Search Results (3,451)

This paper examines the discourse surrounding the relationship between meditation and psychological well-being from a historical perspective. While both Buddhist practitioners and psychotherapists aspire to a state of health, they appear to diverge on what this goal entails and how to achieve it. Influential voices such as Conze and Jung have argued that psychotherapy and Buddhist meditation are incompatible: psychotherapy helps individuals adjust to contemporary society, whereas Buddhism is ultimately designed for detachment from worldly life. At its core, this view rests on an ideological opposition between “tradition” and “modernity,” with the latter interpreted as an exceptional condition. Using Buddhism in Republican China (1912–1949) as a case study, this paper examines how Master Taixu (1890–1947) and the lesser-known mental hygiene advocate Jin Sheng (ca. 1900–?) responded to emerging mental health concerns and the global diffusion of related therapeutic techniques. Analyzing the modern predicament in which the Buddhism–mental health dialogue places them, this research argues their convergence in framing mental illness as a fundamental cognitive obstruction, with meditation alone insufficient as a remedy. Full article

Background: Prophylactic human papillomavirus (HPV) vaccines are crucial for preventing HPV-related cancers. This study aimed to preclinically evaluate a novel recombinant 9-valent HPV vaccine produced in Escherichia coli (E. coli), which targets HPV types 6, 11, 16, 18, 31, 33, 45, 52, and 58, and is based on virus-like particles (VLPs) of the HPV major capsid protein L1. Methods: The molecular weight and purity of HPV L1 protein bands were assessed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) with Coomassie Brilliant Blue staining. The morphology and size distribution of VLPs were characterized using cryo-electron microscopy and DLS. The immunogenicity and durability of the recombinant 9-valent HPV vaccine were evaluated in BALB/c mice and Wistar rats. Mice received single or triple immunizations (2-week intervals) of two vaccine batches or Gardasil^®9 (MSD, USA) control at 1/20 human dose. Antibody responses were monitored via ELISA and pseudovirus neutralization assays over 24 weeks. Rats were administered single or triple immunizations (2-week intervals) of high- (1/10), medium- (1/20), or low-dose (1/40) vaccine or Gardasil^®9 control (1/20), with neutralizing antibodies tracked for 16 weeks. Results: Cryo-electron microscopy and DLS revealed that VLPs of each type appeared as uniformly distributed, spherical or ellipsoidal hollow intact particles with a diameter of approximately 45–65 nm. This vaccine demonstrated robust immunogenicity and long-lasting efficacy in BALB/c mice and Wistar rats, with effects comparable to those of the commercially available vaccine Gardasil^®9. Conclusions: The 9-valent HPV vaccine induces robust and persistent immune responses in mice and rats, strongly supporting further clinical trials. It is expected to be an alternative to marketed vaccines and ease the global supply shortage of 9-valent HPV vaccines. Full article

To examine the complex relationships among physical activity, emotion regulation, psychological stress, and mood states in college students, this study analyzed questionnaire data collected from 494 participants. Network analysis was employed to construct a global association network, compare gender differences, and characterize patterns of directed statistical dependencies via directed acyclic graph (DAG) analysis. The results showed that: (1) the network comprised 25 nodes and 94 non-zero edges, reflecting extensive conditional associations across the four domains; (2) bridge centrality analysis identified cognitive reappraisal, self-related emotions, and anger as key bridge nodes, with cognitive reappraisal exhibiting the highest bridge strength; (3) accuracy and stability analyses yielded a centrality stability coefficient (CS) of 0.749 for strength, indicating adequate network stability; (4) network comparison tests revealed no significant gender differences in overall network structure or global strength, although certain local edge weights differed; (5) DAG analysis suggested that stable directional dependencies were primarily concentrated within individual subsystems, with no marked structural differences observed between male and female groups. In conclusion, physical activity, emotion regulation, psychological stress, and mood states appear to constitute an interconnected psychological adaptation system. Cognitive reappraisal, self-related emotions, and anger likely serve as pivotal bridge nodes warranting priority in future longitudinal research and targeted interventions. Full article

Underground mine tunnels are often characterized by extremely uneven illumination, weak surface textures, and frequent dynamic interference, which severely undermine multi-view photometric consistency and easily induce floating artifacts and spatial divergence in conventional vision-based 3D Gaussian Splatting (3DGS). To address these issues, we propose a LiDAR-guided 3DGS framework for underground tunnel reconstruction based on dynamic-object removal and differentiable unsigned distance field (UDF) regularization. First, a dynamic foreground removal strategy with background restoration is introduced to remove transient foreground disturbances and restore static supervision consistency. Second, LiDAR point clouds are leveraged to initialize Gaussian primitives with a reliable geometric skeleton in weak-texture regions. More importantly, LiDAR priors are further converted into a differentiable UDF field and serve as a persistent geometric constraint. A dual-track mechanism is designed, where continuous geometric attraction pulls mildly deviated Gaussians back toward the physical surface and periodic out-of-bound culling removes severely drifting primitives. Experiments on real underground tunnel and chamber scenes show a clear scene-dependent behavior of the proposed method. In the tunnel scene, the method achieves the best SSIM together with competitive PSNR and LPIPS, while also reducing redundant out-of-bound primitives and improving geometric cleanliness. In the chamber scene, however, its advantages under global full-reference metrics are less evident. These results suggest that the proposed LiDAR-guided and differentiable UDF-regularized framework is particularly beneficial for weak-texture tunnel environments, while further improvement is still needed for chamber scenes with more complex appearance variations. Full article

Demand response is shifting towards continuous coordination of flexible demand, storage, and distributed generation across buildings and prosumer communities. Multi-agent reinforcement learning has gained attention because it can support decentralized execution under partial observability while still learning coordinated behavior through centralized training. This systematic review follows PRISMA 2020 guidance and synthesizes $n=70$ peer-reviewed studies published in the 2021 to 2025 window, covering building clusters, grid-aware district coordination, program-level aggregation, industrial demand response, and transactive energy mechanisms. The results show that the dominant evaluation context is grid-responsive building clusters, with growing reliance on benchmark environments that standardize interfaces and encourage reproducible multi-KPI reporting. Across the methods, centralized training with decentralized execution is the prevailing pattern, often combined with attention-based critics or value factorization to handle heterogeneity and global rewards. Reward design and constraint handling emerge as primary determinants of stability, since objectives mix cost, peak, ramp, comfort, and emissions, while rebound and synchronized behavior are recurring risks. A descriptive and cross-variable quantitative synthesis is also provided, showing that publication activity increased from three studies (4.3%) in 2021 to 28 studies (40.0%) in 2025, with the strongest concentration in 2024–2025. Quantitatively, grid-responsive building clusters accounted for 26 of 70 studies (37.1%), actor–critic methods for 24 studies (34.3%), CityLearn for 16 studies (22.9%), and cost-based evaluation was reported in 64 studies (91.4%), whereas robustness testing appeared in only 16 studies (22.9%). Across the reviewed studies, peak reduction was reported in 55 (78.6%) studies, whereas robustness testing appeared in only 16 studies (22.9%) and transferability or deployment realism in 11 (15.7%), indicating that evaluation remains much stronger for operational performance than for real-world generalization. Full article

Vehicle detection is a fundamental perception task in intelligent transportation systems and autonomous driving. Although state-of-the-art detectors achieve competitive performance under normal conditions, their robustness degrades substantially under adverse conditions such as rain, fog, low illumination, and sensor noise. To address this challenge, we propose RD-DETR, a vehicle detector that incorporates reaction–diffusion mechanisms into deep feature learning. The RDNet backbone adopts a pyramid-based enhancement strategy in which shallow layers preserve fine-grained texture details while deep layers employ reaction–diffusion-inspired dynamics to suppress noise and enhance target representations. The Phase-Guided Spatial Attention (PGSA) module leverages phase-related structural cues that are relatively less sensitive to global illumination and contrast variations, helping recover vehicle boundaries when appearance cues become unreliable under adverse imaging conditions. The Content-Aware Adaptive Fusion Module (CA-AFM) dynamically aggregates multi-scale features according to scene complexity, improving detection across diverse traffic scenarios. Experiments on BDD100K and DAWN show that RD-DETR yields mAP@0.5 improvements of 3.2 and 4.0 percentage points over RT-DETR, respectively, while reducing model parameters by 27.6%, indicating a favorable balance between accuracy and efficiency under the evaluated settings. Full article

Fear of missing out (FoMO) is a cognitive-affective factor that has been consistently linked to problematic social media use (PSMU), but less is known about whether this association differs across severity-based subgroups or changes over time at the node level. This study examined the cross-sectional and two-wave associations between FoMO and PSMU in Chinese university students. Two-wave data were collected one year apart from 853 participants at Time 1 and 817 participants at Time 2. Partial correlation and regression analyses showed that FoMO was positively associated with PSMU. Latent profile analysis identified broad higher- and lower-level subgroups for both FoMO and PSMU. Node-level network analyses further indicated that FoMO and PSMU nodes were positively interconnected. Most subgroup and two-wave network comparisons suggested that overall network structure was relatively stable. The clearest temporal difference emerged in the global strength of the PSMU network. When differences were observed, they were more evident in the relative prominence of specific nodes, including several bridging nodes, than in broader network organization. Overall, the findings suggest that the FoMO-PSMU association is robust, whereas subgroup- and time-related variation appears limited and is better understood as node-level variation within a broader pattern of structural stability. Full article

Background: Metabolic bariatric surgery (MBS) yields significant but heterogeneous recovery patterns. The longitudinal interplay between evolving eating behaviors and health-related quality of life (HRQoL) remains insufficiently characterized. Objectives: To identify trajectories of eating behavior and HRQoL during the first postoperative year and examine their associations with 12-month outcomes. Methods: A total of 244 patients from two hospitals in Taiwan were followed for 12 months. Dutch Eating Behavior Questionnaire, and Impact of Weight on Quality of Life-Lite were assessed. Group-based trajectory modeling (GBTM) identified latent subgroups, and multiple regression analyzed associations with 12-month HRQoL, adjusting for clinical covariates. Results: GBTM identified two distinct trajectories for restrained, emotional, and external eating. For HRQoL, three trajectories emerged: high-start stable (45–50%), moderate-decline (30–35%), and low-start improving (~20%). In the regression model (R² = 0.37, p < 0.001), eating behavior trajectories were not independently associated with total HRQoL at 12 months after adjusting for covariates, including baseline BMI and comorbidities. Specifically, restrained eating (β = −1.42, p = 0.502), emotional eating (β = −10.33, p = 0.110), and external eating (β = −5.33, p = 0.160) trajectories did not significantly predict global HRQoL scores. Conclusions: Postoperative adaptation is characterized by substantial heterogeneity, with a significant subgroup experiencing HRQoL decline despite surgery. While eating behavior trajectories align with domain-specific psychosocial trends, early postoperative clinical factors appear to exert a more dominant influence on total HRQoL during the first year. These findings suggest that multidisciplinary support should target specific vulnerable trajectories to optimize long-term outcomes. Full article

Africa is historically celebrated as the cradle of humankind. However, there is doubt on whether she is maintaining her own originality and position as the motherland and fatherland of all humanity. Although globalisation has impacted all continents and states, its negative effects seem to be skewing towards African and in particular Zimbabwean Shona families. This paper examines how DNA testing has impacted on some of the Shona families in Zimbabwe. The Shona community in Zimbabwe is culturally porous and receptive in terms of traditional, religious, linguistic and cultural values. They embraced Western democracy that is premised on human rights principles, constitutionalism, and citizenship, which, however, do not guarantee their belongingness. As some of the Shona families in Zimbabwe drifted away from the traditional cultural belief system campus, they got into a foreign and alien worldview that is dictated by the host in the name of technology. This has led to excessive reliance on foreign systems that are appearing like global standards yet they are disempowering them and causing them emotional and social distress. The reliance is a result of neocolonialism, linguistic and cultural imperialism that needs decolonisation. Thus, the paper adopts a qualitative approach based on an illuminating multiple case study design of six purposively selected scenarios aired on the The Closure DNA Show programme broadcasted on Zimbabwe Television (ZTV). The Afrocentric paradigm serves as a lens to uncover some of the perceptions of Shona families in Zimbabwe on DNA testing and its implications on parenthood, the family unit, and identity. The findings reveal that DNA testing is perceived as gender divisive and a destroyer of the family unit and exposing children to vulnerability, while it is also perceived positively as a way of (dis)affirming identity, which is crucial among the Shona. The paper recommends that other television programmes be screened based on their implications on gender relations, the family unit and identity. Full article

Background/Objectives: Aging is closely associated with sarcopenia, which has a significant impact on muscle mass and its function. Protein supplementation (PS) brings benefits such as lean mass and strength gains during exercise training. This paper determined the optimal regimen among the composites of variate protein sources and training modalities for older individuals. Methods: We comprehensively searched the electronic databases, namely MEDLINE Complete, PEDro, the Cochrane Library, Google Scholar, EMBASE, and the China National Knowledge Infrastructure, from its inception until December 2025. We included randomized controlled trials (RCTs) that examined the effectiveness of any type of PS combined with one of three exercise types—resistance, aerobic, or multicomponent training—in untrained older adults. The main outcomes used to identify sarcopenia were assessed, including lean mass, handgrip and leg strength, and physical mobility measures. Network meta-analysis (NMA) was performed by a frequentist method using random-effects models. The estimated treatment effect was expressed as the standard mean difference (SMD) with a 95% confidence interval (CI). Any potential factor moderating the treatment effect was determined by the meta-regression analyses, including participant characteristics and methodological factors. Certainty of evidence (CoE) was assessed by the GRADE framework. Results: In total, we included 235 RCTs (20,980 participants) for analyses. A total of 10 protein sources (whey, soy, casein, milk, and the others) were identified, corresponding to 24 monotherapy and combined regimens of PS and exercise. Among the treatment arms, whey plus resistance training was ranked as the most effective treatment for muscle mass (large SMD, 1.29; CoE, moderate) and leg strength (large SMD, 1.16; CoE, moderate); additionally, whey plus multicomponent exercise training achieved the most promising effects on such sarcopenia-related physical indicators such as chair rise (large effect, SMD = 1.09; CoE: high), timed up and go (medium SMD, 0.70; CoE, high), and global mobility score (large SMD, 1.02; CoE, high). Conclusions: The treatment efficacy appears to be moderated by the participant’s conditions, PS resource, and PS dose, particularly the outcome of muscle mass and strength. The present NMA results indicate that whey protein incorporated with resistance training is the optimal program to help combat sarcopenia in older adults. Full article

Accurate and robust localization and mapping in complex and dynamic environments remain a fundamental challenge for autonomous systems. LiDAR–Inertial–Visual Odometry (LIVO) integrates the complementary strengths of LiDAR geometry, visual appearance, and inertial motion constraints. However, existing LIVO systems still suffer from limited adaptability to sensor degradation, weak loop-closure robustness, and insufficient cross-modal consistency modeling. This paper presents a robust multi-sensor SLAM framework that integrates an uncertainty-aware LIVO front-end, a geometry-driven loop-closure module, and a cross-modal consistency factor-graph back-end. We develop an uncertainty-aware iterated error-state Kalman filter (iESKF) to tightly fuse LiDAR, visual, and inertial measurements, with measurement covariances dynamically adjusted according to innovation statistics, feature-matching quality, and observability. To improve global consistency, we propose a multi-channel Binary Triangle Constraint (mBTC) descriptor for LiDAR-based loop detection, which enhances robustness under viewpoint changes and appearance degradation. In addition, we introduce a cross-modal consistency factor to explicitly constrain the relative motion agreement between visual and LiDAR odometries. Extensive experiments on multiple public benchmarks demonstrate improved accuracy, loop-closure reliability, and long-term consistency compared with state-of-the-art LIVO systems. Full article

Hepatitis E virus (HEV) is the leading cause of hepatitis globally and poses particular risks for immunocompromised individuals. Mandatory screening of blood donations for HEV RNA and retrospective individual testing of previous donations (lookback investigations) following a reactive result have been implemented in several countries to protect these patients. This includes Germany, where a sensitivity limit of 2000 IU/mL applies to index donations. In total, 334 HEV RNA-positive blood donations were detected at our blood donation service between 2018 and 2024. Lookback testing was applied in 211 cases, revealing previous HEV RNA-positive donations in 23.1% of donors (n = 48, 76 donations). Although 16 of these retrospectively tested HEV RNA-positive donations have already been transfused, no transfusion-transmitted HEV infection has been reported. The HEV RNA viral load in the lookback donation was below 50 IU/mL in 72.4% of cases. Routine testing effectively prevents highly viremic blood products entering the supply, significantly reducing the infection risk. While the administration of virus particles with low-viremic products cannot be ruled out, the remaining risk appears to be minimal and has been deemed so far acceptable for the safety of blood products. The lookback strategy further supports the screening strategy by retrospectively identifying blood products from low-viremic donations and enabling appropriate risk management. Full article

Accurate vehicle counts from traffic videos are fundamental to traffic measurement and to estimating roadway demand for infrastructure planning and maintenance. However, many vision-based traffic datasets and pretrained models under-represent vehicle types that are prevalent in developing countries, such as the keke (globally known as auto-rickshaw/three-wheeler), which can bias traffic composition estimates and downstream workload indicators. This paper presents a keke-aware vehicle detection and counting pipeline that combines fine-tuned YOLO-based detectors with BoT-SORT/ByteTrack tracking and ROI-based counting, together with a newly curated and publicly released traffic-video dataset that includes a dedicated keke class. The detectors are fine-tuned from pretrained weights on a six-class dataset (bicycle, bus, car, motorcycle, truck, keke) and evaluated on held-out roadside test videos with a manual counting baseline. On the validation split (2088 images; 8400 instances), the fine-tuned YOLO11l model achieves $P=0.752$, $R=0.696$, $\mathrm{mAP}@0.5=0.766$, and $\mathrm{mAP}@0.5:0.95=0.578$, with the keke class attaining $\mathrm{mAP}@0.5=0.772$, while YOLO26l achieves slightly higher overall precision ($P=0.766$) and stronger keke recall and $\mathrm{mAP}@0.5:0.95$. In system-level counting, the selected tuned ROI-based variants produce the most reliable results on the Yola Road downward flow, where keke counts remain close to the manual baseline, but performance is strongly direction- and scene-dependent, with substantially larger errors in the Yola upward flow and the more challenging Mubi Road scene. Flow-rate and ESAL-rate analyses further show that class misclassification can severely distort pavement-loading estimates even when total traffic flow appears close to baseline, underscoring the need for localized class ontologies and robust heavy-vehicle discrimination in mixed-traffic ITS deployments. The released dataset and baseline pipeline provide a practical reference for keke-aware traffic monitoring and for infrastructure-relevant traffic measurement in developing-country contexts. Full article

Background/Objectives: The Global Lung Initiative (GLI) 2022 race-neutral spirometry reference equations were introduced to improve interpretability across populations; however, their performance in Middle Eastern adolescents remains insufficiently validated. This study evaluated the applicability of GLI-2022 among healthy Jordanian adolescents. Methods: Healthy adolescents were recruited from secondary schools across multiple Jordanian cities (July–November 2025). Spirometry was performed according to ATS/ERS standards using a single device and standardized procedures. GLI-2022 predicted values and z-scores were derived for forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), and FEV₁/FVC. Calibration was assessed using mean (SD) z-scores and the proportion below the lower limit of normal (LLN; z < −1.645). Agreement between measured and predicted values was examined using Bland–Altman methods. LLN-based pattern classifications were compared with those obtained using the local reference equation and GLI-2012. Results: A total of 921 adolescents (482 males, 439 females; mean age 15.7–16.0 years) were included. GLI-2022 produced positive mean z-scores for FEV1 (0.51–0.73) and FVC (0.51–0.69), with low proportions below LLN for both indices (<2% in each sex), indicating underestimation of predicted lung volumes. Exact binomial testing confirmed that the observed proportions below LLN for FEV1 and FVC were significantly lower than the expected 5% in both sexes (all p < 0.001). The FEV1/FVC ratio showed smaller deviations (mean z 0.07–0.19), with 4.1% of females and 5.8% of males below LLN, and these proportions did not differ significantly from 5% (female p = 0.444; male p = 0.402). Mean observed-minus-predicted biases for FEV1 were +0.185 L in females and +0.306 L in males, and for FVC were +0.224 L and +0.351 L, respectively; FEV1/FVC bias was −0.15 percentage points in females and +0.60 percentage points in males. LLN-based pattern classification showed 98.7% overall agreement with the local equation and 99.7% with GLI-2012; concordance for obstructive and possible restrictive patterns was 93.5% and 100.0%, respectively. Conclusions: In healthy Jordanian adolescents, GLI-2022 appears to underestimate predicted FEV₁ and FVC, yielding upward-shifted z-scores and fewer volume indices below LLN, while the ratio is less affected. Although LLN-based pattern classification was largely preserved, population-specific validation remains necessary before routine clinical adoption of GLI-2022 in Jordanian adolescents; extrapolation to other Middle Eastern adolescent populations should await additional regional validation. Full article

Background: Agentic artificial intelligence (AI) systems, characterized by autonomous goal-directed behavior, multi-step reasoning, task decomposition, and tool use, are increasingly proposed for healthcare applications. However, their autonomy raises concerns regarding transparency, accountability, and human oversight. While explainable AI (XAI) has been widely studied in traditional predictive models, less is known about how explainability is implemented within agentic architectures. Objective: To map the emerging literature on explainable agentic AI (XAAI) in healthcare and characterize the types, scope, and forms of explainability used in these systems. Methods: A scoping review was conducted following PRISMA-ScR guidelines. PubMed, Embase, IEEE Xplore, and ACM Digital Library were searched through November 2025. Eligible studies described healthcare-related agentic AI systems incorporating explicit explainability mechanisms. Data were extracted on system architecture, explainability type (intrinsic, post hoc, hybrid), explanation scope (local, global), explanation form, and reported clinical outcomes. Results: Nine studies met the inclusion criteria. All systems demonstrated core agentic features, including autonomy, task decomposition, and tool integration, often within multi-agent frameworks. Explainability was predominantly intrinsic and workflow-native, typically delivered through textual reasoning traces and example-based grounding in retrieved clinical evidence. Feature-based and global explanations were comparatively rare and largely confined to hybrid architectures. Across domains including radiology, neurology, psychiatry, and biomedical research, XAAI systems were reported to improve performance and interpretability relative to baseline models in the included studies. However, these findings were derived from heterogeneous, predominantly experimental or retrospective studies, and structured human-in-the-loop oversight was infrequently described. Conclusions: Current XAAI systems appear to emphasize process transparency and evidence grounding rather than mechanistic model-level attribution. The available evidence remains limited and heterogeneous, and findings should be interpreted as early trends rather than established characteristics. Further progress will require standardized evaluation frameworks, clearer reporting of oversight mechanisms, and validation in real-world clinical settings to support safe and trustworthy integration of agentic AI into healthcare practice. Full article