Search Results (17,950)

Background/objectives: To describe and compare Korean AYAs’ and parental perspectives on the family environment in terms of agreement and significant differences and examine which variables were associated with AYAs’ symptom distress. Sample and setting: Self-report data were collected from a total sample of 113 AYAs, recruited from a pediatric-oncology outpatient clinic at a university-affiliated hospital and community group in South Korea. Because each study aim required different data sources, different analytic samples were used. Specifically, 54 AYA–parent dyads were included for Aim 1, whereas self-report data from 111 AYAs with complete data were used for Aim 2. Methods and variables: This subgroup analysis used a quantitative–descriptive, cross-sectional design. AYAs’ and parent perceptions of the family environment (family cohesion and adaptability, family strength, and social support from family) and AYAs’ symptom distress were collected using reliable and validated self-report questionnaires and analyzed using descriptive and inferential statistics. Results: AYAs and their parents showed low (family support) to moderate agreement (family strength, family cohesion, and adaptability) on perceptions of family environment (ICC = 0.374–0.612). AYAs reported significantly lower perceptions of family support than their parents, with a small to moderate effect (p < 0.001, d = 0.48). All family environment variables were correlated with AYAs’ symptom distress (p < 0.05). Among these variables, AYAs’ perceived family strength emerged as the only family environment variable significantly associated with their symptom distress (F = 14.309, p < 0.001, R² = 0.359, R²adj = 0.334), which was stronger during treatment. Conclusions: AYAs’ perceived family strength should be routinely assessed, especially during cancer treatment. Additional nursing interventions focusing on enhancing AYAs’ families as a support group are needed. Full article

Objective: To analyze the perceived facilitators and barriers promoting and hindering the clinical application of the best evidence of clean intermittent catheterization (CIC) in patients after radical hysterectomy (RH). Methods: This study employed a convergent parallel mixed-methods design. Participants included patients undergoing CIC after RH, medical and nursing practitioners and managers in the gynecological department and outpatient clinics at a tertiary-level women’s and children’s hospital in Chengdu. They were included in both components separately. Interview data were managed using Nvivo 11.0 software and analyzed through directed content analysis. Quantitative data were analyzed using SPSS 29.0 statistical software. Results: A questionnaire survey was conducted among 156 healthcare providers and 300 patients. Qualitative interviews were conducted with 11 healthcare workers and 12 patients. At the evidence itself level, evidence meeting clinical needs and evidence lacking practical applicability, respectively, promoted and hindered clinical implementation of the best evidence. At the potential adopters’ level, healthcare professionals’ insufficient professional competence, low willingness to promote implementation, numerous concerns, and lack of autonomy and awareness regarding the importance of the task were significant barriers, but they maintained an overall positive attitude toward the application. At the practical environment level, patient-related perceived barriers predominantly hindered evidence implementation. Additionally, a supportive practice atmosphere, economic feasibility, and talent development opportunities served as key facilitators. However, existing nursing practice content and workflows directly impacted evidence adoption. Conclusions: The promotion and barriers to the clinical application of the best evidence for CIC in RH postoperative patients are multifaceted. Targeted intervention strategies must be developed to facilitate the effective translation of evidence into clinical practice. Full article

To tackle the intermittency of renewable energy and realize the repurposing of abandoned oil and gas wells, this study proposes a compressed air energy storage (CAES)-based cogeneration system integrated with geothermal energy and abandoned oil and gas wells, and conducts a five-dimensional comprehensive analysis covering exergy, exergoeconomic, exergoenvironmental, economic and environmental performance. The optimal operating parameters are determined as air compressed to 200 bar, an ORC turbine inlet pressure of 16 bar and an inlet temperature of 110 °C. The system’s annual total power generation is 2,971,416.5 kWh during low-power daytime operation, and 20,131,785 kWh during high-power nighttime operation. Compared with conventional CAES systems, the proposed system reduces total exergy destruction by 4121.35 kW and increases exergy efficiency from 48.49% to 63.38%. Coolers, geothermal heat exchangers and compressors are the main sources of exergy destruction cost and capital investment, while COM1, HE1 and HOT1 are the key components causing environmental impacts. The system realizes cogeneration of power, hydrogen and pure water, with a static payback period of about 5.4 years and significantly reduced TEWI value at elevated turbine inlet pressure. This system achieves multi-objective synergies in energy efficiency, economy and environment, providing a feasible scheme for the green repurposing of abandoned oil and gas wells and cascaded utilization of renewable energy. Full article

Fast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, while ultra-high-energy cosmic rays (UHECRs; $E\gtrsim {10}^{18}$ eV) remain among the most important unresolved problems in astroparticle physics. This review examines the viability of FRBs and their central engines as sources of UHECRs within a comprehensive multi-messenger framework. We summarize the observational constraints on UHECR source populations imposed by the energy spectrum, nuclear composition, anisotropy measurements, diffuse $\gamma$-ray background, and high-energy neutrino observations, which, together, favor source classes capable of accelerating heavy nuclei with hard injection spectra, modest cosmological evolution, and sufficiently high source densities. We then review the current landscape of FRB progenitor and engine models, including magnetars, supramassive neutron stars, compact-object mergers, and accretion-powered systems, emphasizing their energetics, environments, and particle-acceleration capabilities through relativistic shocks, magnetic reconnection, magnetar wind nebulae, and direct electromagnetic acceleration by ultra-relativistic FRB pulses. We discuss how these scenarios are constrained by neutrino and $\gamma$-ray observations from IceCube, KM3NeT, and Fermi-LAT, as well as by large-scale UHECR anisotropy measurements from the Pierre Auger Observatory and Telescope Array. Finally, we examine the observational tests that will become possible in the coming decade through large samples of localized FRBs, composition-resolved UHECR measurements, next-generation neutrino observatories, and wide-field $\gamma$-ray facilities. We emphasize that FRB dispersion and rotation measures provide unique probes of the baryonic and magnetic environments relevant for UHECR acceleration and propagation, enabling a new form of multi-messenger tomography of cosmic-ray source environments and allowing the FRB–UHECR connection to become a quantitatively testable astrophysical framework. Full article

Accurate object detection remains a major challenge in autonomous systems and surveillance, particularly when objects are partially or fully obscured by occlusions. To address this issue, we revisit FuDensityNet as a multimodal detection framework that jointly leverages 2D RGB images and 3D LiDAR point clouds for robust feature representation. The model integrates spatial and depth cues through low-rank tensor fusion (LRTF) and incorporates an Occlusion Rate (OR) assessment module that estimates the degree of occlusion and dynamically selects the most suitable detection pathway to preserve performance. Experiments on the KITTI and NuScenes datasets indicate that this adaptive strategy improves robustness under high occlusion while maintaining competitive accuracy in less challenging conditions. In particular, FuDensityNet attains 76.6% AP for car detection under “Hard” conditions on KITTI and outperforms several RGB-only and RGB–LiDAR baselines. Owing to its adaptive and modular design, FuDensityNet remains compatible with both 2D and 3D detection pipelines, making it a practical option for real-world environments where visual obstructions are frequent. Full article

Following the PRISMA-ScR framework for scoping reviews, we systematically searched five databases (Scopus, IEEE Xplore, ScienceDirect, SpringerLink, Web of Science) using a Boolean query combining real-time processing, 3D reconstruction, and deformation modelling terms. From 86 records identified, 56 peer-reviewed publications (2009–2025) were retained after two-stage screening and organized into a unified taxonomy covering sensing modalities (RGB-D, LiDAR, tactile), reconstruction pipelines (volumetric fusion, NRSfM, neural radiance fields), and deformation models (FEM, PBD, mass-spring, GNN-based surrogates, differentiable simulators). Of the 56 included works, 60% were published between 2022 and 2025, confirming the field’s rapid growth. Neural and implicit representations account for 20% of contributions, FEM-based methods for 16%, and hybrid or application-specific pipelines for 21%. Four systemic gaps emerge: the absence of a unified physics-aware benchmark; unresolved speed–accuracy trade-offs (PBD achieves >30 FPS on desktop GPUs for 10³–10⁴ vertex meshes but lacks mapping to physical material constants (Young’s modulus, Poisson’s ratio), limiting material fidelity; full-order FEM ensures physically consistent stress–strain behavior but runs at only 1–10 FPS without order reduction; reduced-order FEM recovers interactive rates for low-frequency deformation modes); fragile handling of occlusions and multi-object contact; and limited end-to-end integration of sensing and simulation. The findings support the presentation of a research roadmap centered on model order reduction, differentiable physics, multimodal sensing fusion, and standardized evaluation protocols, with implications for robust digital twins of deformable environments. Full article

Learning agility has become an essential capability for employees working in technology-driven environments characterized by rapid change and uncertainty. Despite increasing attention on learning agility, limited empirical research has examined how different levels of cognitive abilities contribute to its development, particularly among Generation Z employees. This study investigates the cognitive determinants of learning agility by distinguishing between basic cognitive abilities and high-level cognitive abilities and examining their roles across established and start-up companies. A total of 270 Generation Z employees in Indonesia participated in the study, consisting of 135 employees from established companies and 135 from start-up companies. Cognitive abilities were assessed using objective psychometric instruments, where basic cognitive abilities (reasoning, memory, attention, coordination, and perception) were measured using CogniFit, while high-level cognitive abilities were assessed through the Divergent Association Task (DAT) for creativity, the Watson–Glaser Critical Thinking Appraisal for critical thinking, and the FourSight framework for problem-solving. Learning agility was measured using a multidimensional behavioral scale. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that higher-order cognitive abilities play a more prominent role in shaping learning agility than basic cognitive abilities. Creativity and problem solving consistently demonstrate significant positive relationships with learning agility across organizational contexts, while reasoning, critical thinking, and perception show context-dependent effects across organizational environments. These findings suggest that learning agility is primarily driven by generative and evaluative cognitive processes rather than by basic cognitive efficiency alone. The study contributes to a deeper understanding of the cognitive architecture of learning agility and provides insights for organizations seeking to develop adaptive talent in rapidly evolving technological environments. Full article

Background: AI-supported game-based learning (AI-GBL) integrates artificial intelligence mechanisms, including adaptive difficulty adjustment, large language model (LLM) scaffolding, intelligent non-player characters (NPCs), and stealth assessment, into game-based educational environments. Objective: This systematic review synthesizes the empirical evidence on AI-GBL effectiveness, adaptive mechanisms, and intelligent assessment approaches across diverse educational contexts. Method: Following PRISMA 2020 guidelines, 55 peer-reviewed empirical studies (2021–2026) were identified from Web of Science and Scopus databases. Two independent reviewers screened records (κ = 0.89; 100% consensus on disagreements), extracted data using a standardized coding scheme, and assessed methodological quality using a five-criterion rubric. A thematic synthesis approach was adopted due to the heterogeneity of the evidence base. Results: The reviewed studies generally suggest promising positive effects of AI-GBL on knowledge acquisition, intrinsic motivation, and affective engagement under a range of educational conditions. LLM-based scaffolding reduces cognitive load but risks fostering passive dependency; adaptive difficulty adjustment benefits depend critically on the direction and magnitude of adaptation; AI NPCs function as credible instructional partners in both EFL and STEM contexts; stealth assessment achieves AUCs of 0.848–0.913. Challenges include algorithmic bias in assessment models, LLM latency, over-reliance risks, and a near absence of longitudinal evidence. Conclusions: AI-GBL’s effectiveness rests on principled alignment between AI mechanisms and learning theory rather than algorithmic sophistication per se. Equity-by-design approaches and longitudinal evidence constitute the field’s priority research needs. Full article

The use of organophosphate pesticides, such as profenofos, is a pressing environmental concern due to their persistence and toxicity to non-target organisms. For this reason, developing alternatives to remove them from the environment is crucial. The objective of this study was to biosynthesize ZnO nanoparticles (ZnO NPs) using an aqueous extract of Negra Criolla grape seeds for their application in the degradation of profenofos in synthetic solutions. The biosynthesized ZnO NPs had an average size of 52 ± 2 nm and a maximum absorption at 375 nm, characteristic of the surface plasmon resonance of ZnO NPs. The ATR-FTIR spectra showed peaks characteristic of ZnO NPs. The 2² × 3 factorial design showed that the optimal values for pH and ZnO NPs concentration are 5 and 3 g/L, respectively, achieving a primary degradation of profenofos (20 mg/L) of 59 ± 2% after 120 min of UV irradiation. In conclusion, it was demonstrated that ZnO NPs biosynthesized using an aqueous extract of Negra Criolla grape seeds exhibit photocatalytic activity for the degradation of profenofos; however, further studies are needed to evaluate their application in actual contaminated water. Full article

Background/Objectives: The rapid expansion of social media platforms has profoundly changed the way adolescents access reproductive health information. While digital environments increase accessibility to contraceptive content, they also facilitate the dissemination of misinformation, potentially influencing both contraceptive literacy and psychological well-being. The present study aimed to evaluate the relationship between sources of contraceptive information, contraceptive misinformation endorsement, contraceptive knowledge, and mental health indicators among Romanian adolescents and young adults. Methods: A cross-sectional observational study was conducted in a cohort of 210 Romanian adolescents and young adults. Participants completed a structured self-administered questionnaire assessing demographic characteristics, contraceptive information sources, digital health behaviors, contraceptive misconceptions, and contraceptive knowledge. Anxiety and depressive symptoms were evaluated using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) scales. Correlation analyses and multivariable logistic regression models were performed to identify factors associated with poor contraceptive knowledge and moderate-to-severe anxiety. Results: Social media represented the primary source of contraceptive information for 58.1% of participants. Individuals relying predominantly on social media demonstrated significantly lower contraceptive knowledge questionnaire (CKQ) scores compared to those obtaining information from healthcare professionals (5.9 ± 1.8 vs. 8.1 ± 1.7, p < 0.001). Contraceptive misinformation endorsement was inversely correlated with CKQ scores (r = −0.44, p < 0.001) and positively associated with anxiety (r = 0.47, p < 0.001) and depressive symptoms (r = 0.41, p < 0.001). In multivariable analyses, primary reliance on social media (OR 2.21, 95% CI 1.12–4.34, p = 0.022) and low digital health literacy (OR 2.94, 95% CI 1.51–5.71, p = 0.001) were independently associated with poor contraceptive knowledge. Higher misinformation endorsement, infertility-related fears, and high social media exposure were independently associated with moderate-to-severe anxiety. Conclusions: Contraceptive misinformation endorsement was associated with lower contraceptive literacy and poorer psychological outcomes among adolescents and young adults. These findings highlight the growing importance of digital health literacy. However, given the cross-sectional design, the observed relationships should be interpreted as associations rather than causal effects, and longitudinal studies are required to clarify their directionality. Full article

Background/Objectives: Emergency departments (EDs) are particularly prone to medication errors because of urgent treatment environments and high decision density. Automated dispensing cabinets (ADCs) integrated with the hospital information system (HIS) may improve medication safety, yet real-world evidence in centralized-pharmacy settings remains limited. This study evaluated the impact of an HIS-integrated ADC on medication use, medication errors, and nurses’ perception of safety in the ED of a Korean tertiary hospital. Methods: In this prospective before-and-after study, prescribing patterns, medication storage, and related costs were compared in the two months before (Phase 1; September–October 2019) and after (Phase 2; May–June 2020) ADC installation. Medication errors reported through the hospital safety incident reporting system were analyzed over corresponding 6-month windows from July 2019 to June 2020. Long-term sustainability was assessed using follow-up data collected from October to November 2023 (Phase 3), and all 46 ED nurses completed a 5-point Likert-scale survey on perceived medication safety. Results: Daily injectable prescriptions were unchanged (221.1 ± 34.6 vs. 227.7 ± 35.2; p = 0.301), whereas returned injectable prescriptions increased (17.9 ± 5.9 vs. 25.1 ± 6.0; p < 0.001) and non-injectable prescriptions decreased (163.1 ± 42.2 vs. 140.0 ± 22.7; p < 0.001). The number of medication items stored in the ED storage room declined by 95.6%, with a 92.5% reduction in related maintenance cost. Total medication errors decreased from 41 (74.5%) before to 14 (25.5%) after implementation (p < 0.001), with the largest reduction in delivery errors (17 [30.9%] to 2 [3.6%]). These improvements were sustained at the three-year follow-up. Nurses reported high overall satisfaction with medication safety (4.27 ± 0.62 on a 5-point Likert scale). Conclusions: Implementation of an HIS-integrated ADC in the ED was associated with improved prescription patterns, fewer reported medication errors, and enhanced perceived medication safety. In addition, these improvements were sustained over time, indicating stable and consistent implementation of the ADC system. Nurses also reported improved perceptions of medication safety. Full article

Background/Objectives: Sepsis detection relies on clinical variables and scoring systems assumed to perform uniformly across hospital settings. However, sepsis phenotype distributions shift between clinical environments, suggesting that variable importance may be setting dependent. This study aimed to quantify service-specific variability in the discriminatory capacity of clinical variables for sepsis detection and to evaluate whether this heterogeneity translates into differential performance of machine learning models compared to traditional clinical scoring systems. Methods: This stratified sub-analysis of the BIAlert Sepsis cohort (203,755 patients; 11,864 sepsis episodes, 2014–2018) evaluated 61 structured quantitative variables across nine hospital services (≥90 sepsis episodes each). Within each service, the Mann–Whitney–Wilcoxon test (p < 0.01, Holm-corrected) assessed differences between septic and non-septic episodes. Five machine learning models (Random Forest/BIAlert, XGBoost, CatBoost, SVM, Neural Network) and three clinical rules (NEWS, SIRS, qSOFA) were evaluated globally and stratified across four clinical environments. Results: The proportion of significant variables ranged from 95.1% in the Emergency Department (58/61) to 37.7% in the Intensive Care Unit (23/61). Lactate was the only universally significant variable (9/9 services). Clinical scoring systems collapsed in Critical Care (qSOFA and NEWS AUC 0.459). BIAlert maintained the highest AUC across all environments (0.975–0.857). The Friedman test confirmed significant differences (χ² = 28.00, p < 0.001), with BIAlert achieving a mean rank of 1.0. Conclusions: The discriminatory capacity of clinical variables for sepsis detection is not uniform across hospital services. ML models, particularly BIAlert, maintained robust performance where fixed-rule scoring systems failed. Full article

Visually impaired individuals face significant challenges in autonomous mobility and situational awareness. Most existing assistive technologies address isolated tasks, such as object recognition or text reading, while failing to capture broader environmental context. This work addresses this limitation by proposing a scene-sensitive, low-cost assistive system that delivers holistic situational information. We present Vocal-Eyes, an intelligent smart glasses platform that provides periodic audio descriptions of the surrounding environment. The system employs a cloud-based neural processing pipeline in which visual features are extracted using a Transformer-based architecture. Relational context is modeled through scene graph generation, and scene graphs are translated into natural language via a graph-to-text module. A lightweight hardware prototype captures visual data locally, while computationally intensive processing is offloaded to the cloud to reduce power consumption. The experimental results show that relational, scene-based narration produces more coherent and informative descriptions than object-centric approaches while maintaining acceptable periodic latency. Cost analysis further indicates that Vocal-Eyes is significantly more affordable than comparable commercial smart glasses solutions. These results demonstrate that Transformer-based scene understanding with cloud-assisted processing is an effective and practical approach for developing accessible, context-aware assistive technologies for visually impaired users. Full article

Adversarial camouflage has attracted growing research attention owing to its ability to execute multi-view, persistent attacks in real physical environments, outperforming conventional single-view adversarial patches. However, most existing methods are confined to non-targeted attacks, which induce arbitrary incorrect detection results without specifying target categories. This ambiguity weakens attack destructiveness and stealthiness, posing limitations for security evaluation of real-world vision systems. To address this gap, we present TACT, an approach built upon the full-coverage physical camouflage pipeline. By replacing the original category supervision with a predefined target class, TACT redirects the optimization gradient to guide 3D texture toward the target category features. Such a scheme only employs the inherent feature alignment mechanism of off-the-shelf object detectors, without redesigning network modules, defining novel loss functions, or modifying the rendering pipeline. Extensive experiments across digital and physical domains validate its effectiveness: on seven mainstream general-purpose object detectors, TACT-person achieves an average targeted attack success rate of 51.91%, and delivers cross-architecture and cross-version transferability. In physical tests, TACT-bird reduces mAP50-95 by 59.87% on YOLOv8, yet a TCER–TASR gap suggests that the physical pipeline acts as a low-pass filter: coarse-grained target classes transfer robustly while fine-grained ones suffer feature collapse. These results confirm the viability of native supervision redirection and reveal an empirical pattern: coarse-grained target classes transfer more robustly through the physical pipeline than fine-grained ones, suggesting that target class feature granularity consistently influences physical-domain attack effectiveness. Full article

Salt stress represents one of the main challenges for global agricultural production, and digital phenotyping has emerged as a promising alternative for identifying popcorn genotypes tolerant to salt stress. This study evaluated the accumulation of plant pigments in response to salt stress in 49 popcorn genotypes (7 inbred lines and 42 F₁ hybrids). Seeds were subjected to two saline conditions: without salt stress (NS—0 mM NaCl) and salt stressed (SS—100 mM NaCl). The evaluation included physiological parameters, and morphological and colorimetric attributes based on the CIELab color space were analyzed using the GroundEye^® system. Additionally, the salt stress tolerance index (SSTI) was calculated for all assessed genotypes. The SSTI ranged from 0.55 to 0.83, with values closer to 1.0 indicating higher tolerance to the stressor. Among the evaluated genotypes, L472 and four of its hybrids stood out for their salinity tolerance, as they combined efficient maintenance of chlorophyll content with higher SSTI estimates. In contrast, L217 and two of its hybrids were identified as sensitive, exhibiting some of the lowest SSTI estimates and significant accumulation of anthocyanins, which, in this study, indicated a response mechanism to oxidative damage. Digital phenotyping associated with CIELab colorimetric analysis constitutes an objective tool for identifying tolerant genotypes, thereby accelerating breeding programs aimed at developing cultivars adapted to saline environments. Full article