Search Results (69,582)

Mobile health systems have become a central component of the health sector, reshaping how healthcare is accessed, delivered, and experienced, especially in developing countries with limited infrastructure. Mobile health systems are interactive and enable continuous interaction among patients, health professionals, and mobile technology. While these systems improve access to care and patient engagement, they also raise important concerns about how security is handled in highly interactive socio-technological environments. The current state of security assessment for mobile health systems tends to be biased towards technical security controls without addressing the required user interactions during mobile health system development in relation to providing security measures and evidence. This study addresses this gap by proposing a Phase–Role Interaction Security Assessment framework for mobile Health, namely, the PRISM-mH framework. The framework considers three dimensions: interaction-oriented security development lifecycle phases, the roles of the stakeholders involved, and evidence supporting the security requirements. With its help, security can be assessed continuously while taking into consideration the phases and role interactions. This framework is aimed at enhancing the visibility of security during user interactions, while also offering a systematic reference for researchers and practitioners to assess and enhance their security through the mobile health system lifecycle. Considering that PRISM-mH is proposed to address user interaction, technical control, and governance—factors that have not been considered consistently in past mobile health security assessments—this framework opens the door to more comprehensive and evidence-based security assessment, with a goal of enhancing the security position in mobile health development. The author argues that such a framework can help to propose a structure for an agile-driven approach for future studies and provide scoring-based guidance for informing the level of security maturity in mobile health technologies. Full article

Unattended photovoltaic monitoring nodes must maintain authenticated operation under physical access, intermittent connectivity, and environmentally induced key-reconstruction errors. We present a distributed adaptive hardware security framework (DAHSF) combining trusted-gateway threshold authorization, adaptive static random-access memory (SRAM) physical unclonable function (PUF) reconstruction, and incident-driven inspection. Independent epoch secrets separate current authorization from retained historical shares. A nested Bose–Chaudhuri–Hocquenghem (BCH) error-correction scheme reconstructs a fixed 2016-bit SRAM-PUF response, from which universal hashing extracts a 256-bit seed; federated estimation guides decoding effort. The cost and defense trade-offs are assessed under the tested workload, gateway trust, prediction-calibration, and availability conditions. On a 30-node ESP32 platform, DAHSF reduces median startup and mean node power by 4.8% and 4.4% relative to a matched lightweight baseline, with 14.3% more incremental RAM. Predicted-bound decoding reduces mean decoding time from 96 to 59 microseconds. A separate confirmatory cohort records three failures in 120,000 stressed reconstructions; its board-clustered 95% upper bound is $8.6\times {10}^{-5}$. In the moderate attack scenario, the full inspection profile reduces compromised-node exposure by 30.0% relative to a risk-threshold policy and by 18.0% relative to frozen propagation-rate estimates. The recorded control workload has no observed 1 ms MPPT deadline misses, while fault injection still bypasses authorization in 168 of 4000 attempts. Full article

Coronary microvascular dysfunction (CMD) is a major mechanism of myocardial ischemia and adverse cardiovascular outcomes in patients with angina or dyspnea, both in the absence and in the presence of obstructive coronary artery disease. Because the coronary microcirculation cannot be visualized directly with routine angiography, diagnosis depends on physiological assessment. Non-invasive imaging has moved beyond relative perfusion analysis toward quantification of resting and hyperemic myocardial blood flow and flow reserve. Positron emission tomography (PET) remains the most extensively validated non-invasive method and provides robust absolute flow measurements with strong prognostic evidence. Quantitative stress cardiovascular magnetic resonance (CMR) is rapidly maturing, combining high-resolution perfusion mapping with ventricular and tissue characterization without ionizing radiation. Transthoracic Doppler echocardiography offers an accessible, radiation-free estimate of coronary flow velocity reserve, whereas myocardial contrast echocardiography can assess myocardial blood volume and replenishment kinetics. Dynamic cadmium-zinc-telluride single-photon emission computed tomography and stress computed tomography perfusion extend flow quantification to more widely available platforms, although standardization and outcome validation remain less developed. Across modalities, interpretation requires attention to the adequacy of vasodilator stress, resting hemodynamics, diffuse epicardial atherosclerosis, scar, and technical factors. A reduced flow reserve is not synonymous with isolated microvascular disease and cannot identify acetylcholine-provoked vasospasm. This review provides a clinically oriented framework for selecting, performing, interpreting, and reporting non-invasive CMD testing; examines the evidence, strengths, and limitations of each modality; and defines situations in which invasive coronary function testing remains necessary. Future progress will depend on cross-platform standardization, automated quality control, disease- and sex-specific reference ranges, and prospective trials linking imaging-defined endotypes to effective treatment strategies. Full article

The sixth generation (6G) mobile network demands exceptional resilience to support mission-critical applications such as remote surgery, autonomous driving, and industrial automation. Conventional architectures typically separate the wireless access and optical transport domains, which limits coordination and increases vulnerability to cascading failures. This study proposes a cross-domain resilient architecture that integrates federated learning (FL) for optical path prediction with hierarchical reinforcement learning (HRL) for radio resource allocation, coordinated through a virtualised intelligence plane. A continuous-time Markov chain (CTMC) model is developed to characterise network resilience using coverage and throughput metrics, with analytically derived transition probabilities. To mitigate link failures, a load-balancing triple-connectivity (LBTC) scheme based on a correlated failure model (Clayton copula) is introduced, allowing each user to maintain three simultaneous connections to the nearest radio units. The outage probability analysis explicitly incorporates the correlated failure model, ensuring consistency between the theoretical framework and simulation results. Extensive OMNeT++ simulations, aligned with the O-RAN functional split option 7.2 (3GPP TR 38.801) and SD-EON optical transport, demonstrate that the proposed framework reduces end-to-end latency by 33% for URLLC services, increases throughput by 17% for mixed traffic, decreases outage probability by 62%, and reduces recovery time from 8 s to 2 s compared to state-of-the-art baselines, while maintaining acceptable quality of service under failure conditions. Full article

Global climate change severely challenges crop production in tropical and subtropical ecosystems, driving the need for germplasm evaluation. In this study, a sequential evaluation approach was used to identify drought-resilient Thai indigenous upland rice (Oryza sativa L.) accessions. A large-scale screening of 336 accessions at the seedling stage (2022 and 2023) using leaf rolling (LR) scores yielded 23 promising accessions, which were further evaluated across seedling, active tillering, and reproductive flowering stages under severe water-deficit stress. At the seedling stage, accessions ULR 014, ULR 018, and ULR 092 demonstrated consistently outstanding performance, with low leaf rolling scores (LR). At the tillering stage, accessions ULR 086, ULR 014, and ULR 397 exhibited superior post-drought vegetative plasticity, achieving exceptionally high tiller regeneration rates (31.5–36.9%) after re-watering, which serves as a vital recovery mechanism for rainfed direct-seeded systems. Under terminal drought at the flowering stage, accessions ULR 026 (14.8 g/plant; 3.27% yield reduction), ULR 130 (14.3 g/plant; 0.00% yield reduction), ULR 129 (13.0 g/plant; 8.45% yield reduction), and ULR 014 (11.4 g/plant; 12.31% yield reduction) maintained high grain yield potential and yield stability, while ULR 014, ULR 254, and ULR 397 achieved high proportions of 100% completely filled grains under severe stress. Hierarchical cluster analysis classified the selected germplasm into four groups (G1, G2, G3, and G4) based on stage-specific traits. Genotypes in Group 4 (ULR 014, ULR 254, ULR 032, ULR 346, and IR62266) demonstrated comprehensive multi-stage drought resilience with high 100% seed filling (36.9%) and total filled grain (66.5%), while Group 2 (ULR 026, ULR 130, ULR 129, ULR 075, ULR 071, and ULR 003) exhibited the lowest yield reduction under drought (13.3%). The phenotypic variations observed across developmental stages underscore the strong stage dependence of drought response in these local landraces, demonstrating that early-stage resilience does not guarantee reproductive tolerance. The candidate parental sources identified in this study, particularly ULR 014 (high yield maintenance and 100% filled grain under flowering stress), ULR 026 and ULR 130 (yield stability and minimal yield loss), and ULR 397 (strong tiller recovery), offer valuable breeding materials for developing further breeding program. Full article

Because of the increasing negative effects of drought on sectors such as water resources, the economy and agriculture, there is a strong need to study drought and its projections. Given the importance of studying different scenarios regarding drought and climate change, this study presents an integrated modelling framework to generate future drought projections for the Western Black Sea Basin, Türkiye, under four Shared Socioeconomic Pathway (SSP) scenarios derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Monthly precipitation data from seven General Circulation Models—ACCESS-CM2, CanESM5, CNRM-CM6-1, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-LR and MRI-ESM2-0—were statistically downscaled to 31 meteorological observation stations using a Multivariate Adaptive Regression Splines (MARS) approach, trained over the 1979–2014 period. Precipitation records from all 32 available meteorological stations were quality-controlled, and the 31 stations with continuous temperature records were retained for the full analysis, so that both indices could be computed at the same locations. Systematic biases in the downscaled outputs were subsequently corrected using the Quantile Delta Mapping (QDM) method, an essential step that simultaneously reduces distributional errors and preserves the future climate change signal. Both the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were computed at 3-, 6- and 12-month scales, with distribution parameters estimated once over the 1979–2014 window of the bias-corrected model series and held fixed for 2015–2100 so that baseline and future statistics remain comparable. The two indices yield markedly different projections. Although the number of drought events falls, the share of months below the moderate-drought threshold at the 12-month scale rises from 16.5% in the baseline to 47.0% by 2061–2100 under SSP5-8.5 for the SPI and to 88.4% for the SPEI. All 217 station–GCM combinations show the SPEI as the drier index in every scenario and period. The reason is the temperature term: under SSP5-8.5, basin-mean precipitation changes by −20 mm yr⁻¹ by the late century while potential evapotranspiration rises by +192 mm yr⁻¹, so that about 91% of the change in the climatic water balance is attributable to evaporative demand rather than to rainfall. What changes is therefore not how often drought occurs but how long it lasts and how much deficit it accumulates. Projections under SSP1-2.6 indicate substantially less severe drought conditions than those under the higher-emission pathways at every station, although drought exposure still increases relative to the baseline. Full article

The growing demand for data science competencies across professional fields creates a need for curricula that enable management students to develop analytical skills while navigating increasingly complex technological environments. This study examines the design and evaluation of a three-year Data Science track embedded within an undergraduate management program. The curriculum integrates statistics, programming, data management, visualization, artificial intelligence, and project-based learning within a progressively structured, multi-tool environment. A multi-cohort, multi-method design combined longitudinal academic performance data from two independent cohorts with graduates’ attitudes toward data science, career adaptability, course perceptions, and qualitative feedback. Academic performance showed cohort-specific patterns: variability decreased progressively across the three years in Cohort 1, whereas in Cohort 2 it decreased in the second year but increased in the third year. Graduates reported high perceived utility and interest in data science, relatively high career adaptability, and positive perceptions of applied and project-oriented courses, although self-efficacy varied in technically demanding areas. Qualitative findings highlighted authentic projects, progressive learning, and multi-tool exposure, alongside challenges related to technical complexity. The study provides program-level evidence for designing accessible yet rigorous data science curricula and highlights the potential value of progressive curricular integration and applied multi-tool learning for management students. Full article

Background/Objectives: Obesity is a chronic, multifactorial condition driven by complex neuroendocrine and metabolic pathways that limit the long-term success of lifestyle interventions. Pharmacotherapy has advanced from early monoaminergic agents to incretin-based and multi-receptor agonists with substantially improved efficacy. This review aims to provide pharmacodynamics-focused synthesis of current and emerging anti-obesity medications, emphasizing mechanistic targets, receptor interactions, and translational implications. Methods: We conducted a narrative review integrating Phase I–III clinical trials, mechanistic pharmacology data (receptor affinity, potency, and signaling bias), and regulatory summaries for FDA-approved and selected emerging, investigational, repurposed, or regionally approved anti-obesity agents. Inclusion focused on clinical relevance, pharmacodynamic novelty, and weight-loss efficacy. Results: Approved anti-obesity medications act through diverse pathways, including the inhibition of fat absorption (orlistat), the modulation of central appetite circuits (phentermine/topiramate, naltrexone/bupropion), and incretin-based mechanisms (liraglutide, semaglutide, tirzepatide, and the oral GLP-1 receptor agonists semaglutide 25 mg and the nonpeptide orforglipron). GLP-1 receptor agonists achieve approximately 6–15% weight loss, while dual glucose-dependent insulinotropic polypeptide/GLP-1 receptor agonists such as tirzepatide reach 15–20%. Emerging agents—including triple agonists (retatrutide), glucagon-based co-agonists, amylin analogs with Phase III efficacy data, earlier-stage growth differentiation factor 15 (GDF15) pathway agonists, and peripherally restricted cannabinoid-1 receptor inverse agonists—demonstrate enhanced metabolic effects and potential improvements in lean-mass preservation. Obesity-related alterations in pharmacokinetics and pharmacodynamics highlight the need for individualized dosing strategies. Conclusions: Clinical efficacy and tolerability cannot be inferred from receptor identity alone. They emerge from relative receptor potency and efficacy, exposure, tissue access, receptor trafficking, and mechanism-linked adverse effects. Dual and triple agonists can broaden efficacy, but receptor balance and standardized comparative pharmacology remain essential for rational development and individualized dosing. Full article

In recent decades, higher education has seen significant transformations due to rapid technological advancements that have challenged traditional teaching and learning practices. As digital technologies become increasingly integrated into education, institutions are adopting innovative approaches to enhance teaching effectiveness, student engagement, and learning outcomes. Many real-world activities have transitioned into the virtual world, bringing unprecedented challenges to all sectors, particularly in education, leading to the rapid adoption of technology-supported teaching and learning solutions. The fusion of the digital and physical worlds has facilitated the development of the metaverse, a virtual, three-dimensional space in which students can interact with each other and with professors. This “phygital” environment enhances engagement and cooperation while providing innovative and interactive methods for students to experience and understand complex concepts. This paper aims to examine the impact of metaverse integration in open-access higher education institutions and explore students’ perspectives on its potential applications in learning and teaching. The study utilized a convergent parallel mixed-methods approach over two academic years (2022–2023 and 2023–2024) among students aged 18–25 enrolled at a Moroccan public university. Data collected through questionnaires and focus group interviews reveal varied perceptions of the metaverse, highlighting its potential benefits, challenges, and the influence of students’ social, economic, and intellectual contexts on its acceptance. Full article

Background/Objective: Lung cancer remains a leading cause of cancer-related mortality, and access to low-dose CT screening is limited in resource-constrained settings. This study aimed to develop and evaluate an interpretable machine learning model for preliminary, symptom-based lung cancer risk discrimination using clinical indicators and lifestyle factors, without requiring imaging. Methods: A cross-sectional dataset of 561 participants (38 with a prior lung cancer diagnosis), following exclusion of a minors-eligible age bracket for which independent age verification was not possible, was analyzed. Five machine learning algorithms (Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost) were compared using a leakage-free pipeline, with ADASYN class balancing and hyperparameter tuning performed exclusively within cross-validation folds; feature selection was verified to be stable across folds (98% average overlap with the final feature set). The final model was selected solely on the basis of repeated cross-validated performance, without reference to the test set. Model interpretability was assessed using LIME, and a four-tier risk stratification system was developed and calibrated. Results: Random Forest achieved the best cross-validated F1-score (0.850 ± 0.092) among the five candidates and, given a clinically motivated preference for higher sensitivity at comparable F1, was selected as the final model. On the independent test set (n = 113), it achieved Accuracy = 0.956, Recall = 0.875, F1-score = 0.737, and AUC-ROC = 0.912. Sensitivity analysis indicated moderate reliance on a potential diagnostic proxy feature, and balancing-method comparisons (ADASYN, random oversampling, class weighting) confirmed that conclusions were not contingent on the specific resampling technique. Conclusions: The proposed model demonstrates solid discriminative performance and interpretability; however, given the cross-sectional design and limited number of positive cases, results should be interpreted as preliminary risk discrimination rather than validated early-detection prediction, warranting further prospective validation. Full article

Restoring resilient forest structure in the fire-prone, densely stocked forests of the Inland Pacific Northwest depends on mechanical thinning, much of it too steep for conventional ground-based equipment. Winch-assisted (tethered) harvesting has expanded mechanized access to such terrain, but its soil impacts on the ash-cap soils of the Blue Mountains remain poorly characterized, with research focused on near-surface response. This study characterized soil response following two operationally distinct tethered mechanized systems, cut-to-length (CTL) and whole-tree (WTL), within the same 15-ha unit on a 40% ash-cap slope in the Wallowa-Whitman National Forest, Oregon. Bulk density and moisture content were measured at the surface and at 30 cm across pre-harvest, immediate post-harvest, and one-year post-harvest periods, and cross-slope profiles were measured to quantify surface soil displacement. CTL corridors showed minimal immediate surface disturbance due to slash-mat protection but densified significantly by one year, likely driven by environmental exposure rather than machine traffic; sub-surface compaction was transient, relaxing within a year. WTL corridors densified immediately at the surface and remained elevated at one year, while sub-surface bulk density declined significantly below pre-harvest levels by one year, though this decrease may partly reflect sampling variability rather than a purely soil-driven process. Neither system showed sustained compaction at 30 cm after one year. These results indicate that, at this study site and under the operating conditions evaluated, no persistent increase in bulk density was detected at 30 cm depth for either system within the one-year monitoring period, though CTL and WTL produced distinct disturbance timelines that land managers should weigh when selecting equipment for similar terrain. Full article

This review provides an updated overview of recent advances (2024–present) in the synthesis and medicinal applications of thienopyridines and their fused derivatives. Synthetic methodologies are categorized into three strategic approaches: (i) thiophene ring construction onto a preformed pyridine core; (ii) pyridine ring annulation onto a thiophene scaffold—ranging from classical Friedländer and Gould–Jacobs reactions to modern metal-catalyzed, photo-induced, and electrochemical transformations; and (iii) multicomponent reactions for the simultaneous assembly of both heterocyclic rings. Furthermore, the strategic functionalization of preformed thienopyridine scaffolds to access complex tri- and polyheterocyclic systems is discussed. From a biological perspective, the review highlights the scaffold’s expanding therapeutic repertoire, including antibacterial, antifungal, and antiviral activities; potent anticancer applications targeting tumor-associated enzymes and emerging Proteolysis Targeting Chimera (PROTAC)/ Deubiquitinase-Targeting Chimera (DUBTAC) technologies; and modulation of neurological, neuropsychiatric, and metabolic disorders. Finally, recent progress in the agrochemical utility of thienopyridines as insecticides and herbicide safeners is examined. Collectively, this work underscores the enduring relevance of the thienopyridine scaffold as a versatile platform for next-generation drug and agrochemical discovery. Full article

Gender-based violence (GBV) shapes women’s lives beyond a single incident. For Black women in South Carolina, a state in the American South, it intersects with barriers to healthcare, threats to bodily autonomy and structural inequality. This is especially true in South Carolina where significant legal and political restrictions constrain access to reproductive healthcare. This study treats GBV as a sexual and reproductive health crisis and asks how Black women in public service understand and respond to it, drawing on semi-structured interviews with seven Black women public servants in South Carolina across government and nonprofit settings, analyzed thematically through a Black feminist framework. Participants described violence as an ongoing condition shaping access to care, emotional well-being and long-term safety, rather than a single event. Moreover, they defined success through shelter, therapy access and survivor trust rather than prosecution, grounding this framing in lived experience and community trust rather than institutional title. The findings further indicate that, from participants’ professional and community perspectives, legal remedies alone do not fully capture the complexity of GBV. Centering care, access and lived expertise offers a more grounded response to GBV in communities facing sustained structural crisis. Full article

Coastal floodplains face unprecedented threats from deforestation driven by global environmental change. The Sre Ambel floodplain, located in the southwestern coastal region, is one of Cambodia’s main floodplains and plays a critical role in maintaining ecosystem functions pivotal to wildlife and human communities. Over the past three decades, however, it has been at risk due to forest cover loss, which threatens ecosystem processes. This study aimed to investigate temporal and spatial floodplain forest cover loss over the past 24 years using remote sensing data, assess the drivers of deforestation through community social research and a literature review, and assess the anthropogenic influence on deforestation through spatial nonlinear modeling. Approximately 23% (11,182 ha) of forest cover in the Sre Ambel floodplain was lost during 2001–2024. Spatial modeling identified nonlinear conditional associations between mapped forest loss and distances to roads, rivers, villages, and houses. Spatial-block bootstrap analyses supported higher predicted loss probability near roads and at 2 km versus 5 km from village centers, whereas selected river- and house-distance contrasts were not robust. Forest loss peaked during 2006–2010 and again during 2016–2024. Respondents most frequently perceived agricultural expansion, settlement development, land grabbing, timber extraction, and resort development as key deforestation pressures. These results can help guide further floodplain assessment, conservation, restoration, and sustainable land-use planning. Full article

The time cost of care constrains female labour supply; two policy levers act on it: where childcare capacity is sited and how shifts are scheduled, both large combinatorial problems. The crested porcupine optimizer (CPO) initialises at random, guides exploitation by a single random or best individual and resizes its population on a fixed cycle. The proposed multi-strategy CPO (MSCPO) keeps CPO’s four defence operators and replaces those original components with Sobol quasi-opposition initialisation, elite-guided exploitation and monotone fitness-based population reduction. On the 29 CEC2017 functions at 10, 30, 50 and 100 dimensions (30 runs of 30,000 evaluations), MSCPO had the best Friedman mean rank among ten algorithms at every dimension (1.29 against 2.53 for CPO; χ²(9) = 878.5, p < 0.001), and pooled Holm tests separated it from all nine competitors (p ≤ 0.002). A factorial ablation attributed about 70% of the gain to elite-guided exploitation. The advantage over CPO vanished at the full budget of 10⁴D evaluations. In childcare siting, MSCPO ranked first, leaving the dispersion of access 28–35% below the most equal siting rule with at least 99% of the best rule’s employment; in shift scheduling, it ranked fifth, with instrumentation suggesting a limitation of large steps under a rank-key decoder. Full article