Search Results (13,877)

With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. Among these, path planning and target recognition and positioning technologies are particularly critical for applications in intelligent agents with low computational power operating in complex environments. This paper presents a novel approach to path planning on low-resolution lunar surface maps by introducing an improved A* algorithm with an adaptive heuristic function. This innovation enhances robustness in environments with limited map accuracy and enables paths that maintain maximum distance from obstacles. Additionally, we innovatively propose the Dynamic Environment Target Identification and Localization (DETIL) algorithm, which identifies unknown obstacles and employs spatiotemporal clustering to locate points of interest. Our main contributions provide valuable references for the aerospace industry, particularly in lunar exploration missions. The simulation results demonstrate that the improved A* algorithm reduces the maximum elevation difference by 55% and the maximum cumulative elevation difference by 68% compared to the traditional A* algorithm. Furthermore, the DETIL algorithm’s obstacle identification component successfully recognizes all the obstacles along the path, and its spatiotemporal clustering improves the average number of target discoveries by 152% over the conventional DBSCAN clustering approach. Full article

The COVID-19 pandemic and similar public health emergencies have significantly impacted global travel patterns. Analyzing the recovery characteristics of subway station-level passenger flow during the pandemic recovery phase can offer unique insights into public transportation operations and guide practical planning efforts. This pioneering study constructs a station-level passenger flow recovery resilience (PFRR) index during the rapid recovery phase using subway AFC system swipe data. Additionally, it develops an analytical framework based on a multiscale geographically weighted regression (MGWR) model, the improved gray wolf optimization with Levy flight (LGWO), and light gradient boosting machine (LightGBM) regression to analyze passenger flow resilience on weekdays and weekends in relation to land use-related built environment types. Finally, SHAP attribution analysis is used to study the nonlinear relationships between built environment variables and PFRR index. The results show significant spatial heterogeneity in the impact of commercial, recreational, and residential land, as well as POI (points of interest) of leisure and shopping on PFRR. On weekdays, the most relevant built environment variables for PFRR are POI of enterprises and shopping numbers. In contrast, the contribution of built environment variables affecting PFRR of weekend is more balanced, reflecting the recovery of non-essential travel on weekends. Most land use-related built environment variables exhibit nonlinear associations with PFRR values. The proposed analytical framework shows significant performance advantages over other baseline models. This study provides unique insights into subway passenger flow characteristics and surrounding land use-related development layouts under the impact of public health emergencies. Full article

Biofeedback (BFB) is a rehabilitation method, which, among other things, is used for the restitution of motor and gait function. As of now, it has become technically feasible to use BFB training based on target gait parameters to improve the gait function in stroke patients. The walking patterns of stroke patients are generally characterized by significant gait phase asymmetries, mostly of the stance phase and the single stance phase. The aim of the study was to investigate the restoration of gait function using BFB training with gait phases as feedback targets. The study included two patient groups, each of 20 hemiparetic patients in the subacute stage of stroke and a control group of 20 healthy subjects. Each patient group received BFB training with either stance phase or single stance phase as the feedback target, respectively. The patients received a total of 8 to 11 training sessions. Assessments based on clinical scales and gait analysis data (spatiotemporal, kinematic, and EMG parameters) were performed before and after the training course. The score-based clinical assessments showed a significant improvement in both patient groups. According to the assessments of gait biomechanics, the subjects in the Single Stance Phase group had significantly more severe dysfunctions. In both patient groups, the unaffected limb responded to the BFB training, while the stance phase significantly changed after training in the unaffected limb only. The other patient group, trained using the single stance phase as the feedback target, showed no changes in the target parameter either in the affected or in the contralateral limb. The clinical and instrumental assessments showed different, non-equivalent sensitivity. The results of the study demonstrated the possibility to use targeted BFB training to improve walking function. However, a significant effect of such training was only observed with stance phase as the target parameter. A response to training was observed predominantly in the unaffected limb and facilitated the desired increase in the functional ability of the paretic limb. Training based on stance phase as the target parameter is probably preferable for the patient population under study. Full article

Clarifying the driving mechanisms of ecosystem service (ES) supply and demand under urbanization is of significant importance for urban ecological planning and management. However, how the balance of ES supply and demand and its driving mechanisms vary with the degree of urbanization has been little studied. In this study, we analyzed the spatiotemporal changes and the correlations between ES supply and demand and the degree of urbanization in the Zhengzhou Metropolitan Area (ZZMA) from 2000 to 2020 and further explored the driving mechanisms behind these changes. The results showed that, (1) between 2000 and 2020, the ZZMA experienced a deficit in comprehensive ES supply and demand, and regions with rapid urbanization development were more likely to trigger imbalances in ES supply and demand; (2) the spatial mismatch between low–high ES supply and demand was primarily distributed in the built-up areas of various cities, while the high–low spatial mismatch was mostly found in forest and grassland areas; (3) the comprehensive urbanization level of the ZZMA was spatially negatively correlated with the ratio of ES supply and demand. Regions with lower ES balance were more susceptible to disturbances caused by urbanization; (4) population density was the key factor influencing the supply and demand of carbon sequestration, oxygen release, water conservation, and food provision services, while the proportions of forest land and construction areas had the greatest influence on the supply and demand of air purification and leisure services. It is important to ensure the ecological status of the northwestern, southwestern, and central mountainous and forested areas; maintain the agricultural status of the main grain-producing areas in the eastern plains; strengthen ecological restoration and green infrastructure in built-up areas; and formulate differentiated management policies to promote the sustainable supply of ES and safeguard the ecological security of the region. Full article

Food security is the primary condition for the development of human society. The Great River Basin is very important to ensure the accessibility and availability of agricultural irrigation, which is vital for food security. The Yellow River Basin plays a significant role in China’s food security, with counties serving as key administrative units for guaranteeing this security. This study uses the Yellow River Basin in China as a case study to construct an evaluation index system for county-level food security. It assesses the food security of 22 counties (districts) in Ningxia from 2013 to 2022, applying spatial correlation theories and driving factor analysis methods to explore the factors influencing county-level food security. The results reveal the following: (1) Overall, the food security index in Ningxia has been on the rise, but there is significant internal variation among counties. (2) Spatially, the food security index is relatively low in administrative centers, while the irrigation areas along the Yellow River play a crucial role in maintaining food security, and the overall food security index in the central arid areas is improving. (3) Food security is driven by multiple factors including economic, social, and climatic influences. To enhance food security in the Yellow River Basin, it is necessary to manage land resources systematically, improve grain production technology, and balance ecological protection with food security. Full article

This study examined the location preferences and changes in pollution-intensive firms by analyzing the spatiotemporal distribution and drivers in the Yangtze River Economic Belt, a transitional manufacturing region in China. To analyze the distribution of firms under natural growth conditions prior to the implementation of the national “Great Protection of the Yangtze River” policy in 2016, this study utilized data on newly expanded industrial land use from 2007 to 2016. The results indicated that new pollution-intensive firms predominantly focused on water pollution, occupying over 40% of the total area annually. The new pollution-intensive firms preferred the geographic agglomeration siting strategy, mostly along the Yangtze River or in urban agglomerations, while gradually moving westward. The total area and number of new pollution-intensive firms in the Yangtze River Economic Belt showed an overall trend of “inverted U-shaped” variation during the study period, and the average size of the pollution-intensive firms gradually decreased. GeoDetector analysis revealed that geographical factors have always been significant. Local economic factors attracted new pollution-intensive firms, but later in the study period, these factors showed some inhibitory effect on the increase in pollution-intensive firms in the lower reaches. Government intervention worked less effectively but was significantly enhanced after interaction with other factors. Finally, the results suggested that local governments should build a stronger synergy between industrial land policies and environmental regulations to ensure sustainable growth and rational allocation of pollution-intensive firms. Full article

The devastating effects of rain-induced attenuation on communication links operating above 10 GHz during rainy events can significantly degrade signal quality, leading to interruptions in service and reduced data throughput. Understanding the spatial and seasonal distribution of rain heights is crucial for predicting these attenuation effects and for network performance optimization. This study utilized ten years of atmospheric temperature and geopotential height data at seven pressure levels (1000, 850, 700, 500, 300, 200, and 100 hPa) obtained from the Copernicus Climate Data Store (CDS) to deduce rain heights across nine stations in South Africa. The kernel density estimation (KDE) method was applied to estimate the temporal variation of rain height. A comparison of the measured and estimated rain heights shows a correlation coefficient of 0.997 with a maximum percentage difference of 5.3%. The results show that rain height ranges from a minimum of 3.5 km during winter in Cape Town to a maximum of about 5.27 km during the summer in Polokwane. The spatial variation shows a location-dependent seasonal trend, with peak rain heights prevailing at the low-latitude stations. The seasonal variability indicates that higher rain heights dominate in the regions (Polokwane, Pretoria, Nelspruit, Mahikeng) where there is frequent occurrence of rainfall during the winter season and vice versa. Contour maps of rain heights over the four seasons (autumn, spring, winter, and summer) were also developed for South Africa. The estimated seasonal rain heights show that rain-induced attenuations were grossly underestimated by the International Telecommunication Union (ITU) recommended rain heights at most of the stations during autumn, spring, and summer but fairly overestimated during winter. Durban had a peak attenuation of 15.9 dB during the summer, while Upington recorded the smallest attenuation of about 7.7 dB during winter at a 0.01% time exceedance. Future system planning and adjustments of existing infrastructure in the study stations could be improved by integrating these localized, seasonal radio propagation data in link budget design. Full article

The increasing climate-change-induced tropical cyclone phenomena expose the power grid to significant operation risks by disrupting the normal operation of grid components. This paper considers the failure mechanism with respect to critical grid components and reveals a novel spatiotemporal revolution of grid failures during the passage of typhoons. Based on the spatiotemporal evolution of grid failures, a threshold-based emergency scenario set and the corresponding two-stage robust optimization-based emergency dispatch model are developed. The robust emergency dispatch strategy is obtained using a column-and-constraint generation (C&CG) algorithm. The simulation results show that the proposed robust emergency dispatch strategy can guarantee a considerable degree of robustness under multiple emergency scenarios driven by uncertain typhoon conditions. Full article

Neuronal spike patterns are the fundamental units of neural communication in the brain, which is still not fully understood. Entropy measures offer a quantitative framework to assess the variability and information content of these spike patterns. By quantifying the uncertainty and informational content of neuronal patterns, entropy measures provide insights into neural coding strategies, synaptic plasticity, network dynamics, and cognitive processes. Here, we review basic entropy metrics and then we provide examples of recent advancements in using entropy as a tool to improve our understanding of neuronal processing. It focuses especially on studies on critical dynamics in neural networks and the relation of entropy to predictive coding and cortical communication. We highlight the necessity of expanding entropy measures from single neurons to encompass multi-neuronal activity patterns, as cortical circuits communicate through coordinated spatiotemporal activity patterns, called neuronal packets. We discuss how the sequential and partially stereotypical nature of neuronal packets influences the entropy of cortical communication. Stereotypy reduces entropy by enhancing reliability and predictability in neural signaling, while variability within packets increases entropy, allowing for greater information capacity. This balance between stereotypy and variability supports both robustness and flexibility in cortical information processing. We also review challenges in applying entropy to analyze such spatiotemporal neuronal spike patterns, notably, the “curse of dimensionality” in estimating entropy for high-dimensional neuronal data. Finally, we discuss strategies to overcome these challenges, including dimensionality reduction techniques, advanced entropy estimators, sparse coding schemes, and the integration of machine learning approaches. Thus, this work summarizes the most recent developments on how entropy measures contribute to our understanding of principles underlying neural coding. Full article

While many studies have used Earth observations to quantify urbanization in Africa, there is still a lack of empirical evidence on the role of secondary cities in the fastest urbanizing region in the world. Moreover, the diversity of urbanization processes in Africa, which can be more or less compact in terms of land consumption, remains insufficiently acknowledged and under-documented. This empirical study employed mixed methods to address these research gaps. We analyzed and compared the spatiotemporal dynamics of two secondary African cities, Ho (Ghana) and Kpalimé (Togo), between 1985 and 2020. We compared their spatial growth (the rate of urbanization of land) with their respective population growth rates using Landsat TM and ETM+ imagery, and population data. To understand the factors behind eventual differences between the spatial patterns of urbanization of the two cities, our quantitative analysis based on remote sensing was confronted with qualitative data from individual interviews with key stakeholders. Our results showed two distinct urbanization trajectories between 1985 and 2010, with Ho following a more compact pattern than Kpalimé. Since 2010, however, both cities have tended towards urban sprawl, with an urbanization rate greater than the population growth rate. According to the interviews, the main determinants of urban sprawl in these two secondary cities were the absence of housing policies for low-income groups, the absence or inefficacy of urban master plans, the preponderance of single-family housing, and land speculation. Full article

The routine monitoring of eutrophication is an important measure for observing the variation in water quality and protecting the ecological health of lakes. However, in situ information reflects eutrophication levels within a limited distance and period. In this study, we retrieved the trophic level index (TLI) based on Landsat 8 remote sensing images and using a machine learning (ML) method in Liangzi Lake in Hubei Province, China. The results showed that random forest (RF) outperformed other ML algorithms in estimating the TLI, evaluated by its higher fitness through the Monte Carlo method (median values of R², RMSE, and MAE are 0.54, 0.047, and 0.037, respectively). In general, 8% of the areas of Liangzi Lake presented an increasing eutrophication level from 2014 to 2022, and 20.1% of the areas reached a mild eutrophication level in 2022. In addition, we found that temperature and anthropogenic activities may impact the eutrophication conditions of the lake. This work uses remote sensing imagery and a ML method to monitor the dynamics of the lake’s eutrophication status, thereby providing a valuable reference for pollution control measures and enhancing the efficiency of water resource management. Full article

The health status of ecosystems is an important prerequisite for ensuring regional ecological security. Exploring the spatiotemporal patterns, driving mechanisms, and zoning regulation pathways of ecosystem health is of great significance for achieving co-ordinated and sustainable regional ecosystems. This study uses China as a case area and applies the InVEST model to measure integrated ecosystem services and incorporates it into an evaluation framework for ecosystem health based on the “Vigor-Organization-Resilience-Ecosystem Services” (VORS) model. It reveals the spatiotemporal evolution characteristics of ecosystem health in China from 2000 to 2020 and employs the geodetector and spatiotemporal geographically weighted regression model to analyze the main influencing factors and spatial differentiation characteristics, thereby exploring ecological management zoning and optimization pathways. The study results show that (1) during the study period, the overall ecosystem health level in China showed a declining trend, dropping from 0.397 in 2000 to 0.377 in 2020. (2) Overall, China’s ecosystem health exhibits strong spatial positive correlation and spatial clustering characteristics, with a basic pattern of lower values in the northwest and higher values in the southeast. (3) Vegetation coverage, population density, density of road network, and per capita GDP are the main influencing factors of ecosystem health in China. (4) China is divided into five types of Ecological Management Zones: Ecological Conservation Zone, Ecological Enhancement Zone, Ecological Buffer Zone, Ecological Remediation Zone, and Ecological Reshaping Zone, with differentiated strategies proposed for optimizing ecosystem health in each zone. Full article

Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky–Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety. Full article

Mudflat wetland, one of the 27 surface elements identified by the International Geographic Data Committee, has undergone substantial transformations with the rapid growth of the social economy and marine hazards, resulting in significant changes in its area and distribution. Quick identification of mudflat wetland evolution is vital to improve the mudflat ecological service value. We employed object-oriented and decision tree classification methods to map the mudflat wetland in the Yellow Sea using the Landsat time series from 1983 to 2020. The Improved Spectral Water Index (IWI) was established by combining the characteristics of many ratio indices and using ratio operation and quadratic power operation. The coefficient of variation (CV) of the IWI was calculated, and the range of the intertidal zone in 1983, 1990, 2000, 2010, and 2020 was obtained by using a threshold method. The results indicate that the mudflat wetland area decreased continuously from 1983 to 2020, with a reduction of 337.38 km²/10a. Among the total area, the natural wetland experienced a decline of 446.9 km²/10a, with the most drastic changes occurring between 2000 and 2010. In contrast, the area of the human-made wetland increased by 109.56 km²/10a. Over the 38 years, the tidal flat has undergone the most drastic reduction, with an average of 157.45 km²/10a. From 1983 to 2020, the intertidal zone area decreased, with a reduction of 429.02 km²/10a. Human activities were the key factors causing mudflat wetland loss. Based on these findings, we propose several policy suggestions. This study provides a scientific basis for understanding the synergetic evolution mechanism of coastal resources utilization and mudflat wetland protection under global change. Full article

Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors’ search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O₃ concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles. Full article