic regression analyses demonstrated that model constructs significantly predicted actual traffic violations and accidents. These findings provide theoretical insights into risky driving determinants and practical guidance for developing targeted interventions and effective traffic safety policies for young drivers in urban China. Full article

Food addiction is characterized by compulsive consumption and impaired control over highly palatable foods, with neurobiological mechanisms analogous to substance use disorders. The Yale Food Addiction Scale 2.0 (YFAS 2.0) is the most widely used instrument to assess these symptoms; however, its psychometric properties have not been validated in Mexican adults. To perform the cross-cultural adaptation of the YFAS 2.0 and validate its psychometric properties for the identification of food addiction in the Mexican adult population. A cross-sectional study was conducted in 500 Mexican adults aged 20 years or older. Participants completed the cross-culturally adapted YFAS 2.0. Exploratory and hierarchical factor analyses were conducted. Reliability was assessed using Cronbach’s alpha and omega coefficients, and model fit was evaluated through global fit indices. The scale showed high internal consistency (α = 0.88; ωt = 0.87; ωh = 0.89). The Kaiser–Meyer–Olkin index was 0.815 and Bartlett’s test was significant (χ² = 4367.88; df = 595; p < 0.001). Exploratory factor analysis supported a unidimensional structure, with the first factor explaining 21.3% of the total variance. In the hierarchical model, all items loaded substantially onto the general factor. Fit indices indicated excellent model fit (CFI = 0.99; TLI = 0.98; RMSEA = 0.001; RMR = 0.004). The YFAS 2.0 is a valid and reliable instrument for identifying food addiction symptoms in Mexican adults. It may be useful in clinical practice and research on eating disorders. Full article

Background/Objectives: Myelofibrosis (MF) is a myeloproliferative neoplasm characterized by the replacement of healthy bone marrow (BM) with malignant and fibrotic tissue. In a healthy state, bone marrow is composed of approximately 60–70% fat cells, which are replaced as disease progresses. Proton density fat fraction (PDFF), a non-invasive and quantitative MRI metric, enables analysis of BM architecture by measuring the percentage of fat versus cells in the environment. Our objective is to investigate variance in quantitative PDFF-MRI values over time as a marker of disease progression and response to treatment. Methods: We analyzed existing data from three cohorts of mice: two groups with MF that failed to respond to therapy with approved drugs for MF (ruxolitinib, fedratinib), investigational compounds (navitoclax, balixafortide), or vehicle and monitored over time by MRI; the third group consisted of healthy controls imaged at a single time point. Using in-house MATLAB programs, we performed a voxel-wise analysis of PDFF values in lower extremity bone marrow, specifically comparing the variance of each voxel within and among mice. Results: Our findings revealed a significant difference in PDFF values between healthy and diseased BM. With progressive disease non-responsive to therapy, the expansion of hematopoietic cells in BM nearly completely replaced normal fat, as determined by a markedly reduced PDFF and notable reduction in the variance in PDFF values in bone marrow over time. Conclusions: This study validated our hypothesis that the variance in PDFF in BM decreases with disease progression, indicating pathologic expansion of hematopoietic cells. We can conclude that disease progression can be tracked by a decrease in PDFF values. Analyzing variance in PDFF may improve the assessment of disease progression in pre-clinical models and ultimately patients with MF. Full article

Background/Objectives: The field of dietary supplements is changing and evolving quickly. Registered Dietitian Nutritionists are recognized as experts in nutrition and familiarity with the usage of dietary supplements is expected. However, education on the use of dietary supplements is not equal across accredited dietetic education programs, which can lead to disparities in dietitians’ feelings of preparedness, attitudes, and consequently experience of comfort regarding dietary supplements. The purpose of this study was to create the D-SCOPE Questionnaire (Dietitians’ Survey of Comfort, Opinions, and Preparedness in Education in Supplements) and validate it. This questionnaire assesses Registered Dietitian Nutritionists’ feelings of preparedness, comfort with use, and general attitudes in the field of dietary supplements. Methods: Face and content validity was established with dietitian, nutritionist, and statistician input. For recruitment, 2000 national randomly selected emails were obtained from the Commission on Dietetic Registration. Registered Dietitian Nutritionists (n = 248) responded to the survey email request. Descriptive statistics (reported as means ± standard deviation), principal axis factoring (exploratory factor analysis) with a direct oblimin rotation and Cronbach’s a reliability analysis were used for validation techniques. Results: Five factors were created, which explained about 63% of the variance in the questionnaire. The questionnaire was generally reliable, but the factor structure could change with a non-US population. Conclusions: As a unit, the D-SCOPE Questionnaire shows validity and reliability in assessing Registered Dietitian Nutritionists’ perceptions of preparedness and attitudes in the area related to dietary supplements. Full article

Cowpea (Vigna unguiculata [L.] Walp) is a vital food security crop in sub-Saharan Africa, including Eswatini. The productivity of the crop is low (<600 kg/ha) in the country due to a lack of improved, locally adapted, and farmer-preferred varieties with biotic and abiotic stress tolerance. The objective of the study was to assess the agronomic performance of newly developed elite cowpea mutants to select best-yielding and adapted pure lines for production and genetic improvement in Eswatini. A total of 30 cowpea genotypes, including 24 newly developed advanced mutant lines, their 3 founder parents and 3 local checks, were profiled for major agronomic traits in two selected sites (Lowveld Experiment and Malkerns Research Stations) using a 6 × 5 alpha lattice design with three replications. A combined analysis of variance revealed that the genotype x location interaction effects were significant (p < 0.05) for germination percentage (DG %), days to flowering (DTF), days to maturity (DMT), number of pods per plant (NPP), pod length (PDL), number of seeds per pod (NSP), hundred seed weight (HSW), and grain yield (GYD). Elite mutant genotypes, including NKL9P7, BRR4P11, SHR9P5, and NKL9P7-2 exhibited higher grain yields at 3158.8 kg/ha, 2651.6 kg/ha, 2627.5 kg/ha, and 2255.8 kg/ha in that order. The highest-yielding mutant, NKL9P7, produced 70%, 61%, and 54% more grain yield than the check varieties Mtilane, Black Eye, and Accession 792, respectively. Furthermore, the selected genotypes displayed promising yield components such as better PDL (varying from 13.1 to 26.3 cm), NPP (15.9 to 26.8), and NSP (9.8 to 16.2). Grain yield had significant positive correlations (p < 0.05) with DG %, NSP, and NPP. The principal component analysis (PCA) revealed that 81.5% of the total genotypic variation was attributable to the assessed quantitative traits. Principal component (PC) 1 accounted for 48.6%, while PC 2 and PC 3 contributed 18.9% and 14% of the overall variation, respectively. Key traits correlated with PC1 were NPP with a loading score of 0.91, NSP (0.83), PDL (0.73), GYD (0.68), HSW (0.58), DMT (−0.60), and DTF (−0.43) in a desirable direction. In conclusion, genotypes NKL9P7, BRR4P11, SHR9P5, NKL9P7-2, Bira, SHR3P4, and SHR2P7 were identified as complementary parents with relatively best yields and local adaptation, making them ideal selections for direct production or breeding. The following traits, NPP, NSP, PDL, GYD, and HSW, offered unique opportunities for genotype selection in the cowpea breeding program in Eswatini. Full article

The primary aim of this study was to explore the influence of abiotic factors on weed development in maize fields, with the goal of informing more effective weed management practices. We focused on identifying key environmental, edaphic, and agricultural variables that contribute to weed infestations, particularly before the application of spring herbicide treatments. Field investigations were conducted from 2018 to 2021 across selected maize-growing regions in Hungary. Over the four-year period, a total of 51 weed species were recorded, with Echinochloa crus-galli, Chenopodium album, Portulaca oleracea, and Hibiscus trionum emerging as the most prevalent taxa. Collectively, these four species accounted for more than half (52%) of the total weed cover. Altogether, the 20 most dominant species contributed 95% of the overall weed coverage. The analysis revealed that weed cover, species richness, and weed diversity were significantly affected by soil properties, nutrient levels, geographic location, and tillage systems. The results confirm that the composition of weed species was influenced by several environmental and management-related factors, including soil parameters, geographical location, annual precipitation, tillage method, and fertilizer application. Environmental factors collectively explained a slightly higher proportion of the variance (13.37%) than farming factors (12.66%) at a 90% significance level. Seasonal dynamics and crop rotation history also played a notable role in species distribution. Nutrient inputs, particularly nitrogen, phosphorus, and potassium, influenced both species diversity and floristic composition. Deep tillage practices favored the proliferation of perennial species, whereas shallow cultivation tended to promote annual weeds. Overall, the composition of weed vegetation proved to be a valuable indicator of site-specific soil conditions and agricultural practices. These findings underscore the need to tailor weed management strategies to local environmental and soil contexts for sustainable crop production. Full article

Objectives: Previous literature has already discussed the effects of age and sex on the diagnosis and treatment of malocclusion problems. However, this effect varies among different ethnic groups. These differences have not yet been investigated in many populations, such as Arab orthodontic patients and residents of Israel. Therefore, it is crucial to understand such variations in specific populations for better diagnosis and treatment. The main aim of this study is to provide novel knowledge concerning skeletal classes I and II among a cohort of Arab patients who are citizens of Israel. We used parameters obtained from lateral cephalograms to understand the variations among different sex and age subgroups. We also examined the correlations and performed principal component analysis (PCA). Methods: This study was based on the coded records of 394 Arab patients diagnosed with skeletal Class I occlusion (SCIO) or skeletal Class II malocclusion (SCIIMO), according to the individualized ANB (Calculated_ANB) of Panagiotidis and Witt. Results: Among patients with SCIO, males had a significantly more horizontal growth pattern (PFH/AFH) and anterior mandible rotation (ML-NSL) than females. Regarding patients with SCIIMO, female adults had more hyperdivergent jaw bases than adolescents (ML-NL) and a more posteriorly rotated mandible (ML-NSL). Spearman’s analysis revealed many significant correlations, like Calculated_ANB, ANB angle, and Wits appraisal. The PCA results showed a remarkable ability to explain 88.6% of the sample variance using four principal components. Conclusions: This research revealed new information regarding Arab orthodontic patients diagnosed with skeletal class I or II. The results demonstrate the differences between the two classes. In addition, this study demonstrated the variation and correlation of cephalometric parameters among different sex and age subgroups in skeletal class I and II Arab patients, especially considering Calculated_ANB. Therefore, this study highlights the need to consider these differences when diagnosing patients and to distinguish the differences across different sex and age subgroups in the diagnosis and treatment process. Furthermore, the PCA results showed the importance of ML-NSL, SN-Pg, PFH/AFH ratio, and NL-ML in explaining the data variance. Full article

Background/Objectives: Dystonia, traditionally regarded as a purely motor disorder, is now increasingly recognized as involving clinically significant non-motor symptoms (NMSs) that can adversely affect patients’ health-related quality of life (HRQoL). This study aimed to assess HRQoL in Romanian patients with isolated dystonia and to evaluate the impact of two key NMSs, depression and cognitive impairment, on their HRQoL. We hypothesized that depression would have a greater adverse effect on HRQoL than cognitive impairment. Methods: A cross-sectional study was conducted involving 65 adult Romanian patients with isolated dystonia. HRQoL was measured using the Short Form-36 Health Survey (SF-36), including the physical component summary (PCS) and mental component summary (MCS). Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), and cognitive impairment was assessed using the Montreal Cognitive Assessment (MoCA). Descriptive statistics, correlation analysis, and parametric and non-parametric tests were used. Multiple regression analysis was employed to evaluate associations between NMS and HRQoL. Results: The mean (SD) age was 56.6 (14.3) years, and 80% of participants were female. Depression and cognitive function were significantly associated with PCS (0.33 and −0.51, respectively) and MCS (0.26 and −0.78, respectively). Multiple regression analysis showed that the two NMS explained 38% of the variance in PCS and 58% of the variance in MCS. Depression had a greater impact on PCS and MCS than cognitive impairment (−0.47 vs. 0.33 and −0.72 vs. 0.16, respectively). Cognitive impairment (MoCA < 26) was present in 35.4% of patients, while 46.2% had at least mild depressive symptoms (PHQ-9 ≥ 5); 23.1% met criteria for moderate-to-severe depression (PHQ-9 ≥ 10). Depressive symptoms showed strong negative correlations with all SF-36 domains, while cognitive performance correlated modestly. Conclusions: Both depression and cognitive impairment have a significant negative impact on HRQoL in dystonia, with depression having a stronger effect, as we hypothesized. Routine screening for non-motor symptoms is essential to support better clinical outcomes and enhance patients’ quality of life. Full article

Background: Telehealth is an essential component of modern healthcare, and it was especially relevant during the COVID-19 pandemic, but disparities in digital and technological literacy among health professionals may limit its equitable adoption and impact. Objective: This study seeks to validate an eight-item telehealth literacy survey among health professionals in Central–South Chile and to examine demographic and behavioral determinants of literacy levels, developing predictive models to identify key factors. Methods: In this cross-sectional study, 2182 health professionals from urban and rural centers in Central–South Chile completed the adapted survey along with questions on age, gender, nationality, and frequency of telehealth use. We assessed internal consistency (Cronbach’s α), explored factor structure via exploratory factor analysis (EFA), and tested associations using Pearson correlations, t-tests, one-way ANOVA, and both linear and multinomial logistic regressions. Results: The instrument demonstrated high reliability (Cronbach’s α = 0.92) and a two-factor structure explaining 65% of variance. Age negatively correlated with literacy (r = −0.26; p < 0.001), while the frequency of telehealth use showed a positive correlation (r = 0.26; p < 0.001). Female professionals and those in urban settings scored significantly higher on telehealth literacy (p = 0.005 and p < 0.001, respectively). The reduced multinomial model achieved moderate classification accuracy (51.65%) in distinguishing low, medium, and high literacy groups. Conclusions: The validated survey is a reliable tool for assessing telehealth literacy among health professionals in Chile. The findings highlight age, gender, and geographic disparities, and support targeted digital literacy interventions to promote equitable telehealth practice. Full article

Cardiac fibrosis following myocardial infarction plays a critical role in the formation of scar tissue and contributes to ventricular arrhythmias, including ventricular tachycardia and sudden cardiac death. Current clinical diagnostics use electrical and structural markers, but lack precision due to low spatial resolution and absence of molecular information. In this paper, we employed line scan Raman microspectroscopy to classify sheep myocardial tissue into muscle, necrotic, granulated, and fibrotic tissue types, using collagen as a molecular biomarker. Three spectral regions were evaluated: region A (600–2960 cm⁻¹), region B (600–1399 cm⁻¹ and 1751–2960 cm⁻¹), and region C (1400–1750 cm⁻¹), which includes the prominent collagen-associated peaks at 1448 cm⁻¹ and 1652 cm⁻¹. Linear and nonlinear principal component analysis (PCA) and support vector machines (SVMs) were applied for dimensionality reduction and classification, with nonlinear models specifically addressing the nonlinearity of collagen formation during fibrogenesis. Histological validation was performed using Masson’s trichrome staining. Raman bands associated with collagen in region C consistently outperformed regions A and B, achieving the highest explained variance and best class separation in both binary and multiclass PCA models for both linear and nonlinear approaches. The ratio of collagen-related peaks enabled stage-dependent tissue characterization, confirming the nonlinear nature of fibrotic remodeling. Our findings highlight the diagnostic potential of collagen-associated Raman bands for characterizing myocardial fibrosis. The proposed PCA-SVM framework demonstrates robust performance even with limited sample size and has the potential to lay the foundation for real-time intraoperative diagnostics. Full article

To address the challenges of low density, loose structure, high utilization costs, and inadequate molding effects of corn straw char under ambient temperature and pressure conditions, this study investigated the utilization of waste soybean powder (WSP) as a binder to produce biochar pellets via ultrasonic-assisted processing. A single-factor experiment was initially conducted to assess the effects of key variables. Subsequently, a Central Composite Rotatable Design (CCRD) was employed to evaluate the individual and interactive effects of these variables, in which pellet density and durability served as response indicators. Regression models for both responses were developed and validated using analysis of variance (ANOVA). The results indicated that, at a 0.05 significance level, the mixing ratio of corn straw char to WSP and molding pressure had highly significant effects on pellet density, while pelleting time had a significant effect and ultrasonic power had no significant influence. All four factors significantly affected pellet durability, and their interactions were further analyzed. The optimal conditions were a mixing ratio of 45%, pelleting time of 33 s, an ultrasonic power of 150 W, and a molding pressure of 5 MPa, yielding pellets with a density of 1140.41 kg/m³ and a durability of 98.54%. These results demonstrate that WSP is an effective binder for the ultrasonic-assisted fabrication of biochar pellets. Full article

This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry. Full article

Durum wheat, the world’s second most cultivated wheat species, is a staple crop, critical for global food security, including in Ethiopia where it serves as a center of diversity. However, climate change and genetic erosion threaten its genetic resources, necessitating genomic studies to support conservation and breeding efforts. This study characterized genome-wide diversity, population structure (STRUCTURE, principal coordinate analysis (PCoA), neighbor-joining trees, analysis of molecular variance (AMOVA)), and selection signatures (F_ST, Hardy–Weinberg deviations) in Ethiopian durum wheat by analyzing 376 genotypes (148 accessions) using an Illumina Infinium 25K single nucleotide polymorphism (SNP) array. A set of 7842 high-quality SNPs enabled the assessments, comparing landraces with cultivars and breeding populations. Results revealed moderate genetic diversity (mean polymorphism information content (PIC) = 0.17; gene diversity = 0.20) and identified 26 loci under selection, associated with key traits like grain yield, stress tolerance, and disease resistance. AMOVA revealed 80.1% variation among accessions, with no significant differentiation by altitude, region, or spike density. Landraces formed distinct clusters, harboring unique alleles, while admixture suggested gene flow via informal seed exchange. The findings highlight Ethiopia’s rich durum wheat diversity, emphasizing landraces as reservoirs of adaptive alleles for breeding. This study provides genomic insights to guide conservation and the development of climate-resilient cultivars, supporting sustainable wheat production globally. Full article

The arid-hot valleys of Sichuan Province contain extensive engineered gravel deposits, where ecological restoration has become the predominant remediation strategy. Accelerating vegetation recovery and continuously improving productivity are important prerequisites for the protection of regional biodiversity. We employed fertilization and sowing cultivation to facilitate ecological restoration. We have conducted continuous ecological experiments for two years using the following experimental treatments, covering indigenous soil, adding organic fertilizer, and applying compound fertilizer and organic fertilizer, with six types of sowing established under each soil treatment: monoculture and pairwise mixed cropping utilizing Elymus dahuricus (EDA), Dactylis glomerata (DGL), and Medicago sativa (MSA). Through the analysis of variance and the calculation of effect factors, our results indicated that compound fertilizer and organic fertilizer adding significantly improved vegetation cover and increased aboveground biomass, and the highest productivity was observed in the mixed sowing treatment of EDA and MSA. The effect coefficient model analysis further showed that the combination of EDA and MSA resulted in the highest selection and compensation effects on aboveground productivity. Two potential mechanisms drive enhanced productivity in mixed grasslands: the strengthening of the selection effect via increased legume nitrogen fixation, and the enhancement of the compensation effect through niche differentiation among species. Full article

This study examines the regional disparities in public perceptions of decarbonization and the acceptance of the bioeconomy within Western Macedonia, a Greek region undergoing structural economic change. While the environmental benefits of decarbonization, such as reduced carbon emissions and improved air quality, are widely acknowledged, perceptions of economic and social outcomes, including investments, new business development, and policy support, vary significantly across sub-regions. To this end, a structured survey was conducted among 765 residents, utilizing Likert-scale items to assess attitudes, with demographic data providing a contextual framework. Statistical analyses, incorporating techniques such as one-way analysis of variance (ANOVA), Kruskal–Wallis, and multiple regression, were employed to explore spatial variations and identify the primary drivers of bioeconomy acceptance. The results indicate that perceived government action, visible investment, new enterprises, and a positive view of public sentiment are all significant predictors of acceptance, with institutional support showing the strongest influence. The findings reveal that certain areas feel less engaged in the transition, expressing skepticism about its benefits, while others report more optimism. This disparity in perception underscores the necessity for targeted policy interventions to ensure inclusive and equitable participation. The study emphasizes the necessity for regionally responsive governance, enhanced communication strategies, and tangible local development initiatives to cultivate public trust and support. The study makes a significant contribution to the broader discourse on just transitions by emphasizing the role of place-based perceptions in shaping sustainable change. Full article