Search Results (292)

The process of urbanization has intensified the urban heat environment, with the degradation of thermal conditions closely linked to the morphological characteristics of different functional zones. This study delineated urban functional areas using a multivariate dataset and investigated the seasonal and threshold effects of landscape and architectural features on land surface temperature (LST) through boosted regression tree (BRT) modeling and Spearman correlation analysis. The key findings are as follows: (1) LST exhibits significant seasonal variation, with the strongest urban heat island effect occurring in summer, particularly within industry, business, and public service zones; residence zones experience the greatest temperature fluctuations, with a seasonal difference of 24.71 °C between spring and summer and a peak temperature of 50.18 °C in summer. (2) Fractional vegetation cover (FVC) consistently demonstrates the most pronounced cooling effect across all zones and seasons. Landscape indicators generally dominate the regulation of LST, with their relative contribution exceeding 45% in green land zones. (3) Population density (PD) exerts a significant, seasonally dependent dual effect on LST, where strategic population distribution can effectively mitigate extreme heat events. (4) Mean building height (MBH) plays a vital role in temperature regulation, showing a marked cooling influence particularly in residence and business zones. Both the perimeter-to-area ratio (LSI) and frontal area index (FAI) exhibit distinct seasonal variations in their impacts on LST. (5) This study establishes specific indicator thresholds to optimize thermal comfort across five functional zones; for instance, FVC should exceed 13% in spring and 31.6% in summer in residence zones to enhance comfort, while maintaining MBH above 24 m further aids temperature regulation. These findings offer a scientific foundation for mitigating urban heat waves and advancing sustainable urban development. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

This study demonstrates the successful fabrication of high-performance reaction-bonded silicon carbide (RBSC) ceramics through an optimized liquid silicon infiltration (LSI) process employing multi-modal SiC particle gradation and nano-carbon black (0.6 µm) additives. By engineering porous preforms with hierarchical SiC distributions and tailored carbon sources, the resulting ceramics achieved a compressive strength of 2393 MPa and a flexural strength of 380 MPa, surpassing conventional RBSC systems. Microstructural analyses revealed homogeneous β-SiC formation and crack deflection mechanisms as key contributors to mechanical enhancement. Ultrafine SiC particles (0.5–2 µm) refined pore architectures and mediated capillary dynamics during infiltration, enabling nanoscale dispersion of residual silicon phases and minimizing interfacial defects. Compared to coarse-grained counterparts, the ultrafine SiC system exhibited a 23% increase in compressive strength, attributed to reduced sintering defects and enhanced load transfer efficiency. This work establishes a scalable strategy for designing RBSC ceramics for extreme mechanical environments, bridging material innovation with applications in high-stress structural components. Full article

The microstructure and mechanical properties of welded joints of API 5L X-52 steel plates cladded with AISI 316L-Si austenitic stainless steel were evaluated. The gas metal arc welding process with pulsed arc (GMAW-P) and controlled arc oscillation were used to join the bimetallic plates. After the root welding pass, buttering with an ERNiCrMo-3 filler wire was performed and multi-pass welding followed using an ER70S-6 electrode. The results obtained by optical and scanning electron microscopy indicated that the shielding atmosphere, welding parameters, and electric arc oscillation enabled good arc stability and proper molten metal transfer from the filler wire to the sidewalls of the joint during welding. Vickers microhardness (HV) and tensile tests were performed for correlating microstructural and mechanical properties. The mixture of ERNiCrMo-3 and ER70S-6 filler materials presented fine interlocked grains with a honeycomb network shape of the Ni–Fe mixture with Ni-rich grain boundaries and a cellular-dendritic and equiaxed solidification. Variation of microhardness at the weld metal (WM) in the middle zone of the bimetallic welded joints (BWJ) is associated with the manipulation of the welding parameters, promoting precipitation of carbides in the austenitic matrix and formation of martensite during solidification of the weld pool and cooling of the WM. The BWJ exhibited a mechanical strength of 380 and 520 MPa for the yield stress and ultimate tensile strength, respectively. These values are close to those of the as-received API 5L X-52 steel. Full article

The diffusion effect of river cooling is critical for mitigating the urban heat island effect in riverside areas and for establishing an urban cooling network. River cooling effect diffusion is influenced by the two-dimensional (2D) and three-dimensional (3D) urban morphology of surrounding areas. However, the characteristics of 2D/3D urban morphology that facilitate efficient river cooling effect diffusion remain unclear. This study establishes a technical framework to analyze river cooling effect diffusion resistance (RCDR) across different urban morphologies, using the Huangpu River waterside area in Shanghai as a case study. Seven urban morphology indicators, derived from both 2D and 3D dimensions, were developed to characterize the river cooling effect diffusion resistance. The relative contributions and marginal effects were analyzed using the Boosted Regression Tree (BRT) model. The study found that (1) river cooling effect diffusion was heterogeneous, with four typical patterns; (2) the Landscape Shape Index (LSI) and Blue-green Space Ratio (BGR) significantly impacted cooling effect diffusion; and (3) optimal cooling effect diffusion occurred when the blue-green space occupancy ratio exceeded 20% and building density ranged from 0.1 to 0.3. This study’s technical framework offers a new perspective on river cooling effect diffusion and heat island mitigation in riverside spaces, with significant practical value and potential for broader application. Full article

This paper clarified the correlation among quality of life (QoL) indicators, exercise implementation level, and exercise habits in middle-aged and older adults under identical exercise intervention conditions. The survey items were anthropometric and physiologic measurements, physical strength measurements, and exercise habits. During the 3-month experimental period, a weekly “health exercise course” served as the primary intervention. For all participants, anthropometric and physiologic measurements, physical strength tests, questionnaire surveys, and other surveys were conducted before and after the experiment; then, the pre- and post-intervention effects were compared. After the exercise intervention, significant differences were observed among middle-aged and older adults in terms of various parameters, such as weight, fat rate, diastolic pressure, systolic pressure, sit-up, standing on one foot, lower limb extension force, activity of daily living (ADL), and subjective well-being (PGC). ADL and PGC changed significantly in the participants who engaged in exercise more than twice a week. However, the participants who engaged in exercise for less than twice a week showed no significant differences in any parameters except the life satisfaction (LSI) mean; the LSI increased in the “Less than twice a week” exercise group but decreased in the “More than twice a week” group. In terms of average walking time per session, the “More than 30 min” exercise group showed significant differences in ADL and PGC, whereas the “Less than 30 min” group showed significant differences only in the LSI. The influence of exercise on QoL indicators of middle-aged and older adults, under the same exercise intervention conditions, is related to their exercise habits. This study highlights the benefits of physical exercise in middle-aged and older adults, emphasizing the importance of regular and sustained exercise for this population. Furthermore, the study provides a scientific basis for improving QoL in middle-aged and older adults, thus, to some extent, addressing the concerns related to the growing population of older adults. Full article

Landslide susceptibility assessment (LSA) plays a crucial role in disaster prevention and mitigation. Traditional random selection of non-landslide samples (labeled as 0) suffers from poor representativeness and high randomness, which may include potential landslide areas and affect the accuracy of LSA. To address this issue, this study proposes a novel Landslide Susceptibility Index–based Semi-supervised Fuzzy C-Means (LSI-SFCM) sampling strategy combining membership degrees. It utilizes landslide and unlabeled samples to map landslide membership degree via Semi-supervised Fuzzy C-Means (SFCM). Non-landslide samples are selected from low-membership regions and assigned membership values as labels. This study developed three models for LSA—Convolutional Neural Network (CNN), U-Net, and Support Vector Machine (SVM), and compared three negative sample sampling strategies: Random Sampling (RS), SFCM (samples labeled 0), and LSI-SFCM. The results demonstrate that the LSI-SFCM effectively enhances the representativeness and diversity of negative samples, improving the predictive performance and classification reliability. Deep learning models using LSI-SFCM performed with superior predictive capability. The CNN model achieved an area under the receiver operating characteristic curve (AUC) of 95.52% and a prediction rate curve value of 0.859. Furthermore, compared with the traditional unsupervised fuzzy C-means (FCM) clustering, SFCM produced a more reasonable distribution of landslide membership degrees, better reflecting the distinction between landslides and non-landslides. This approach enhances the reliability of LSA and provides a scientific basis for disaster prevention and mitigation authorities. Full article

Landslides are among the most destructive natural hazards in the Western Ghats region of Kerala, driven by complex interactions between geological, hydrological, and anthropogenic factors. This study aims to generate a high-resolution Landslide Susceptibility Level Map (LSLM) using a machine learning (ML)-based Random Forest (RF) model integrated with Geographic Information Systems (GIS). A total of 231 historical landslide locations obtained from the Bhukosh portal were used as reference data. Eight predictive factors—Stream Order, Drainage Density, Slope, Aspect, Geology, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI)—were derived from remote sensing and ancillary datasets, preprocessed, and reclassified for model input. The RF model was trained and validated using a 50:50 split of landslide and non-landslide points, with variable importance values derived to weight each predictive factor of the raster layer in ArcGIS. The resulting Landslide Susceptibility Index (LSI) was reclassified into five susceptibility zones: Very Low, Low, Moderate, High, and Very High. Results indicate that approximately 17.82% of the study area falls under high to very high susceptibility, predominantly in the steep, weathered, and high rainfall zones of the Western Ghats. Validation using Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) analysis yielded an accuracy of 0.890, demonstrating excellent model performance. The output LSM provides valuable spatial insights for planners, disaster managers, and policymakers, enabling targeted mitigation strategies and sustainable land-use planning in landslide-prone regions. Full article

In the face of economic, social, and environmental pressures, the issue of sustainable development has garnered widespread attention in the Logistics Service Supply Chain (LSSC) with risk attitudes under Technical Output Uncertainty. In this regard, this paper first constructs an optimal emission reduction investment game model for an LSSC composed of Logistics Service Integrators (LSIs) and Logistics Service Providers (LSPs) against the backdrop of Technical Output Uncertainty. To this end, it quantifies the participants’ risk attitudes using a mean-variance model to analyze optimal emission reduction investment decisions for centralized and decentralized LSSC under different levels of risk tolerance. Subsequently, it designs a joint contract with altruistic preferences for sharing emission reduction costs in the LSSC. This contract analyzes the parameter constraints for achieving Pareto optimization within the supply chain. Finally, the study employs a case simulation to analyze the changes in expected revenues for centralized LSSC and joint contracts under different risk tolerance levels. The study reveals that (1) in a centralized LSSC, under risk-neutral attitudes, there exists a unique optimal emission reduction investment, which yields the highest expected return from emission reduction. However, under risk-averse attitudes, the expected return is always lower than the optimal expected return under risk neutrality. (2) In a decentralized LSSC, the emission reduction investment decisions of the Logistics Service Providers are similar to those in a centralized LSSC. (3) Under risk-neutral attitudes, the cost-sharing and altruistic preference-based joint contract can also coordinate the risk-averse LSSC under certain constraints, and by adjusting the cost-sharing and altruistic preference parameters, the expected returns can be reasonably allocated. Full article

Carbon emission forecasting is a critical step in addressing climate change and effective environmental management. However, previous studies have concentrated mainly on socioeconomic factors, with less attention directed toward the significant impact of urban form. To address the shortcomings of previous studies, this study introduced three types of landscape indices that can characterize urban form and combined them with conventional socioeconomic factors to create a new carbon emission forecasting method. The enhanced STIRPAT and PLUS models were employed to forecast future changes in various socioeconomic factors and urban form, with the aim of forecasting carbon emissions in 21 cities of Guangdong during 2025–2060. The results confirm that urban form has an obvious influence on carbon emissions. In comparison to the baseline model, which considered only socioeconomic factors, the incorporation of urban form into the carbon emission forecast resulted in a reduction in the mean absolute percentage error from 7.16% to 6.18%. Moreover, carbon emissions were found to be positively correlated with GDP per capita, energy intensity, permanent population, share of secondary sector, LSI, and PLADJ but negatively correlated with PD. Furthermore, Guangdong will not be able to accomplish its “carbon peaking” objective around 2030, except in a low-carbon situation. Our proposed method could enhance the rationality of carbon emission forecasting, thereby providing a reasonable decision-making basis for low-carbon management. Full article

Urban forests play a crucial role in enhancing vegetation cover and bolstering the ecological functions of cities by expanding green space, improving ecological connectivity, and reducing landscape fragmentation. This study examines these dynamics in Jinhua City, China, utilizing Landsat 8 satellite imagery for all four seasons of 2023, accessed through the Google Earth Engine (GEE) platform. Fractional vegetation cover (FVC) was calculated using the pixel binary model, followed by the classification of FVC levels. To understand the influence of landscape structure, nine representative landscape metrics were selected to construct a landscape index system. Pearson correlation analysis was employed to explore the relationships between these indices and seasonal FVC variations. Furthermore, the contribution of each index to seasonal FVC was quantified using a random forest (RF) regression model. The results indicate that (1) Jinhua exhibits the highest average FVC during the summer, reaching 0.67, while the lowest value is observed in winter, at 0.49. The proportion of areas with very high coverage peaks in summer, accounting for 50.6% of the total area; (2) all landscape metrics exhibited significant correlations with seasonal FVC. Among them, the class area (CA), percentage of landscape (PLAND), largest patch index (LPI), and patch cohesion index (COHESION) showed strong positive correlations with FVC, whereas the total edge length (TE), landscape shape index (LSI), patch density (PD), edge density (ED), and area-weighted mean shape index (AWMSI) were negatively correlated with FVC; (3) RF regression analysis revealed that CA and PLAND contributed most substantially to FVC, followed by COHESION and LPI, while PD, AWMSI, LSI, TE, and ED demonstrated relatively lower contributions. These findings provide valuable insights for optimizing urban forest landscape design and enhancing urban vegetation cover, underscoring that increasing large, interconnected forest patches represents an effective strategy for improving FVC in urban environments. Full article

The Grand Canal serves as a vital water transportation route, a UNESCO World Cultural Heritage site, and an ecological corridor. It is currently undergoing coordinated transformation through infrastructure development, heritage preservation, and ecological restoration. However, existing research has primarily focused on either cultural heritage conservation or localized ecological issues, with limited attention to the spatial relationship between landscape patterns and ecological quality along the entire corridor. To address this gap, this study examines eight sections of the Grand Canal and develops a gradient analysis framework based on equidistant buffer zones. The framework integrates the Remote Sensing Ecological Index (RSEI) with landscape pattern indices to assess ecological responses across spatial gradients. A Multi-scale Geographically Weighted Regression (MGWR) model is applied to reveal the spatially heterogeneous effects of landscape patterns on ecological quality. From 2013 to 2023, landscape patterns showed a trend toward increasing agglomeration and regularity. This is indicated by a rise in the Aggregation Index (AI) from 91.24 to 91.38 and declines in both patch density (PD) from 8.45 to 8.20 and Landscape Shape Index (LSI) from 199.74 to 196.72. During the same period, ecological quality slightly declined, with RSEI decreasing from 0.66 to 0.57. The effects of PD and Shannon’s Diversity Index (SHDI) on ecological quality varied across canal sections. In highly urbanized areas such as the Tonghui River, these indices were positively correlated with ecological quality, whereas in less urbanized areas like the Huitong River, negative correlations were observed. Overall, the strength of these correlations tended to weaken with increasing buffer distance. This study provides a scientific foundation for the integrated development of ecological protection and spatial planning along the Grand Canal and offers theoretical insights for the refined management of other major inland waterways. Full article

In protected areas of the Brazilian savannas (Cerrado), Land Surface Phenology (LSP) is influenced by both precipitation and fire, but the nature of these relationships remains unexplored. Here, we assessed the impacts of precipitation and fire on LSP metrics derived from the Normalized Difference Vegetation Index (NDVI) at Emas National Park (ENP). Using TIMESAT, along with the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and 30-m Harmonized Landsat Sentinel (HLS) products, we investigated these effects in both grassland and woodland areas. To evaluate the effects of precipitation, we identified the driest and wettest seasonal cycles between 2002 and 2023 and analyzed the relationships between accumulated rainfall during the rainy season and each of the 13 TIMESAT metrics. To assess the effects of fire, three major events were examined: 1 September 2005 (affecting 45% of the park’s area), 12 August 2010 (90%), and 10 July 2021 (21%). The burned grassland area and the subsequent vegetation recovery following the 2021 event were analyzed in detail using a non-burned control site and LSP metrics extracted from the HLS product, covering both pre- and post-disturbance cycles. The results indicated that the metrics most positively correlated to precipitation were Amplitude (AMP), End of Season (EOS), Large and Small Seasonal Integrals (LSI and SSI), and Rate of Increase at the Beginning of the Season (RIBS). The highest correlation coefficients were found in woodland areas, which were less affected by fire disturbance than grassland areas. Similar trends were observed in the behavior of AMP, EOS, and SSI in response to both precipitation and fire, with fire exerting a stronger influence. By decoupling the fire effects from rainfall influence using the control site, we identified Base Level (BL), SSI, EOS, AMP, and Values at the End and Start of the Season (VES and VSS), as the metrics most sensitive to fire and subsequent vegetation recovery in burned areas. The effects of fire were evident for most metrics, both during the disturbance cycle and in the post-fire cycle. Our study underscores the importance of combining MODIS and HLS time series to understand vegetation phenology in the Cerrado. Full article

In the face of increasingly complex urban challenges, a critical question arises: can urban ecosystems maintain resilience, vitality, and sustainability when confronted with external threats and pressures? Taking Kunming—a plateau-mountainous city in China—as a case study, this research constructs an urban ecosystem resilience (UER) assessment model based on the DPSIR (Driving forces, Pressures, States, Impacts, and Responses) framework. A total of 25 indicators were selected via questionnaire surveys, covering five dimensions: driving forces such as natural population growth, annual GDP growth, urbanization level, urban population density, and resident consumption price growth; pressures including per capita farmland, per capita urban construction land, land reclamation and cultivation rate, proportion of natural disaster-stricken areas, and unit GDP energy consumption; states measured by Evenness Index (EI), Shannon Diversity Index (SHDI), Aggregation Index (AI), Interspersion and Juxtaposition Index (IJI), Landscape Shape Index (LSI), and Normalized Vegetation Index (NDVI); impacts involving per capita GDP, economic density, per capita disposable income growth, per capita green space area, and per capita water resources; and responses including proportion of natural reserve areas, proportion of environmental protection investment to GDP, overall utilization of industrial solid waste, and afforestation area. Based on remote sensing and other data, indicator values were calculated for 2006, 2011, and 2016. The entire-array polygon indicator method was used to visualize indicator interactions and derive composite resilience index values, all of which remained below 0.25—indicating a persistent low-resilience state, marked by sustained economic growth, frequent natural disasters, and declining ecological self-recovery capacity. Forecasting results suggest that, under current development trajectories, Kunming’s UER will remain low over the next decade. This study is the first to integrate the DPSIR framework, entire-array polygon indicator method, and Grey System Forecasting Model into the evaluation and prediction of urban ecosystem resilience in plateau-mountainous cities. The findings highlight the ecosystem’s inherent capacities for self-organization, adaptation, learning, and innovation and reveal its nested, multi-scalar resilience structure. The DPSIR-based framework not only reflects the complex human–nature interactions in urban systems but also identifies key drivers and enables the prediction of future resilience patterns—providing valuable insights for sustainable urban development. Full article

The purpose of this study was to assess whether a simple and reproducible battery of jump tests can distinguish between patients affected by chronic ankle instability (CAI) and control subjects. The hypothesis was that patients with CAI would demonstrate lower performance compared to healthy subjects during jumping tasks. Twenty-one young, active adults aged 18 to 45 years affected by CAI were matched for sex, age, and body mass index (BMI) to a control group of 21 healthy subjects without history of lower limb pathology. Jumping ability was instrumentally assessed by an infrared optical acquisition system using a test battery, including mono- and bipodalic vertical squat jumps, countermovement jumps (CMJs), a drop jump (DJ), and a side-hop test. Patients with CAI had significantly worse monopodalic CMJ, DJ, and side-hop test scores in their involved limb compared to the non-dominant limb of healthy individuals. Pathological limbs of CAI patients reported inferior results compared to non-dominant limbs of healthy individuals while performing monopodalic CMJs, DJs, and side-hop tests (p < 0.05). No statistically significant differences were found between the two groups in the limb symmetry index (LSI) while performing monopodalic CMJs and DJs (p = 0.072 and p = 0.071, respectively), while a difference was found between the two groups, in favor of healthy subjects, while performing monopodalic side-hop tests (p < 0.01). A reproducible battery of jump tests performed with a simple and low-cost instrument can be applied in the clinical setting allowing for reliable measurements of functional ability of subjects with CAI. Our findings support the idea that side-hop tests could be more accurate than vertical jump tests for detecting functional deficits in patients suffering from CAI. Full article