Search Results (1,529)

Economic and technical barriers to agricultural technology adoption are widely documented, but less is known about how social and psychological factors shape farmers’ intention to adopt smart irrigation systems in post-communist transition economies. This study examines smart irrigation adoption intentions among 368 farmers in Albania, an EU-candidate country characterised by smallholder farming, land fragmentation and uneven technological diffusion. Drawing on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and motivation perspectives, we analyse how social influence, self-efficacy and task-technology fit shape adoption intentions. Partial Least Squares Structural Equation Modelling (PLS-SEM) shows that social influence is the strongest direct predictor of intention to use smart irrigation systems, while self-efficacy and task-technology fit also contribute to farmers’ perceptions and intentions. Perceived usefulness has a stronger role than perceived ease of use, suggesting that farmers prioritise practical benefits such as water efficiency, productivity and farm-level utility over usability alone. The findings show how social embeddedness, farmer confidence and task compatibility shape smart irrigation intentions in a post-communist agricultural context, and suggest that policy should strengthen peer learning, farmer champions and community-based diffusion mechanisms. Full article

Background: Patients with stage I rectal cancer generally have favorable outcomes after curative surgery, but prognosis is not completely homogeneous. This study explored the prognostic association of preoperative inflammation–nutrition indices with overall survival and developed an interpretable internally validated machine learning survival model. Methods: We retrospectively included 475 patients with primary pathological stage I rectal adenocarcinoma who underwent curative-intent radical surgery at Sichuan University West China Hospital between 2018 and 2021. Patients downstaged to ypStage I after neoadjuvant therapy or treated by local transanal excision without lymph node dissection were excluded. Candidate predictors included age, sex, carcinoembryonic antigen, and routinely available preoperative inflammation–nutrition indices. LASSO-Cox regression was used for feature selection. Six survival models were developed and evaluated using 1000 bootstrap resamples with out-of-bag internal validation. Model performance was assessed at 60 months using time-dependent AUC, C-index, Brier score, calibration, and decision curve analysis. SHAP analysis was used for model interpretation. Results: During a median follow-up of 68 months, 30 deaths occurred. LASSO-Cox regression identified five predictors: age, lymphocyte-to-white blood cell ratio, fibrinogen-to-lymphocyte ratio, albumin-to-alkaline phosphatase ratio, and neutrophil-to-HDL cholesterol ratio. In bootstrap out-of-bag internal validation, XGBoost-Cox achieved the highest, although only marginally higher, discriminative performance among the evaluated models, with a 60-month time-dependent AUC of 0.783, a C-index of 0.774, and a Brier score of 0.0507. The calibration intercept and slope of XGBoost-Cox were 0.519 and 1.070, respectively. SHAP analysis identified age as the most influential predictor, followed by the selected inflammation–nutrition indices. Decision curve analysis suggested potential clinical utility within threshold probabilities from 1% to 20%, although this finding remains exploratory. Conclusions: Preoperative inflammation–nutrition indices may contribute to overall survival prognostic stratification in primary pathological stage I rectal cancer. External validation in larger multicenter cohorts is required before clinical application. Full article

Background: India accounts for one-fifth of global cervical cancer deaths, yet Human Papillomavirus (HPV) vaccination coverage remains low despite the World Health Organization (WHO) recommendations to initiate vaccination at age 9. With the national HPV vaccination campaign in 2026 and its approval for integration into India’s Universal Immunization Programme (UIP), strengthening physician knowledge, beliefs, and confidence in recommending the vaccine is critical. Methods: In 2023, the American Cancer Society and Cancer Foundation of India catalyzed two national medical societies, the Federation of Obstetric and Gynaecological Societies of India and the Indian Academy of Pediatrics, to educate physicians on HPV vaccination using a train-the-trainer (ToT) model. A mixed-methods evaluation, using the RE-AIM Framework, included pre- and post-training surveys assessing changes in knowledge, beliefs, confidence, and intent, as well as monthly engagement surveys and project reports analyzed using descriptive and inferential statistics. A fidelity assessment evaluated consistency of training delivery. Twenty in-depth interviews explored physician reactions and implementation of learnings, analyzed using a rapid approach. Results: A total of 18,206 physicians were trained. The post-training respondent group demonstrated higher HPV vaccination knowledge scores and more favorable responses in confidence, beliefs, and intent than the pre-training respondent group. The fidelity assessment demonstrated consistent delivery overall, with some variability in role play facilitation. Interviews highlighted increased physician confidence, peer knowledge sharing, and community engagement following the training. Medical societies reported strong program adoption and plans for continued efforts. Conclusions: A ToT physician training cascaded through medical societies is a feasible strategy to prepare physicians to recommend the HPV vaccine and address parental concerns at scale. Full article

Background/Objectives: Youth are increasingly engaged in mental health research, but many models remain consultative rather than participatory. We examined the Technology and Adolescent Mental Health Internship (TAMI) as an implementation-focused instrumental case study of a structured youth participatory action research internship for high school students recruited through schools serving Chicago’s West and South Sides. Methods: The case study included an embedded multimethod formative evaluation with participatory interpretation by a post-program youth advisory board (n = 8). In Year 3, 18 participants completed baseline surveys, and 16 provided matched pre–post surveys (matched survey completion = 89%). Measures included a study-developed five-item research-task self-efficacy indicator, post-program attitudes and intentions, and four open-ended items. We conducted one exploratory paired-samples t-test and an inductive descriptive content analysis. Results: In exploratory analyses, mean research-task self-efficacy scores increased from 21.8 (SD = 2.1) at baseline to 23.8 (SD = 1.4) post-program, t(15) = 4.09, p = 0.001, d_z = 1.02. Among the 15 respondents who responded to each of these post-program items, all reported being confident or very confident explaining youth mental health research, and all reported being at least somewhat likely to participate in future research, advocacy, or mental health activities. Participants described learning related to literature review, research design, ethics, data interpretation, and science communication. They recommended a longer program, additional applied practice, and more guest speakers. Conclusions: The TAMI was delivered within one program cycle and was viewed favorably by respondents. This case illustrates how youth-generated questions, adult scaffolding, collaborative interpretation, and youth co-authorship can be incorporated into a structured research internship. Larger, independent evaluations are needed to assess outcomes, sustainability, and transferability. Full article

Teacher adoption of virtual reality (VR) in education appears constrained despite the technology’s potential as an immersive multimodal learning environment. This cross-sectional online survey, based on convenience and snowball sampling, examined adoption perceptions among 408 Romanian teachers from primary, secondary, and tertiary levels using an integrated Technology Acceptance Model and Theory of Planned Behavior framework. Seven constructs were measured on five-point Likert scales and analyzed through internal consistency indices, confirmatory factor analysis, HTMT discriminant validity assessment, structural equation modeling, Spearman correlations, nonparametric group comparisons, supplementary manifest-score regression, and descriptive thematic coding of open-ended responses. The seven-factor CFA model showed acceptable fit (CFI = 0.945, TLI = 0.936, RMSEA = 0.068), and composite reliability and AVE supported convergent validity across all constructs. However, HTMT indicated limited discriminant validity between attitude toward using VR and attitude toward the behavior of adopting VR (HTMT = 0.928). In the SEM model, perceived usefulness showed the largest standardized association with attitude toward using VR, while behavioral intention was mainly associated with attitudinal evaluations and subjective norm; perceived behavioral control showed a weaker standardized path. All scales showed acceptable internal consistency (Cronbach’s $\alpha =0.81$–0.95), and construct means exceeded the scale midpoint (range: 3.45–4.03), indicating generally positive but differentiated perceptions. Supplementary manifest-score regression was consistent with the SEM results: the unified attitude factor showed the strongest statistical association with behavioral intention, followed by subjective norm and perceived behavioral control. Descriptive thematic coding of open-ended responses identified training, infrastructure, equipment access, curriculum-aligned content, cost, technical support, and time as recurrent perceived conditions associated with self-reported VR adoption intentions. The findings suggest that educational VR adoption should be interpreted through self-reported human factors and perceived implementation conditions, including perceived control, access to immersive equipment, practical training, and institutional support. Full article

The rapid proliferation of generative artificial intelligence (GenAI) has reshaped university students’ learning practices, yet the association between GenAI-assisted learning and procrastination remains insufficiently understood. Drawing on the Theory of Planned Behavior as an established framework, this study offered a contextual extension of TPB and procrastination research into the GenAI domain by examining the association between students’ use of GenAI for learning task completion and GenAI-assisted learning procrastination among 1243 Chinese university students. Path analysis showed that students’ use of GenAI was negatively associated with GenAI-assisted learning procrastination (β = −0.195, p < 0.001). Behavioral attitude (β = −0.164, p < 0.001), subjective norm (β = −0.171, p < 0.001), perceived behavioral control (β = −0.331, p < 0.001), and behavioral intention (β = −0.137, p < 0.001) were each negatively associated with GenAI-assisted learning procrastination. The indirect associations linking GenAI use to GenAI-assisted learning procrastination through these Theory of Planned Behavior constructs were statistically significant, with the total indirect association accounting for approximately 30.9% of the total association (standardized indirect association = −0.086, p < 0.01). Learning GenAI anxiety moderated the association between behavioral intention and GenAI-assisted learning procrastination, with the negative association being stronger among students with higher anxiety (b = −0.268, SE = 0.040, p < 0.001) than among those with lower anxiety (b = 0.006, SE = 0.052, p = 0.915). The model explained 71.6% of the variance in procrastination in GenAI-assisted learning. Given the cross-sectional design and the sample’s specific characteristics, these findings should be interpreted as conditional associations rather than causal evidence, and their generalizability requires further investigation. Nevertheless, the results highlighted the roles of beliefs, intentions, and emotional experiences in understanding procrastination in GenAI-assisted learning. Full article

Generative artificial intelligence (GenAI) is rapidly transforming teaching, learning, research, and institutional practices in higher education, increasing the need to understand how university professionals engage with AI technologies in both functional and ethical contexts. This study examined AI adoption among sampled Generation Y (Gen Y) and Generation Z (Gen Z) higher education professionals by investigating the relationships among AI Competency Capability (AICC), AI Utilisation Behaviour (AIUB), Perceived Usability (PU), Perceived Strategic Value (PSV), AI Ethical Awareness (AIEA), AI Self-Efficacy (AISE), Responsible AI Utilisation Behaviour (RAIUB), Academic Engagement (AE), Perceived Performance Outcomes (PPO), and AI Adoption Intention (AAI). Adaptive Structuration Theory (AST) and Dual-Process Theory (DPT) were employed as complementary interpretive perspectives rather than as theories directly tested by the structural model. Data were collected from 870 higher education professionals employed at Saudi Arabian universities and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), Partial Least Squares Multi-Group Analysis (PLS-MGA), and the Measurement Invariance of Composite Models (MICOM) procedure. The PLS-MGA identified statistically significant between-group differences only for the relationships between AICC and AIUB, which were numerically larger among the sampled Gen Z participants, and between AIEA and Responsible AIUB, which were numerically larger among the sampled Gen Y participants. All remaining differences represented sample-specific numerical variations rather than statistically confirmed between-group differences. Given the cross-sectional design and differences in age, career stage, institutional role, and professional experience, the findings should be interpreted as sample-specific associations rather than fixed generational characteristics. This study introduces the Gen-AI Dual Competency Alignment Framework (GADCAF) as a provisional conceptual and interpretive framework, intended to guide future research on AI competency, responsible AI utilisation, and organisational AI integration rather than as a validated theoretical model. The findings advance understanding of the complementary functional and ethical dimensions of AI adoption in higher education. Full article

As organizations lean more heavily on their IT systems, managing cyber risk is gaining increasing importance. Organizations are often challenged to determine which cybersecurity risk framework they should adopt. Choosing the right framework can have a significant impact on the quality of governance, operational resilience, and assurance in risk reporting. However, most prevalent cybersecurity risk frameworks vary significantly in their intent, design, and analytical approach. This makes it difficult for organizations to understand how each framework may meet their business needs. This study presents an AI-enhanced multi-criteria decision support approach for evaluating cybersecurity risk frameworks. The model incorporates machine learning-driven risk scoring as a conceptual input layer, enhancing the objectivity and analytical rigor of the comparison without executing new predictive algorithms. The methodology includes a hybrid approach of literature review, document analysis, and multi-criteria decision analysis (MCDA) to compare and rank NIST CSF, ISO 27001, FAIR, OCTAVE, and CRAMM based on eight criteria that are designed to represent modern requirements for risk frameworks, including governance, scalability, quantitative focus, and interoperability. These criteria also reflect differences in security metrics supported by each framework to provide an organized means to compare qualitative versus quantitative measurement methodologies. The results indicate that NIST CSF performs the best overall in agility, business alignment, and interoperability. ISO 27001 outperforms all others in established governance and compliance. FAIR outperforms all others in quantitative risk analysis and provides superior analytical depth that other frameworks do not offer. OCTAVE and CRAMM function well in legacy systems but lack scalability and are not well-suited for modern distributed systems. Robustness analysis shows that the ranking of NIST CSF, ISO 27001, and FAIR is consistent under different weighting combinations and industry types. The result of this research demonstrates that a combined or hybrid approach to cybersecurity risk framework selection, such as using NIST CSF with FAIR, can give organizations a more well-rounded foundation for applying machine learning-enabled risk analytics with cyber controls. This research also offers a reusable decision support tool that organizations can leverage when aligning their risk priorities to the features of cybersecurity risk frameworks. Full article

As generative AI technologies become increasingly embedded in educational cloud platforms, understanding their impact on teacher professional development is essential. Grounded in the stimulus–organism–response (S–O–R) framework, this study investigates how AI-driven stimuli—hedonicity, interactivity, and immersion—influence teachers’ self-regulation and resilience. Using structural equation modeling, data were collected from in-service teachers in Taiwan who actively utilize educational cloud platforms. The results reveal that all three AI-driven stimuli significantly enhance teachers’ self-regulation and resilience, which in turn are significantly associated with perceived value co-creation intentions. Specifically, self-regulation enables teachers to manage goals effectively, while resilience supports their recovery from technical setbacks. The findings indicate that self-regulation positively influences resilience, and both appear to mediate the relationship between perceived AI-driven stimuli and teachers’ value co-creation intentions. This study highlights the potential role of teachers’ psychological adaptability in AI-enhanced environments. Practical implications suggest that platform developers and administrators should prioritize AI features that foster self-directed learning and emotional engagement to promote collaborative willingness and professional alignment within modern educational ecosystems rather than implying proven macro-level transformation. Full article

Background: Stroke is a leading cause of neurological disability worldwide, with upper limb impairment affecting approximately 70% of survivors and only 5–20% achieving complete dexterity recovery at six months. Brain–computer interface (BCI) neurorehabilitation decodes motor intentions from electroencephalographic (EEG) signals to deliver synchronized functional electrical stimulation (FES) and virtual reality feedback, creating a closed-loop neurofeedback system that reinforces motor learning. While existing evidence supports BCI efficacy and safety in chronic stroke, its feasibility, safety, and cost-effectiveness during the acute and subacute phase (2 to 12 weeks post-stroke), when neuroplasticity is heightened, remain underexplored. Furthermore, there is a paucity of data regarding preliminary health economic analyses for BCI rehabilitation in acute stroke rehabilitation settings. Methods: This prospective, open-label, single-arm pragmatic feasibility pilot trial will recruit 12 patients with hemorrhagic or ischemic stroke (2–12 weeks post-stroke) undergoing inpatient rehabilitation from a public healthcare institution. Up to 15 sessions of BCI-rehabilitation of 30 min each using the recoveriX system will be supervised by a trained therapist or clinical research assistant (4–5 sessions/week over 3–4 weeks), followed by standard occupational therapy within 30–60 min of BCI-rehabilitation. Primary outcomes assessing feasibility and adherence include eligibility and recruitment rate (%/screened); tolerability using self-rated System Usability Scale (SUS) score; within-session adherence > 80%/240 trials, summated for completed trials per patient; programme completion number > 80% of scheduled (>12/15) sessions; and training-related adverse events per patient ≤ 17% (≤2/12 sessions). Secondary outcome measures include clinical efficacy by arm impairment scale using hemiplegic Upper Limb Fugl–Meyer Motor Assessment (FMA-UE), hand function using Action Research Arm Test (ARAT), admission and discharge functional status (Functional Independence Measure-FIM (18–126), Modified Barthel Index-MBI (0–100), stroke impact scale (SIS_3.0), arm, participation domains), and economic analysis. All outcomes will be measured by trained therapists/researchers at baseline week 0, week 3–4 (post-BCI-rehabilitation), and week 12 and 24 (follow-up). BCI-rehabilitation EEG-derived electrophysiological correlates of recovery will be extracted to better understand participant progress over time. An incremental cost-utility analysis will compare the BCI-rehabilitation participants against propensity-matched historical controls from the TTSH stroke rehabilitation registry (2017 to 2025), stratified by baseline motor severity. Discussion: This study will provide preliminary evidence on feasibility, tolerability, safety, clinical efficacy, and cost-effectiveness of early BCI-rehabilitation in acute/subacute stroke to better inform clinicians on its implementation. Full article

PhotonAssay™ is a rapid, non-destructive gold assay method that analyses large-mass crushed or pulverised samples while reducing turnaround time, hazardous reagent use, and environmental footprint relative to conventional fire assay. Its growing adoption, however, has exposed a persistent misconception, that CRIRSCO-based reporting codes approve certain analytical methods as being “compliant.” This paper clarifies the reporting position for PhotonAssay™ and outlines the key requirements for its use in Exploration Results, Mineral Resource, and Ore/Mineral Reserve reporting, including sampling context, preparation protocol, quality assurance/quality control and method validation. It also frames adoption as a four-stage journey—awareness, consideration, intent, and implementation—in which peer learning, testwork, business-case development, and deployment planning are as important as analytical performance. Particular attention is given to representative sampling and to interpreting differences from fire assay, especially in coarse-gold systems where larger assay charges are likely to yield more representative results. Two test methods are recommended, the transitional feasibility study (bias between assay methods) and heterogeneity study (bias and precision). The PhotonAssay™ adoption journey is a multidisciplinary process, requiring engagement between practitioners and executives, and between geoscientists, mining and minerals engineers, chemists, data scientists and corporate managers. Full article

Gamification increasingly influences learning experiences, cognitive engagement, and behavioral performance in digital learning, cognitive training, and health intervention contexts. However, the mechanisms underlying cognitive factors, along with related research hotspots and evolutionary trends, have not been adequately synthesized. Using the Web of Science Core Collection, this study analyzes 813 publications on gamification and cognitive factors published between 2012 and 2024. Using a bibliometric method, CiteSpace was utilized to analyze publication trends, collaborations, keyword co-occurrence, cluster structures, burst terms, cited references, and knowledge-map visualizations. The cluster analysis produced 10 interrelated themes: “flipped classroom,” “active learning,” “continuance intention,” “dementia,” “executive function,” “cognitive control training,” “computational thinking,” “cognitive training,” “cognitive load” and “user experience”. Potential future directions suggested by the bibliometric patterns include: (1) expanding gamification across educational contexts; (2) refining gamification theory models that focus on cognitive processes by examining user experience and cognitive load as potential mechanisms that link gamification design features to outcomes such as motivation, self-efficacy, task performance, and continuance intention; (3) promoting applications in cognitive training, cognitive impairment intervention, and digital health; (4) optimizing experimental design, data collection, interdisciplinary collaboration, and personalized design; and (5) clarifying how gamification shapes cognitive processes such as attention allocation, cognitive load regulation, problem solving, executive function, and computational thinking. This study does not aim to establish causal effects; rather, it uses bibliometric evidence to reveal the developmental trajectory, thematic structure, and emerging directions of research on gamification and cognitive factors. Full article

Cross-domain collaborative filtering effectively alleviates the data sparsity issue but raises serious privacy concerns. Federated learning has been integrated into cross-domain collaborative filtering to reduce these risks by securely exchanging embeddings or model parameters. However, the current federated paradigm often relies on the simple alignment of coarse-grained representations, while propagating information on a rigid local graph. Without fine-grained preference modeling, models easily suffer from representation collapse. The inherent sparsity of local graphs further limits their robustness. To address these issues, we propose P2P-IAGR, a privacy-preserving peer-to-peer cross-domain collaborative filtering framework based on intent-adaptive graph reconstruction. Specifically, our framework first disentangles user and item representations into fine-grained latent intent prototypes. We perturb these prototypes using Local Differential Privacy (LDP) and securely exchange them across domains. A contrastive learning strategy is then used for cross-domain alignment. Next, guided by the combined cross-domain intent prior, P2P-IAGR differentiably reconstructs an augmented graph view. We apply a dual-view structural contrastive learning objective to dynamically inject external collaborative signals into the sparse local topology. Extensive experiments on real-world datasets show that P2P-IAGR significantly outperforms state-of-the-art methods, achieving an average improvement of 5.85% in NDCG and 6.89% in HR. Full article

With the deep integration of artificial intelligence technology into education, AI literacy has emerged as a core competence indispensable for pre-service teachers. However, its formation mechanisms and sustainable cultivation pathways remain to be further explored. This study integrates the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) to construct a theoretical model, in which Individual Innovation (II) and Self-Efficacy (SE) serve as antecedents, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) as mediators, Behavioral Intention (BI) as a proximal variable, AI literacy as the outcome variable, gender and major as moderating variables, and grade and AI exposure time as control variables, exploring the influencing factors and mechanisms of pre-service teachers’ AI literacy. Through a questionnaire survey of 778 pre-service teachers, Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) were employed for sequential empirical analysis. The PLS-SEM results reveal that II and SE were significantly and positively associated with AI literacy through the serial mediation of PU, PEOU, and BI. The fsQCA further identified four distinct equifinal configurations associated with high AI literacy: “High-efficacy Practice-Oriented”, “High-Behavioral-Intention-Oriented”, “High-Innovativeness-Oriented”, and “Long-Term-Development-Oriented”. The findings demonstrate that the improvement of pre-service teachers’ AI literacy follows multiple equifinal mechanisms, necessitating a shift beyond the single-training mindset. Accordingly, this study proposes differentiated cultivation pathways, providing theoretical foundations and practical references for normal universities to deliver targeted and sustainable AI literacy training. It also offers empirical evidence and strategic support for the sub-goals of SDG 4 concerning teacher capacity-building and the digital transformation of education, which aim to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Full article

Autonomous driving technology is regarded as an effective approach to improving traffic safety and reducing accidents. However, its performance in complex urban scenarios, particularly at intersections, still requires improvement. To enhance trajectory prediction and tracking control, a planning and control framework based on interacting-vehicle trajectory prediction is proposed. First, a deep learning trajectory prediction model integrating an attention mechanism and driving intention recognition is developed to predict the future trajectories of interacting vehicles with high accuracy. Based on the prediction results, collision-avoidance trajectory planning is performed. Then, a trajectory tracking controller combining compensated explicit model predictive control (C-EMPC) and PID control is designed to achieve both high tracking accuracy and real-time performance. The proposed framework is evaluated in a typical intersection scenario through simulation. Results show that the proposed framework can improve driving safety and efficiency in the tested scenario. Furthermore, hardware-in-the-loop experiments indicate the real-time execution feasibility and effectiveness of the proposed method. Full article