Search Results (350)

In the canon of Soviet travel writings of the 1920s–30s, Andrei Bely’s lesser-known book Veter s Kavkaza (1928, not reprinted since its first publication) and the essay Armenia (1929) are something of an oddity. They are generally seen аs an active attempt on his part to become a Soviet writer. This attempt by all accounts had very limited success, but the intention was genuine, and it enters into a most intriguing constellation with the more successful travel writings of the same period that ostensibly are based on the same practice of participatory observation as was practiced by members of LEF and other literary groups. Bely’s writings are more about observation itself than they are about anything else. His entire approach to the subject matter of his travel narratives is based on an obsessive mapping of the topography of his journey in an attempt to learn (by his own account) the Goethean art of seeing—not just the physical topography but also the past and the future of the human landscape in its revolutionary transformation. Ultimately, Bely’s spatially focused narrative seeks to see and represent time, and for this reason suffers the most spectacular failure, which Bely the Kantian and Bely the Symbolist wants to celebrate, but Bely the Soviet writer desperately tries to overcome. The article examines this failure in the broader political and artistic context of the time. Full article

Emotional and workload-related states unfold dynamically during immersive virtual reality (VR) experiences, yet reliable physiological modeling in such environments remains challenging. We investigated whether multi-channel facial electromyography (fEMG), combined with spatio-temporal deep learning, can (i) accurately classify calibrated facial expressions across participants and (ii) transfer to spontaneous, task-elicited behavior in immersive VR. Twelve adults completed a calibration phase involving four intentional expressions (smile, frown, raised eyebrow, neutral), followed by VR scenes designed to elicit emotional, cognitive, physical, and dual task demands. After participant-level physiological normalization, a single shared Convolutional Neural Network–Temporal Convolutional Network (CNN–TCN) model was trained and evaluated using leave-one-participant-out (LOPO) validation. The model achieved strong cross-participant performance (Macro-F1 = 0.88 ± 0.13; ROC-AUC = 0.95 ± 0.06). When applied to unlabeled spontaneous VR task-elicited fEMG recordings, the trained model generated continuous expression classes. Derived static and temporal expression features showed scene-dependent modulation and False Discovery Rate (FDR)-surviving associations, primarily with perceived physical demand (NASA-TLX). The observed muscle activation patterns were physiologically plausible and aligned with Facial Action Coding System (FACS)-based interpretations of underlying muscle activity. These findings demonstrate that end-to-end spatio-temporal modeling of raw fEMG enables facial expression sensing in immersive VR using a single shared model following physiological normalization. The proposed framework bridges calibrated expression learning and spontaneous task-elicited behavior, supporting privacy-preserving, continuous and physiologically grounded monitoring in human-centered VR applications. Full article

Auditory Brain–Computer Interfaces (BCIs) constitute the vital intervention for profound sensorineural hearing loss where the auditory nerve is compromised, yet their clinical efficacy remains restricted by substantial biological bottlenecks and limited spectral resolution. This review critically examines the evolutionary paradigm of auditory restoration, tracing the transition from static physical replacement to dynamic biological symbiosis. We systematically analyze physiological barriers across cochlear, brainstem, and cortical levels, elucidating how rigid interfaces provoke chronic tissue responses and why linear encoding protocols fail in distorted central tonotopy. The article synthesizes emerging methodologies in material science, demonstrating how soft, bio-integrated electronics and biomimetic topologies effectively address mechanical impedance mismatches. Furthermore, the trajectory of neural encoding is evaluated, highlighting the paradigm shift from traditional envelope extraction to deep learning-driven non-linear mapping and adaptive closed-loop neuromodulation. Finally, the potential of high-resolution modulation techniques, including optogenetics and sonogenetics, alongside AI-facilitated intent perception for active listening, is assessed. It is concluded that future neuroprostheses must evolve into symbiotic systems capable of seamlessly integrating with neural plasticity to enable high-fidelity cognitive reconstruction. Full article

Obesity is a major public health problem with a continuously increasing global prevalence and is associated with various chronic diseases and substantial social and economic burdens. As dietary modification and physical activity alone often have limited effectiveness in achieving sustained weight loss, dietary supplements intended for weight reduction are widely used. However, evidence on the efficacy and safety of these supplements is inconsistent, and consumer use intentions tend to be driven by subjective beliefs and insurance-like perceptions. Accordingly, this study reviewed recent evidence on L-carnitine, green tea extract, glucomannan, and Garcinia cambogia, supplements for which weight loss effects have been proposed, to assess their efficacy and safety and to highlight the importance of supplement selection aligned with consumer use contexts. PubMed, MEDLINE, and Google Scholar databases were searched for studies published between 1 January 2020 and 10 October 2025. Although some studies have reported improvements in weight and metabolic indicators, consistent scientific evidence has not yet been established. This review emphasized the need for purpose-driven supplement selection that integrates efficacy, safety, usage context, and evidence level, and the importance of consumers’ critical information appraisal capacity, supported by structured information provision and education. Full article

Background/Objectives: We conducted a scoping review to map peer-reviewed evidence on occupational stress, burnout, and quality of life among radiographers and radiologic technologists and to identify measurement tools and reported consequences. Methods: Searches were conducted in Web of Science, Scopus, and PubMed. Eligible studies enrolled radiographers/radiologic technologists who were healthy adults; assessed at least one target construct (occupational stress, burnout, or quality of life) using validated instruments; and used cross-sectional, experimental, quasi-experimental, longitudinal, or mixed-methods designs. Articles published from 1995 onward in English, French, Spanish, or Portuguese were considered. Two reviewers independently screened, extracted data, and appraised methodological quality using Quality Assessment with Diverse Studies (QuADS). The synthesis was narrative only. Results: Of 2701 records, 10 studies from nine countries met inclusion. Most were cross-sectional, and two used mixed methods. Sample sizes ranged from 38 to 864. Frequently used instruments included MBI-HSS, OSI-R, HSE Indicator Tool, and SOC-13. Across studies, radiographers reported high stress and burnout—particularly emotional exhaustion and depersonalization—alongside reduced quality of life in multiple domains. Recurrent stressors involved workload and staffing pressures, role demands, anxiety about radiation exposure, and limited recognition. These factors were associated with intention to leave and a lower sense of coherence. Conclusions: The evidence base is largely cross-sectional, uses heterogeneous measures, and often relies on modest samples, with overall methodological quality mostly moderate. Findings indicate a persistent psychosocial risk profile in radiography and underscore the need for organizational and managerial actions—such as workplace physical activity programs—to reduce stress and burnout and protect the quality of life in this workforce. Full article

Compensatory Health Beliefs (CHBs) are cognitions that the negative effects of unhealthy behaviors can be offset by healthy ones. While their role in single behaviors is established, their mechanisms in regulating multiple health behaviors remain empirically unclear, particularly whether CHBs facilitate or inhibit actual cross-behavior compensation between physical activity (PA) and healthy eating (HE). This study tested a dual-path model proposing that CHBs are associated with immediate intention compensation via moral licensing and with long-term cross-behavior inhibition through reduced self-efficacy. A cross-sectional online survey of 366 university students assessed general CHBs, domain-specific social cognitive variables (self-efficacy, intention, planning), and self-reported PA (IPAQ-SF) and HE (calculated from reported food consumption as a dietary guideline adherence score). Data were analyzed using Structural Equation Modeling. We found that CHBs were positively associated with PA intention but negatively linked to HE intention, planning, and behavior. CHBs were also negatively related to HE self-efficacy, which was subsequently associated with lower PA planning, indicating a cross-behavior inhibition pathway. In conclusion, CHBs are linked to lower health behavior engagement through two pathways: short-term intention-based licensing across domains and self-efficacy erosion that inhibits integrated planning. This integrated model highlights the importance of addressing both CHBs and self-efficacy in health interventions. Full article

Objective: From a family perspective, this study aimed to examine the effects of a 12-week digital platform-based parent–child exercise intervention on children’s behavioral level (physical activity), psychological level (physical exercise attitude), and mental health. Methods: This study included 218 students aged 10–11 years who underwent a 12-week standardized parent–child exercise intervention. The intervention group completed two structured parent–child tasks per week through a digital platform (Ding Talk App) while maintaining regular physical education classes; the control group only maintained their regular physical education classes. Assessments were conducted using the Physical Activity Rating Scale, Exercise Attitude Scale, and mental health scales (The Spence Children’s Anxiety Scale (SCAS) and The Patient Health Questionnaire-9 (PHQ-9)) at four stages: pre-intervention (T1), mid-intervention (T2), post-intervention (T3), and a 2-month follow-up period (T4). The intervention effects and effect sizes (ηp²) were examined using Repeated Measures Analysis of Variance. Results: At the behavioral level, a significant group × time interaction was found for physical activity volume (F = 17.651, p = 0.04, ηp² = 0.138), indicating the presence of a moderate effect. At the psychological level, significant interactions were observed across exercise attitude dimensions (behavioral attitude: F = 3.699, p = 0.002, ηp² = 0.033; perceived behavioral control: F = 4.189, p = 0.006, ηp² = 0.037; subjective norm: F = 4.616, p < 0.001, ηp² = 0.040) and mental health measures (depression: F = 4.009, p = 0.026, ηp² = 0.044; anxiety: F = 3.1, p = 0.016, ηp² = 0.020), though no significant effect was found for behavioral intention (F = 1.346, p = 0.259, ηp² = 0.012), with all significant effects being relatively weak. Conclusions: The home–school collaborative, digital platform-based parent–child exercise intervention demonstrated positive effects on children’s physical activity volume, exercise attitudes, and mental health, offering a feasible and promising strategy to support more integrated child health promotion initiatives. Full article

Ship-to-ship (STS) transfer has become a major concern for maritime security and regulatory authorities, as it is frequently exploited for smuggling and other illicit activities. Accurate and timely identification of STS events is therefore essential for effective maritime supervision. Existing monitoring approaches, however, suffer from two inherent limitations: AIS-based surveillance is vulnerable to intentional signal shutdown or manipulation, and remote-sensing-based ship detection alone lacks digital identity information and cannot assess the legitimacy of transfer activities. To address these challenges, we propose a Multi-source Observation framework with SAR and AIS (MOSA), which integrates SAR imagery with AIS data. The framework consists of two key components: STS-YOLO, a high-precision fine-grained ship detection model, in which a dynamic adaptive feature extraction (DAFE) module and a multi-attention mechanism (MAM) are introduced to enhance feature representation and robustness in complex maritime SAR scenes, and the SAR-AIS Consistency Analysis Workflow (SACA-Workflow), designed to identify suspected abnormal STS behaviors by analyzing inconsistencies between physical and digital ship identities. Experimental results on the SDFSD-v1.5 dataset demonstrate the quantitative performance gains and improved fine-grained detection performance of STS-YOLO in terms of standard detection metrics. In addition, generalization experiments conducted on large-scene SAR imagery from the waters near Panama and Singapore, in addition to multi-satellite SAR data (Capella Space and Umbra) from the Gibraltar region, validate the cross-regional and cross-sensor robustness of the proposed framework. The effectiveness of the SACA-Workflow is evaluated qualitatively through representative case studies. In all evaluated scenarios, the SACA-Workflow effectively assists in identifying suspected abnormal STS events and revealing potential AIS inconsistency indicators. Overall, MOSA provides a robust and practical solution for multi-scenario maritime monitoring and supports reliable detection of suspected abnormal STS activities. Full article

Rapid urbanization and spatial constraints in high-density residential areas pose significant challenges to public health and well-being. This study investigates the mechanisms by which the visual environment of urban micro public spaces shapes residents’ psychophysiological responses to encourage spontaneous physical activity and advance active health. Using machine learning and semantic segmentation, 9 core visual elements across 20 micro public space scenes in high-density urban neighborhoods were quantified. An immersive virtual reality (VR) experiment was conducted, collecting synchronized multimodal psychophysiological data from 60 participants, which yielded 600 valid observations. Through an analytical framework combining Self-Organizing Map (SOM) clustering and Random Forest (RF) modeling, three distinct functional archetypes were identified: Restoration-Supporting, Activity-Promoting, and Stress-Inducing. The Activity-Promoting archetype was most effective in fostering spontaneous activity intention, characterized by a high proportion of activity areas, a moderate sky view factor, and minimal physical barriers. RF modeling further pinpointed pedestrian density, activity area ratio, and green space ratio as key visual drivers of health-promoting outcomes. Based on these findings, a “Visual Activation for Active Health” framework is proposed. It posits that moderate visual-environmental stimulation is the core mechanism for transforming passive spaces into health-promotive settings, thereby establishing a theoretical foundation for the evidence-based design of healthy and sustainable urban environments. Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article

The exoskeleton rehabilitation robot is a structural robot that uses the actuator to control, so as to construct a human–robot collaborative rehabilitation training system to realize the perception and decoding of patients and promotes the recovery of limb function and neural remodeling. This review focused on the synergistic advancement of physical and information interaction in exoskeleton rehabilitation robotics. This review systematically retrieved literature related to the synergistic advancement of physical and information interaction in exoskeleton rehabilitation robotics. Publications from 2011 to 2025 were searched for across the EI, IEEE Xplore, PubMed, and Web of Science databases. The included studies mainly covered the period from 2018 to 2025, reflecting recent technological progress. This article summarizes the collaborative progress of physical and informational interaction in exoskeleton rehabilitation robots. The physical and information interaction is manifested in the bionic structure, physiological information detection and information processing technology to identify human movement intention. The bionic structural design is fundamental to realize natural coordination between human and robot to improve the following of movements. The active participation and movement intention recognition accuracy are enhanced based on multimodal physiological signal detection and information processing technology, which provides a clear direction for the development of intelligent rehabilitation technology. Full article

Modern lifestyles are increasingly plagued by physical inactivity, social disconnection, digital addiction, and excessive time indoors—factors that negatively impact the health and well-being of both humans and their companion dogs (Canis familiaris). Evidence shows that nature exposure, physical activity, and human–animal bond (HAB) each enhance physical, mental, and social well-being, yet these domains have rarely been examined together as an integrated therapeutic triad. We introduce a new conceptual framework of bonded green exercise, defined as shared physical activity between a bonded human and dog in natural environments. Synthesizing existing evidence across human and canine sciences into a testable conceptual integration, we posit that bonded green exercise may plausibly activate evolutionarily conserved, synergistic mechanisms of physiological, behavioural, and affective co-regulation. Four testable hypotheses are proposed: (H1) triadic synergy: combined domains produce greater benefits than additive effects; (H2) heterospecific benefit: parallel health gains occur in both species; (H3) behavioural amplification: dogs acts as catalysts to drive human participation in nature-based activity; and (H4) scalable health promotion: bonded green exercise represents a low-cost, accessible, One Health approach with population-level potential. This framework highlights how intentional, shared physical activity in nature may potentially offer a novel low-cost and accessible model for enhancing health, lifespan, welfare, and ecological stewardship across species. Full article

The global rise in nursing home (NH) populations presents substantial challenges, as residents frequently experience physical and cognitive decline, low physical activity, and high fall risk. This study evaluates the effectiveness of the BeSt Age App, a tablet-based, staff-supported mHealth intervention designed to promote physical activity and prevent falls among NH residents. Primary outcomes were fall incidence and fall risk (assessed using Berg Balance Scale [BBS] and Timed Up and Go [TUG]); fear of falling was a secondary outcome. In a cluster-randomized controlled trial across 19 German NHs, 229 residents (mean age = 85.4 ± 7.4 years; 74.7% female) were assigned to an intervention group (IG) or control group (CG). The 12-week intervention comprised twice-weekly, tablet-guided exercise sessions implemented by NH staff. Mixed models and generalized estimating equations were used under an intention-to-treat framework. The IG showed significantly greater improvement in BBS scores than the CG (group × time: F(1, 190.81) = 8.25, p = 0.005, d = 0.22), while group × time changes in TUG performance, fear of falling, and fall incidence were nonsignificant. These findings demonstrate the feasibility of a staff-mediated mHealth approach to fall prevention in NH residents, showing significant improvements in BBS scores as one functional indicator of fall risk, while TUG, fall incidence and fear of falling showed no change. Full article

Grounded in the circumplex model, this study examined how need-thwarting teaching styles, control and chaos, are combined by Physical Education teachers (as perceived by students) and how these combinations differ in relation to students’ basic psychological needs and motivational outcomes within (experiences and perceived learning) and outside (intention to be physically active) the Physical Education context. A total of 431 Spanish secondary school students (M_age = 14.92; 53% girls) participated. Latent profile analysis identified three profiles: (1) high control—moderate chaos (35%), (2) moderate control—high chaos (9%), and (3) high demanding—low chaos (56%). Mean comparisons revealed that students in the “high demanding—low chaos” profile reported the most adaptive outcomes, including greater autonomy and competence satisfaction, more positive Physical Education experiences, higher perceived learning, and stronger intentions to be physically active. Conversely, the “moderate control—high chaos” profile was linked to the most maladaptive outcomes, characterized by greater basic psychological needs frustration and poorer experiences, learning, and physical activity intentions. The “high control—moderate chaos” profile yielded intermediate results. Overall, findings indicate that chaotic teaching, especially in its abandoning form, was associated with the worst quality of students’ motivation, while a demanding approach may be comparatively less harmful but still detrimental. Teacher training should therefore reduce controlling and chaotic practices and foster autonomy support and structure. Full article

In the face of rapid aging, depression in later life has become a prominent issue in urban public health and environmental research. As potential places for social activities and emotional healing, the social stayability of shaded community spaces is an essential environmental factor influencing the mental well-being of the elderly. In order to overcome the challenge of depression relief in later life, it is important to investigate what attributes of social stayability in shaded spaces influence the mental well-being of the elderly, as well as their gap structures. This study innovatively develops a fuzzy multi-criteria decision-making method and builds an analytical framework combining Fuzzy-BWM and VIKOR to comprehensively evaluate three dimensions of physical accessibility, facilities, and spatial conditions, and environmental comfort and safety of shaded spaces. Using the Pioneer community in Panyu, Guangzhou, and the Yuehan community in Macau as empirical cases, this study integrates expert judgment and residents’ perception data to identify the key attributes and gap structure of shaded space stayability in mitigating depression-related psychological risk and promoting emotional restoration and psychological well-being among older adults. The results show that facilities and spatial conditions have the greatest impact on social stayability. The two attributes of sitting comfort and public service facilities are the dominant factors that affect stay intention and emotional recovery. Environmental comfort and safety have a secondary but stable supporting effect on psychological security. This study reveals the coupling relationship between functional configuration and perceptual experience and advocates for the transformation of urban renewal thinking from spatial optimization to psychological health promotion. This study’s results offer theoretical support and policy implications for building restorative, inclusive, and age-friendly cities. The findings provide a quantitative basis for decision making regarding sustainable community space governance and intervention prioritization. Full article