Search Results (43,178)

China’s rural population flowing into highly urbanized areas has led to the spatial reconstruction of urban–rural land use. Exploring the laws and trends of urban–rural land use in highly urbanized areas is of great significance in promoting rural transformation. This paper takes the southern Jiangsu region as a research area and uses a system dynamics (SD) model to simulate the demand for different land types based on economic, social, policy, and environmental (ESPE) factors. Future land use simulation (FLUS) is used to simulate the spatial evolution trend of urban–rural land use based on point–axis elements. The results show that the agricultural production space is severely squeezed by the urban living space. Under the scenario of rapid expansion, the decrease in arable land quantity and the demand area for rural residential areas are the largest. Under the scenario of high-quality development, the decrease in arable land area and the demand for land in rural residential areas are lowest. Based on the spatial simulation, it is reported that the areas with more intense land use spatial reconstruction in the three scenarios are mainly concentrated in the region’s urban–rural border areas. The future evolution of urban–rural land is summarized into three models: (1) single-center-driving expansion, (2) patchy expansion near the city center, and (3) multi-center-driving expansion. This paper proposes targeted policy recommendations to provide a scientific reference for solving the conflict between urban and rural land use. Full article

The simplified tax system is the most common special tax regime in the Russian Federation in terms of the number of taxpayers. Tax revenues from the simplified tax system account for 6% of the structure of tax revenues of the consolidated budgets of the constituent entities of the Russian Federation and more than 93% of the structure of tax revenues from special tax regimes. The purpose of this study is to identify and assess the factors influencing tax revenues from the tax levied in connection with applying the simplified system of taxation (taxable object—income reduced by the amount of expenses). The objective of this study is to determine a set of factors used by economists to model the level of tax revenues and to conduct a corresponding econometric analysis of the influence of the selected factors on the dependent variable to identify characteristics of the simplified taxation system functioning in the Russian Federation. The object of this study is the per capita tax revenue from the tax levied in connection with applying the simplified system of taxation (the object of taxation is income reduced by expenses) in the Russian Federation. The subject of the research is a set of economic relations, which arise because of tax-legal relations between tax authorities and taxpayers in relation to the calculation of the tax levied in connection with the application of the simplified taxation system. This study’s hypothesis is that the amount of tax revenues is influenced by factors characterizing the economic situation and development of small and medium businesses in the constituent territories of the Russian Federation. This study was conducted in 83 constituent territories of the Russian Federation in 2020–2022. The research methods are statistical analysis and econometric modeling on panel data. During this study, six econometric models were constructed. Based on the results of specification tests, the least squares dummy variables model was selected. The results of the modeling show that the tax rate, the number of taxpayers, and the real average per capita monetary income of the population have a statistically significant impact on the per capita tax revenue under the simplified tax system (the object of taxation is income reduced by the number of expenses). As a result, the focus of economic policy at both macro and meso levels should be on the support of small and medium-sized enterprises in the early stages of their life cycle, as well as on the increase of the purchasing power of the population. Based on the results obtained, it is possible to forecast the revenue side of the budgets of the constituent entities of the Russian Federation. Full article

The development of sports tourism is of great significance in promoting regional cultural exchanges, boosting economic development, accelerating the construction of national fitness, promoting the development of the sports industry, and advancing ecological environmental protection. With the integrated application of exploratory spatial data analysis and gray correlation analysis model, this article takes the Yangtze River Delta region as the research object and comprehensively explores the pattern evolution characteristics and influencing factors of its sports tourism development space. The study found that (1) the total amount of sports tourism resources in the Yangtze River Delta region has accumulated in fluctuation and iteration, and the types are constantly enriched; (2) the spatial pattern of sports tourism resources in the Yangtze River Delta region shows the evolution characteristics of “agglomeration–dispersion–agglomeration” over time; (3) the spatial evolution hot spots of sports tourism resources in the Yangtze River Delta region have experienced the following characteristics “unipolar-multipolar-area-wide-suburban”, and the center of gravity of spatial evolution has experienced the process of east–west linear development and north–south diffusion; and (4) the spatial development of sports tourism in the Yangtze River Delta region has experienced the process of “policy + sports + transportation” drive, “economic + social” drive, economic drive, and total-factor drive in different periods. The results of the study can help optimize the allocation of sports and tourism resources in the Yangtze River Delta region, further realize the in-depth integration and development of sports, culture, and tourism, and enhance the regional economy and public service level. Full article

The system inertia insufficiency brought on by a high percentage of wind power access to a power grid can be effectively resolved by wind-storage collaborative participation in primary frequency regulation (PFR). However, the impact of energy storage participation in system-frequency regulation is significantly influenced by its state of charge (SOC). In this paper, considering the SOC of energy storage (ES) and the stochastic characteristics of wind turbine (WT) output, the control strategy of wind-storage collaborative participation in the PFR of a system is proposed. Firstly, a WT adaptive inertia control and a model of storage droop control were constructed. Additionally, to prevent the problem of secondary frequency drop brought on by a separate rotational kinetic energy control, a wind-storage collaborative frequency-regulation control scheme was constructed. Secondly, considering changes in wind speed and the SOC of ES, an improved dynamic droop-control strategy for ES is proposed. This strategy was combined with the adaptive inertia control of the WT to establish the PFR of the WT collaborative participation system. Lastly, a simulation example of a two-region, four-machine system was used to validate the efficacy of the frequency-control strategy presented in this paper. The results show that a significant percentage of WTs connected to a power grid can effectively have their frequency-response ability improved by wind-storage collaborative control. Full article

Wastewater treatment is a critical process for ensuring water quality and public health, particularly in the context of increasing environmental challenges such as pollution and water scarcity. Artificial intelligence (AI) has emerged as a transformative technology capable of optimizing various wastewater treatment processes, such as contaminant removal, energy consumption, and cost-efficiency. This study presents a comprehensive bibliometric analysis of AI applications in wastewater treatment, utilizing data from Scopus and Web of Science covering 4335 publications from 1985 to 2024. Utilizing machine learning techniques such as neural networks, fuzzy logic, and genetic algorithms, the analysis reveals key trends in the role of the AI in optimizing wastewater treatment processes. The results show that AI has increasingly been applied to solve complex problems like membrane fouling, nutrient removal, and biofouling control. Regional contributions highlight a strong focus on advanced oxidation processes, microbial sludge treatment, and energy optimization. The Latent Dirichlet Allocation (LDA) model further identifies emerging topics such as real-time process monitoring and AI-driven effluent prediction as pivotal areas for future research. The findings provide valuable insights into the current state and future potential of AI technologies in wastewater management, offering a roadmap for researchers exploring the integration of AI to address sustainability challenges in the field. Full article

This paper describes a numerically efficient method for optimizing the high power transfer efficiency (PTE) of a resonant cavity-based Wireless Power Transfer (WPT) system for the wireless charging of smart clothing. The WPT system under study unitizes a carbon steel closet intended to store smart clothing overnight as a resonant cavity. The WPT system is designed to operate at 865.5 MHz; however, the operating frequency can be adjusted over a wide range. The main reason behind choosing a resonant cavity-based WPT system is that it has several advantages over the competitive WPT methods. Specifically, in contrast to its Far-field Power Transfer (FPT) and Inductive Power Transfer (IPT) counterparts, resonant cavity-based WPTs do not exhibit path loss and significant PTE sensitivity to the distance between the Tx and Rx coils and misalignment, respectively. The non-uniformity of the fields within the closet is addressed by using an optimized Yagi-like transmitting antenna with an additional element affecting the waveguide mode phases. The changes in the mode phases increase the volume inside the cavity, where the PTE values are higher than 50% (the high PTE region). In the present study, the model decomposition method is adapted to substantially accelerate the process of finding the optimal WPT system parameters. Additionally, the decomposition method explains the mechanism responsible for extending the high PTE region. The generalized scattering matrices are computed using the full-wave simulator Ansys HFSS for three sub-models. Then, the calculated S matrices are combined to evaluate the system’s PTE. The decomposition method is validated against full-wave simulations of the original WPT system’s model for several different parameter value combinations. The simulated results obtained for a sub-optimal model are experimentally verified by measuring the PTE of a real-life closet-based WPT system. The measured and calculated results are found to be in close agreement with the maximum measured PTE, as high as 60%. Full article

Exploring NPP changes and their corresponding drivers is significant for the achievement of sustainable ecosystem management and in addressing climate change. This study aimed to explore the spatiotemporal variation in NPP and analyze the effects of vegetation and climate change on the global NPP from 2003 to 2020. Methodologically, the Theil–Sen and Mann–Kendall methods were used to study the spatiotemporal characteristics of global NPP change. Moreover, a ridge regression model was built by selecting the vegetation indicators of the leaf area index (LAI) and fraction vegetation coverage (FVC) and the climate factors of CO₂, shortwave downward solar radiation (R_sd), precipitation (P), and temperature (T). Then, the relative contributions of each factor were evaluated. The results showed that, over the previous two decades, the global mean NPP reached 503.43 g C m⁻² yr⁻¹, with a fluctuating upward trend of 1.52 g C m⁻² yr⁻¹. The regions with a significant increase in NPP (9.22 g C m⁻² yr⁻¹) were mainly located in Central Africa, while the regions with decreasing NPP (−3.21 g C m⁻² yr⁻¹) were primarily in the Amazon Rainforest in northern South America. Additionally, CO₂, the LAI, and the FVC exhibited positive contributions to the NPP trend, with the predominant factors being CO₂ (relative contribution of 32.22%) and the LAI (relative contribution of 21.96%). In contrast, the contributions of R_sd and precipitation were relatively low (<10%). In addition, the contributions varied at different land cover and climate zone scales. The CO₂, LAI, FVC, and temperature were the predominant factors affecting NPP across the vegetation types. At the scale of climate zones, CO₂ was the predominant factor influencing changes in vegetation NPP. As the climate gradually transitioned towards temperate and cold regions, the contribution of the LAI to NPP increased. The findings of this study help to clarify the effects of vegetation and climate change on the ecosystem, providing theoretical support for ecological environmental protection and other related initiatives. Full article

Background: Runs of homozygosity (ROHs), continuous homozygous regions across the genome, are often linked to consanguinity, with their size and frequency reflecting shared parental ancestry. Homozygosity mapping (HM) leverages ROHs to identify genes associated with autosomal recessive diseases. Whole-exome sequencing (WES) improves HM by detecting ROHs and disease-causing variants. Methods: To streamline personalized multigene panel creation, using WES and ROHs, we developed a methodology integrating ROHMMCLI and HomozygosityMapper algorithms, and, optionally, Human Phenotype Ontology (HPO) terms, implemented in a Django Web application. Resorting to a dataset of 12,167 WES, we performed the first ROH profiling of the Portuguese population. Clustering models were applied to predict consanguinity from ROH features. Results: These resources were applied for the genetic characterization of two siblings with epilepsy, myoclonus and dystonia, pinpointing the CSTB gene as disease-causing. Using the 2021 Census population distribution, we created a representative sample (3941 WES) and measured genome-wide autozygosity (F_ROH). Portalegre, Viseu, Bragança, Madeira, and Vila Real districts presented the highest F_ROH scores. Multidimensional scaling showed that ROH count and sum were key predictors of consanguinity, achieving a test F1-score of 0.96 with additional features. Conclusions: This study contributes with new bioinformatics tools for ROH analysis in a clinical setting, providing unprecedented population-level ROH data for Portugal. Full article

Data have become a virtual factor of production, and when integrated with the traditional factors of labor, capital, and land form digital labor, digital capital, and digital land, thereby generating a multiplier effect that contributes to the comprehensive revitalization of rural areas. This paper utilizes panel data from 30 provinces (autonomous regions and municipalities) in China from 2013 to 2023 and employs a double machine learning model to empirically test the impact mechanism of rural digitalization on the integration of rural industries. The results indicate that digital villages significantly promote the integrated development of rural industries through three direct pathways—digital industry development, digital information infrastructure, and digital service levels—with this conclusion remaining valid after a series of robustness tests. A mechanism analysis shows that digital villages facilitate the integration of rural industries through three indirect pathways—alleviating urban–rural factor mismatches, adjusting the agricultural–industrial structure, and promoting agricultural technological advancement—with this conclusion still valid after various robustness tests. The heterogeneity results show that there is significant variability in how digital villages promote the development of integrated rural industries, with the effects being more pronounced in major grain-producing and eastern regions compared to non-major grain-producing and central-western regions. Based on this, this paper proposes policy recommendations focused on accelerating digital village construction, formulating differentiated strategies, and alleviating factor mismatches, aiming to provide references for achieving rural revitalization. We mainly propose countermeasures and suggestions from three aspects: digital dividend, differentiation strategy, and element mismatch. Our main purpose in writing this article is to make up for the shortcomings of existing theories, enrich the theoretical system of digital rural construction, contribute Chinese solutions for digital rural construction around the world, and improve the word’s level of digital rural construction. Full article

Infrared thermography is a non-invasive measurement method that can accurately describe immediate temperature changes of an object. In the case of continuous in vivo hand measurements, extracting correct thermal data requires a first step of image segmentation to identify regions of interest. This step can be difficult due to parasitic hand movements. It is therefore necessary to regularly readjust the segmented areas throughout the recording. This process is time-consuming and presents a particular obstacle to studying a large number of areas of the hand and long duration sequences. In this work, we propose an automated segmentation methodology that can automatically detect these regions on the hand. This method differs from previous literature because it uses a secondary visual camera and a combination of computer vision and machine learning feature identification. The obtained segmentation models were compared to models segmented by two human operators via Dice and Intersection-over-Union coefficients. The results obtained are very positive: we were able to decompose the images acquired via IRT with our developed algorithms, regardless of the temperature variation, and this with processing times of less than a second. Thus, this technology can be used to study the long-term thermal kinetics of the human hand by automatic feature detection, even in situations where the hand temperature experiences a significant variation. Full article

The growth of climate investment and financing highlights the growing global focus on climate change. In China, fostering the coordinated development of climate investment and financing and green finance is a crucial step toward accomplishing sustainable development objectives and addressing climate challenges effectively. By constructing the indicator system of climate investment and financing and green finance, and using the entropy method and the coupled coordination model, we comprehensively explored the development level and the coupling coordination relationship between climate investment and financing and green finance in 30 provinces in China during the period of 2013–2022. The findings of the research indicate that the development of climate investment and financing and green finance in China as a whole shows a growth trend, but the development of climate investment and financing remains relatively underdeveloped, and noticeable variations can be observed among regions. In respect to the level of coupling coordination, the overall coordination level of each province has not yet attained the desired level of optimality, and despite the yearly increase in the coordination degree at the national level, it is still in the state of mild dislocation. The eastern coastal region has the highest level of coupling coordination and is in the stage of verging on dislocation. And the great northwestern region has the lowest level of coordination and is in the stage of moderate dislocation. These findings provide an important reference for the formulation and execution of climate investment and financing and green finance policies in China. Full article

Wildfires are affecting natural ecosystems worldwide, causing economic and human losses and exacerbated by climate change. Models of fire severity and fire susceptibility are crucial tools for fire monitoring. This case study analyses a fire event on 3 September 2019 in Vilcabamba parish, Loja province, Ecuador. This article aims to assess the severity and susceptibility of a fire through spectral indices and multi-criteria methods for establishing a fire action plan proposal. The methodology comprises the following: (i) the acquisition of Sentinel-2A products for the calculation of spectral indices; (ii) a fire severity model using differentiated indices (dNBR and dNDVI) and a fire susceptibility model using the Analytic Hierarchy Process (AHP) method; (iii) model validation using Logistic Regression (LR) and Non-metric Multidimensional Scaling (NMDS) algorithms; (iv) the proposal of an action plan for fire management. The Normalised Burn Ratio (NBR) index revealed that 10.98% of the fire perimeter has burned areas with moderate-high severity in post-fire scenes (2019) and decreased to 0.01% for post-fire scenes in 2021. The Normalised Difference Vegetation Index (NDVI) identified 67.28% of the fire perimeter with null photosynthetic activity in the post-fire scene (2019) and 5.88% in the post-fire scene (2021). The Normalised Difference Moisture Index (NDMI) applied in the pre-fire scene identified that 52.62% has low and dry vegetation (northeast), and 8.27% has high vegetation cover (southwest). The dNDVI identified 10.11% of unburned areas and 7.91% using the dNBR. The fire susceptibility model identified 11.44% of the fire perimeter with null fire susceptibility. These results evidence the vegetation recovery after two years of the fire event. The models demonstrated excellent performance for fire severity models and were a good fit for the AHP model. We used the Root Mean Square Error (RMSE) and area under the curve (AUC); dNBR and dNDVI have an RMSE of 0.006, and the AHP model has an RMSE of 0.032. The AUC = 1.0 for fire severity models and AUC = 0.6 for fire susceptibility. This study represents a holistic approach by combining Google Earth Engine (GEE), Geographic Information System (GIS), and remote sensing tools for proposing a fire action plan that supports decision making. This study provides escape routes that considered the most significant fire triggers, the AHP, and fire severity approaches for monitoring wildfires in Andean regions. Full article

Lightweight structures, characterized by rapid assembly, are vital for creating habitats in outdoor environments, but their implementation in high-plateau cold regions encounters significant challenges in heating and ventilation. This paper systematically introduces the environmental characteristics and reviews the demands and primary influencing factors of indoor environments in these regions. The advantages and limitations of underground lightweight construction are also discussed. Current research indicates that evaluation methods for air quality in high-altitude cold regions require further development. Reducing building heat loss and minimizing cold air infiltration can enhance indoor environments and lower energy consumption. However, it is essential to establish effective ventilation strategies to prevent the accumulation of air pollutants. Then, potential passive ventilation improvement measures suitable for the environmental characteristics of high-cold plateaus are outlined. The application potential and possible limitations of these measures are summarized, providing references for future research. Finally, the main research methods for ventilation and heating within building interiors are organized and discussed. Findings indicate that computational fluid dynamics models are predominantly used, but they demonstrate low efficiency and high resource consumption for medium- to large-scale applications. Integrating these models with network models can achieve a balance of high computational accuracy and efficiency. Full article

In response to the difficulties and poor timeliness in detecting feeding metallic foreign objects during high-yield continuous crushing operations in coal mines, this paper proposes a new method for detecting metallic foreign objects, combining pulsed eddy current testing with the Truncated Region Eigenfunction Expansion (TREE) method. This method is suitable for the harsh working conditions in coal mine crushing stations, which include high dust, strong vibration, strong electromagnetic interference, and low temperatures in winter. A model of the eddy current field of feeding metallic foreign objects in the truncated region is established using a coaxial excitation and receiving coil with a Hall sensor. The full-cycle time-domain analytical solution for the induced voltage and magnetic induction intensity of the reflective field under practical square wave signals is obtained. Simulation and experimental results show that the effective time range, peak value, and time to peak of the received voltage and magnetic induction signals can be used to classify and identify the size, thickness, conductivity, and magnetic permeability of feeding metallic foreign objects. Experimental results meet the actual needs for removing feeding metallic foreign objects in coal mine sites. This provides core technical support for the establishment of a predictive fault diagnosis system for crushing equipment. Full article

Given the increasing environmental pressures, it is essential that agriculture achieves the goal of sustainable and low-carbon development. In 2010, China, as the top carbon emitter, introduced a policy on agricultural land lease (ALL), which has been met with considerable approval from farmers and has resulted in a notable surge in the rate of ALL within the country. Nevertheless, the question of how the ALL policy affects agricultural carbon emissions (ACEs) remains unanswered. What are the transmission mechanisms? To answer these questions, this paper presents an equilibrium model that accounts for the heterogeneous production efficiency among farmers. It offers a theoretical analysis of the impact of ALL policy on agricultural carbon emission reduction (ACER) and presents an empirical test of this impact using a difference-in-differences (DID) model. Our research shows that the ALL policy gives impetus to ACER. This conclusion persists even after conducting the robustness and endogeneity tests. The mechanism posits that the policy achieves ACER through reducing the proportion of rural agricultural employees. Heterogeneity analysis indicates that the policy effect is significant in both the northern and southern regions of China. Nonetheless, the effect is only observable in economically developed areas, regions with high chemical fertilizer application rates, and areas with restricted agricultural progress. This study elucidates the connection between land transfer and agricultural carbon emissions, offering empirical evidence to support the advancement of green and low-carbon agricultural development. Full article