Search Results (77)

Social media has become an important platform for the public to obtain information, express emotions, seek help, and participate in collaborative responses during major natural disasters. However, existing studies have mainly focused on the intensity of disaster-related discussions, emotional evolution, or patterns of information dissemination, while systematic examination remains limited regarding how public risk perception is associated with specific behavioral responses and whether these associations vary across disaster stages, degrees of regional impact, and user types. Using the stimulus–organism–response (SOR) framework as an organizing heuristic, this study examines the 2023 Beijing–Tianjin–Hebei extreme rainfall event. A total of 31,102 Sina Weibo posts were collected. By integrating LDA topic modeling, risk-perception dictionary matching, a BERT-based semantic robustness check, and chi-square tests, this study analyzes descriptive associations between public risk perception and behavioral responses. The results show that Weibo discussions evolved across disaster stages, with topics shifting from meteorological warnings and disaster reports to mutual aid, rescue operations, material support, and post-disaster reflection. Public risk perception shifted from information-oriented to relationship-oriented cognition, while behavioral responses transitioned from information attention to emergency mutual aid, sustained support, and reflective expression. Further analysis indicates that uncertainty perception was mainly associated with information attention; susceptibility perception co-occurred with help-seeking and emotional support; positive trust perception was strongly associated with emotional support; and negative trust perception co-occurred with questioning, accountability, and reflective suggestions. Because the negative-trust classifier performed substantially less well than the other dimensions, findings involving negative trust are treated as exploratory. Heterogeneity analyses show that observed perception–behavior associations varied across disaster stage, regional impact, and user type. These findings deepen understanding of social-media discourse during disasters while supporting cautious, stage-, region-, and group-sensitive risk communication. Full article

Flood disaster risk assessment serves as an important foundation for formulating regional sustainable development strategies. This study establishes a risk assessment model for flood disasters in small and medium-sized rivers based on a theoretical framework integrating Geographic Information Systems (GIS), the Delphi method, and Bayesian networks (Delphi–BNs). An indicator system for the assessment was developed from three dimensions: hazard, vulnerability, and exposure. Hazard is represented by flood inundation area and depth; vulnerability is indicated by population distribution and economic layout; and exposure is reflected by road accessibility. By constructing a GIS-based Bayesian network and employing the Delphi method to create a probabilistic and spatially explicit model, this approach quantifies various sources of uncertainty in the assessment process, enabling a probabilistic expression of risk. Based on the risk assessment results, a stratified, phased flood emergency rescue and personnel transfer plan was established, designating extremely high-risk areas as the core zones for the first phase of personnel transfer, high-risk areas as the second-phase rescue zones, and medium-risk areas as the third-phase rescue zones, thereby providing clear operational guidance for flood emergency response in the basin. The Delphi–BN assessment framework developed in this study focuses on the core elements of flood disaster risk formation, organically integrates expert experience with spatial big data, and effectively overcomes the limitations of traditional assessment methods, such as strong subjectivity, insufficient accuracy, and poor quantification. It achieves a refined and quantitative assessment of flood risk in small and medium-sized river basins in semi-arid regions. The outcomes of this research contribute to a clearer understanding of both the driving mechanisms and the spatial patterns of regional flood risk. Furthermore, they establish a scientifically credible and operationally relevant foundation for key disaster-response decisions, encompassing timely emergency actions, phased population transfers, and the optimized deployment of limited emergency resources. Full article

The transition towards smart megacities has deeply integrated Artificial Intelligence (AI) with power–transport networks. While AI empowers complex operations like multi-network coordinated dispatch and emergency rescue, current algorithm-centric perspectives largely ignore its massive physical energy costs. Accordingly, this Perspective examines the dual role of AI, considering it not only as an intelligent decision-support tool but also as a potential source of additional stress on physical infrastructure. First, through a structured synthesis of the representative literature, we deconstruct the functional dependencies between algorithms and physical infrastructures, identifying how AI reshapes the operational paradigms of power, ground transport, and aerial networks under routine and emergency scenarios. We then introduce the concept of the “Computation–Energy Paradox.” Integrating conceptual analysis with a quantitative case study of a typical community, we illustrate a plausible failure mechanism: during extreme disasters, intensified AI invocation for emergency management generates surging computational loads, which paradoxically exacerbate power shortages and reduce the operating margin of already weakened systems. In addition, we analyze core engineering bottlenecks, including spatiotemporal computation–energy mismatches and physical constraints in extreme edge environments. To address these challenges, we outline a prospective roadmap encompassing lightweight emergency AI and computation–power-coordinated offloading mechanisms. Finally, the sustainable development of such systems suggests a paradigm shift: AI must evolve from a purely virtual algorithm into a physical component of an integrated compute–power–transport system. Full article

Effective emergency relief allocation in dynamic post-disaster environments depends critically on accurate and timely demand information. From a sustainability perspective, improving allocation accuracy is essential for using scarce rescue resources efficiently and supporting resilient disaster response. However, existing demand forecasting approaches frequently exhibit systematic bias, leading to resource misallocation and diminished rescue outcomes. Although deploying on-site assessment teams can partially mitigate this limitation, a unified framework that systematically embeds field assessment feedback into operational allocation processes remains lacking. To bridge this gap, this study proposes a multi-agent joint assessment-allocation model that facilitates coordinated operations between demand assessment and resource distribution activities. The sequential decision-making process is formulated as a Markov Decision Process (MDP), and deep reinforcement learning is employed to coordinate the actions of assessment and allocation teams, enabling allocation policies to be continuously refined through real-time field feedback. By improving the match between actual demand and material supply, the proposed model aims to support more resource-efficient disaster response under demand uncertainty. An empirical case study based on the 2025 Dingri County earthquake in Tibet is conducted to validate the proposed framework. Results demonstrate that integrating assessment feedback substantially improves resource allocation performance: in multi-site rescue scenarios, the framework increases the number of rescued individuals, reduces mission completion time, and enhances overall demand satisfaction. Further sensitivity analysis reveals that a moderate increase in team size strengthens cross-site coordination, whereas excessive team deployment yields diminishing returns and may generate operational redundancy. These findings suggest that sustainable emergency management depends not only on the availability of relief resources, but also on the efficient coordination of real-time information acquisition and material allocation. The proposed framework offers a generalizable approach for integrating real-time information acquisition with dynamic relief allocation. It improves the efficient utilization of scarce rescue resources, reduces avoidable operational redundancy, and strengthens the resilience of emergency response systems, thereby contributing to sustainable disaster risk reduction. Full article

Chronic respiratory diseases (CRDs) represent a significant global mortality burden, largely driven by viral-triggered exacerbations. In the elderly, susceptibility to viral pathogens is critically linked to the “interferon gap”—a kinetic delay in innate antiviral signaling resulting from immunosenescence and Th2-skewed inflammaging. While traditional vaccines provide pathogen-specific protection, their efficacy is often compromised by age-related immune hyporesponsiveness and antigenic drift. This perspective paper proposes a dual-phase, virus-agnostic immunomodulatory platform designed to restore mucosal immune competence and provide a rapid-response intervention for incipient exacerbations. Rather than acting as a pathogen-specific vaccine, the platform serves as a comprehensive host immune-rejuvenation engine and cellular adjuvant platform. The platform consists of two integrated stages: Allopriming and Alloantigen Inhalation Recall (AIR). Allopriming utilizes AlloStim^® (activated, allogeneic Th1 cells) to leverage the evolutionarily conserved allo-rejection response, establishing a lung mucosal reservoir of allo-specific Th1 tissue-resident memory cells (Trm). Building on previously published Phase I/II data showing that Allopriming reverses biomarkers of immunosenescence and sustains durable heterologous antiviral responsiveness, the AIR strategy is introduced as a patient-administered rescue mechanism for frail CRD patients. AIR is designed to activate pre-positioned Trm cells at the earliest onset of symptoms, inducing a high-magnitude IFN-γ surge in the lung mucosa. By bridging the senescent “interferon gap” with the rapid effector kinetics of Trm activation, this approach represents a novel paradigm toward reconstituting youthful-like antiviral mucosal immunity to both enhance vaccine efficacy in the elderly and protect against both seasonal pathogens and emerging viral triggers (“Disease X”) of CRD. Future randomized studies in long-term care settings are planned to evaluate clinical outcomes in high-risk populations. Full article

The rapid growth of hiking tourism presents a critical challenge for balancing visitor safety with the sustainable management of ecologically fragile mountain environments. Traditional models developed in urban settings struggle to capture the highly non-linear, heterogeneous, and zero-inflated characteristics of wilderness trekking behavior. In order to quantify the nonlinear and threshold-based effects of environmental variables on hikers’ spatial decisions in unstructured wilderness and to identify distinct behavioral regimes for segmented management, this study introduces an explainable machine learning framework to reconstruct hikers’ spatial decision-making in a complex mountainous system in Inner Mongolia, China. Random Forest (RF), XGBoost, and LightGBM were compared in predicting trail density and the Euclidean distance to the nearest trail. Results show that transforming behavioral traces into continuous proximity surfaces dramatically improves model performance, with XGBoost achieving the highest predictive accuracy for Trail_Dist. By integrating the SHapley Additive exPlanations framework, this study moves beyond black-box prediction to reveal the nonlinear mechanisms driving hiker behavior. Key findings include: (1) Nighttime light range exhibits a U-shaped threshold effect as the primary anthropogenic attractor. (2) Elevation shows an exponential inhibitory trend above 1238 m. (3) Strong spatial coupling exists between elevation and slope, alongside a landscape compensation effect where high Normalized Difference Vegetation Index (NDVI) areas attract off-trail movements. This research provides a robust methodological pathway for predicting behavior in unstructured outdoor environments. It offers a scientific foundation for smart scenic area management, including optimized route planning, precise ecological protection zoning, and targeted emergency rescue preparedness. Full article

To address high uncertainty, dynamic evolution, and limited information in emergency decision-making for major sudden disasters, this paper proposes a sliding-window game-theoretic method with four reference points for emergency response selection. Firstly, interval-valued T-spherical fuzzy sets are adopted to capture decision-makers’ uncertain and hesitant evaluations in interval form. Subsequently, a four-reference-point framework, including the external, internal, average development speed, and ideal proximity reference points, is established to reflect stage-dependent psychological baselines. Furthermore, criterion weights are updated by a sliding-window game-theoretic combination weighting scheme that integrates entropy, anti-entropy, criteria importance through intercriteria correlation, and the coefficient of variation, and performs rolling updates across stages. Prospect values are then computed relative to the four reference points and aggregated to rank alternatives at each stage. Finally, a case study of the 2024 Huludao extreme rainfall event applies the proposed method to evaluate four candidate schemes across six criteria over three decision stages. Results show that rescue cost has the highest weight in all stages, while the importance of rescue speed decreases and social impact increases as the response progresses. The proposed method identifies a comprehensive flood relief scheme led by the People’s Liberation Army and the People’s Armed Police Force as the best option in all stages, because it achieves the highest comprehensive prospect values among all alternatives. Comparative analyses indicate more consistent identification of the optimal scheme than existing approaches, supporting sustainable and resource-efficient disaster management. Full article

Humanitarian crises such as natural disasters and armed conflicts are increasing in frequency and intensity, posing major challenges to the sustainable protection of vulnerable populations. Rapid and equitable identification of individuals at highest risk is essential for efficient allocation of limited emergency resources and for strengthening community resilience. This study proposes an intelligent, privacy-aware decision-support platform for citizen-level vulnerability assessment that supports social sustainability and resilient crisis management. The platform integrates heterogeneous data from healthcare institutions, municipal civil records, and emergency rescue services to construct multidimensional vulnerability profiles based on social conditions, medical status, and geographical accessibility. The dataset was collected in Algeria in collaboration with the Algerian Civil Protection and consolidated into a fully anonymised dataset of approximately 5000 individual records reflecting realistic crisis scenarios. Five supervised machine learning models (Decision Tree, Random Forest, Support Vector Machine (RBF), XGBoost, and Logistic Regression) were evaluated under class-imbalance conditions using SMOTE and class weighting. The Random Forest model achieved the best performance, with an F1-Macro score of 0.710 and a recall of 0.569 for the high-risk class (95% confidence interval: [0.431, 0.706]). These results demonstrate that the proposed platform enables transparent, data-driven prioritisation of emergency interventions, contributing to sustainable humanitarian response, improved public resource allocation, and enhanced resilience of vulnerable communities. Full article

Emergency and Disaster Assembly Areas (EDAA) are designated safe zones where basic needs can be met until temporary shelters are established following natural or man-made disasters like floods, fires, earthquakes, explosions, or chemical incidents. Promptly relocating disaster victims to these areas is crucial for minimizing loss of life and facilitating effective search and rescue operations by maintaining an uninterrupted flow of information. To prepare for disasters like earthquakes, which cause significant material and emotional damage to large populations, sustainable disaster management must be ensured to evaluate site suitability, correct deficiencies, and avoid inappropriate locations. This study will examine the evaluation criteria for EDAAs established by the Tunceli Provincial Disaster and Emergency Management Authority (AFAD) in terms of area, structure, security, and accessibility, taking into account the region’s specific characteristics. Based on a literature review, eleven criteria have been proposed and ranked using the Besson mean ranking method. Areas have been classified into four categories (e.g., adequate, not suitable) using the optimistic, pessimistic, and comprise approaches of the Assignment Rule Driven by Attitudes (ARDA) and the ORESTE-Sort method. The examination of 19 EDAA provides two perspectives: an optimistic view that recommends classifying eleven areas as first class and using all areas as they are, and a pessimistic view that calls for urgent improvements in three areas and states that one area (EDAA 1) is deemed unsuitable due to its assignment to class ${\mathrm{K}}_4$. It is also advised that the second area should not be used, despite being rated as class ${\mathrm{K}}_3$, due to its proximity to the river and its slope characteristics. The study also performs a sensitivity analysis of the method and provides recommendations for future research. Full article

Against the backdrop of the suddenness and inherent uncertainty of emergencies, pre-disaster emergency facility location and emergency relief stockpiling are critical for improving the efficiency and sustainability of emergency response. This paper focuses on the emergency response network design problem considering uncertain transportation time and emergency demands. We cluster historical disaster events and extract cluster-specific statistical features, such as the average value, mean absolute deviation, and probabilistic statistical distance of uncertain parameters, constructing an ambiguity set based on the disaster feature and multivariate probability distribution information. Then, to minimize the total rescue cost, a feature-driven two-stage distributionally robust optimization model is formulated to determine reliable pre-disaster emergency facility locations, inventory decisions, and post-disaster resource allocation strategies. Finally, through an earthquake case in Sichuan Province of China, this work verifies that incorporating disaster clustering information enables a superior trade-off between the robustness and conservatism of emergency rescue decisions. Compared with the benchmark model, the proposed method displays better out-of-sample performance and can effectively enhance the sustainability of emergency response in uncertain environments. Full article

Hair loss disorders, particularly androgenetic alopecia (AGA), are common conditions that carry significant psychosocial impact. Current standard therapies, including minoxidil, finasteride, and hair transplantation, primarily slow progression or re-distribute existing follicles and do not regenerate lost follicular structures. In recent years, regenerative medicine has been associated with a gradual shift toward approaches that aim to restore follicular function and architecture. Stem cell-derived conditioned media and exosomes have shown the ability to activate Wnt/β-catenin signaling, enhance angiogenesis, modulate inflammation, and promote dermal papilla cell survival, resulting in improved hair density and shaft thickness with favorable safety profiles. Autologous cell-based therapies, including adipose-derived stem cells and dermal sheath cup cells, have demonstrated the potential to rescue miniaturized follicles, although durability and standardization remain challenges. Adjunctive interventions such as microneedling and platelet-rich plasma (PRP) further augment follicular regeneration by inducing controlled micro-injury and releasing growth and neurotrophic factors. In parallel, machine learning-based diagnostic tools and deep hair phenotyping offer improved severity scoring, treatment monitoring, and personalized therapeutic planning, while robotic Follicular Unit Excision (FUE) platforms enhance surgical precision and graft preservation. Advances in tissue engineering and 3D follicle organoid culture suggest progress toward producing transplantable follicle units, though large-scale clinical translation is still in early development. Collectively, these emerging biological and technological strategies indicate movement beyond symptomatic management toward more targeted, multimodal approaches. Future progress will depend on standardized protocols, regulatory clarity, and long-term clinical trials to define which regenerative approaches can reliably achieve sustainable follicle renewal in routine cosmetic dermatology practice. Full article

Background: Opioids and nonsteroidal anti-inflammatory drugs (NSAIDs) for clavicle fracture pain management carry significant adverse effect and allergic reaction risks. This study assessed ultrasound-guided nerve block (USNB) efficacy for acute clavicle fracture pain in emergency department (ED) patients, providing an alternative to NSAIDs and opioids with fewer adverse effects. Methods: This retrospective, single-center observational study was conducted in accordance with Methods of Medical Record Review Studies in Emergency Medicine Research guidelines. Adult patients (≥20 years) who presented to the ED with traumatic clavicle fractures between 1 January 2015 and 30 November 2023 were included. Of the 343 eligible patients, 12 received ultrasound-guided nerve blocks (USNB) and 331 received standard care. To improve exchangeability, 1:10 matching with replacement was performed according to patients’ characteristics, such as age, sex, initial pain score, and comorbidities. The primary outcome was pain relief, assessed via the pain intensity difference (PID) on the Numerical Rating Scale within 360 min post-intervention. Meaningful pain relief was defined as a PID ≥ 4. Secondary outcomes included rescue opioid use, ED length of stay, hospital length of stay, and USNB-associated complications, such as vascular puncture, nerve injury, or local anesthetic systemic toxicity. Data were analyzed using time-course, time-to-event (time to meaningful pain relief), and linear regression analyses. Results: A total of 12 patients in the USNB group and 85 matched patients in the standard care group were analyzed after baseline characteristics matching with replacement. Compared to standard care, USNB was associated with significantly greater pain relief (p < 0.001). In the time-to-event analysis, USNB led to a 3.41-fold faster achievement of meaningful pain relief compared with that achieved with standard care (HR = 3.41; 95% CI, 1.47–7.90; p = 0.004). No significant differences were observed between groups in rescue opioid use, ED length of stay, or hospital length of stay. No USNB-associated complication developed in the USNB group. Conclusions: In patients with traumatic clavicle fractures, USNB provides more rapid and sustained pain relief than standard analgesic care in the ED, without increasing the ED length of stay. Large prospective studies are needed to confirm these findings. Full article

In the initial phase of large-scale disasters, delayed external relief supplies make scientific local emergency supply allocation crucial—not only for reducing casualties, but also for advancing sustainable disaster response, a key link in enhancing post-disaster resilience. Existing research mostly focuses on cross-regional material allocation while overlooking local challenges like low resource efficiency and unbalanced supply–demand dynamics. To tackle these limitations in the existing research, this study develops a multi-objective collaborative local emergency supply allocation model centered on sustainability. It uses an improved TOPSIS method to quantify the urgency of needs in disaster-stricken areas, prioritizing material distribution to vulnerable regions in line with the principle of “no vulnerable area left neglected in relief efforts”. The study also integrates the entropy weight method and analytic hierarchy process (AHP) to ensure rational indicator weighting, and designs a double-layer encoded genetic algorithm to obtain optimal allocation schemes that balance efficiency, fairness, and sustainability. Validated using the 2013 Ya’an Earthquake case study, the model outperforms traditional local allocation approaches: it boosts resource utilization efficiency by reducing material shortage rates, accelerates post-disaster recovery by shortening response times, and improves allocation fairness. Findings provide empirical support for the establishment of “local–external” collaborative rescue systems and sustainable disaster risk reduction frameworks. Empirical calculations using case-specific data and real-world estimates verify the model’s practical applicability: it meets the requirements for fair and rapid allocation needs, aligns with the goals of sustainable disaster management, and lowers the carbon footprint of relief operations by lessening reliance on long-distance external materials. Full article

The health and operational continuity of emergency responders are fundamental pillars of sustainable and resilient disaster management systems. These personnel operate in high-risk environments, exposed to intense physical, environmental, and psychological stress. This makes it crucial to monitor their health to safeguard their well-being and performance. Traditional methods, which rely on intermittent, voice-based check-ins, are reactive and create a dangerous information gap regarding a responder’s real-time health and safety. To address this sustainability challenge, the convergence of the Internet of Things (IoT) and wearable biosensors presents a transformative opportunity to shift from reactive to proactive safety monitoring, enabling the continuous capture of high-resolution physiological and environmental data. However, realizing a field-deployable system is a complex “system-of-systems” challenge. This review contributes to the field of sustainable emergency management by analyzing the complete technological chain required to build such a solution, structured along the data workflow from acquisition to action. It examines: (1) foundational health sensing technologies for bioelectrical, biophysical, and biochemical signals; (2) powering strategies, including low-power design and self-powering systems via energy harvesting; (3) ad hoc communication networks (terrestrial, aerial, and space-based) essential for infrastructure-denied disaster zones; (4) data processing architectures, comparing edge, fog, and cloud computing for real-time analytics; and (5) visualization tools, such as augmented reality (AR) and heads-up displays (HUDs), for decision support. The review synthesizes these components by discussing their integrated application in scenarios like firefighting and urban search and rescue. It concludes that a robust system depends not on a single component but on the seamless integration of this entire technological chain, and highlights future research directions crucial for quantifying and maximizing its impact on sustainable development goals (SDGs 3, 9, and 11) related to health, sustainable cities, and resilient infrastructure. Full article

Effective post-disaster management requires continuous and reliable monitoring of the evolving disaster situation. While remote sensing provides objective measurements of ground deformation, social media data offer dynamic insights into public perception and disaster progression. However, integrating these complementary data sources to achieve sustained monitoring of disaster remains a challenge. To address this, we propose a novel framework that combines Sentinel-1 SAR data with Sina Weibo posts to improve dynamic earthquake impact assessment. Physical damage was quantified using D-InSAR-derived deformation. Disaster-related locations were identified using a fine-tuned pre-trained language model, and public sentiment was inferred through prompt-based few-shot learning with a large language model. Spatiotemporal analysis was performed to examine the relationship between sentiment dynamics and varying levels of physical damage, followed by an analysis of topic transitions within regional semantic networks to compare discussion patterns across areas. A case study of the 2023 Jishishan earthquake demonstrates the framework’s capability to continuously track disaster evolution: regions experiencing severe physical damage exhibit clear concentrations of negative sentiment, whereas increases in positive sentiment coincide with areas where rescue operations are effectively underway. These findings indicate that integrating the two data sources improves continuous disaster monitoring and situational awareness, thereby supporting emergency response. Full article