Search Results (16,625)

The use of multimodal data can effectively compensate for the lack of temporal resolution in streetscape imagery-based studies and achieve hourly refinement in the study of street walkability dynamics. Exploring the 24 h dynamic pattern of urban street walkability and its diurnal variation characteristics is a crucial step in understanding and responding to the accelerated urban metabolism. Aiming at the shortcomings of existing studies, which are mostly limited to static assessment or only at coarse time scales, this study integrates multimodal data such as streetscape images, remote sensing images of nighttime lights, and text-described crowd activity information and introduces a novel approach to enhance the simulation of pedestrian perception through a visual–textual multimodal deep learning model. A baseline model for dynamic assessment of walkability with street as a spatial unit and hour as a time granularity is generated. In order to deeply explore the dynamic regulation mechanism of street walkability under the influence of diurnal shift, the 24 h dynamic score of walkability is calculated, and the quantification system of walkability diurnal change characteristics is further proposed. The results of spatio-temporal cluster analysis and quantitative calculations show that the intensity of economic activities and pedestrian experience significantly shape the diurnal pattern of walkability, e.g., urban high-energy areas (e.g., along the riverside) show unique nocturnal activity characteristics and abnormal recovery speeds during the dawn transition. This study fills the gap in the study of hourly street dynamics at the micro-scale, and its multimodal assessment framework and dynamic quantitative index system provide important references for future urban spatial dynamics planning. Full article

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method. Full article

Drought is one of the most complex natural hazards, characterized by its slow onset, persistent nature, diverse sectoral impacts (e.g., agriculture, water resources, ecosystems), and dependence on meteorological, hydrological, and socioeconomic factors. Over the years, significant scientific effort has been devoted to developing methodologies that address its multifaceted nature, reflecting the interdisciplinary challenges of drought analysis. However, previous reviews have typically focused on individual methods, while this study presents a unified, multidisciplinary framework that integrates multiple drought analysis methods and links them to key factors guiding method selection. To address this gap, five widely used methods—index-based, remote sensing, threshold-level methods (TLM), impact-based methods, and the storyline approach—are critically evaluated from a multidisciplinary perspective. In addition, the study examines spatial and temporal trends in scientific publications, illustrating how the application of these methods has evolved over time and across regions. The primary objective of this review is twofold: (1) to provide a holistic, state-of-the-art synthesis of these methods, their applications, and their limitations; and (2) to evaluate and prioritize the critical decision-making factors, including drought type, data type/availability, study scale, and management objectives that influence method selection. By bridging this gap, the paper offers a conceptual decision-support framework for selecting context-appropriate drought analysis methods. However, challenges remain, including the vast diversity of methods beyond the scope of this review and the limited consideration of less influential factors such as user expertise, computational resources, and policy context. The paper concludes with insights and recommendations for optimizing method selection under varying circumstances, aiming to support both drought research and effective policy implementation. Full article

Background: The metabolic dysfunction-associated steatotic liver disease (MASLD) represents an escalating global health concern, with effective treatments still lacking. Given its complex pathogenesis, multi-targeted strategies are highly desirable. Methods: This study reports the isolation of four flavone C-glycosides (FCGs) from Dianthus superbus L. and explores their potential in treating MASLD. The bioactivity and underlying mechanisms of FCGs were systematically evaluated by integrating network pharmacology, molecular docking, and zebrafish model validation. Results: Network pharmacology analysis revealed that FCGs may modulate multiple MASLD-related pathways, including lipid metabolism, insulin signaling, inflammation, and apoptosis. Molecular docking further confirmed strong binding affinities between FCGs and key protein targets involved in these pathways. In the zebrafish model of MASLD induced by egg yolk powder, FCGs administration markedly attenuated obesity, hepatic lipid accumulation, and liver tissue damage. Furthermore, FCGs improved lipid metabolism and restored locomotor function. Molecular analyses confirmed that FCGs upregulated PPARγ expression to promote lipid metabolism, restored insulin signaling by enhancing INSR, PI3K, and AKT expression, and suppressed inflammation by downregulating TNF, IL-6 and NF-κB. Additionally, FCGs inhibited hepatocyte apoptosis by elevating the BCL-2/BAX ratio. Conclusions: These findings highlight the multi-pathway regulatory effects of FCGs in MASLD, underscoring its potential as a novel therapeutic candidate for further preclinical development. Full article

African hydrological systems are incredibly complex and highly sensitive to climate variability. This review synthesizes observational data, remote sensing, and climate modeling to understand the interactions between fluvial processes, water cycle dynamics, and anthropogenic pressures. Currently, these systems are experiencing accelerating warming (+0.3 °C/decade), leading to more intense hydrological extremes and regionally varied responses. For example, East Africa has shown reversed temperature–moisture correlations since the Holocene onset, while West African rivers demonstrate nonlinear runoff sensitivity (a threefold reduction per unit decline in rainfall). Land-use and land-cover changes (LULCC) are as impactful as climate change, with analysis from 1959–2014 revealing extensive conversion of primary non-forest land and a more than sixfold increase in the intensity of pastureland expansion by the early 21st century. Future projections, exemplified by studies in basins like Ethiopia’s Gilgel Gibe and Ghana’s Vea, indicate escalating aridity with significant reductions in surface runoff and groundwater recharge, increasing aquifer stress. These findings underscore the need for integrated adaptation strategies that leverage remote sensing, nature-based solutions, and transboundary governance to build resilient water futures across Africa’s diverse basins. Full article

Accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is fundamental to ensuring safe operation. However, due to the complex electrochemical processes during battery operation and the limited availability of training data, accurate estimation of the state of health remains challenging. To address this, this paper proposes a prediction framework based on dual-view voltage signal features and an improved Long Short-Term Memory (LSTM) neural network. By relying solely on readily obtainable voltage signals, the data requirement is greatly reduced; dual-view features, comprising kinetic and aggregated aspects, are extracted based on the underlying reaction mechanisms. To fully leverage the extracted feature information, Scaled Dot-Product Attention (SDPA) is employed to dynamically score all hidden states of the long short-term memory network, adaptively capturing key temporal information. The experimental results based on the NASA PCoE battery dataset indicate that, under various operating conditions, the proposed method achieves an average absolute error below 0.51% and a root mean square error not exceeding 0.58% in state-of-health estimation, demonstrating high predictive accuracy. Full article

Brachycephalic head shape in dogs has been associated with behavioural traits that may enhance their appeal as companion animals, contributing to their popularity. However, it remains unclear whether these behavioural differences are directly linked to head shape or are mediated by factors such as body size, demographics, and dog-keeping practices. Drawing on two large-scale owner surveys (N = 5613) and cephalic index estimates for 90 breeds, we investigated the relationship between head shape and eight behavioural variables (four personality traits and four behavioural problems), while controlling for 20 demographic and dog-keeping characteristics, as well as body size. Our results show that behavioural differences among head shapes are only partly attributable to head shape itself; some are explained by confounding variables. Specifically, brachycephalic dogs appeared predisposed to positive behaviours (e.g., calmness, fewer behavioural problems), but these traits were often obscured by their small body size and low training experience. These findings highlight the complex interplay between morphology, behaviour, and environment, and emphasize the role of training and management in supporting the behavioural well-being of popular brachycephalic breeds. This has important implications for owners, breeders, and welfare professionals aiming to align aesthetic preferences with behavioural and welfare outcomes. Full article

Currently, most studies evaluating storm surges are conducted at the provincial level, and there is a lack of detailed research focusing on cities. This paper focuses on the urban scale, using some fine-scale data of coastal areas obtained through remote sensing images. This research is based on the Hazard–Exposure–Vulnerability (H-E-V) framework and PPRR (Prevention, Preparedness, Response, and Recovery) crisis management theory. It focuses on 52 Chinese coastal cities as the research subject. The evaluation system for the disaster response capabilities of Chinese coastal cities was constructed based on three aspects: the stability of the disaster-incubating environment (S), the risk of disaster-causing factors (R), and the vulnerability of disaster-bearing bodies (V). The significance of this study is that the storm surge capability of China’s coastal cities can be analyzed based on the results of the evaluation, and the evaluation model can be used to identify its deficiencies. In this paper, these storm surge disaster response capabilities of coastal cities were scored using the entropy weighted TOPSIS method and the weight rank sum ratio (WRSR), and the results were also analyzed. The results indicate that Wenzhou has the best comprehensive disaster response capability, while Yancheng has the worst. Moreover, Tianjin, Ningde, and Shenzhen performed well in the three aspects of vulnerability of disaster-bearing bodies, risk of disaster-causing factors, and stability of disaster-incubating environment separately. On the contrary, Dandong (tied with Qinzhou), Jiaxing, and Chaozhou performed poorly in the above three areas. Full article

The maritime sector faces escalating demands to minimize emissions and optimize operational efficiency under tightening environmental regulations. Although technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twins (DT) offer substantial potential, their deployment in real-time ship performance analytics is at an emerging state. This paper proposes a machine learning-driven framework for real-time ship performance management. The framework starts with data collected from onboard sensors and culminates in a decision support system that is easily interpretable, even by non-experts. It also provides a method to forecast vessel performance by extrapolating Key Performance Indicator (KPI) values. Furthermore, it offers a flexible methodology for defining KPIs for every crucial component or aspect of vessel performance, illustrated through a use case focusing on fuel oil consumption. Leveraging Artificial Neural Networks (ANNs), hybrid multivariate data fusion, and high-frequency sensor streams, the system facilitates continuous diagnostics, early fault detection, and data-driven decision-making. Unlike conventional static performance models, the framework employs dynamic KPIs that evolve with the vessel’s operational state, enabling advanced trend analysis, predictive maintenance scheduling, and compliance assurance. Experimental comparison against classical KPI models highlights superior predictive fidelity, robustness, and temporal consistency. Furthermore, the paper delineates AI and ML applications across core maritime operations and introduces a scalable, modular system architecture applicable to both commercial and naval platforms. This approach bridges advanced simulation ecosystems with in situ operational data, laying a robust foundation for digital transformation and sustainability in maritime domains. Full article

Rapid urbanization and escalating disaster risks necessitate resilient urban governance systems. Smart city initiatives that leverage digital technologies—such as the internet of things (IoT), big data analytics, and artificial intelligence (AI)—demonstrate transformative potential in enhancing emergency management capabilities. However, empirical evidence regarding their causal impact and underlying mechanisms remains limited, particularly in developing economies. Drawing on panel data from 275 Chinese prefecture-level cities over the period 2006–2021 and using China’s smart city pilot policy as a quasi-natural experiment, this study applies a multi-period difference-in-differences (DID) approach to rigorously assess the effects of smart city construction on emergency management capabilities. Results reveal that smart city construction produced a statistically significant improvement in emergency management capabilities, which remained robust after conducting multiple sensitivity checks and controlling for potential confounding policies. The benefits exhibit notable heterogeneity: emergency management capability improvements are most pronounced in central China and in cities at the extremes of population size—megacities (>10 million residents) and small cities (<1 million residents)—while effects remain marginal in medium-sized and eastern cities. Crucially, mechanism analysis reveals that digital technology application fully mediates 86.7% of the total effect, whereas factor allocation efficiency exerts only a direct, non-mediating influence. These findings suggest that smart cities primarily enhance emergency management capabilities through digital enablers, with effectiveness contingent upon regional infrastructure development and urban scale. Policy priorities should therefore emphasize investments in digital infrastructure, interagency data integration, and targeted capacity-building strategies tailored to central and western regions as well as smaller cities. Full article

Ensuring a sustainable and healthy human environment in university dormitories is essential for students’ learning, living, and overall health and well-being. To address this need, we carried out a series of systematic field measurements of the noise levels at 30 dormitories in three representative major urban universities in a major provincial capital city in China and designed and implemented a comprehensive questionnaire and surveyed 1005 students about their perceptions of their acoustic environment. We proposed and applied a sustainability–health-oriented, multidimensional assessment framework to assess the acoustic environment of the dormitories and student responses to natural sound, technological sounds, and human-made sounds. Using the Structural Equation Modeling (SEM) approach combined with the field measurements and student surveys, we identified three categories and six factors on student health and well-being for assessing the acoustic environment of university dormitories. The field data indicated that noise levels at most of the measurement points exceeded the recommended or regulatory thresholds. Higher noise impacts were observed in early mornings and evenings, primarily due to traffic noise and indoor activities. Natural sounds (e.g., wind, birdsong, water flow) were highly valued by students for their positive effect on the students’ pleasantness and satisfaction. Conversely, human and technological sounds (traffic noise, construction noise, and indoor noise from student activities) were deemed highly disturbing. Gender differences were evident in the assessment of the acoustic environment, with male students generally reporting higher levels of the pleasantness and preference for natural sounds compared to female students. Educational backgrounds showed no significant influence on sound perceptions. The findings highlight the need for providing actionable guidelines for dormitory ecological design, such as integrating vertical greening in dormitory design, water features, and biodiversity planting to introduce natural soundscapes, in parallel with developing campus activity standards and lifestyle during noise-sensitive periods. The multidimensional assessment framework will drive a sustainable human–ecology–sound symbiosis in university dormitories, and the category and factor scales to be employed and actions to improve the level of student health and well-being, thus, providing a reference for both research and practice for sustainable cities and communities. Full article

Background: With increasing pharmacokinetic evidence suggesting the inadequacy of conventional dose intravenous co-amoxiclav (IVCA) 1.2 g Q8H in targeting Enterobacterales, our institution antibiotic guidelines optimised dosing recommendations for diabetic foot infection (DFI) management to 1.2 g Q6H in August 2023. In this study, we aim to evaluate the efficacy and safety of the optimised dose IVCA in DFI treatment. Methods: In this single-centre cohort study, patients ≥ 21 years with DFI, creatinine clearance ≥ 50 mL/min, and weight > 50 kg, who were prescribed IVCA 1.2 g Q8H (standard group (SG)), were compared with those prescribed IVCA 1.2 g Q6H (optimised group (OG)). Patients who were pregnant, immunocompromised, had nosocomial exposure in last 3 months, or received < 72 h of IVCA were excluded. The primary efficacy outcome was clinical deterioration at end of IVCA monotherapy. The secondary efficacy outcomes include 30-day readmission and mortality, empiric escalation of antibiotics, lower limb amputation, and length of hospitalisation. The safety outcomes include hepatotoxicity, renal toxicity, and diarrhoea. Results: There were 189 patients (94 in SG; 95 in OG) included. Patients in SG (31.9%) were twice as likely to experience clinical deterioration compared to OG (16.8%) (odds ratio: 2.31, 95% confidence interval: 1.16–4.62, p < 0.05). There were statistically more patients who had 30-day all-cause mortality in SG (5.3%) compared to OG (0%) (p < 0.05). Furthermore, 30-day readmission due to DFI in SG (26.6%) was higher compared to OG (11.6%) (p < 0.05). Empiric escalation of IV antibiotics was required for 14.9% patients in SG and 6.3% patients in OG (p = 0.06). There was no statistical difference for lower limb amputation (p = 0.72), length of hospitalisation (p = 0.13), and the occurrence of safety outcomes in both groups. Conclusions: This study suggests IVCA 1.2 g Q6H is associated with the decreased likelihood of clinical deterioration and is likely as safe as IVCA 1.2 g Q8H. The optimised dose of IVCA may help reduce the use of broad-spectrum antibiotics due to clinical deterioration. Full article

Land cover, topography, precipitation, and socio-economic factors exert both direct and indirect influences on urban land surface temperatures. Within the broader context of global climate change, these influences are magnified by the escalating intensity of the urban heat island effect. However, the interplay and underlying mechanisms of natural and socio-economic determinants of land surface temperatures remain inadequately explored, particularly in the context of cold-region cities located in the northern temperate zone of China. This study focuses on Changchun City, employing multispectral remote sensing imagery to derive and spatially map the distribution of land surface temperatures and topographic attributes. Through comprehensive analysis, the research identifies the principal drivers of temperature variations and delineates their seasonal dynamics. The findings indicate that population density, night-time light intensity, land use, GDP (Gross Domestic Product), relief, and elevation exhibit positive correlations with land surface temperature, whereas slope demonstrates a negative correlation. Among natural factors, the correlations of slope, relief, and elevation with land surface temperature are comparatively weak, with determination coefficients (R²) consistently below 0.15. In contrast, socio-economic factors exert a more pronounced influence, ranked as follows: population density (R² = 0.4316) > GDP (R² = 0.2493) > night-time light intensity (R² = 0.1626). The overall hierarchy of the impact of individual factors on the temperature model, from strongest to weakest, is as follows: population, night-time light intensity, land use, GDP, slope, relief, and elevation. In examining Changchun and analogous cold-region cities within the northern temperate zone, the research underscores that socio-economic factors substantially outweigh natural determinants in shaping urban land surface temperatures. Notably, human activities catalyzed by population growth emerge as the most influential factor, profoundly reshaping the urban thermal landscape. These activities not only directly escalate anthropogenic heat emissions, but also alter land cover compositions, thereby undermining natural cooling mechanisms and exacerbating the urban heat island phenomenon. Full article

The Millard’s rat (Dacnomys millardi), a threatened murid endemic to Southeast Asian montane rainforests and the sole member of its monotypic genus, faces escalating endangered risks as a Near Threatened species in China’s Biodiversity Red List. This ecologically specialized rodent exhibits diagnostic morphological adaptations—hypertrophied upper molars and cryptic pelage—that underpin niche differentiation in undisturbed tropical/subtropical forests. Despite its evolutionary distinctiveness, the conservation prioritization given to Dacnomys is hindered due to a deficiency of data and unresolved phylogenetic relationships. Here, we integrated morphological analyses with the first complete mitogenome (16,289 bp in size; no structural rearrangements) of D. millardi to validate its phylogenetic placement within the subfamily Murinae and provide novel insights into genetic diversity erosion. Bayesian and maximum likelihood phylogenies robustly supported Dacnomys as sister to Leopoldamys (PP = 1.0; BS = 100%), with an early Pliocene divergence (~4.8 Mya, 95% HPD: 3.65–5.47 Mya). Additionally, based on its basal phylogenetic position within Murinae, we propose reclassifying Micromys from Rattini to the tribe Micromyini. Codon usage bias analyses revealed pervasive purifying selection (Ka/Ks < 1), constraining mitogenome evolution. Genetic diversity analyses showed low genetic variation (CYTB: π = 0.0135 ± 0.0023; COX1: π = 0.0101 ± 0.0025) in fragmented populations. We propose three new insights into this genetic diversity erosion. (1) Evolutionary constraints: genome-wide evolutionary conservation and shallow evolutionary history (~4.8 Mya) limited mutation accumulation. (2) Anthropogenic pressures: deforestation-driven fragmentation of habitats (>20,000 km²/year loss since 2000) has reduced effective population size, exacerbating genetic drift. (3) Ecological specialization: long-term adaptation to stable niches favored genomic optimization over adaptive flexibility. These findings necessitate suitable conservation action by enforcing protection of core habitats to prevent deforestation-driven population collapses and advocating IUCN reclassification of D. millardi from Data Deficient to Near Threatened. Full article

Blasting is an effective technique for fragmenting rock in open-pit mining operations. Blasting operations produce either boulders or fine fragments, both of which increase costs and pose environmental risks. As a result, predicting the mean fragmentation size (MFS) distribution of rock is critical for assessing blasting operations’ quality and mitigating risks. Due to the limitations of empirical and statistical models, several researchers are turning to artificial intelligence (AI)-based techniques to predict the MFS distribution of rock. Thus, this study uses three AI tree-based algorithms—extra trees (ET), gradient boosting (GB), and random forest (RF)—to predict the MFS distribution of rock. The prediction accuracy of the models is optimized utilizing the tree-structured Parzen estimators (TPEs) algorithm, which results in three models: TPE-ET, TPE-GB, and TPE-RF. The dataset used in this study was collected from the published literature and through the data augmentation of a large-scale dataset of 3740 blast samples. Among the evaluated models, the TPE-ET model exhibits the best performance with a coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and max error of 0.93, 0.04, 0.03, and 0.25 during the testing phase. Moreover, the block size (XB, m) and modulus of elasticity (E, GPa) parameters are identified as the most influential parameters for predicting the MFS distribution of rock. Lastly, an interactive web application has been developed to assist engineers with the timely prediction of MFS. The predictive model developed in this study is a reliable intelligent model because it combines high accuracy with a strong, explainable AI tool for predicting MFS. Full article