Search Results (2,027)

Regional reassessments of agricultural carbon emission efficiency are essential for improving the sustainability of food production systems under climate constraints. This study evaluates agricultural carbon emission efficiency (ACEE) across China’s major grain-producing zone (GPZ), major grain-consuming zone (GSZ), and grain production–consumption balanced zone (GBZ) during 2003–2022, excluding Hong Kong, Macao, Taiwan, and Tibet due to data limitations. A super-efficient EBM–GML model incorporating both desirable and undesirable outputs is employed to measure ACEE at the provincial level, with comparisons conducted within each functional zone and nationally unified efficiency values used as a benchmark. Spatial dependence is examined using Moran’s I, and a spatial Durbin model is applied to identify driving factors and spatial spillover effects. The results indicate that the average efficiency levels differ systematically across functional grain zones, following the order GBZ > GPZ > GSZ, while several provinces experience notable changes in their relative rankings. Carbon emissions increase in the earlier period and decline in later years, whereas efficiency exhibits an opposite temporal pattern, reflecting a gradual transition of grain production systems from extensive input-driven growth toward more sustainability-oriented practices. Substantial regional disparities in ACEE are also observed. Rational industrial organization and efficient allocation of production resources contribute to positive spillover effects on neighboring regions, whereas natural disasters and inefficient resource distribution tend to weaken such effects. These findings suggest that functional grain zones provide an effective framework for capturing intra-regional heterogeneity and should be adopted as the basic unit for efficiency assessment and the formulation of differentiated governance strategies. Full article

The deformation of surrounding soil primarily governs the behavior of underground structures. Consequently, variations in their external geometry significantly affect their overall seismic response. Moreover, large soil deformations and structural uplift caused by liquefaction severely threaten their seismic safety. While most previous studies have focused on conventional rectangular subway stations, the seismic performance of novel varying-span structures remains largely unexplored. In this study, nonlinear dynamic time-history analyses are conducted to investigate the soil–structure interaction (SSI) of large unequal-span subway stations in liquefiable sites. Furthermore, the seismic responses of both the structure and the surrounding soil are systematically evaluated under various burial depths of the liquefiable layer. Finally, a U-shaped foundation reinforcement method is proposed to mitigate structural uplift. The results show that unequal-span structures suppress liquefaction in lateral soil, whereas significant liquefaction occurs beneath the base slab and cantilevered middle slabs. The burial depth of the liquefiable layer has a negligible effect on the liquefaction state directly under the center span. Regarding structural response, global uplift follows a spatial pattern that peaks at the center span and gradually attenuates laterally. Although the proposed U-shaped reinforcement effectively reduces both total and differential uplift, it does not fundamentally change the underlying liquefaction mechanism. Specifically, reinforcing the soil under cantilevered sections minimizes differential uplift while enhancing the overall economic efficiency of the seismic design. These findings provide a scientific basis for optimizing the seismic resilience of complex underground structures, contributing to the development of resource-efficient and disaster-resilient urban underground infrastructure in liquefaction-prone regions. Full article

Age estimation is an important component of forensic investigation, with applications in criminal casework, immigration assessments, and disaster victim identification. Determining whether an individual is a minor or an adult, or estimating the age at death of unidentified remains, can have significant legal and humanitarian implications. Traditional forensic age estimation methods rely primarily on anthropological and radiological assessment of skeletal development and degeneration; however, these approaches may be limited by subjectivity, population-specific reference standards, and reduced precision in adult age estimation. In recent years, molecular biomarkers have emerged as promising complementary tools for age prediction. Molecular approaches, including DNA methylation profiling, Y-chromosome-associated markers, RNA-based biomarkers, mitochondrial DNA alterations, proteomic signatures, and telomere length analysis, reflect biological processes associated with aging and may provide objective indicators that can be measured from biological samples. Among these methods, DNA methylation-based models currently demonstrate the strongest predictive performance and represent the most extensively studied molecular strategy for forensic age estimation. Nevertheless, several challenges remain before widespread forensic implementation can be achieved, including tissue specificity, environmental influences on biomarker stability, population variability, and the need for robust validation across laboratories and forensic sample types. This review summarises the current molecular approaches investigated for forensic age estimation, evaluates their biological basis and methodological limitations, and discusses their potential integration into forensic workflows. While molecular techniques offer promising avenues for improving age estimation, further standardisation, validation, and careful interpretation are required before they can be routinely applied in forensic practice. Full article

Low-temperature stress during the jointing stage severely disrupts the coordination of the source–flow–sink system in winter wheat. To elucidate the underlying mechanism, three wheat cultivars with different winter habits (Zhenmai 12, Jimai 22, and Shannong 38) were selected and subjected to six temperature levels (−6 °C to 8 °C) and three stress durations (2–6 days). The effects of vascular bundle traits on the transport of photosynthetic products, dry matter distribution, and yield formation were analyzed. The results showed that Zhenmai 12 and Jimai 22 completely ceased photosynthesis under 0 °C and −3 °C, respectively. The leaf vascular bundle area continuously decreased with increasing low-temperature stress, while the proportion of xylem and phloem initially increased by approximately 15% and 10%, respectively, before rapidly decreasing to 65% of the control value. In the stem, the three vascular bundle parameters initially increased by 20%, 25%, and 20%, respectively, before quickly decreasing to 50%. Changes in the vascular bundle structure weakened the transport capacity of assimilates, with dry matter in leaves and stems decreasing by 15–20% and 10%, respectively, while the root dry matter increased by 20–30%. Correlation analysis revealed highly significant relationships (p < 0.001) between vascular bundle parameters and yield components. Principal component and cluster analyses indicate that the area of leaf and stem vascular bundles, maximum net photosynthetic rate, and water use efficiency may be key indicators in explaining the variation in yield. Radar plots further validated this finding, showing that Zhenmai 12 and Jimai 22 are more sensitive to changes in the maximum net photosynthetic rate, while Shannong 38 exhibits a greater sensitivity to changes in water use efficiency. Based on existing research on photosynthetic pathways and dry matter distribution, this study innovatively investigates the potential relationship between material transport and yield formation under low-temperature stress during the jointing stage from the perspective of anatomical structure and functional coupling. The findings provide new insights into understanding the structural impact of low-temperature stress on crop yield formation and offer theoretical support for identifying the structural basis of limited material transport under stress and for developing disaster diagnostic models driven by structural parameters. Full article

Global climate change and increasingly complex geological conditions have led to more frequent landslides in the red-bed sedimentary layers of western Sichuan, China, posing severe threats to human safety and hindering progress toward regional Sustainable Development Goals (SDGs), particularly those related to disaster risk reduction and ecological protection. To address this challenge and advance sustainable disaster management, this study proposes a lightweight hybrid model, termed Transformer–Attention–LSTM, which integrates the global attention mechanism of Transformers with the local time-series modeling capabilities of Long Short-Term Memory networks. Focusing on the Kuyaogou landslide, the model achieves an optimal balance between parameter scale, sequence length, and prediction accuracy. The mean Coefficient of Determination (R²) values for the test samples in the X, Y, and Z directions reached 0.948, representing enhancements of 9.9%, 4.2%, and 2.3%, respectively, compared to the suboptimal Attention–LSTM model. Concurrently, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were reduced to 9.23 mm and 7.17 mm, respectively. Based on these displacement predictions, the landslide evolution stage was determined by calculating the tangent angle, indicating that the Kuyaogou landslide will remain in a stable creep phase over the ensuing ten-day period with low overall risk of rapid movement, though localized instability requires continued monitoring. This research provides a ‘small, fast, and accurate’ paradigm for red-bed landslide displacement prediction, offering scientific support for disaster prevention and emergency decision-making. The framework demonstrates potential for broader application in monitoring other geological hazards, thereby contributing to the implementation of sustainable development strategies in geohazard-prone regions. Full article

Sustainable spatial development requires land-use allocation that aligns with reflects the environment’s biophysical capacity. However, rapid urbanization and agricultural expansion often result to spatial mismatches between land utilization and land capability, the reby increasing environmental degradation and disaster vulnerability. East Java Province, one of Indonesia’s most densely populated regions, has experienced significant land-use transformation driven by demographic pressure and economic development. This study aims to evaluate the environmental carrying capacity by assessing the spatial compatibility among land capability, existing land use, and the Provincial Spatial Plan (RTRWP) using a Geographic Information System (GIS)-based analytical approach. Land capability was determined based on key biophysical parameters, including slope gradient, soil texture, drainage conditions, erosion susceptibility, effective soil depth, and flood hazard. Spatial overlay analysis was employed to identify areas of conformity and mismatch between land capability and both current and planned land uses. The results indicate that only approximately 52% of the provincial area is utilised in accordance with its land capability. In comparison, the remaining 48% exhibits varying degrees of spatial mismatch. Erosion is identified as the dominant limiting factor, affecting more than 43% of the region, particularly in mountainous and hilly landscapes. Furthermore, over 60% of East Java falls within Land Capability Classes III–VII, indicating moderate to severe environmental constraints on limitations intensive land use. High levels of spatial mismatch are concentrated in the southern upland districts—such as Pacitan, Trenggalek, southern Malang, and Lumajang, which are highly susceptible to landslides, as well as in the northern lowland corridor, including the Surabaya–Gresik–Sidoarjo metropolitan region, which faces a significantly flood risk. These findings suggest that land-use practices exceeding environmental carrying capacity substantially amplify disaster risk. Therefore, integrating land capability assessment into spatial planning and zoning regulations is essential and for promoting ecosystem-based disaster risk reduction and achieving sustainable spatial development in East Java Province. Full article

Posttraumatic stress disorder (PTSD) has been linked to impaired physical function, which in turn predicts falls, morbidity, and mortality. However, few studies have used objective measures such as handgrip strength to assess physical function. In this cross-sectional study, we investigated associations of average PTSD symptom severity and symptom domain severity with measures of maximum handgrip strength and handgrip asymmetry from 11/2021–12/2023, among 381 male firefighters and emergency medical responders who responded to the World Trade Center disaster, using covariate-adjusted linear regression models. PTSD was diagnosed using the Structured Clinical Interview for the DSM-5 in 17% of responders. Greater overall PTSD average symptom severity was associated with weaker maximum handgrip strength (β = −4.43 lbs; 95%; CI: −8.77, −0.09; p = 0.045). Higher re-experiencing symptom severity was associated with weaker maximum handgrip strength (β = −4.17 lbs; 95% CI: −8.13, −0.22; p = 0.039). Avoidance symptoms were associated with weaker handgrip strength in adjusted models (β = −4.14 lbs; 95% CI: −7.56, −0.73; p = 0.018), and associated with a larger negative difference assessing for hand asymmetry (β = −2.20 lbs; 95% CI: −4.18, −0.22; p = 0.029). Findings suggest that PTSD may contribute to long-term physical decline, even in populations with high baseline fitness. Full article

In the context of increasingly frequent and severe natural disasters, scientifically measuring and analyzing the efficiency of natural disaster emergency management in China is of great practical significance for enhancing the performance of the emergency management system and promoting its systematic and high-quality development. This study first applies a super-efficiency SBM-DEA model with undesirable outputs to systematically measure the efficiency of China’s natural disaster emergency management system during the period 2019–2023. Subsequently, the Dagum Gini coefficient and Kernel Density estimation are employed to examine regional disparities and dynamic evolution across eastern, central, western, and northeastern China. Finally, the coefficient of variation and spatial econometric models are applied to test the spatial convergence characteristics of emergency management efficiency. The results indicate that: (1) China’s overall disaster emergency management efficiency remains at a relatively low level and exhibits a fluctuating trend characterized by an initial increase followed by a decline. The regional distribution pattern of emergency efficiency is ranked as “Northeast > Central > West > East”. (2) The average annual contributions of intra-regional disparities, inter-regional disparities, and transvariation density to the overall variation in national emergency management efficiency are 27.58%, 39.90%, and 32.53%, respectively, indicating that inter-regional disparities and transvariation density are the dominant sources of systemic differences among regional subsystems. (3) The national distribution of emergency management efficiency displays a bimodal pattern, indicating polarization; however, the secondary peak is relatively flat, suggesting a weakening trend of provincial-level polarization and a gradual narrowing gap with high-efficiency regions. (4) σ-divergence is observed at the national level and in the central region, while both absolute and conditional β-convergence exist to varying degrees at the national level and across all four regions. Nevertheless, the enhancement of natural disaster emergency management efficiency has not yet realized a system-level transition from convergence in growth rates to convergence in efficiency gaps. In addition, economic development, technological progress, urbanization, and industrial structure exert significantly heterogeneous effects on disaster emergency management efficiency across different regions. Full article

Earthquake disasters often cause communication base stations to fail, severely hindering rescue operations and information transmission. While traditional air-ground collaborative emergency communication systems can rapidly restore communications, they still face challenges such as the “time gap” caused by the endurance limitations of unmanned aerial vehicle (UAV) and the “spatial blind spots” resulting from the uncertainty of road disruptions. These issues reduce the continuity and reliability of system services. To address the robustness of air-ground platform coordinated deployment and path planning under uncertain road disruptions, this paper proposes a two-stage distributionally robust deployment and path planning (DRDPRP) method for fixed-wing UAV and ground unmanned vehicles (UGVs) in post-disaster emergency communications. This method constructs a distributionally robust uncertainty set based on a probabilistic distance metric to characterize road disruption risks. It establishes a two-stage distributionally robust optimization model to jointly optimize the deployment and paths of fixed-wing UAV and UGVs. Concurrently, it employs the Column and Constraint Generation (C&CG) algorithm as the solution framework, combined with branch-and-bound and local optimization strategies to enhance computational efficiency. Simulation results demonstrate that this method generates more robust collaborative deployment plans under road disruption uncertainties, thereby enhancing the continuity and reliability of post-disaster emergency communication systems. Full article

Rapid post-disaster building damage assessment requires recognizing explicit structural failures and interpreting implicit situational cues in visually complex scenes. Whereas conventional automated methods are often confined to detecting explicit damage patterns, human perception naturally integrates both types of information into a holistic risk judgment. This study presents an exploratory investigation into the neural signatures underlying this integrated judgment process using electroencephalography. A modified paradigm was employed to probe the cognitive dynamics of risk evaluation in participants with civil engineering backgrounds. Although participants were instructed only to identify damaged buildings without explicit severity grading, event-related potential analysis revealed systematic, graded neural responses that scaled with damage severity. This suggests that the brain encodes damage-related information not as a binary state but as a continuous spectrum of perceived risk, implicitly processing severity, even in the absence of explicit instructions. Furthermore, single-trial analysis demonstrated that time-domain features contain robust discriminative information, verifying the feasibility of decoding these latent judgments from brain activity. These findings provide a physiological basis for developing future cognition-informed algorithms and human-in-the-loop frameworks, bridging the semantic gap to enhance the reliability of automated disaster assessment. Full article

Background/Objectives: Post-traumatic stress disorder (PTSD) is a common psychiatric consequence of trauma, and adolescents may be particularly vulnerable after large-scale disasters. Sleep disturbances and circadian preference may play a role in PTSD symptomatology. This study aimed to investigate the associations between PTSD, sleep quality, sleep problems, and chronotype in adolescents exposed to an earthquake. Methods: This cross-sectional study included 201 adolescents aged 12–18 years: 92 diagnosed with PTSD and 109 earthquake-exposed controls without a DSM-5 psychiatric disorder. Participants completed the Children’s Posttraumatic Stress Reaction Index (CPTS-RI), Pittsburgh Sleep Quality Index (PSQI), Children’s Sleep Habits Questionnaire (CSHQ), and Children’s Chronotype Questionnaire (CCTQ). Group differences, correlation analyses, and binary logistic regression analyses were performed. Results: Adolescents with PTSD had significantly higher CPTS-RI, PSQI, CSHQ, and CCTQ scores compared with controls (all p < 0.001), indicating poorer sleep quality, more sleep problems, and a greater tendency toward eveningness. PTSD severity was positively correlated with sleep problems, impaired sleep quality, and eveningness. In logistic regression analysis, poor sleep quality (p < 0.001) and clinically significant sleep problems (p = 0.011) were independently associated with PTSD, whereas chronotype was not. Conclusions: Sleep disturbances are more strongly associated with PTSD than chronotype in earthquake-exposed adolescents. Assessment and treatment of sleep problems may represent an important therapeutic target following large-scale trauma. Full article

Land subsidence has emerged as a critical geohazard affecting major urban centers worldwide, particularly in coastal and deltaic regions where intensive groundwater extraction and rapid urbanization are prevalent. It is estimated that subsidence threatens more than 1.6 billion people globally, with reported subsidence rates exceeding 100 mm/year in several rapidly urbanizing cities and cumulative ground lowering exceeding 10 m in extreme cases such as Mexico City. This review provides a comprehensive synthesis of the hydrogeological drivers, impacts, and sustainable mitigation pathways of land subsidence based on a systematic literature review of 167 peer-reviewed studies following the PRISMA framework and bibliometric network analysis. The findings confirm that groundwater extraction is the dominant driver, causing pore pressure decline and irreversible consolidation of compressible aquitards, while geological conditions, recharge imbalance, and climate variability strongly influence subsidence magnitude and persistence. The consequences are severe and multidimensional, including increased flood risk, infrastructure damage, groundwater storage loss, ecosystem degradation, and significant socio-economic impacts. Global case studies from major subsiding cities demonstrate that subsidence often contributes more to relative sea-level rise and urban flood vulnerability than climate-driven ocean rise alone. Mitigation strategies, including groundwater regulation, managed aquifer recharge, water-sensitive urban design, geotechnical stabilization, and satellite-based monitoring, have shown effectiveness but remain limited when implemented independently. This study proposes an integrated management framework combining continuous monitoring, hydrogeological assessment, sustainable groundwater management, engineering and nature-based solutions, and governance integration. The findings highlight that early intervention, groundwater sustainability, and coordinated policy actions are essential to reduce subsidence and enhance long-term urban resilience. These insights support the achievement of Sustainable Development Goal 11 (Sustainable Cities and Communities), particularly in strengthening disaster risk reduction and climate resilience in subsidence-prone urban areas. Full article

Oil spills are severe environmental disasters that cause long-lasting damage to marine ecosystems and impose significant economic costs, underscoring the urgent need for efficient detection and monitoring technologies. Conventional field-based observation methods, while valuable, are constrained by limited spatial coverage, high costs, and labor-intensive processes, making them impractical for large-scale or rapid-response applications. To overcome these challenges, satellite remote sensing has been used as an effective alternative for oil spill monitoring. In particular, the advent of Google Earth Engine (GEE), a cloud-based geospatial platform, has transformed oil spill research by enabling scalable management and analysis of large satellite remote sensing datasets. This review synthesizes studies employing GEE for oil spill detection, across marine environments and interconnected aquatic systems, focusing on methodologies based on optical imagery and synthetic aperture radar data and approaches that integrate machine learning techniques. The analysis underscores that GEE enhances oil spill monitoring by facilitating rapid data processing, supporting reproducible workflows, and expanding access to multi-source satellite data. Furthermore, this review highlights the necessity of incorporating very-high-resolution satellite data and achieving tighter integration of external deep learning framework within GEE to improve detection accuracy and the operational applicability in complex marine and coastal contexts. Full article

Extreme climate events in Australia are increasing. Since 2019, fires and floods have devastated all states and territories in Australia, leading to a reckoning via several government inquiries, including a Royal Commission, on how governments, emergency services, communities, and individuals prepare for, respond to, and recover from such catastrophic events. It also raises the question of how the media reports and reacts to these events; in Australia, the national broadcaster, the Australian Broadcasting Corporation (ABC), has taken on the role of emergency broadcaster. This paper employs a cross-sectional design to examine how media practitioners from ABC Canberra navigate their role as emergency broadcasters, how they prepare for and respond to emergencies, and how they interact with the community during those events. This examination includes reflections and memories from a series of interviews we conducted with these practitioners about the catastrophic bushfires in 2019/2020 in the Australian Capital Territory (ACT) region. Using this design and a Bourdieusian lens, the study examined the practices of media practitioners during a catastrophic emergency and their perceptions of preparedness for future disasters. We examined how training (cultural capital), networks (social capital), online expertise (digital capital), and experience (habitus) contribute to preparedness in emergency broadcasting. The study has both a theoretical and practical contribution: theoretically, it expands Bourdieu’s cultural production model by applying it to a form of broadcasting that has not been examined in this way; practically, it contributes to our understanding of media practitioners and how they practice during emergency broadcasting. Full article

The frequency and severity of extreme weather events have increased due to climate change, with floods emerging as one of the most common climate change-induced disasters the world over. South Africa is one of the countries most susceptible to floods in Southern Africa. Among the main factors exacerbating the impact of floods, particularly in urban areas in Africa, is waste. This article contributes solutions in dealing with the flood and solid waste challenges in urban informal settlements in South Africa through exploring the potential benefits of knowledge systems integration in tackling such challenges. Using the case of the Quarry Road West informal settlement in eThekwini Municipality, KwaZulu-Natal province, South Africa, the paper discusses the roles played by scientific, practitioner and local knowledge systems in responding to flood risk and solid waste challenges in this area over the years and the benefits that could be realised if these knowledge systems are deployed in a systematically integrated manner. Full article