Search Results (947)

Greenscaping, a key sustainable practice, helps cities combat rising temperatures and climate change. Urban parks, a pivotal greenscaping element, mitigate the urban heat island (UHI) effect. In this study, we utilized high-resolution remote sensing imagery (GF-2 and Landsat 8, 9) and in situ measurements to analyze the seasonal thermal regulation of different park types in Zhengzhou, China. We calculated vegetation characteristic indices (VCIs) and landscape patterns (LMs) and employed boosted regression tree models to explore their relative contributions to land surface temperature (LST) across different seasons. Our findings revealed that urban parks lowered temperatures by 0.65 °C, 1.41 °C, and 2.84 °C in spring, summer, and autumn, respectively, but raised them by 1.92 °C in winter. Amusement parks, comprehensive parks, large parks, and water-themed parks had significantly lower LSTs. The VCI significantly influenced LST in autumn, with trees having a stronger cooling effect than shrubs. LMs showed a more prominent effect than VCIs on LST during spring, summer, and winter. Parks with longer perimeters, larger and more dispersed green patches, higher plant species richness, higher vegetation heights, and larger canopies were associated with more efficient thermal reduction in an urban setting. The novelty of this study lies in its detailed analysis of the seasonal thermal regulation effects of different types of urban parks, providing new insights for more effective urban greenspace planning and management. Our findings assist urban managers in mitigating the urban surface heat effect through more effective urban greenspace planning, vegetation community design, and maintenance, thereby enhancing cities’ potential resilience to climate change. Full article

This study analyses the dynamics of green infrastructure (GI) in the cities of Bujumbura, Kinshasa, and Lubumbashi. A remote sensing approach, combined with landscape ecology metrics, characterized this analysis, which was based on three Landsat images acquired in 2000, 2013, and 2022 for each city. Spatial pattern indices reveal that GI was suppressed in Bujumbura and Kinshasa, in contrast to Lubumbashi, which exhibited fragmentation. Furthermore, the values of stability, aggregation, and fractal dimension metrics suggest that Bujumbura experienced rather intense dynamics and a reduction in the continuity of its GI, while Kinshasa showed weaker dynamics and tendencies towards patch aggregation during the study period. In contrast, Lubumbashi exhibited strong dynamics and aggregation of its GI within a context of significant anthropization. The evolution of the Normalized Difference Vegetation Index demonstrates a sawtooth pattern in the evolution of tall vegetation patches in Bujumbura, compared to a gradual decrease in Kinshasa and Lubumbashi. It is recommended that urban growth in these cities should be carefully planned to ensure the integration of sufficient GI. Full article

Climate change intensifies the frequency and severity of extreme weather events, profoundly altering demographic landscapes globally and within the United States. This study investigates their impact on migration patterns, using propensity score matching and LASSO techniques within a larger regression modeling framework. Here, we analyze historical population trends in relation to climate risk and exposure metrics for various hazards. Our findings reveal nuanced patterns of climate-induced population change, including “risky growth” areas where economic opportunities mitigate climate risks, sustaining growth in the face of observed exposure; “tipping point” areas where the amenities are slowly giving way to the disamenity of escalating hazards; and “Climate abandonment” areas experiencing exacerbated out-migration from climate risks, compounded by other out-migration market factors. Even within a single county, these patterns vary significantly, underscoring the importance of localized analyses. Projecting population impacts due to climate risk to 2055, flood risks are projected to impact the largest percentage of areas (82.6%), followed by heatwaves (47.4%), drought (46.6%), wildfires (32.7%), wildfire smoke (21.7%), and tropical cyclone winds (11.1%). The results underscore the importance of understanding hyperlocal patterns of risk and change in order to better forecast future patterns. Full article

Variations in species distribution, population structure, and behavior can provide a portfolio effect that buffers populations against rapid environmental change. Although diversity has been identified as a goal for effective resource management and genetic and demographic tools have been developed, life history remains challenging to quantify. In this study, we demonstrate a novel metric of life history diversity using telemetry data from migratory fish. Here, we examined diversity in the outmigration behavior of juvenile Chinook salmon (Oncorhynchus tshawytscha) released in the Sacramento River, California, between 2007 and 2017. In this synthesis, we examined a wide variety of landscape and demographic drivers at high resolution by incorporating many individual telemetry studies, with variability in release location by year, environmental conditions, and all runs of salmon that are present in the watershed. When years were grouped by shared hydrologic conditions, variation in travel time was significantly higher in wet years. Further, our model showed a negative effect of warm temperatures at low flows on the variation in migration movements. This suggests that enhanced hydrologic connectivity increases the variation in migration time, a representation of habitat complexity and biocomplexity, despite the degraded state of this watershed and the weakened state of these populations. Variation in migration behavior could buffer species from current and future environmental changes, such as climate effects on precipitation and temperature. Hence, behavioral metrics generated from telemetry studies can be used to understand life history diversity and the potential effects of environmental fluctuations. Full article

At least 10 global digital elevation models (DEMs) at one-arc-second resolution now cover Earth. Comparing derived grids, like slope or curvature, preserves surface spatial relationships, and can be more important than just elevation values. Such comparisons provide more nuanced DEM rankings than just elevation root mean square error (RMSE) for a small number of points. We present three new comparison categories: fraction of unexplained variance (FUV) for grids with continuous floating point values; accuracy metrics for integer code raster classifications; and comparison of stream channel vector networks. We compare six global DEMs that are digital surface models (DSMs), and four edited versions that use machine learning/artificial intelligence techniques to create a bare-earth digital terrain model (DTM) for different elevation ranges: full Earth elevations, under 120 m, under 80 m, and under 10 m. We find edited DTMs improve on elevation values, but because they do not incorporate other metrics in their training they do not improve overall on the source Copernicus DSM. We also rank 17 common geomorphic-derived grids for sensitivity to DEM quality, and document how landscape characteristics, especially slope, affect the results. None of the DEMs perform well in areas with low average slope compared to reference DTMs aggregated from 1 m airborne lidar data. This indicates that accurate work in low-relief areas grappling with global climate change should use airborne lidar or very high resolution image-derived DTMs. Full article

In the digital era, cloud computing stands as a pivotal tool in the transformative journey of enterprises, with China’s tech landscape serving as a prime exemplar. However, when enterprises embrace cloud computing, they face complex hurdles, mainly technical ones. To understand how the complexity of cloud migration affects their digital transformation, our research meticulously constructed business process models for business deployment in both non-cloud and cloud contexts, spanning the IaaS, PaaS, and SaaS levels. By harnessing China’s public cloud market data, we constructed the Cloud Migration Complexity Index, providing a tangible metric to gauge the intricacies of cloud migration and their implications on digital transformation. The findings illustrate that a decrease in cloud migration complexity significantly accelerates digital transformation, with the reduction in SaaS complexity having the most profound impact. Analyzing businesses of varying scales, the diminishing complexity of SaaS predominantly boosts digital transformation for non-SME enterprises, while reduced PaaS complexity is most beneficial for SMEs. This study advocates for the government to expand the PaaS market and suggest that cloud providers develop more PaaS-based products to optimize cloud migration both technically and economically. Full article

While most studies focused on the impact of intensive agriculture on the barn owl’s diet, little is known about the effect of cropland abandonment. We compared the taxon composition/evenness and feeding guild structure of small mammal prey identified in pellets collected before (2004) and after (2012) the abandonment of 9% of cultivated fields within a cultural landscape. Data on prey abundance per pellet were analysed through non-metric multidimensional scaling and permutational, paired tests. Prey taxon evenness in 2012 was significantly lower than in 2004. That induced a shift in prey taxon composition as indicated by the significantly lower dietary similarity compared with the random expectation. The increasing and declining abundance of Murinae and Crocidurinae, respectively, had the largest contribution to the differentiation of the diet spectrum. Insectivorous prey was significantly more abundant in 2004 compared to 2012, while the opposite was true for omnivorous prey. Our results suggest that even a small fraction of abandoned crops in the landscape might induce a detectable shift in the barn owl’s food niche. The dietary effects are similar to those observed after agricultural intensification, that is, an increase in the abundance of generalists to the detriment of specialist mammal prey. Full article

Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale deployment of CF-MIMO presents significant challenges in processing signals in a scalable manner. This study introduces an innovative methodology that leverages the capabilities of Dynamic Mode Decomposition (DMD) to tackle the complexities of Channel Estimation in CF-MIMO wireless systems. By extracting dynamic modes from a vast array of received signal snapshots, DMD reveals the evolving characteristics of the wireless channel across both time and space, thereby promising substantial improvements in the accuracy and adaptability of channel state information (CSI). The efficacy of the proposed methodology is demonstrated through comprehensive simulations, which emphasize its superior performance in highly mobile environments. For performance evaluation, the most common techniques have been employed, comparing the proposed algorithms with traditional methods such as MMSE (Minimum Mean Squared Error), MRC (Maximum Ration Combining), and ZF (Zero Forcing). The evaluation metrics used are standard in the field, namely the Cumulative Distribution Function (CDF) and the average UL/DL Spectral Efficiency. Furthermore, the study investigates the impact of DMD-enabled Channel Estimation on system performance, including beamforming strategies, spatial multiplexing within realistic time- and delay-correlated channels, and overall system capacity. This work underscores the transformative potential of incorporating DMD into massive MIMO wireless systems, advancing communication reliability and capacity in increasingly dynamic and dense wireless environments. Full article

Pollination mapping and modeling have opened new avenues for comprehending the intricate interactions between pollinators, their habitats, and the plants they pollinate. While the Lonsdorf model has been extensively employed in pollination mapping within previous studies, its conceptualization of bee movement in agricultural landscapes presents notable limitations. Consequently, a gap exists in exploring the effects of forest fragmentation on pollination once these constraints are addressed. In this study, our objective is to model pollination dynamics in fragmented forest landscapes using a modified version of the Lonsdorf model, which operates as a distance-based model. Initially, we generated several simulated agricultural landscapes, incorporating forested and agricultural habitats with varying forest proportions ranging from 10% to 50%, along with a range of fragmentation degrees from low to high. Subsequently, employing the modified Lonsdorf model, we evaluated the nesting suitability and consequent pollination supply capacity across these diverse scenarios. We found that as the degree of forest fragmentation increases, resulting in smaller and more isolated patches with less aggregation, the pollination services within landscapes tend to become enhanced. In conclusion, our research suggests that landscapes exhibiting fragmented forest patch patterns generally display greater nesting suitability due to increased floral resources in their vicinity. These findings highlight the importance of employing varied models for pollination mapping, as modifications to the Lonsdorf model yield distinct outcomes compared to studies using the original version. Full article

Traditional urban design often overlooks the synchronisation of human and ecological connectivities, typically favouring corridors for ecological continuity. Our study challenges this convention by introducing a computational design approach, meta-connectivity, leveraging the deep generative models performing cross-domain translation to integrate human–wildlife landscape connectivity in urban morphology amidst the planetary urbanisation. Utilising chained Pix2Pix models, our research illustrates a novel meta-connectivity design reasoning framework, combining landscape connectivity modelling with conditional reasoning based on deep generative models. This framework enables the adjustment of both human and wildlife landscape connectivities based on their correlative patterns in one single design process, guiding the rematerialisation of urban landscapes without the need for explicit prior ecological or urban data. Our empirical study in East London demonstrated the framework’s efficacy in suggesting wildlife connectivity adjustments based on human connectivity metrics. The results demonstrate the feasibility of creating an innovative urban form in which the land cover guided by the connectivity gradients replaces the corridors based on simple geometries. This research thus presents a methodology shift in urban design, proposing a symbiotic approach to integrating disparate yet interrelated landscape connectivities within urban contexts. Full article

Accurate solar power generation forecasting is paramount for optimizing renewable energy systems and ensuring sustainability in our evolving energy landscape. This study introduces a pioneering approach that synergistically integrates Boosting Cascade Forest and multi-class-grained scanning techniques to enhance the precision of solar farm power output predictions significantly. While Boosting Cascade Forest excels in capturing intricate, nonlinear variable interactions through ensemble decision tree learning, multi-class-grained scanning reveals fine-grained patterns within time-series data. Evaluation with real-world solar farm data demonstrates exceptional performance, reflected in low error metrics (mean absolute error, 0.0016; root mean square error 0.0036) and an impressive R-squared score of 99.6% on testing data. This research represents the inaugural application of these advanced techniques to solar generation forecasting, highlighting their potential to revolutionize renewable energy integration, streamline maintenance, and reduce costs. Opportunities for further refinement of ensemble models and exploration of probabilistic forecasting methods are also discussed, underscoring the significance of this work in advancing solar forecasting techniques for a sustainable energy future. Full article

The emotional responses and perceptual preferences of individuals for urban public spaces are shaped by their interactions with the physical environment. Emotions and perceptions are inextricably linked, forming the basis of people’s spatial experience. For instance, the presence of dense city buildings can result in feelings of crowding and friction. By improving the urban landscape, it is possible to reduce the stress experienced by citizens. In this study, architectural styles and building facade colors were examined to explore design approaches and features of stress-relieving building facades and identify metrics that measure participants’ stress-relief when viewing buildings. The color of 600 buildings in Japan and Taiwan was analyzed to understand stress-relief from architecture. Semi-structured interviews were conducted with 70 participants who viewed images of 30 buildings. The semantic differential method with a seven-point image scale was employed to assess the stress-relieving potential of different architectural styles and colors. The findings of this study indicated that participants perceived that architectural colors influenced feelings of relief. Additionally, they anticipated variations in architectural colors contingent on architectural usage patterns. To substantiate this observation, three principles—city image, identity, and spiritual atmosphere—were identified as fundamental elements in designing cities for livability. The three principles are illustrated by several case studies for a detailed understanding of their applicability in biodesign practices. Full article

Predicting forest cover change (FCC) and screening development scenarios are crucial for ecological resilience. However, quantitative evaluations of prioritizing forest change scenarios are limited. Here, we took five shared socio-economic pathways (SSPs) representing potential global changes, namely SSP1: sustainability, SSP2: middle of the road, SSP3: regional rivalry, SSP4: inequality, and SSP5: fossil-fueled development, which were constructed by integrated assessment and climate models. We modeled them with the patch-generating land use simulation (PLUS) and constructed a multi-dimensional landscape connectivity index (MLCI) employing forest landscape connectivity (FLC) indices to assess forest development in Fujian Province, Southeast China. The MLCI visualized by radar charts was based on five metrics, including forest patch size (class area (CA), number (patch density (PD), isolation (landscape division index (DIVISION), aggregation (mean nearest-neighbor index (ENN_MN), and connectance index, (CONNECT). The results indicate that FC will remain above 61.4% until 2030, with growth observed in SSP1 and SSP4. Particularly, FC in SSP4 substantially increased, converted from cropland (1140.809 km²) and grassland (645.741 km²). SSP4 has the largest MLCI values and demonstrates significant enhancements in forest landscape integrity, with CA, ENN_MN and CONNECT increasing greatly. Our study offers valuable approaches to and insights into forest protection and restoration. Full article

Electrifying aircraft, a crucial advancement in the aviation industry, aims to cut pollutive emissions and boost energy efficiency. Traditional aircraft depend on fossil fuels, which contribute significantly to greenhouse gas emissions and environmental pollution. Despite progress in electric propulsion and energy storage technologies, challenges such as low energy density and integration issues persist. This paper provides a comprehensive thematic and bibliometric analysis to map the research landscape in aircraft electrification, identifying key research themes, influential contributors, and emerging trends. This study applies natural language processing to unstructured bibliographic data and cross-sectional statistical methods to analyze publications, citations, and keyword distributions across various categories related to aircraft electrification. The findings reveal significant growth in research output, particularly in energy management and multidisciplinary design analysis. Collaborative networks highlight key international partnerships, with the United States and China being key research hubs, while citation metrics highlight the impact of leading researchers and institutions in these countries. This study provides valuable insights for researchers, policymakers, and industry stakeholders, guiding future research directions and collaborations. Full article

In modern cyber-physical systems, the integration of AI into vision pipelines is now a standard practice for applications ranging from autonomous vehicles to mobile devices. Traditional AI integration often relies on cloud-based processing, which faces challenges such as data access bottlenecks, increased latency, and high power consumption. This article reviews embedded AI vision systems, examining the diverse landscape of near-sensor and in-sensor processing architectures that incorporate convolutional neural networks. We begin with a comprehensive analysis of the critical characteristics and metrics that define the performance of AI-integrated vision systems. These include sensor resolution, frame rate, data bandwidth, computational throughput, latency, power efficiency, and overall system scalability. Understanding these metrics provides a foundation for evaluating how different embedded processing architectures impact the entire vision pipeline, from image capture to AI inference. Our analysis delves into near-sensor systems that leverage dedicated hardware accelerators and commercially available components to efficiently process data close to their source, minimizing data transfer overhead and latency. These systems offer a balance between flexibility and performance, allowing for real-time processing in constrained environments. In addition, we explore in-sensor processing solutions that integrate computational capabilities directly into the sensor. This approach addresses the rigorous demand constraints of embedded applications by significantly reducing data movement and power consumption while also enabling in-sensor feature extraction, pre-processing, and CNN inference. By comparing these approaches, we identify trade-offs related to flexibility, power consumption, and computational performance. Ultimately, this article provides insights into the evolving landscape of embedded AI vision systems and suggests new research directions for the development of next-generation machine vision systems. Full article