Search Results (275)

Background: Diabetes mellitus is a major public health concern in Saudi Arabia, affecting approximately 18.3% of adults. Although over 95% of people in the Qassim region own smartphones, limited information exists regarding how and why patients with diabetes utilize digital health technologies. Objective: This study employed an extended Technology Acceptance Model (TAM) incorporating trust, privacy concerns, and self-efficacy to investigate the factors influencing digital health technology adoption among patients with diabetes in Qassim Unaizah, Saudi Arabia. Methods: A cross-sectional study was conducted from November 2025 to January 2026 following institutional review board approval. The quantitative phase utilized a structured survey (n = 203) measuring TAM constructs via validated 5-point Likert scales. Descriptive statistics, Pearson correlations, and one-way ANOVA were performed. Qualitative themes were derived from open ended responses to contextualize quantitative findings. Results: Most participants were male (62.6%), with a mean age of 47.2 years (SD ± 13.1). Smartphone ownership was nearly universal (99.5%), and 82.8% used blood glucose tracking applications. All TAM constructs exhibited significant positive correlations with behavioural intention (p < 0.001): attitude (r = 0.450), perceived usefulness (r = 0.438), self-efficacy (r = 0.394), trust (r = 0.379), ease of use (r = 0.340), and privacy concern (r = 0.309). Mean scores indicated strong acceptance: perceived usefulness (4.31, SD ± 0.39), behavioural intention (4.21, SD ± 0.44), and attitude (4.17, SD ± 0.42). Daily usage was reported by 63.1% of participants, 81.8% expressed satisfaction, and 63.1% reported that digital tools greatly improved their diabetes management. Privacy concerns were notably low (mean 1.70, SD ± 0.89, reverse-coded; Cronbach’s α = 0.921). Supplementary qualitative content analysis of optional open-ended comments (n = 40 respondents) identified six recurring topics: clinical utility, digital literacy, trust, data security awareness, patient empowerment, and social support. Conclusions: Patients with diabetes in Qassim Unaizah demonstrate substantial digital health adoption, predominantly driven by perceived usefulness, positive attitudes, and self-efficacy. The extended TAM effectively explains adoption within this Saudi Arabian context. Findings support interventions emphasizing clinical benefits, user friendly design, and trust building to optimize digital health utilization in diabetes care. Full article

Background/Objectives: Psilocybin-assisted therapy has progressed from early proof-of-concept work to a substantially expanded evidence base, with four pivotal studies published in 2025–2026 that were not incorporated into earlier reviews. This review synthesizes the pharmacological and neurobiological foundations of psilocybin and appraises clinical evidence across major depressive disorder (MDD), treatment-resistant depression (TRD), post-traumatic stress disorder (PTSD), cancer-related distress, and substance use disorders, emphasizing long-term durability, expanding indications, and methodological limitations. Methods: A narrative review was conducted using a targeted search of PubMed/MEDLINE, Embase, Scopus, and Web of Science (January 2000–April 2026), emphasizing 2025–2026 publications. Predefined eligibility principles prioritized randomized controlled trials, long-term follow-up studies, and systematic reviews; formal PRISMA procedures were not applied, consistent with the narrative design. Results: Four 2025–2026 studies extend the evidence base: a 52-week follow-up confirming dose-dependent maintenance of antidepressant benefit after a single 25 mg psilocybin session; the first pilot study in Veterans with severe TRD, reporting a 60% response rate at three weeks; the first safety trial in PTSD, where symptom improvement tracked self-transcendent experience intensity but worsened with session anxiety; and a living review of 15 randomized trials confirming a meaningful antidepressant effect while identifying functional unblinding as a substantial threat to effect estimates. Conclusions: Evidence supports psilocybin-assisted therapy as mechanistically distinct and clinically promising, most strongly in MDD and TRD, with preliminary support in PTSD and Veterans. Functional unblinding, small open-label designs, narrow safety populations, and absent SSRI-integration protocols constrain the current conclusions. Full article

Background/Objectives: Human papillomavirus (HPV) is the leading cause of cervical cancer and anogenital malignancies, yet its clinical presentation, genotype distribution, co-infection patterns, and viral load are poorly characterised in tertiary-care referrals. This study aimed to identify predictors of HPV positivity and describe genotype, co-infection, and relative viral copy number (Cq values) in this setting. Methods: A retrospective case–control study of 234 patients (97 HPV-positive, 137 HPV-negative) used a 37-genotype qPCR platform at a tertiary-care hospital between January-December 2025. Demographic data, clinical diagnosis categories, genotype profiles, co-infection patterns, and cycle quantification (Cq) values were recorded, and multivariable logistic regression identified independent predictors of HPV positivity. Results: HPV positivity was 41.5% (97/234). Only the clinical diagnosis category independently predicted HPV status. Compared with non-specific presentations, patients with anogenital or viral warts (aOR: 4.25; 95% CI: 1.49–12.14; p = 0.007) and vaginal or vulvar inflammation (aOR: 2.08; 95% CI: 1.19–3.63; p = 0.010) had higher odds of positivity. High-risk genotypes were the second most frequently detected category (40.00% of detections), after low-risk genotypes (48.21%), with HPV-16 leading among high-risk types. Co-infection occurred in 44.3% of HPV-positive patients, mostly involving mixed-risk genotypes. Patients with anogenital warts had lower Cq values than those with urinary tract complaints (p = 0.011), reflecting higher relative viral copy numbers per swab. Conclusions: HPV positivity and relative viral copy number (as approximated by Cq values) tracked more closely with clinical presentation than with demographic background. The dominance of low-risk and high-risk genotypes and the prevalence of mixed-risk co-infections were consistent with the symptomatic profile of the cohort. These findings support interpreting HPV results in light of clinical presentation and extended genotyping with relative viral copy number quantification in tertiary settings. Full article

Quality management in mega construction projects is increasingly supported by digital technologies, yet quality information often remains fragmented across inspection systems, reality-capture platforms, Building Information Modelling (BIM) environments and enterprise systems. This fragmentation limits traceability, delays corrective actions and weakens the connection between quality performance, contractual obligations and financial accountability. This study develops a closed-loop digital QA/QC framework that integrates BIM, mobile field inspection, reality capture, artificial intelligence (AI) augmentation and enterprise resource planning (ERP) within a unified governance architecture. Following a Design Science Research approach, the study proposes a seven-layer framework linking quality events to procurement, financial control, and project-management processes while supporting role-based decision-making through data democratisation mechanisms. The framework extends conventional ERP-enabled quality management by explicitly incorporating procurement (MM) and financial-control (FI/CO) functions, including supplier-quality management, cost-of-poor-quality tracking and quality-linked payment governance. An illustrative project scenario and sensitivity analysis are used to demonstrate the application of the proposed KPI and evaluation structure. By treating integration as the primary design objective, the framework provides a foundation for enterprise-wide digital quality management, lifecycle information continuity and digital-twin readiness in mega construction projects. The contribution itself is evaluated through an illustrative Design Science Research demonstration and an assumption-bounded sensitivity analysis rather than through field data, with future empirical validation specified through a controlled before-and-after case-study protocol. Full article

Vessel detection and multi-object tracking (MOT) in marinas remain challenging because of occlusions, small targets, cluttered backgrounds, and changing illumination. This paper presents a controlled, feasibility-focused comparative evaluation of YOLO-driven vessel detection and tracking in a fixed-camera marina environment, comparing YOLOv11 with the more attention-oriented YOLOv12 detector family. A dataset of 3546 annotated images was collected using a static ground-level camera at a marina in the northern Adriatic, Croatia. YOLOv11s, YOLOv11m, YOLOv12s, and YOLOv12m were fine-tuned using transfer learning, evaluated across five random seeds, and integrated into a common tracking-by-detection pipeline with Kalman filter (KF) and extended Kalman filter (EKF) motion models. Detection was assessed using COCO-style mAP metrics, while tracking was evaluated using MOT and HOTA-based metrics. YOLOv11m achieved the highest mean test-set mAP@50–95 (0.7458 ± 0.0027) and significantly outperformed YOLOv11s and YOLOv12s. Although YOLOv12m did not achieve the highest frame-level mAP, it obtained the highest mean AssA and IDF1 when averaged across the two motion models. KF achieved higher mean HOTA, DetA, AssA, MOTA, and IDF1 than EKF, whereas EKF achieved only marginally higher MOTP; the KF advantage was statistically significant for IDF1. Overall, the results show that frame-level detection accuracy does not necessarily determine downstream tracking performance, thereby supporting the joint evaluation of detector and motion-model choices. Full article

Background/Objectives: First-line therapy for advanced gastric cancer (AGC) has evolved from cytotoxic chemotherapy to HER2-targeted and immune checkpoint inhibitor (ICI)-based regimens, yet conversion surgery (CS) outcomes across these strategies remain poorly characterized. We describe CS outcomes and prognostic factors by first-line strategy at a single center. Methods: We retrospectively reviewed 187 patients with AGC who began first-line therapy from 2011, grouped as cytotoxic (CTX, n = 127), HER2-targeted (trastuzumab, n = 21), or ICI (n = 39). CS was defined as resection after response, including an extended oligometastatic definition (n = 74). Overall survival (OS) was measured from chemotherapy initiation, and prognostic factors were assessed by Cox regression. Results: Median OS was 14.8, 18.9, and 20.9 months for CTX, trastuzumab, and ICI, respectively (p = 0.048). CS rates were comparable (41%, 33%, and 38%; p = 0.794). Pathological response was more pronounced after trastuzumab/ICI (grade 3 and ypStage 0/1; both p < 0.001). Among CS cases, R0 resection (hazard ratio [HR] 0.31) and trastuzumab/ICI therapy (HR 0.37) were independent favorable factors, whereas high inflammatory–nutritional indices (NLR, CAR) were independent poor prognostic factors. OS was comparable between oligometastatic and conventional CS (p = 0.324). Conclusions: In this hypothesis-generating study, response depth tracked tumor biology, whereas survival was determined by R0 resection, targeted/ICI therapy, and host inflammatory–nutritional status—two largely dissociable axes informing biology- and host-based selection of CS candidates for prospective testing. Full article

Eye-tracking technology, which offers the potential to objectively measure user behavior in digital news environments, has drawn growing interest in journalism studies. This study aims to systematically review studies examining news consumption through the eye-tracking method and evaluate the thematic orientations, methodological characteristics, and findings from a holistic perspective. As a result of a search conducted in the Web of Science database and the subsequent multi-stage screening process, 69 studies were included in the analysis. The findings reveal that, since 2008, the field has undergone a thematic evolution extending from news interface design to platform comparisons, and from there to disinformation and media literacy. Most studies rely on experimental quantitative methods; overcoming the limitations of self-report measures through objective behavioral metrics constitutes the primary methodological contribution of this body of research. Nevertheless, homogeneous student samples, the artificiality of laboratory settings, and dependence on momentary measurements stand out as the major limitations of the field. This study demonstrates that the eye-tracking method in journalism studies evolved from a tool for measuring isolated variables into an interdisciplinary perspective that holistically reveals the relationships among attention, cognitive processing, and media literacy. Full article

Building supervisory control requires accurate, prior-consistent, and computationally tractable thermal models. This study proposes a physics-guided neural ordinary differential equation (PG-NODE) framework for thermal prediction and model predictive control (MPC). It combines a resistance–capacitance (RC)-inspired reference, a neural residual, RC-prior directional regularization, and validation-based checkpoint selection; the selected predictor remains fixed during MPC operation. Using 30 min EnergyPlus data from five zones, the framework was evaluated through held-out prediction, Gaussian noise and control-input-mismatch tests, ablation, and surrogate-based closed-loop experiments. Long short-term memory achieved the lowest prediction root mean square error (RMSE), whereas PG-NODE achieved the lowest RMSE among the evaluated neural ODE models and the lowest directional inconsistency rate among learned nonlinear models. In five-seed × five-window BACK SPACE experiments using independently trained, fixed PG-NODE surrogate environments rather than direct EnergyPlus interaction, fixed-block analysis supported lower reference-tracking RMSE for PG-NODE-MPC than for rule-based control and lower tariff-weighted normalized control effort than for the extended Kalman filter-based RC-MPC benchmark. This benchmark achieved the lowest descriptive mean reference-tracking RMSE and total objective. Mean PG-NODE-MPC optimization time was 0.86 s. Results suggest the potential for low-frequency building management system supervisory decision support, subject to physical command mapping and staged field validation. Full article

Extended object tracking (EOT) has emerged as a critical challenge in modern surveillance and autonomous systems, where high-resolution sensors yield multiple measurements per target in a single scan. This task necessitates the simultaneous estimation of an object’s spatial extent and kinematic states by systematically assimilating distributed detections. In practical scenarios, however, sensor visibility constraints often restrict measurements to partial contours rather than the full object geometry, posing a significant hurdle for accurate state estimation. This paper proposes a robust EOT algorithm that addresses these challenges by extracting elliptical features—including size, orientation, and noise characteristics—in an affine-invariant manner from incomplete measurement sets. By formulating the ellipse-fitting problem through an algebraic solution, the proposed method effectively reconstructs the target’s morphology even under severe self-occlusion. The tracking framework is established as a Bayesian inference problem, utilizing a standard Kalman filter as a recursive estimator to ensure both computational efficiency and numerical robustness. In Monte Carlo simulations in which measurements are restricted to the sensor-facing contour, the proposed method reduces the average extent estimation error, quantified by the Gaussian Wasserstein distance, to 9.15 m, compared with 64.84–70.62 m for three state-of-the-art baselines, while retaining real-time feasibility with an average processing time of 0.94 ms per scan. Experiments on real-world LiDAR vehicle-tracking data further confirm consistent and accurate extent estimation under partial observability and complex maneuvers. Full article

In this paper, the concept of symmetry is applied to design active trajectory tracking control of autonomous distributed drive electric vehicles considering energy efficiency—that is, the construction and solution of active trajectory tracking controllers are symmetrical. This paper proposes a hierarchical control strategy consisting of upper-level control and lower-level control to improve trajectory tracking accuracy of DDEVs considering energy-efficiency characteristics. In the upper-layer control, a sliding mode active disturbance rejection (ADRC) controller is developed to control the front wheel steering angle and active yaw moment to achieve tracking of the desired trajectory, in which an extended state observer (ESO) is synthesized to estimate and compensate for internal model uncertainties and external environmental disturbances. In the lower-layer control, a multi-objective optimization algorithm based on Karush–Kuhn–Tucker (KKT) conditions is designed to realize the torque distribution control for improving energy efficiency and vehicle stability of the distributed drive electric vehicle. Finally, a joint simulation platform based on Matlab/Simulink-CarSim (version 2019) is established for simulation verification. The performances of ADRC, linear quadratic regulator controller (LQR), and model predictive controller (MPC) are compared in snake-like and double-lane-change maneuvers. Simulation results show that the proposed controller can effectively reduce motor energy consumption while maintaining trajectory tracking accuracy and handling stability. This work provides a certain engineering design solution for motion control of intelligent electric vehicles. Full article

Background/Objectives: The primary goal of this research was to investigate the relationship between the utilization of social media platforms, its influences on health status perceptions and health information-seeking tendencies of individuals who underwent surgical procedures. Methods: This study was designed and executed utilizing a descriptive and cross-sectional design. The research cohort comprised 376 surgical patients selected from operative clinics who satisfied the specified eligibility requirements for participation. Data collection was carried out through a structured Patient Demographic Questionnaire alongside the Health-Seeking Behavior and Health Perception Scales. For statistical evaluation of the gathered data, descriptive metrics, both parametric and non-parametric analytical techniques, and correlation tracking frameworks were employed. Results: According to the study analysis, participants under the age of 54 achieved significantly higher web-based health information-seeking scores (15.79 ± 5.50) than those in the older demographic group (14.10 ± 5.83; t = 2.886, p < 0.05). In terms of gender variations, female patients demonstrated more prominent scores in both digital and comprehensive health-seeking tasks compared to male counterparts (t = 2.316, t = 2.130; p < 0.05). Academic background elicited significant statistical fluctuations across all sub-dimensions (F = 5.655, F = 7.080, F = 7.690, F = 9.877; p < 0.001), revealing that individuals with only primary school education presented the lowest baseline values. Furthermore, individuals restricting their daily internet exposure to one hour or less demonstrated noticeably lower scores than peers maintaining an extended daily digital presence (F = 4.791, F = 4.232, F = 3.549; p < 0.05). Conclusions: This study found that women and individuals with higher levels of education exhibit more frequent online health-seeking behaviors, which were found to be more prominent among individuals who spend more time online and actively use social media platforms. Full article

The episodic aggregation of shallow-sea organisms near coastal nuclear power plant water intakes can obstruct filtration facilities and threaten cooling-water circulation. Reliable multi-object tracking is therefore required for continuous monitoring and warning-oriented decision support. However, underwater targets commonly show weak texture, similar appearance, partial occlusion, and current-driven nonlinear motion, which cause trajectory fragmentation and identity switches in conventional trackers. This study proposes a feature-enhanced DeepSORT framework for shallow-sea biological target tracking and intake-invasion warning. An improved YOLOv8 detector is used as the detection front end, while the main methodological contribution is an enhanced tracking module. Efficient Channel Attention, RepVGG, and an enhanced Squeeze-and-Excitation block are incorporated into the Re-ID feature extractor to improve appearance discrimination under turbid and low-texture conditions. An extended Kalman filter is further introduced to improve motion prediction for drifting, turning, and short-term occluded targets. Based on the tracked trajectories, equivalent density, and velocity component toward the intake, an invasion-intensity index and graded warning strategy are established. Experiments on shallow-sea biological video data show that the proposed tracker improves IDF1 from 55.7% to 56.3%, MOTA from 43.6% to 44.7%, and MOTP from 74.3% to 75.1% compared with DeepSORT, while reducing identity switches from 779 to 735. These results indicate that the proposed method can provide more stable trajectory information for early warning of biological blockage risks at coastal nuclear power plant intakes. Full article

Background/Objectives: Clinical artificial intelligence (AI) is increasingly moving from proof-of-concept development into clinical evaluation, regulatory review, and routine care. This scoping review aimed to map and synthesise empirical evidence on clinical AI evaluation after model development, focusing on deployment-related robustness, post-development monitoring, and lifecycle oversight in practice. Methods: We conducted a scoping review in accordance with Joanna Briggs Institute guidance and reported findings using PRISMA-ScR. MEDLINE, Embase, Scopus, and Web of Science Core Collection were searched with no lower date restriction within each database’s available indexed coverage and with a common upper search date of 28 February 2026. Searches were supplemented by backward and forward citation tracking. Grey literature, preprint servers, and regulatory databases were not systematically searched because eligibility was restricted to full-text, peer-reviewed empirical studies and empirically grounded implementation or monitoring reports. Findings were synthesised using descriptive evidence mapping and inductive thematic synthesis. Results: Eighteen studies or empirically grounded reports were included. Evidence was organised into five strata: direct live or post-deployment monitoring studies; near-live bridge studies generating prospective outputs without guiding care; methodological monitoring and maintenance studies; deployment-relevant robustness and predeployment safety studies; and governance, implementation, readiness, and human-factors studies. Three themes emerged: trustworthiness after development was conditional and context-dependent; algorithmovigilance extended beyond aggregate performance tracking to include operational, workflow, fairness, contextual, and user-feedback signals; monitoring was more actionable when linked to corrective pathways, governance structures, and institutional readiness. Sociotechnical failures included automation-bias signals, workflow burden, reasoning–conclusion misalignment, and workflow-fit problems. Conclusions: Post-development clinical AI evaluation remains a layered and emerging field rather than a mature monitoring literature. Direct live evidence is limited, concentrated in high-income settings, and weighted towards radiology. The findings should be interpreted as synthesis-informed rather than as empirically validated standards for lifecycle oversight. Full article

The BepiColombo spacecraft, designed by ESA and JAXA, is currently in its cruise phase toward Mercury. Among the scientific investigations is the Mercury Orbiter Radio-science Experiment (MORE), which will exploit a multi-frequency microwave tracking system with an advanced Ka-band transponder to achieve its objectives pertaining to Mercury’s geodesy and fundamental physics. Leveraging precise measurements from this state-of-the-art radio tracking system, MORE is expected to provide new insights into Mercury’s interior, refining and expanding upon the findings of the MESSENGER mission. This work evaluates the performance of MORE’s gravity and rotation experiment, specifically assessing how BepiColombo’s improved radio tracking data can reduce uncertainties in the determination of Mercury’s gravity field, Love number $k_2$, and rotational state. Differently from previous covariance analyses, this work includes errors in the dynamical model to assess the experiment’s performance under controlled mismodeling conditions. We present the results of a numerical simulation covering BepiColombo’s extended two-year orbital phase, with scientific operations set to begin in 2027. Full article

The Poisson multi-Bernoulli mixture (PMBM) filter is an effective approach for multi-object tracking in complex scenarios. However, its performance deteriorates when surviving objects spawn, as the PMBM filter only classifies detected objects as either new-born or surviving, thereby ignoring information from the surviving objects and preventing timely identification of spawning events. To address this limitation, this paper proposes the Dynamic Hybrid Detection-Gamma Gaussian inverse Wishart Spawning Poisson multi-Bernoulli mixture (DHD-GGIW-SPMBM) filter, which models spawning objects independently using a Bernoulli process to enhance tracking accuracy. The probability generating functional is employed to derive the recursive prediction and update equations of the proposed filter, and its conjugacy after prediction and update is formally proven. Additionally, a dynamic hybrid detection method is introduced to evaluate the consistency between measurements and theoretical samples, enabling the detection of spawning events. The detection results guide an evidential Gaussian mixture model (EGMM) for fuzzy partitioning of the spawning process, reducing errors under closely spaced and high-clutter conditions. Simulation results demonstrate that, compared with existing spawning-capable filters, the proposed DHD-GGIW-SPMBM filter achieves superior tracking performance, faster identification of spawned objects, and robust operation in complex scenarios. Full article