Search Results (2,898)

Based on data from 208 students involved in entrepreneurship studies at Tecnológico de Monterrey, Mexico, this paper examines whether prior experience with AI, expectations, gender, and age reinforce future AI use. To achieve this objective, we applied binary logistic regression with random oversampling to balance the dataset. We complemented it with additional model performance metrics, including the confusion matrix, sensitivity, specificity, and area under the ROC curve. The results show that prior experience with AI, age-related technology use, and positive expectations regarding AI are associated with a higher likelihood of reinforcing future AI use. In terms of gender, the results indicate a gender gap favoring women, who are more likely to use AI when they perceive greater utility and confidence, as well as a stronger desire to succeed. Full article

Generative Artificial Intelligence (GAI) is rapidly transforming higher education by introducing new mechanisms for supporting the development of advanced cognitive processes and research-related capabilities. This study examines how postgraduate students employ GAI to develop their cognitive and research talent, conceptualized here as higher-order academic skills such as analysis, synthesis, and critical reasoning, across six domains: literature review, theoretical development, research design, data analysis, academic writing, ethical use, and challenges encountered—signaled explicitly rather than listed line by line. We administered a validated multidimensional scale to 214 postgraduate students, and the results indicate a moderate overall use of GAI, with notably high involvement in practices that emphasize ethics and responsibility. Students reported clear cognitive benefits in tasks involving information processing, linguistic refinement, and conceptual clarification while showing caution toward delegating higher-order analytical or theoretical reasoning to AI systems. Key challenges included limited institutional training, concerns about data privacy and academic integrity, and difficulties evaluating the originality and reliability of AI-generated content. Inferential analyses indicated significant differences based on gender, academic level, and general technology proficiency, whereas no differences emerged across age groups, departments, or specializations. Overall, this study demonstrates how GAI can contribute to the development of higher-level cognitive skills and research competencies, with “moderate use” operationalized as consistent but selective engagement across domains, while underscoring the need for structured training, clear guidelines, and teaching approaches that foster the responsible and effective incorporation of AI within postgraduate research. The results highlight practical implications for higher education, including the importance of institutional training programs, governance frameworks for responsible AI use, and pedagogical models that foster critical engagement with GAI. Full article

The alpine meadows on the Qinghai-Tibetan Plateau function as critical reservoirs for regional water resources, yet face severe degradation driven by climate warming and overgrazing. Although establishing Poa pratensis artificial grasslands is a common restoration strategy, their effectiveness in recovering hydrological functions along restoration chronosequence remains poorly quantified. This study evaluated the changes in water conservation capacity and its drivers across a degradation–restoration sequence in the Qilian Mountains comprising alpine meadow (AM), degraded meadow (DM), and 2-, 3-, and 13-year artificial grasslands (AG2, AG3, AG13). Vegetation characteristics, soil structural properties, and water-holding indices were measured to assess restoration outcomes. The results showed that compared to AM, water-holding capacity at 0–30 cm in DM declined by 75.3–85.8%, primarily due to fragmentation of the mattic epipedon and deterioration of soil aggregates. While artificial restoration improved vegetation traits and some soil properties, hydrological recovery exhibited a distinct lag. Specifically, soil water-holding capacity in artificial grasslands showed no statistically significant improvement compared to DM. Even in AG13, soil water storage remained significantly lower than that in AM. Mantel tests and regression analyses identified root mass density and mean weight diameter as the primary drivers governing water conservation capacity. These findings reveal that artificial grasslands cannot serve as functional hydrological reservoirs in a timely manner, highlighting the importance of conserving intact alpine ecosystems. Full article

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy marked by substantial molecular heterogeneity and variable response to gemcitabine-based therapy. While KRAS mutations are nearly universal, the broader RTK-RAS and MAPK signaling architecture and its relationship to treatment response remain incompletely defined. We conducted an integrative clinical-genomic analysis of 184 PDAC tumors stratified by age at diagnosis and gemcitabine exposure, interrogating somatic alterations across curated RTK-RAS/MAPK gene sets. Conversational artificial intelligence agents (AI-HOPE-RTK-RAS and AI-HOPE-MAPK) enabled dynamic cohort construction and pathway-level analyses, with findings validated using standard statistical methods. In late-onset PDAC, ERBB2 and RET mutations were significantly enriched in gemcitabine-treated tumors. Early-onset cases demonstrated differential enrichment of CACNA2D family alterations in non-treated tumors and higher frequencies of FLNB and TP53 mutations in treated disease. Importantly, late-onset patients not treated with gemcitabine who lacked RTK-RAS or MAPK alterations exhibited significantly improved overall survival. These findings reveal age- and treatment-dependent pathway dependencies beyond canonical KRAS status and support a precision oncology framework in PDAC. Conversational AI facilitated rapid, multidimensional clinical–genomic integration to uncover clinically relevant signaling substructures. Full article

Dry eye disease (DED) is a complex, multifactorial, progressive disease that has consequences both for individuals and society. Symptoms reported by patients include discomfort in the eye and periodic blurred vision, while in the broader perspective, the disease is associated with economic burdens and challenges for healthcare systems. Globally, dry eye disease remains a growing problem observed in many countries. It is estimated that symptoms of dry eye syndrome occur in approximately 10 to 20 per cent of people over the age of 40. This prevalence is on the rise, which is associated with both the aging population and increased incidence among younger adults. In this group, factors such as contact lens wear and prolonged use of digital devices are considered to be contributing factors. Further epidemiological studies, conducted in different regions of the world, covering diverse populations and a wide range of age groups, with a particular focus on younger cohorts, may contribute to a more accurate understanding of the prevalence of dry eye disease. There are more and more methods of diagnosing DED. In addition to well-known procedures like the Schirmer test or tear break-up time, there are also methods that focus on the evaluation of the tear film or imaging of the ocular surface. Moreover, usage of artificial intelligence is also playing a significant role in it. However, the key issue in individual cases is introducing the most effective treatment based on combining available substances, including corticosteroids, antibiotics and supplements, which leads to a reduction in inflammation and improvement in visual comfort. Full article

The objective of this paper is to propose a sociological and interdisciplinary framework for analyzing the digitization of emotions in adolescence. This contribution aims to promote theoretical reflection and inform educational and political interventions in the digital age, framing adolescents’ digital experiences as emotionally embodied and socially integrated processes. These aspects are of paramount importance due to the rapid proliferation of digital technologies and artificial intelligence, which have precipitated a profound transformation in the emotional, relational, and educational experiences of adolescents. The role of digital and AI-based environments in mediating communication is expanding beyond the scope of simple facilitation. These environments are increasingly implicated in the production, modulation, and regulation of emotions, thereby influencing developmental trajectories and identity formation processes. This phenomenon is theorized as a socio-technical process, wherein emotions are embodied, narrated, and governed within digital environments. The article introduces the concept of digital emotional embodiment, drawing on the sociology of emotions, theories of embodiment, and critical perspectives on artificial intelligence. Specifically, the concept refers to the manner in which adolescents experience and express emotions through avatars, images, emojis, algorithmic feedback, and AI-mediated interactions. Therefore, it is imperative to underscore the evolution of empathy, which is progressively configured as a virtualized and datafied process, diverging from the tradition established by Hume and characterized by sympathy. In contemporary processes, shaped by the logic of platforms, recommendation systems, and emotionally reactive technologies, conventional emotional concepts have undergone deconstruction, and digital constructs are undergoing a gradual restructuring. In this context, AI systems do not merely reflect adolescents’ emotions but rather actively contribute to the construction of emotional narratives, influencing emotional regulation, social connection, and future orientation. Digital environments have been shown to encourage emotional expressiveness, experimentation, and inclusivity. Conversely, they have the capacity to encourage emotional standardization, dependency, and forms of affective vulnerability, particularly during a sensitive developmental stage such as adolescence. Full article

Seed aging is a major constraint for plant establishment in arid and semi-arid ecosystems, where poor seed vigor directly limits species persistence and restoration success. Desert species are particularly vulnerable to storage- and stress-induced deterioration, yet practical strategies to recover germination capacity in aged seeds remain limited. This study aimed to quantify aging-induced losses in germination performance and to evaluate whether exogenous gibberellic acid (GA₃) can partially restore seed vigor through physiological, biochemical, and hormonal regulation. Fresh seeds (FS), naturally aged (NA), and artificially aged (AA) seeds of four desert species (Salsola affinis C.A.Mey., Trigonella arcuata C.A.Mey., Ceratocarpus arenarius L., and Alyssum desertorum Stapf) were exposed to graded GA₃ concentrations (0–500 mg L⁻¹). Germination indices (GP, GR, GI, VI), antioxidant enzymes (SOD, POD, CAT), lipid peroxidation (MDA), phytohormones (IAA, ABA, cytokinins), and multivariate trait relationships were assessed. Without GA₃, NA reduced germination potential by 22.8–33.6%, while AA caused more severe losses of 42.4–67.8%, depending on species. Germination rate declined by 15.7–32.5% under NA and 36.4–65.2% under AA. GA₃ application improved all germination indices up to 200 mg L⁻¹ (GA200), which increased GP by 22.8–32.0% and vitality index by 17.0–28.5% compared with GA₀, whereas GA500 showed diminishing returns. Aging suppressed antioxidant enzymes by 15–20% (NA) and 30–45% (AA) and increased MDA by up to 50%, while GA200 enhanced SOD, POD, and CAT and reduced MDA by 8–18%. Aging also reduced IAA and cytokinins (~28–50%) and increased ABA (27.7–77.4%), with GA200 partially restoring hormonal balance. In conclusion, GA₃ at an optimal dose (200 mg L⁻¹) partially reverses aging-induced physiological and hormonal constraints, improving germination and vigor, although recovery remains limited under advanced deterioration. Full article

Background/Objectives: The aim of the study was to evaluate the diagnostic accuracy of a commercial artificial intelligence (AI) algorithm originally developed for screening mammography when applied to symptomatic women presenting to a tertiary outpatient clinic. Methods: This single-center, retrospective diagnostic accuracy study included women who presented with breast symptoms to a tertiary outpatient clinic between January and June 2013 and underwent digital mammography. An AI algorithm cleared by the U.S. Food and Drug Administration (FDA)-cleared AI algorithm was applied to all mammograms and generated continuous malignancy scores ranging from 1 to 100. Mammographic breast density was classified according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) by two experienced radiologists. Histopathology, when available, or otherwise a minimum of 2 years of clinical and imaging follow-up served as the reference standard. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis with calculation of the area under the curve (AUC) and 95% confidence intervals (CI) derived by patient level bootstrap resampling (n = 2000). Analyses were performed for the overall cohort and stratified by breast density (non-dense [BI-RADS A–B] vs. dense [BI-RADS C–D]). Results: A total of 78 women (mean age, 55 ± 11 years) were included, of whom 16 had histopathological verification of suspicious lesions with proven breast cancer in 14 patients and 62 were classified based on follow-up alone. In the overall cohort (156 breasts, including 15 breasts with malignancies), the AI algorithm achieved an AUC of 0.96 (95% CI: 0.86–1.00). Performance remained high in non-dense breasts (AUC = 0.96; 95% CI: 0.88–1.00) and dense breasts (AUC = 0.99; 95% CI: 0.93–1.00), with no statistically significant difference observed between density subgroups (DeLong test, p = 0.36), although subgroup comparisons were underpowered. Decision curve analysis suggested a consistent positive net benefit across a wide range of threshold probabilities in both density groups. Conclusions: In this preliminary, single-center retrospective cohort, a screening-trained AI algorithm showed promising diagnostic accuracy when applied to symptomatic mammograms. These findings require validation in larger, contemporary, multicenter cohorts before clinical implementation. Full article

The use of generative artificial intelligence (GenAI) in academic contexts has expanded rapidly in recent years, yet limited evidence exists regarding its prevalence across educational levels or its association with psychological and academic variables among adolescents and young adults. This exploratory study aimed to examine the prevalence of GenAI use for learning-related academic purposes among pre-university and university students, including gender differences, and to analyze its relationship with self-esteem, self-efficacy, academic confidence, and academic anxiety. The sample comprised 1043 participants aged 13 to 23 years (M = 16.16, SD = 2.42; 59.1% female) who completed self-report measures. Structural equation modeling was conducted controlling for gender, age, and Internet use. Overall, 95% of students reported using GenAI for academic purposes, with higher usage among university than pre-university students and among female than male students. GenAI use was significantly associated with higher academic anxiety, although the effect size was small, and no significant associations were observed with the remaining variables. These findings suggest that while GenAI use is widespread, its associations with psychological and academic variables appear to remain limited. Full article

Background/Objectives: Nurses’ turnover intentions are strong predictors of actual turnover, which increases costs, reduces care quality, and destabilies the workforce. This study aimed to identify the key predictors of nurses’ turnover intentions using advanced machine learning methods and to explore how demographic, well-being, and work environment factors contribute to these intentions. Methods: Cross-sectional data were collected from 2459 nurses employed across various healthcare institutions. We used multichannel invitation and snowball sampling. An artificial neural network regression model was applied, combined with iterative feature selection and SHAP analysis, to identify the most important predictors of turnover intentions and to examine nonlinear and context-dependent relationships among variables. Results: Seven predictors explained 49.8% of the variance in turnover intentions, outperforming traditional linear models. Age was the strongest predictor, with younger nurses demonstrating a substantially higher likelihood of intending to leave; this association was nonlinear, with intentions decreasing more sharply at older ages. Job satisfaction and burnout were also strong predictors, particularly among younger nurses. Four work environment factors further contributed to turnover intentions: managerial support functioned as a protective factor, interpersonal conflict increased intentions to leave, limited professional development opportunities were strongly associated with higher turnover intentions, and role conflict showed heterogeneous effects. Conclusions: Machine learning approaches enhance understanding of complex workforce dynamics and enable more precise identification of high-risk groups. The findings support age-sensitive retention strategies, proactive monitoring of nurse well-being, and organizational interventions to strengthen managerial support and professional development, ensuring workforce stability and sustainable healthcare service delivery. Full article

Artificial intelligence (AI)-mediated tools have rapidly penetrated student life and become a valuable resource for seeking help with academic assignments/tasks, psychological problems, and social interactions. This study aims to investigate the levels and associations of AI-help-seeking behavior (AI-HSB), anxiety, stress, and depression among university students in the United Arab Emirates (UAE). In addition, it examines the factors associated with AI-HSB based on the selected demographic (gender, marital status, age, academic year, employment status, major, and nationality), as well as anxiety, stress, and depression. This study employed a descriptive cross-sectional design among 433 university students, who were recruited via an online Google Form between 1 October 2025 and 10 December 2025. The study utilized validated Arabic versions of the AI-HSB scale and the anxiety, stress, and depression scale. Descriptive statistics, Pearson correlation, and predictive analyses were conducted using SPSS v 25. Results indicated that students reported moderate reliance on AI-HSB despite moderate to severe levels of psychological distress, with particular emphasis on anxiety. The AI-HSB was positively associated with anxiety, stress, and depression amongst the participants. Furthermore, both depression and the students’ academic year emerged as the only significant predictors of AI-HSB, explaining a modest but meaningful proportion of variance with an exact percentage of 18.1%. AI tools may partially circumvent stigma by offering privacy and anonymity; however, cultural expectations around interpersonal support, trust, and authority may simultaneously limit students’ willingness to rely on non-human agents for emotional care. Full article

Organophosphate flame retardants (OPFRs) are widely used in consumer products and have attracted extensive attention due to their potential hazards. In this study, the concentration of OPFRs in cosmetic sponges, the migration of these compounds, and the assessment of dermal exposure risk are reported for the first time. Twelve OPFRs were detected in cosmetic sponges, with concentrations ranging from not detected (ND) to 9624 ng·g⁻¹ and a total detection frequency (DF) of 75.58% (n = 86). A migration experiment was designed to evaluate the skin load of OPFRs from cosmetic sponges using the Strat-M^TM artificial membrane, and the reliability of the method was verified. The daily exposure of females (age: 11–40 years) to OPFRs through dermal contact with cosmetic sponges under different use conditions and for different age groups was assessed. The use of wet cosmetic sponges resulted in persistent and higher OPFRs exposure. Although the calculation of the hazard ratio indicated an acceptable health risk from OPFRs contained in cosmetic sponges, the toxicity results based on the L-929 cell line highlight that the potential toxicity risk caused by the migration of OPFRs from cosmetic sponges cannot be neglected. Full article

To meet the growing demand for autonomous decision-making and real-time optimization in industrial manufacturing, integrating Artificial Intelligence-Generated Content (AIGC) services with Industry 5.0 can enable real-time industrial intelligence. The effectiveness of a generative model is closely related to the current state of the production environment. However, existing studies often ignore the dynamic temporal relationship between generative models and production environments, especially in industrial scenarios with large model transmission delays and random AIGC task arrivals. Therefore, we define a novel metric, namely the Age of Model (AoM), to measure the freshness of generative models with respect to current industrial tasks. We then formulate an average-AoM-minimization problem that jointly considers LoRA-based fine-tuning, wireless transmission and resource allocation. To solve this problem, we propose a Hybrid-Action Multi-Agent Proximal Policy Optimization (HA-MAPPO) algorithm. The proposed algorithm follows the centralized training and decentralized execution (CTDE) paradigm and introduces a Main-Agent Priority State Strategy to support coordinated training and independent execution. In addition, a multi-head output structure is designed to handle the hybrid-action space, which includes discrete fine-tuning association decisions and continuous transmission resource allocation. Simulation results show that the proposed scheme outperforms all benchmark methods. Specifically, the cumulative rewards are improved by approximately $11.13\%$, $20.32\%$, $36.61\%$, and $38.78\%$ compared with the four benchmark algorithms, respectively. These results demonstrate that the proposed scheme can significantly reduce the average AoM while providing high-quality and timely industrial AIGC services. Full article

Background: Warfarin pharmacogenomics is critical due to its narrow therapeutic index and significant interpatient variability. While machine learning (ML) can predict anticoagulation control status (ACS), its “black-box” nature limits clinical translatability. Explainable Artificial Intelligence (XAI) addresses this by providing interpretable insights. This study applied ML and XAI to a warfarin pharmacogenomic dataset to predict poor ACS and explain model decisions. Methods: A post hoc analysis was conducted on a cross-sectional dataset of 232 patients receiving warfarin for ≥6 months. Data included age, gender, interacting drugs, SAMe-TT2R2 score, and genotypes for CYP2C9, VKORC1, and CYP4F2. Poor ACS was defined as time in therapeutic range (TTR) < 70%. The dataset was split into training (70%) and testing (30%) cohorts. Three models, Random Forest, XGBoost, and Logistic Regression, were developed and evaluated using AUC-ROC, sensitivity, and specificity. XAI techniques, including permutation importance and SHapley Additive exPlanations (SHAP), were employed for global and local interpretability. Results: Of 232 patients, 141 (60.8%) had poor ACS. XGBoost and Random Forest demonstrated comparable predictive accuracy (AUC-ROC: 0.67), outperforming Logistic Regression. Sensitivity was 0.83 and 0.79 for XGBoost and Random Forest, respectively. However, specificity was modest for both ensemble methods (Random Forest: 0.48; XGBoost: 0.41) and extremely low for Logistic Regression (0.04), indicating poor discrimination, particularly for identifying patients with adequate anticoagulation control. Globally, important predictors included the age, SAMe-TT2R2 score, CYP2C9 (*2/*2), female gender, and VKORC1 (C/T). XAI revealed predictions were primarily driven by VKORC1, CYP4F2, SAMe-TT2R2 scores, and drug interactions. Concordance between XAI predictions and actual ACS was 78% for adequate and 88.6% for poor ACS. SHAP analysis showed VKORC1 provided a stable risk signal (mean absolute SHAP: 1.44 ± 0.49 in concordant cases), while CYP2C9 was a high-variance, high-impact driver of discordance (mean SHAP: 3.44 ± 3.79 in discordant cases). Conclusions: ML models, particularly ensemble methods, show modest ability to predict poor warfarin control with limited ability to correctly identify patients with adequate control from our dataset. XAI transforms these models into interpretable tools, with SHAP analysis attributing predictions to specific genetic and clinical features. While predictive accuracy remains modest, this approach enhances transparency and provides a foundation for generating hypotheses that may ultimately support clinical decision-making in pharmacogenomic-guided warfarin therapy. Full article

Background: Diabetes mellitus is a global health challenge, especially among homeless people. Early prediction of diabetes can reduce treatment costs and improve interventions. This study aimed to identify predictors of diabetes among homeless adults by utilizing artificial intelligence and providing recommendations for diabetes prevention. Methods: A case-control study of 150 homeless adults in Giza, Egypt (99 diabetes cases and 51 controls), analyzed 43 variables collected through interviews and physiological measures, with missing data imputed. Feature selection using recursive feature elimination and univariate and correlation analyses reduced the predictors to 13 variables. The class imbalance was addressed using synthetic minority over-sampling on the training set. Six models and a stacking ensemble with XGBoost as a meta-learner were evaluated using 5-fold cross-validation and performance metrics, including the accuracy, precision, recall, F1-score, and AUC-ROC. Results: The key predictors included BMI, systolic blood pressure, triceps skinfold thickness, waist circumference, lifestyle factors, comorbidities, diastolic blood pressure, age, medication adherence, educational level, marital status, duration of residence, and diabetes knowledge. Individual classifiers achieved a moderate performance (accuracy: 56.7–70.0%, F1-score: 0.686–0.781). The stacking ensemble substantially outperformed individual models, achieving a 95.45% accuracy, a 100% precision, a 93.75% recall, a 0.968 F1-score, and a 0.979 AUC-ROC on the test set. Conclusions: Machine learning models can reliably predict diabetes. The proposed hybrid stacking model outperformed conventional classifiers in terms of the prediction performance, highlighting the benefits of ensemble learning and sophisticated resampling strategies in dealing with imbalanced medical data. It is recommended that healthcare institutions integrate AI-powered diagnostic assistance technology into clinical processes to aid in the early detection and treatment of diabetes. Full article