Multidisciplinary Digital Publishing Institute (Switzerland)
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The Digital Unconscious and Post-Disaster Recovery in the Cinema of Haruka Komori
No full textHow does digital technology mediate decision-making and shape our understanding of disaster recovery? I address this question by examining both the administrative and cinematic uses of digital images in the reconstruction process following the 2011 Great East Japan Earthquake. Post-disaster digital mediation is characterized by the administrative use of what has been termed “operational images,” designed not for interpretation but for action, particularly in disaster response and prevention. I connect the social and ethical dimensions of post-disaster recovery with the ontological dimensions of the technological characteristics of digital photography. By comparing Japanese independent filmmaker Haruka Komori’s digital filmmaking practice with the operational images utilized by administrative and research bodies, I aim to demonstrate how her particular digital aesthetics elicit the latent capacity of the “digital unconscious” and offer new modes of perceiving post-disaster recovery, in contrast to both other forms of post-disaster digital mediation and to analog photography. Through close analyses, I argue that her work articulates an alternative vision of recovery—one rooted not in spatial management or predictive planning, but in physical attachment to place, trust in the future, and imaginative engagement with survivors and the dead
Explainable Artificial Intelligence for Rehospitalization and Financial Burden of Fertile Women in Orthopedic Care
No full textBackground: Fertile women represent a socially and medically significant patient group, yet little research has examined their rehospitalization behavior and financial burden in clinical settings. This study develops predictive and explainable artificial intelligence for rehospitalization and medical costs among reproductive-age orthopedic patients. Methods: Electronic health records of 83 women (aged 15–49) at a major university hospital in Korea were analyzed. Six machine learning models were developed, and model performance was assessed using accuracy, the area under the curve, the root mean square error and its scaling invariant divided by the interquartile range (RMSE/IQR). Shapley Additive Explanations were applied to interpret predictors of rehospitalization. Additional analyses explored determinants of patients’ total and uncovered medical costs. Results: The random forest outperformed other models in predicting rehospitalization (area under the curve 0.92 vs. 0.73 for logistic regression). Key predictors included major disease, systolic blood pressure, platelet count, age, and treatment costs. The random forest also yielded lower error rates than linear regression in forecasting patients’ costs (e.g., RMSE/IQR for total cost: 1.05 vs. 1.14). Several factors—such as blood pressure, pulse, and hematocrit—were influential for both rehospitalization and costs. Conclusions: Predictive and explainable artificial intelligence can support medical centers in anticipating the rehospitalization and financial burden of fertile women. By integrating medical and socioeconomic determinants, hospitals may design strategies that enhance patient rehospitalization while addressing broader societal priorities in women’s health
System Value Assessment and Heterogeneous Cost Allocation of Long-Duration Energy Storage Systems: A Public Asset Perspective
No full textLong-duration energy storage (LDES) can deliver system-wide flexibility and decarbonization benefits, yet investment is often hindered because these benefits are diffuse and not fully monetized under conventional market structures. A public-asset-oriented valuation and cost-allocation framework is proposed for LDES. First, LDES externality benefits are quantified through a system-level optimization-based simulation on a stylized aggregated regional network, with key indicators including thermal generation cost, carbon penalty, renewable curtailment cost, involuntary load shedding, and end-user electricity expenditures. Second, LDES investment costs are allocated among thermal generators, renewable operators, grid entities, and end users via a benefit-based Nash bargaining mechanism. In the case study, introducing LDES reduces thermal generation cost by 3.92%, carbon penalties by 5.59%, and renewable curtailment expenditures by 7.07%, while eliminating load shedding. The resulting cost shares are 46.9% (renewables), 28.7% (end users), 22.4% (thermal generation), and 0.5% (grid entity), consistent with stakeholder-specific benefit distributions. Sensitivity analyses across storage capacity and placement further show diminishing marginal returns beyond near-optimal sizing and systematic shifts in cost responsibility as benefit patterns change. Overall, this framework offers a scalable, economically efficient, and equitable strategy for cost redistribution, supporting accelerated LDES adoption in future low-carbon power systems
A Demand Prediction-Driven Algorithm for Dynamic Shared Autonomous Vehicle Relocation: Integrating Deep Learning and System Optimization
No full textThis paper develops a dynamic repositioning mechanism for shared autonomous vehicles (SAVs) driven by travel demand. A prediction model for SAV travel demand is constructed by the proposed GRU-FC network. On this basis, an integer programming model for empty-vehicle dispatching which aims to maximize the SAV revenue while minimizing the costs of vehicle relocation and operation is formulated. The results indicate that, relative to relying solely on natural vehicle dispatching, the proposed dispatching scheme reduces empty vehicle dispatches by 21.00% and increases total system profit by 38.89%. The findings theoretically improve the dynamic optimization theory of SAV dispatching and provide theoretical support for algorithm design based on the “demand-pull” principle. The method proposed in this paper is beneficial to optimizing the dynamic vehicle dispatching theory of SAVs. It helps to boost system revenue, reduce empty driving costs, alleviate traffic pressure, and lower energy consumption and environmental pollution, thereby fostering sustainable urban mobility and supporting the Sustainable Development Goals of clean energy and sustainable cities
Emerging Resident Concerns as Signals of a Paradigm Shift in the Spatial Infrastructure for Integrated Community Care: Focusing on Yeonpyeong Island, a Medically Isolated Declining Region of Korea
No full textAcross East Asia, rapid population aging and regional decline threaten the sustainability of rural and island communities. Yeonpyeong Island provides a critical context for examining how spatial infrastructure shapes older residents’ daily challenges. The aim of this study is to identify how older adults evaluate their housing and community environments and to determine whether these perceptions signal a transition toward more integrated and community-based care settings. Using a primary quantitative survey of 102 older residents, supplemented by contextual input from a local representative, the study analyzes how health decline, mobility constraints, and housing obsolescence interact with aspirations for service-integrated and socially connected living. Composite scores for perceived home modification needs remained consistently in the mid-to-upper range (approximately 3.5–4.0 on a 5-point scale). Acceptance of alternative, cohousing-type community housing also remained above the midpoint (approximately 3.5–4.1), reflecting an unusually high level of openness in a setting traditionally characterized by low receptivity to residential change and limited local housing alternatives. Safety risks, poor accessibility, and inadequate facilities function as push factors, while preferences for shared programs, proximity-based reassurance, and integrated hubs operate as pull factors, together signaling readiness for more supportive communal living. By integrating Push–Pull Theory with Environmental Press and Life-Space perspectives, the study contributes theoretically by extending these frameworks to the community scale and empirically by providing resident-level evidence from an under-researched island context. The findings highlight how older adults act as evaluators of their environments, articulating practical signals for spatial restructuring and integrated care planning
Human and AI Reviews Coexist: How Hybrid Review Systems Enhance Trust and Decision Confidence in E-Commerce
No full textThis research investigates how hybrid review systems integrating human-generated reviews and AI-generated summaries shape consumer trust and decision-related confidence. Across three controlled experiments conducted in simulated e-commerce environments, when and how hybrid reviews enhance consumer evaluations were examined. Study 1 demonstrates that hybrid reviews, which combine the emotional authenticity of human input with the analytical objectivity of AI, elicit greater levels of review trust and decision confidence than single-source reviews. Study 2 employs an experimental manipulation of presentation order and demonstrates that decision confidence increases when human reviews are presented before AI summaries, because this sequencing facilitates more effective cognitive integration. Finally, Study 3 shows that AI literacy strengthens the positive effect of perceived diagnosticity on confidence, while information overload mitigates it. By explicitly testing these processes across three experiments, this research clarifies the mechanisms through which hybrid reviews operate, identifying authenticity and objectivity as dual mediators, and sequencing, literacy, and cognitive load as critical contextual moderators. This research advances current theories on human–AI complementarity, information diagnosticity, and dual-process cognition by demonstrating that emotional and analytical cues can jointly foster trust in AI-mediated communications. This integrative evidence contributes to a nuanced understanding of how hybrid intelligence systems shape consumer decision-making within digital marketplaces
Climate Change and Health Systems: A Scoping Review of Health Professionals’ Perceptions and Readiness for Action
No full textClimate change is one of the greatest challenges of our time, with direct implications for sustainable development, the physical and mental health of populations, and the functioning of health systems. Strengthening the resilience and sustainability of health systems through mitigation and adaptation strategies requires the active involvement of health professionals. This scoping review explores health professionals’ perceptions of climate change and its impacts on public health and health systems, as well as their operational preparedness and the barriers to adaptation. The literature review was conducted in three phases (20 December 2024, 20 January 2025, and 20 March 2025) using the Web of Science, Scopus, and PubMed databases, covering the period 2016–2025 and following PRISMA guidelines. Of the 1888 studies initially identified, 36 met the predefined inclusion and exclusion criteria. The findings showed that while health professionals recognize climate change as a current threat to public health and health systems, they are not adequately prepared to address its impacts. The main barriers to addressing climate change are related to a lack of information and awareness, inadequate training, limited time, lack of supportive leadership, failure to integrate sustainable practices into daily clinical practice and, above all, inadequate funding. Based on these findings, there is an urgent need to develop policies that promote the active participation of health professionals in the design and implementation of climate change mitigation and adaptation strategies. At the same time, there is a need to strengthen research activity through both synchronous and diachronic studies in order to gather information on the sustainability and resilience of health systems
Downhill Running-Induced Muscle Damage in Trail Runners: An Exploratory Study Regarding Training Background and Running Gait
No full textThis study aimed to assess the effect of a downhill-running (DR) bout on muscle damage biomarkers. It also examined whether training background and gait kinematics may influence DR-induced muscle damage and strength loss. Thirty-six experienced trail runners (25 men, 11 women), participants of a 106 km ultra-trail, performed a 5 km DR bout at 15% decline and at an intensity equivalent to their first ventilatory threshold. Muscle damage biomarkers (creatine kinase, lactate dehydrogenase, and myoglobin) were analyzed before and 30 min after the DR protocol, and also before and after the UT race. Isometric strength was assessed before and after DR, and gait parameters were recorded during DR. All muscle damage biomarkers increased following DR (d = 0.19 to 1.85). Lactate dehydrogenase concentrations after the race and DR were associated (r = 0.64). Athletes who habitually performed downhill repetitions showed reduced creatine kinase (182 ± 73 U/L vs. 290 ± 192 U/L; p < 0.05; d = 0.64) and greater squat strength retention (4 ± 10% vs. −9.1 ± 16.8%; p <0.05; d = 0.87). Ankle plantar flexion and squat strength retention were inversely correlated with vertical oscillation (r = −0.44) and step length (r = −0.37), respectively. In summary, lactate dehydrogenase response to a short DR bout could indicate an athlete’s readiness to handle ultra-trail-induced muscle damage, although further research is needed to confirm it. In addition, despite the exploratory nature of the study, regularly performing downhill intervals and adopting a more terrestrial gait pattern appear to soften strength loss and muscle damage response to DR
Experimental Investigation of Acoustic Signal Characteristics of Blockages in Highway Tunnel Drainage Pipelines Using Distributed Acoustic Sensing
No full textThis study aims to quantitatively assess blockage conditions in highway tunnel drainage pipelines using acoustic wave signals. A full-scale physical model of a drainage pipeline was constructed to simulate six blockage ratio conditions ranging from 12.5% to 75%. Distributed Acoustic Sensing (DAS) technology was employed to collect acoustic signals along the pipeline. Time-domain analysis and Fast Fourier Transform (FFT)-based frequency-domain analysis were conducted to compare the waveform amplitude and dominant frequency components between blocked and unobstructed pipeline sections. The results demonstrate a significant increase in time-domain amplitude at the blockage location, with a maximum enhancement of up to 50% compared to unobstructed sections. In the frequency domain, this phenomenon is particularly pronounced within specific dominant frequency bands (core frequency bands). For instance, the 395–405 Hz band was identified as the core band under the 50% blockage ratio condition. Furthermore, the time-domain amplitude at the blockage shows a positive correlation with the blockage ratio (12.5–75%). The comprehensive analysis indicates that the time-domain characteristics of DAS-based acoustic signals can effectively identify both the location and severity of blockages in highway tunnel drainage pipelines. This research provides fundamental data for evaluating the blockage state of tunnel drainage systems based on acoustic signatures
Whistleblowing in Emerging Financial Systems: Model Development and Mixed-Methods Evidence from Banks in Qatar
No full textWhistleblowing is a key mechanism of financial governance; however, its effectiveness varies across institutional and cultural contexts. This study examines the factors influencing whistleblowing effectiveness in Qatar’s banking sector, employing an integrated model grounded in the Stimulus–Organism–Response framework and Prosocial Behavior theory. A mixed-methods design combined survey data from 354 banking employees with qualitative text analysis. Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that Training and awareness were the strongest predictors of whistleblowing effectiveness, followed by Transparency and Accountability, and Reporting and Monitoring Mechanisms. At the same time, Legislative and Policy Framework were not significant. Fear of Retaliation partially mediated these relationships, underscoring the importance of psychological safety and trust. Thematic analysis confirmed these findings, highlighting leadership credibility, anonymity, and independent reporting as key enablers, while cultural norms such as hierarchy and loyalty remained barriers. The results indicate that effective whistleblowing in Qatar is less dependent on formal regulation and more on cultivating trust, transparency, and credible protection mechanisms. The study extends behavioral theory to financial ethics, offering practical insights for strengthening integrity systems in emerging financial sectors