Blockchain has gained attention for its potential to verify health insurance records, allow secure data sharing, and protect patient privacy. Despite these benefits, blockchain adoption in Egypt remains limited, and little research has examined its drivers and barriers. This study contributes to the limited research on blockchain adoption in Egyptian public hospitals by extending the TOE model with context-specific factors and examining adoption decisions at the organizational level. The study was conducted in public hospitals operating within the Universal Health Insurance System (UHIS) across six governorates. A quantitative approach was used, with data collected from a stratified random sample of 228 senior management and IT professionals across 53 public hospitals, analyzed using PLS-SEM. The results show that relative advantage, financial capability, and perceived trust positively affect adoption intention, whereas perceived risk and complexity negatively affect it. Security and privacy, as well as government support and regulations, significantly enhance perceived trust but do not directly affect adoption intention. Top management support and hospital readiness also strengthen perceived trust. The model explains 67.1% of the variance in blockchain adoption intention (R2 = 0.671) and shows predictive relevance (Q2 = 0.662). Perceived trust positively influences adoption intention and mediates selected relationships in the model, particularly for security and privacy, as well as government support and regulations, which indirectly influence adoption through trust. The proposed model provides practical insights for Egyptian public hospitals, blockchain providers, the Ministry of Health, and policymakers in designing strategies that may facilitate blockchain adoption in public hospitals in Egypt.

Implementing Live Long Walk Strong: Practice-based evidence on adapting a physical therapy program across diverse contexts.

Wireless Sensor Networks (WSNs) are popular for monitoring purposes, but it is found that the networks' lifetime may be restricted by energy limitations and load imbalance. Therefore, the authors propose a new energy-balancing protocol called Density-Energy Gradient-based Relay Routing (DEGR) that combines local node density and residual energy gradients in order to choose relay nodes for the dynamic formation of optimal routing paths to maximize routing efficiency in mobile sink-enabled WSNs. In contrast to clustering protocols and static sector based routing, the DEGR dynamically determines the relay nodes which are not only energy abundant but also strategically located in dense areas, thus avoiding hotspots and extending network lifetime. DEGR combined with an adaptive clustering model and circular sink mobility, provides balanced distribution of energy and low latency delivery of data. The results of the simulations in MATLAB show that DEGR outperforms Low-Energy Adaptive Clustering Hierarchy (LEACH), Energy-aware Clustering and Data Routing Algorithm (ECDRA), LEACH with Mobile Sink (MS-LEACH), and Mobility and Energy Adaptive Density-based Clustering (MEADC) protocols. It increases the lifespan of the first node death by approximately 54% compared to LEACH, decreases the variance of energy consumed by approximately 40%, and keeps the packet delivery ratio above 90% all the time. These results show the relative performance advantage of DEGR in the simulations in the specified simulation framework.

Enhancing cross-regional transferability of super-resolution-based flood surrogate models for data-scarce catchments.

The Medly remote management program, which has demonstrated effectiveness in a speciality care setting, enables comprehensive management of patients with heart failure by integrating the Medly digital therapeutic with defined clinical roles. One-time funding from Ontario Health was awarded to 3 community-based organizations to implement Medly for the first time outside a speciality care setting. The objectives of this project were to evaluate implementation success and identify the facilitators and barriers to implementing this intervention in 3 community-based settings. A multiple case study approach was used to determine implementation success using the Proctor implementation outcomes and barriers and facilitators based on constructs of the Consolidated Framework for Implementation Research (CFIR). Qualitative insights were collected through semi-structured interviews (n = 20) with implementation team members 3 months after enrollment of the first patient. Quantitative data for the implementation outcomes were obtained from a secondary analysis of project status reports analyzed 6-months after go-live. Although there was evidence of intervention fidelity and perceptions of acceptability and appropriateness toward the Medly program at all 3 sites, implementation of the Medly program was found to be successful at only one site based on the key Proctor outcomes of feasibility and adoption. The interviews revealed common implementation facilitators across sites related to characteristics of the intervention, including relative advantage and intervention design quality. Between-site differences related to the CFIR constructs of the inner and outer setting highlight unique features that resulted in differing levels of implementation success. Specifically, facilitators included access to a predefined patient pool, maturity of the organization and communication channels and staffing resources while barriers included lack of time to execute the implementation plan as intended. This work demonstrates that remote management programs, like Medly, can be translated from a speciality care setting and implemented with fidelity in community settings. However, certain contextual conditions are important to enhance the likelihood of success of the implementation, particularly when time constraints are imposed.

Costly punishment in social bonds includes reactive penalties for transgressions and proactive sanctions against fair behaviors; however, a unified neurocomputational framework is lacking. Using neuroimaging and computational modeling, we tested how future interaction prospects modulate both forms of punishment. Participants, acting as second- or third-party punishers, responded to offers from partners with or without future interactions, imposing harsher punishment on both fair and unfair offers from non-future-interacting individuals, consistent with lower self-reported closeness. Heightened aversion to disadvantageous inequity, indexed by dorsal anterior cingulate activity, mediated increased reactive punishment toward non-future-interacting wrongdoers, whereas punishment of future-interacting transgressors engaged the dorsolateral prefrontal cortex (dlPFC). A stronger preference for relative advantage and reduced harm sensitivity, linked to the dlPFC and temporoparietal junction, mediated greater proactive punishment toward non-future-interacting individuals. Proactive punishment also exhibits collective retaliation against others' unfairness. These findings establish a unified neurocomputational framework that integrates the mechanisms by which future interaction prospects regulate costly punishment within social bonds.

BackgroundTo address the delayed provision of obstetric emergency care in Ghana and Liberia, our team previously implemented a mobile obstetric emergency referral system (MORES) connecting rural health facilities (RHFs) and district hospitals through a South-South-North triangular collaboration. This secondary qualitative data analysis aimed to synthesize the perceived barriers and benefits associated with the adoption of MORES in Ghana and Liberia. To guide this cross-context synthesis, we utilized Rogers’ Diffusion of Innovation Theory.MethodsThe analysis included individual interviews conducted among 29 district hospital providers and 33 rural health facility (RHF) workers in Liberia. In Ghana, 11 midwives at a district hospital participated in a focus group discussion. The codes were developed utilizing an inductive process. Thematic analysis was utilized to compare adoption across countries through the Diffusion of Innovation Theory.FindingsFour themes were identified from the healthcare workers in Ghana and Liberia: compatibility, relative advantage, resource barriers, and limited implementation. According to healthcare workers, MORES was compatible with everyday workflow and had significant relative advantage, contributing positively to adoption. Healthcare workers were able to prioritize patient conditions upon presentation with increased referral information. Resource barriers and the limited implementation across districts restricted widespread adoption of MORES. Healthcare workers in both countries cited inconsistent access to ambulances as a barrier to complete obstetric referrals and in Liberia, the need for financial support for data and network connectivity was referenced. There were nuances by country, within the four themes. In Ghana, providers used MORES to facilitate follow-up on patients who did not complete referrals, contributing to MORES’ relative advantage.ConclusionsMORES has the potential to reduce obstetric emergency referral delays in Ghana and Liberia. If the resource barriers and limited implementation of MORES are addressed, healthcare workers may continue to adopt and use the MORES system. Policy makers can address referral delays through the scale up of MORES, a compatible intervention with great relative advantage.

Green loans and energy-efficient technology adoption: Evidence on sectoral heterogeneity in SMEs

Socioeconomic disadvantage and sepsis survival in Australian intensive care units: A retrospective cohort study.

Altered interoceptive-exteroceptive profile in Generalized Anxiety Disorder: Evidence from bayesian psychophysics and metacognitive modeling.

Accommodative Demand and Near Addition in Early-Onset Primary Angle-Closure Disease: A Comparative Study.

The performance of proton exchange membrane fuel cells (PEMFCs) under high-load conditions is strongly affected by coupled flow and mass-transfer processes in cathode flow channels. In this study, a three-dimensional multiphysics model was used to investigate the effects of parameterized periodically perturbed channels on PEMFC transport and output performance. Five perturbed channel configurations, including sawtooth, sinusoidal, parabolic, trapezoidal, and elliptical channels, were compared with a conventional straight channel under identical geometric constraints. Combined with the field synergy principle and vortex-structure analysis, the mass-transfer enhancement mechanism was discussed from two aspects: periodic converging–diverging modulation and local curvature continuity. The results show that the elliptical channel provides the most favorable trade-off among the tested configurations. At 0.7 V, it achieves an EEC of 3.65 × 10−4 and increases the net power by 12.14% compared with the straight channel. Its peak power density reaches 0.798 W/cm2, corresponding to an 18.40% increase. The elliptical channel also improves oxygen utilization, reduces average liquid-water saturation, and provides a more uniform temperature distribution at the reaction interface. Additional amplitude-wavelength sensitivity analysis indicates that the relative advantage of the elliptical channel remains generally stable within the tested parameter range, although the benefit weakens when the amplitude decreases or the wavelength increases. These results provide comparative evidence for understanding how periodic perturbation geometry affects secondary-flow formation and mass-transfer enhancement in PEMFC cathode channels.

Priority effects outweigh nitrogen deposition in shaping C3–C4 grass competition in desert steppe ecosystems

Recent advances in technology and the impact of COVID-19 have expanded the adoption of digital health. Synchronous collaborative telehealth between optometry and ophthalmology has been used to expedite specialist eye care in Western Australia since 2011. Optometrists perform an in-person assessment and participate in videoconferencing with ophthalmology to facilitate shared decision-making. This study utilises existing implementation theory to assess factors influencing implementation success, sustainability and scalability of this telehealth model. Regional, rural, and remote Western Australia. In-depth interviews were undertaken using an interview guide based on the Consolidated Framework for Implementation Research (CFIR). Deductive analysis was used to code themes to the framework, and inductive analysis was used for themes relating to sustainability and scalability. Sixteen clinical and non-clinical staff with experience in telehealth for eye care in rural Western Australia. Key themes identified using the CFIR included the relative advantage of telehealth (innovation), optimising workflow and role clarification (implementation process), funding (external setting), technological infrastructure (inner setting), a champion, and ownership and adaptability (individuals). Stakeholder buy-in, resource allocation, integration into public health infrastructure, asynchronous telehealth, and artificial intelligence were themes pertaining to both sustainability and scalability. This study proposes key considerations in the implementation and maintenance of a collaborative telehealth model for eye care in rural areas which may be used as a basis for guideline development or to replicate the model in other contexts.

ABSTRACT Objectives Latino/a immigrants evidence a relative health advantage compared to US-born counterparts, a trend that dissipates as they navigate a myriad of challenges tied to the immigration and adaptation process known as acculturative stressors. Recognizing the vast heterogeneity situated within the Latino/a immigrant population, experiences of acculturative stress differ among immigrant groups based on a range of demographic, social, and migration-related factors. Design In a sample of adult immigrants (ages 18–44; N = 391) residing in an emerging immigrant community, we conducted a cluster analysis to identify classes of respondents (i.e. ‘clusters of acculturative stressors’) and evaluated differences among these clusters across demographic, social support, and acculturation level variables. Results Results revealed that participants fell into one of three clusters: (a) high in family and systemic acculturative stress; (b) high in marital and parental stress; or (c) low in acculturative stress in general. Participants in the first group tended to be unauthorized immigrants, thereby facing more acculturative stress. Participants in the third group tended to have higher English proficiency, education, income, health insurance, and emotional and instrumental support, highlighting the buffering impact of residing within positive social contexts. Conclusion These findings underscore the need for interventions that could be tailored to distinct subgroups of immigrants such as those which could increase familial and social connectedness and support, especially for those who are isolated and marginalized (cluster 1) as well as those experiencing familial and parenting stress (cluster 2). For those who endure comparatively less acculturative stress (cluster 3), low-touch interventions that maintain resilience may be important to maintain the wellbeing of this subset of immigrants. Together, these strategies have the potential to improve the health of this population and thereby reduce inequities.

To evaluate early ΔSUVmax% for predicting pathological complete response (pCR) in HER2-positive breast cancer patients undergoing neoadjuvant therapy (NAT), to build a combined [68Ga]Ga-HER2 Affibody PET/CT model, and to compare its performance with magnetic resonance imaging (MRI). 45 patients underwent both [68Ga]Ga-HER2 Affibody PET/CT and MRI at baseline and after two cycles of NAT. Univariate logistic regression identified parameters associated with pCR. After assessing multicollinearity, combined PET (ΔSUVmax%, TBR2) and MRI (ADC2,ΔADC%) models were constructed. The model performance was evaluated using leave-one-out cross-validation (LOOCV) method. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis were performed. 27 of the 45 enrolled patients achieved pCR (60%). After treatment, the pCR group showed significantly lower PET/CT parameters, a significantly higher ΔSUVmax%, and significantly higher MRI-derived ADC and ΔADC% values compared to the non-pCR group. The combined PET model showed a LOOCV AUC of 0.887 (95% CI: 0.781-0.969), significantly higher than that of the combined MRI model (0.642, 95% CI: 0.474-0.803, p = 0.015). Calibration was numerically better for the PET model (p = 0.404) than for the MRI model (p = 0.309), indicating a relative advantage for PET. Decision curve analysis indicated higher clinical net benefit for the PET model across threshold probabilities of 10%-90%. ΔSUVmax% is a promising early predictor of pCR.The [68Ga]Ga-HER2 Affibody PET/CT combined model provides superior predictive value compared with MRI‑based model and could serve as a promising biomarker to guide personalized treatment decisions.

BackgroundGenomic prediction (GP) is a central component of modern plant breeding, enabling the early selection of superior genotypes based on genomic marker data. Classical GP models, such as genomic best linear unbiased prediction (GBLUP), operate within the data modeling culture and typically assume additive genetic effects, with extensions required to model non-additive effects such as dominance and epistasis. In contrast, machine learning (ML) models from the algorithmic modeling culture can flexibly model complex, non-additive genetic relationship but often lack direct grounding in quantitative genetic theory and interpretability. To bridge these gaps, we propose 2NPLGBM, a hybrid genomic prediction approach that integrates quantitative genetics with ML. This method introduces a two-matrix (2NP) genotype representation by concatenating additive (Z) and dominance (W) matrices, which are then used as input to a Light Gradient Boosting Machine (LGBM), enabling the simultaneous modeling of additive, dominance, and higher-order genetic interactions (AA, AD, DD).ResultsThe 2NPLGBM model was evaluated using six years of test-cross hybrid maize trial data across four agronomic traits (grain yield, plant height, days to silking, and days to anthesis) under five cross-validation schemes simulating temporal: Leave-One-Year-Out (LOYO), Rolling Window (RW), and genetic generalization: Five-Fold, and tester-based schemes (Tester CV0 and Tester CV00). Compared to GBLUP, 2NPLGBM achieved an average of 5% improvement in predictive accuracy under temporal validations and over 15% gains under tester-based schemes, particularly for flowering traits (days to silking and days to anthesis). Performance was generally comparable to LGBM, with both ML models outperforming GBLUP for most traits. Under Tester CV0, 2NPLGBM showed its strongest relative advantage over LGBM for flowering traits, suggesting improved capture of interaction-related genetic signals, whereas LGBM generally performed best for plant height and grain yield. In five-fold CV and Tester CV00, GBLUP remained competitive for some traits, while both machine learning models showed reduced gains, with LGBM slightly outperforming 2NPLGBM. In addition, 2NPLGBM generally improved selection efficiency over GBLUP and, in most cases, LGBM, indicating enhanced ability to capture complex genetic signals relevant for hybrid ranking, particularly for flowering traits, whereas LGBM tended to achieve the highest selection efficiency for plant height and grain yield. Feature interpretation using Shapley Additive exPlanations (SHAP) confirmed that non-additive interactions contributed substantially to prediction accuracy for highly heritable traits. It also revealed trait-specific architectures, additive effects dominated flowering traits, while dominance effects contributed more to plant height and yield. Classical variance component analysis supported these findings, indicating high dominance contributions of 17.3% for yield and 8.2% for plant height.Conclusion The 2NPLGBM model integrates quantitative genetic theory with machine-learning, bridging classical statistical (data-model) and algorithmic modeling cultures. bridging classical statistical (data model) and algorithmic modeling cultures. By jointly modeling additive and non-additive effects it can improve predictive accuracy, interpretability, and selection efficiency in test-cross hybrids. Future work should explore multi-trait and multi-environment extensions, integration of environmental covariates, and the inclusion of multi-omic data to further strengthen predictive power and interpretability.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13007-026-01545-2.

HighlightsWhat are the main findings?University students demonstrated moderately positive attitudes toward telemental health services, with attitude emerging as the strongest predictor of behavioral intention to use these services.The lack of trust in mental health professionals was the most prominent help-seeking barrierWhat are the implications of the main findings?Universities and providers should strengthen privacy features and expand telemental health options.There is a need for culturally tailored awareness campaigns, trust-building communication, and gender-sensitive service design to support telemental health adoption among Saudi university students.Background: Mental health concerns are notably common among students attending universities in Saudi Arabia, and low engagement with psychological services has been widely documented in this population group. Telemental health has emerged as a promising alternative under Vision 2030’s digital transformation agenda, although the determinants of university students’ intentions to use these services have received limited empirical attention in Saudi Arabia. Objective: This study examined attitudes toward telemental health services, perceived barriers to seeking psychological help, and predictors of behavioral intentions to use telemental health services among university students in Saudi Arabia, based on the Technology Acceptance Model and Theory of Planned Behavior. Methods: A cross-sectional design was employed using an online, self-administered questionnaire. A total of 236 undergraduate students from three large universities in Riyadh were recruited using convenience sampling methods. We examined demographic variables, telemental health attitude variables (ease of use, usefulness, subjective norms, trust in telemental health, relative advantage, intentions, and attitudes), and barrier subscales (fear of stigma, trust in mental health professionals, difficulties in self-disclosure, perceived devaluation, and lack of knowledge) among university students. Descriptive statistics, Welch’s t-tests, and multiple linear regression analyses were conducted using SPSS (version 29). Results: Participants demonstrated moderately positive attitudes toward telemental health (M = 74.15, SD = 16.11) and reported moderate overall barriers (M = 50.76, SD = 14.44), with trust in mental health professionals being the most prominent barrier. The regression model explained 58.0% of the variance in behavioral intentions (F(19, 211) = 15.35, p < 0.001). Attitude was the strongest predictor (β = 0.534, p < 0.001), followed by trust in telemental health, sex, and difficulty in self-disclosure. Conclusions: Culturally tailored awareness campaigns, trust-building communication, and gender-sensitive service design are recommended to promote the adoption of telemental health by Saudi university students. These efforts align with Vision 2030’s digital health priorities and may support the equitable expansion of mental healthcare access in this population.

Craniosynostosis, the premature fusion of calvarial bones, requires surgical intervention within a critical time-period. Early referral potentiates the application of less invasive treatments. Previous studies from the United States have demonstrated that socioeconomically disadvantaged groups experience delayed referrals, older age at surgery and require more complex interventions. This study investigated the relationship between socioeconomic status and management for non-syndromic single suture craniosynostosis within Australia's universal healthcare system. A retrospective cohort study was completed at an Australian craniofacial centre from 2018 to 2024. Independent variables (Index of Relative Socioeconomic Advantage and Disadvantage score, rurality, sex, interpreter use) and outcomes (age at referral/surgery, time between referral and appointment, procedure type, complications, and length of stay) were analysed with univariate and multivariate models. Totally, 200 patients were included. Age at referral and surgery were negatively correlated with Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) percentile. Multiple linear regression models demonstrated that for each one-unit increase in the IRSAD decile, age at referral decreased by 24.26 days (p < 0.001) and age at surgery decreased by 23.29 days (p = 0.003). No other factors were found to affect age at referral or age at surgery, and there were no differences in type of surgery, complications or length of stay. This study found that patients from socioeconomically advantaged backgrounds were referred earlier and underwent surgery at a younger age compared to disadvantaged counterparts. However, patients underwent the same surgical procedures, indicating that in Australia's universal healthcare setting, patients are falling within the critical window for intervention regardless of socioeconomic status.

To explore the influencing factors promoting the cancer pain management (CPM) behavior of pharmacists through the behavioral model and to provide a basis for formulating targeted intervention strategies. This study constitutes the third phase of a large‑scale cross‑sectional project carried out in medical institutions of all levels across different income regions of China. In the present phase, we combine the capability-opportunity-motivation (COM‑B) model with the theoretical domain framework (TDF). Univariate and multivariate analyses were conducted on the collected data, and the influencing factors of pharmacists' CPM behavior were explored through the predictive model. A total of 1466 hospital pharmacists from 157 cities in China participated in the survey. Multivariate analysis in this study revealed no significant correlation between pharmacists' CPM behavior and the economic level of the city they work in or whether their institution has passed the GMP-ward review. Pharmacists' department, capability, and opportunity are positively associated with pharmacists' CPM behavior. The regression coefficients are 0.048 for department, 0.081 for capability, and 0.649 for opportunity, indicating their relative strengths in predicting CPM behavior. Among them, opportunity has the strongest predictive effect on pharmacists' CPM behavior. Research shows that pharmacists' department, capability, and opportunity are significantly positively correlated with CPM behavior and have a relative advantage in predicting behavior. This finding provides a basis for formulating measures to promote pharmacists' management of cancer pain.