Search Results (79)

Ecological security pattern (ESP) planning typically emphasizes ecological structure and connectivity, but may provide limited information on long-term functional degradation occurring within ecologically important areas. To address this gap, this study develops a baseline-referenced framework for diagnosing ecological resilience degradation (ERD) by integrating ecosystem service value (ESV) dynamics, minimum cumulative resistance (MCR) modeling, and explainable machine learning (XGBoost–SHAP). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, ERD was operationally defined as long-term functional degradation within ecologically important components of the 2000 baseline network, with net ESV decline from 2000 to 2020 used as the functional-degradation signal. Two complementary ERD patterns were distinguished: source erosion and corridor interruption. The results showed that ERD exhibited a pronounced core–periphery pattern, with higher degradation intensity concentrated along the Guangzhou–Foshan–Dongguan–Shenzhen urban development corridor. Integrated ERD covered 5452.69 km², of which source erosion accounted for 4238.32 km² (77.73%) and corridor interruption for 1214.37 km² (22.27%). Source erosion represented the dominant ERD pattern in terms of spatial extent. The XGBoost model showed moderate predictive performance under spatial block cross-validation (mean validation R² = 0.638 ± 0.043), and SHAP analysis indicated that vegetation condition, proximity to water bodies, nighttime light, and elevation were among the most influential factors associated with spatial variation in ERD intensity. NDVI contributions shifted from positive to negative around 0.65, while the distance-to-water response changed most rapidly within 200–300 m. These values are interpreted as empirical transition ranges in the model response. Overall, the proposed framework links long-term ecosystem functional degradation with baseline ecological network position and provides a spatially explicit basis for identifying functionally degraded ecological areas and supporting differentiated spatial prioritization and ecological management. Full article

Reliable pedestrian perception under low illumination is important for nighttime monitoring in smart built environments. However, visible-light detectors often lose texture and edge information, whereas conventional RGB–infrared fusion may introduce cross-modal noise and discard discriminative cues during scale conversion. This study proposes Multimodal Wavelet–Spectral DETR (MWSD), a frequency-aware RGB–infrared detection framework. MWSD employs a dual-branch Multimodal Fusion Feature Sampling backbone for cross-modal interaction. The Multimodal Frequency-Domain Feature Enhancement (MFFE) module produces input-dependent Fourier modulation within shared detection features, rather than reconstructing a fused image or independently fusing modality-specific spectra. Haar wavelet upsampling and downsampling (HWU and HWD) construct a bidirectional feature pyramid by using frequency components to guide adjacent-level scale conversion, rather than performing image-level wavelet reconstruction. This coordinated design combines residual spectral enhancement with wavelet-guided multi-scale fusion in an end-to-end detector. On LLVIP, MWSD achieves 96.7% $mA{P}_{50}$ and 63.0% $mA{P}_{50:95}$. On M³FD, it achieves 87.1% and 59.0%, respectively. The model requires 45 ms per 640 × 640 image on an NVIDIA RTX 4090 GPU. These results support frequency-aware multimodal detection as a visual perception approach for nighttime safety monitoring. Full article

Artificial light at night (ALAN) is an increasing component of urban environmental change, affecting vegetation dynamics and ecosystem functioning. Satellite nighttime light (NTL) data serve as proxies for urbanization and artificial illumination, aiding the analysis of vegetation responses to human pressures. However, NTL–vegetation relationships are often poorly synthesized, and ALAN is rarely included in frameworks linking urban vegetation, climate, and human drivers. Drawing on a 2014–2025 Scopus and Web of Science search, this review of 22 articles categorizes findings as (i) Lights Track Urbanization, (ii) Vegetation Modulates Light, and (iii) ALAN Shapes Ecology. Results show strong geographical concentration in China, followed by the United States, and high heterogeneity in sensors, metrics, and methods. Increasing nighttime radiance is consistently associated with vegetation decline and higher environmental pressure, while vegetation modulates light through canopy structure and phenology. ALAN effects on plant phenology are reported but vary relative to climatic drivers and are highly context-dependent. Despite these advances, the field remains methodologically inconsistent and geographically biased. This review highlights the need for harmonized multi-sensor frameworks that integrate radiance, vegetation, and climate data to improve assessments of urban environmental change and to support biodiversity conservation and light-sensitive urban planning, thereby preserving ecosystem service functions. Full article

How the diffusion of green innovation technologies translates into regional landscape sustainability is still poorly resolved, in part because most studies rely on a single data source that cannot separate an adoption signal from confounding climatic and terrain influences. To make progress on this identification problem, an empirical framework is assembled that fuses openly licensed observations, Landsat and Sentinel-2 imagery, OpenStreetMap layers, NPP-VIIRS nighttime lights, and public statistical yearbooks, and embeds them in a spatial econometric design, so that the adoption–pattern–service–sustainability chain can be traced across 72 county-level units spanning Ningxia, eastern Gansu, and northern Shaanxi over 2013–2022. Pixel- and object-level integration, entropy weighting, and principal component reduction jointly deliver a fused representation whose coefficient of determination against held-out reference data exceeds 0.85 while the reconstruction error falls by roughly a third relative to single-source baselines. A spatial Durbin specification then decomposes adoption’s association with sustainability into a dominant direct component and a smaller, distance-bounded spillover, and roughly one-quarter of the total travels through landscape reconfiguration; the result survives the placebo, subsample, and variable-substitution checks, and is strongly conditioned by the terrain and local economic capacity. These findings favour a spatially coordinated, capacity-targeted transition policy rather than uniform deployment. Two caveats should be read alongside them: adoption is measured through proxies whose validity, though corroborated against county-level green-patent and installed-capacity records, is not perfect, and external validation across contrasting landscapes remains outstanding. Full article

Objective: To explore nursing team perceptions of sleep and sleep-disrupting factors in critically ill pediatric patients. Methodology: Convergent mixed-methods study comprising an exploratory qualitative and descriptive cross-sectional quantitative. For the qualitative component, the population consisted of the PICU nursing team with ≥3 years of experience until theoretical saturation was reached. Semi-structured interviews were conducted regarding their perceptions of sleep in critically ill children. Data were analyzed using transcription, immersive reading, and coding. For the quantitative component, the population was selected using convenience sampling without exclusion. The variables were years of experience, professional category, and factors affecting rest. Data were collected using an ad hoc Likert-type questionnaire. For the analysis, the median and interquartile range were calculated for quantitative variables, and frequencies and percentages were calculated for categorical variables. Bivariate analysis was performed using the Mann-Whitney U test. Results: Ten interviews were conducted, revealing three main categories: “Factors Affecting Sleep,” with noise, nighttime interventions, and light as the most frequent codes; “Consequences of Sleep Disturbances,” with delirium and recovery as the most frequent codes; and “Perception of Sleep in the PICU and Need for Professional Awareness”. For the quantitative results, 78.3% (n = 65) of the nursing team participated. Among the factors affecting rest, the most relevant were “Inadequately controlled pain,” identified as “Highly relevant” by 84.6%, and “Delirium,” “Withdrawal syndrome,” “Light,” “Nocturnal environmental noise,” and “Nighttime interventions/procedures,” identified as “Highly relevant” by over 70%. In contrast, “Continuous nocturnal glucose administration” was identified as “Slightly/Not relevant” by almost 40%. Differences between nurses and nursing assistants were observed regarding the perceived relevance of nocturnal environmental noise, delirium, physical restraints and continuous nocturnal glucose administration (p ≤ 0.05). Conclusions: Qualitative and quantitative results concur in identifying, from the perspective of nursing professionals in a single PICU, noise, light, interventions, and delirium as negative factors for rest. Full article

Global environmental change has shifted disaster risk from being predominantly natural hazard-dominated to increasingly socially driven. While the Social Vulnerability Index (SoVI) is widely used to quantify these dynamics, its construction remains hindered by uncertainty in weighting algorithms and spatial aggregation schemes. This study assesses SoVI in Taiwan across three harmonized study years (2015, 2020, and 2025) by bridging data innovation, spatial representation, and methodological synthesis. To offer fine-scale spatial representation, we integrate gridded nighttime light, urban built-up volume, and population density to shift vulnerability assessments from conventional administrative borders to functional, data-driven Human Agglomeration Zones (HAZs). Furthermore, we examined algorithmic uncertainty by comparing the variance-driven Principal Component Analysis (PCA) against the Entropy Weight Method (EWM). We found that integrating gridded socioeconomic data can effectively identify small contiguous HAZ patches that were commonly omitted by administrative divisions. The results also reveal a twofold pattern of vulnerability. PCA produced comparatively stable rankings and repeatedly identified south-central counties as high-vulnerability areas, whereas EWM generated more concentrated weights and a stronger reordering of vulnerability patterns in 2025 as the weight assigned to population density increased sharply. Ultimately, by proposing a synthesized multi-method framework, a comprehensive SoVI was established to mitigate algorithmic bias, providing policymakers with a multidimensional tool that aligns long-term structural investments with short-term dynamic monitoring. Full article

CONSTANS-LIKE (COL) genes are pivotal regulatory components in the photoperiodic flowering pathway of plants. These genes can be regulated by both photoreceptors and the circadian clock, modulating plant flowering responses under specific day lengths by regulating florigen levels. However, the COL gene family in Litchi chinensis Sonn. has not yet been characterized. In this study, we identified eight COL family members in litchi and classified them into three subgroups based on phylogenetic analysis. The analysis of cis-regulatory elements within the promoters of LcCOLs revealed a wide distribution of elements associated with light, hormone, and stress responses. Transcript expression profiling indicated that most LcCOLs exhibited relatively high expression levels in leaf buds, leaves, and young fruits. Diurnal expression analysis under natural photoperiod conditions revealed that the expression peaks of all LcCOLs, with the exception of LcCOL1, occurred during the nighttime. The heterologous overexpression of LcCOL7 and LcCOL8, the closest homologs to AtCO, in Arabidopsis thaliana significantly delayed the flowering time under both long-day (LD) and short-day (SD) conditions, indicating that these genes act as repressors of flowering. This study provides a foundational basis for elucidating the molecular mechanisms underlying litchi flowering regulation and identifies promising candidate genes for the molecular breeding of litchi flowering-related agronomic traits. Full article

Understanding how ecological quality and human activity co-evolve in densely populated watersheds is essential for sustainable land management, yet spatially explicit long-term evidence remains limited. This study investigated the spatiotemporal dynamics and coupling coordination between ecological quality and multi-dimensional human activity intensity in the Huai River Basin (approximately 269,000 km²) from 2012 to 2024. An Improved Remote Sensing Ecological Index (IRSEI) was constructed by integrating EVI, wetness, dryness, land surface temperature, and a salinity index through annual principal component analysis. A composite Human Activity Intensity (HAI) index combining nighttime light, built-up intensity, and population density was derived with objectively determined weights. The coupling coordination degree (CCD) model and a pixel-level four-quadrant classification were then applied to characterize the human–environment interaction. Results showed that the basin-wide mean IRSEI declined from 0.564 in 2012 to 0.516 in 2020, before recovering to 0.566 in 2024, while HAI increased moderately by 16.9%. CCD improved slightly from 0.451 to 0.480, indicating limited but positive coordination gains. Four-quadrant transitions revealed that high-ecology, low-activity areas expanded, low-ecology, low-activity areas contracted, whereas low-ecology, high-activity zones persisted as stable pressure cores. These findings demonstrate that ecological recovery and human activity intensification can coexist spatially, but persistent high-pressure areas require targeted management interventions. Full article

Linear heritage corridors are increasingly exposed to spatially heterogeneous pressures from climate change and human activities, yet integrated geospatial frameworks for corridor-scale risk identification remain limited. Taking the Beijing–Hangzhou Grand Canal as a representative linear World Heritage corridor, this study developed a GIS-based spatiotemporal assessment framework to quantify natural risk, anthropogenic pressure, and their coupled patterns during 1995–2024. Approximately 350 canal segments were constructed as comparable assessment units and linked with 49 heritage sites and 18 World Heritage canal sections through a multi-scale spatial framework integrating canal sections, buffer zones, and heritage sites. Natural risk was characterized using extreme temperature, precipitation, and drought indices, while anthropogenic pressure was represented by nighttime lights, population density, impervious surface, and road density. The results reveal a clear north–south gradient in integrated natural risk, with higher values concentrated in the southern canal sections. Among the three natural-risk modules, temperature, precipitation, and drought contributed weights of 0.594, 0.242, and 0.164, respectively, indicating the dominant role of heat-related processes. The first two principal components of anthropogenic pressure explained 80.8% of the total variance. Four dominant coupling types were identified, among which the dual high-pressure type was concentrated mainly in the southern canal and marked the most critical areas of compound risk. This study provides a geospatial approach for hotspot detection and spatial decision support for the conservation of large linear heritage systems. Full article

Assessing the ecological and environmental quality of old industrial cities is crucial for understanding the spatial heterogeneity of ecological quality and its associated factors during regional transformation. Taking Tangshan, a typical old industrial city in China, as a case study, this study employed Landsat 8/9 remote sensing imagery and multi-source auxiliary data from 2015 to 2024 to calculate annual Remote Sensing Ecological Index (RSEI) values using a unified multi-year standardization and principal component analysis framework. Global and local Moran’s I analyses were conducted to examine spatial clustering patterns, and the Optimal-Parameter Geographical Detector (OPGD) was used to quantify the spatial correspondence between RSEI and selected natural and anthropogenic explanatory factors. The results indicate the following. (1) The mean RSEI in Tangshan fluctuated between 0.34 and 0.54 from 2015 to 2024, exhibiting significant interannual variability. (2) Higher RSEI values were primarily distributed in the northern mountainous and southern coastal ecological zones, while lower values were concentrated in the central and eastern industrial-mining zones. (3) The global Moran’s I was significantly positive in all years (0.702–0.778, p = 0.001), indicating the persistence of spatial clustering; the proportion of non-significant local spatial units decreased from 72.00% in 2015 to 69.46% in 2024. (4) Land use/land cover (LULC) exhibited the most consistently high explanatory power. Elevation (ELE), nighttime light (NTL), and built-up intensity (BUILT) also formed a leading group of spatially associated factors, although their relative ranking varied between the optimal-parameter results and the robustness analysis. Slope (SLOPE), annual precipitation (Pre), and annual mean temperature (Tmean) generally showed relatively lower explanatory power. Interaction detection showed that pairwise factor combinations generally had higher q values than individual factors, with LULC × ELE showing consistently high explanatory power in representative years. This study provides a scientific reference for ecological and environmental monitoring and differentiated management in old industrial cities. Full article

Street lighting systems are essential for ensuring nighttime road safety and visibility. The integration of solar photovoltaic (PV) systems into street lighting infrastructure improves energy efficiency and sustainability; however, the mismatch between daytime energy generation and nighttime lighting demand requires effective energy management solutions. In addition, long-distance electrical connections introduce voltage drop constraints, which are often overlooked in conventional design approaches. This study addresses the integration of lighting design, electrical constraints, and techno-economic performance in nanogrid-based LED street lighting systems for secondary roads. A unified framework is developed to evaluate lighting performance, PV–battery sizing, voltage drop behavior, and lifecycle cost under different system architectures. Optimal pole spacing and luminaire ratings are determined using DIALux, while PV–battery configurations are optimized using HOMER Pro based on site-specific solar irradiance. The analysis focuses on voltage drop as the key electrical constraint and examines its impact under decentralized and centralized nanogrid configurations (25%, 50%, and 100%) in both stand-alone and grid-connected modes. The results show that increasing centralization reduces component redundancy but significantly increases cable length, conductor sizing, and infrastructure cost. A techno-economic assessment with lifecycle cost and sensitivity analysis indicates that a 25% centralized configuration reduces total system cost by approximately 23% compared to fully decentralized systems while avoiding excessive cabling costs. These findings demonstrate that voltage drop and electrical infrastructure constraints play a decisive role in determining optimal system design, highlighting the importance of system-level integration rather than isolated optimization of lighting or energy components. Full article

Satellite remote sensing has rapidly evolved from an experimental support tool into a structural component of preventive archaeology and cultural heritage governance. Drawing on scientific publications and policy-oriented grey literature from 2010–2025, this study provides an integrated review of how optical, SAR, and multi-sensor satellite data are used to detect archaeological sites, monitor landscape and structural change, and support risk-informed planning across diverse legal and institutional contexts. A multi-platform workflow combines AI-assisted semantic querying (Consensus), bibliometric searches (Scopus), and the collaborative management and geospatial visualisation of references through Zotero, VOSviewer (1.6.19), and QGIS (3.44)-based literature mapping, thereby linking thematic trends, co-authorship networks, and geographical patterns of research and regulation. The results show non-linear but marked publication growth, a strongly interdisciplinary profile, and the consolidation of international hubs that drive advances in Sentinel-2-based prospection, Landsat and night-time lights urbanisation metrics, and SAR time series for deformation, looting, and conflict-damage mapping. Parallel analysis of grey literature and institutional initiatives (Copernicus Cultural Heritage Task Force, national “extraordinary plans”, regional declarations, and UNESCO guidelines) reveals the codification of satellite Earth observation within rescue archaeology protocols, emergency archaeology, and long-term conservation strategies. Overall, the evidence indicates a transition towards data-driven, multi-sensor, and multi-scalar research, underpinned by open satellite data, reproducible workflows, and AI-supported evidence synthesis. Full article

The giant panda holds a critical position in global biodiversity conservation, yet the ongoing fragmentation of its habitat poses a severe threat to the long-term viability of its survival. This study focused on the giant panda habitat in the Xiangling region and systematically analyzed the mechanisms through which human socioeconomic activities drive habitat fragmentation. The analysis was based on data from 2000 to 2023, encompassing land use, population density, transportation networks, mining activities, and nighttime light emissions, utilizing a methodology that integrated Principal Component Analysis, the Moving Window method, trend analysis, and the Geodetector model. The findings reveal the following: First, the degree of habitat fragmentation has intensified over time with significant spatial heterogeneity, exhibiting a pattern of “low fragmentation in the core areas and high fragmentation in the periphery,” where areas of very high fragmentation have expanded markedly along the habitat edges. Second, the trend in fragmentation demonstrates an overall improvement in the core zones, particularly within the Giant Panda National Park, where over 70% of the area shows reduced fragmentation; conversely, nearly 30% of the peripheral areas continue to degrade. Third, the driving factors of habitat fragmentation exhibit bi-factor enhancement and nonlinear enhancement effects, with land use identified as the dominant factor. The study recommends enhancing the overall connectivity and ecological functionality of the habitat through measures such as refining land-use planning, constructing ecological corridors, implementing hierarchical management, and promoting community co-management. Full article

Autonomous driving technology is a core enabler for new energy vehicle industrial upgrading and a critical pillar for achieving sustainable development goals (SDGs), especially sustainable urban mobility, low-carbon transportation, and efficient intelligent transportation systems (ITS). However, unstable nighttime low-light perception severely restricts autonomous driving deployment, hindering sustainable transportation development—rooted in visual feature degradation and cross-modal imbalance that impair 3D object detection (autonomous driving’s core perception technology). To address this and advance sustainable autonomous driving, this paper proposes a Bird’s-Eye View (BEV)-based multi-modal 3D object detection approach tailored for nighttime scenarios, integrating low-light adaptive components while preserving the original BEV pipeline. Without modifying core inference, it enhances low-light robustness and cross-modal fusion stability, ensuring reliable perception for sustainable autonomous driving operation. Extensive experiments on the nuScenes nighttime subset quantify performance via rigorous metrics (NDS, mAP, mATE). Results show the method outperforms BEVFusion with negligible parameter/inference overhead, achieving 1.13% NDS improvement. This validates its effectiveness and provides a sustainable technical tool for autonomous driving perception, promoting new energy vehicle popularization, optimizing urban ITS efficiency, reducing perception-related accidents and carbon emissions, and directly contributing to transportation and socio-economic sustainability. Full article

Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human–wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-image suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Specifically, Stage 1 uses a pretrained You Only Look Once (YOLO)-family detector for permissive animal localization and empty-trigger suppression, and Stage 2 uses a lightweight EfficientNet-based binary classifier to confirm puma on detector crops and gate downstream actions. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 s. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-image classifier while maintaining high recall. On the held-out test set ($N=1434$ events), the proposed two-stage cascade achieves precision $0.983$, recall $0.975$, F1 $0.979$, accuracy $0.986$, and balanced accuracy $0.983$, with only 8 false positives and 12 false negatives. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention. Full article