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Time series dynamic prediction of surface deformation in mining areas can provide reference data for coal mine safety and production, which has important impacts. The combination of interferometric synthetic aperture radar (InSAR) technology and the probability integral method (PIM) is commonly used for predicting deformation. However, most surface subsidence prediction in mining areas is based on the static PIM parameters, failing to achieve the three-dimensional (3D) dynamic deformation prediction. This paper proposed a 3D deformation dynamic prediction model (InSAR-3D-CTPIM) between InSAR deformation observations and dynamic coordinate-time PIM (CTPIM) parameters, which can realize the prediction of east–west, north–south, and vertical series deformation caused by mining. The method has been validated by simulation experiments and real experiments in the mining area of Jiansheng Coal Mine in Baisha Reservoir, Henan Province, China. The results showed that the modeling accuracy was improved by 34.3% compared to the traditional multi-rate model, and the accuracy was improved by 28.5% compared to the vertical deformation obtained by the traditional static PIM method. The InSAR-3D-CTPIM model can be used to predict the evolutionary history of basin-wide surface deformation dynamics in coal mining areas, and provide a reference for the early warning and prediction of geological hazards in coal mining areas. [ABSTRACT FROM AUTHOR]

In order to give full play to the advantages of local governance structure, it is necessary to explore the influence of governance structure on college students' cultural identity and self-confidence. In this paper, a questionnaire survey was conducted on four college students in Henan Province, and the regression analysis method was used to analyze the survey data. Based on the analysis results, from the perspective of local governance structure, the relationship between local governance structure and college students' cultural identity and self-confidence is demonstrated. This paper puts forward the following hypotheses: first, whether the structure of local autonomy has an impact on the cultural identity of college students; second, whether the relationship between college students' identity and selfconfidence is independent; thirdly, whether the local self-control structure can improve the self-confidence of college students; Finally, the influence and degree of local self-government structure and cultural identity of college students on college students' self-confidence. The results show that the local structure can improve the cultural level of college students, with an increase of about 10.5%, and the impact on the cultural self-confidence of college students is relatively small, indicating that there is a correlation between cultural identity and local governance structure, and the correlation between cultural self-confidence is small, and the relationship between cultural identity and self-confidence is independent. The reason for this is that the short-term effect of the local governance structure is significant, and the long-term effect is not good, so it is necessary to cooperate with social participation measures, university system and other measures to enhance students' cultural self-confidence. [ABSTRACT FROM AUTHOR]

Addressing the excessive input and inefficient use of agrochemicals are crucial for global food security, environmental protection, and human health. This paper offers a new idea from the perspective of outsourcing agricultural production. The impact of outsourcing on the frequency and intensity of agrochemical inputs were theoretically analyzed and empirically tested using a field survey of 1211 farmers in Heilongjiang, Henan, and Hunan, the major food-producing areas in China. A Logit regression framework was used to analyze the effect, a conditional mixture process (CMP) method was used to address potential endogeneity concerns, and a mediation effect model was used to dissect the mechanism. The results show that the effect of outsourcing on both input frequency and input intensity of agrochemicals was positive at the 1% significance level. The positive effect conclusion still holds even after addressing the potential endogeneity concerns, and in the sub-sample estimates for maize, wheat, and rice. We conclude that outsourcing can improve the utilization efficiency of agrochemicals by increasing the frequency of agrochemical inputs, but fail to solve the excessive agrochemical inputs and even leads to a further increase in the intensity of agrochemical inputs. Moreover, the mechanism for an increase in agrochemical input intensity due to outsourcing was explored, and it is more likely to be caused by inhibiting farmers' investment in soil improvement measures. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The South-to-North Water Diversion Project (SNWDP) is believed to drive the next phase of sustainable productivity growth, meeting growing water demand, so as to address increasing environmental sustainability challenges. The Middle Route of SNWDP is regarded as an extremely large long-distance inter-basin water diversion project, which has benefited Henan, Hebei, Tianjin and Beijing since 2014 with great sustainable changes to the cities, groundwater, ecological environment, industrial structure and social development of the beneficiary areas. Taking the number of industrial enterprises above designated size (IEDS) in the water-receiving areas as the research object, this paper takes the year of policy implementation 2014 as the basic time point, evaluating the change of the number of IEDS in the beneficiary areas of the Middle Route of SNWDP through difference-in-difference model. The results show that: (1) The Middle Route of SNWDP promotes the additional growth of the number of IEDS in the beneficiary areas. (2) When the Middle Route of SNWDP promotes the growth of the number of IEDS in beneficiary areas, there is no regional difference for regions with different development levels. (3) The reasons why the Middle Route of SNWDP contributes to the additional growth of the number of IEDS are composed of promoting mixed ownership reform of enterprises in beneficiary areas, increasing water supply and increasing population. However, the Middle Route of SNWDP has not had a significant impact on the traditional total factor productivity or the components of production factors, technology and capital. From the final outcome, the South-to-North Water Diversion Project has played a facilitating role in the sustainable development of large-scale enterprises in the water-receiving areas. [ABSTRACT FROM AUTHOR]

This paper explores the intersection of spirituality and martial arts through an in-depth examination of Shaolin Kung Fu as practiced within the Shaolin Monastery in Henan Province, China. Conducting fieldwork from April 2018 to August 2020, the study employs theories of practice and embodiment to understand how religious teachings are interwoven into physical movements and martial practices. This phenomenological analysis of bodily movement integrates the physical body with the sense of self, highlighting the relationship between individual habitus and the agency of the moving body, referred to as the "lived body." It argues that Shaolin martial artists engage in a form of dynamic embodiment, where Chan Buddhist doctrines are embedded within the "lived body," influencing both movement and perception. This process allows practitioners to interpret and interact with their socio-cultural environment through a religious lens, highlighting the deep connection between spiritual beliefs and bodily actions. The paper presents a comprehensive approach, combining intensive participant observation, historical analysis, and a close look at both the communal and individual aspects of monastic life and learning. By examining narratives, daily routines, and pedagogical methods, the study reveals how the socio-cultural setting of the monastery fosters collective habitus and transforms the individual habitus of practitioners. It shows how this transformation contributes to the development of a spiritual dimension in their martial performance. By highlighting the link between the habitus, the lived body, and the dynamic embodiment, the paper extends the current discourse on embodiment, offering a novel perspective on the relationship between spirituality, culture, and physical practice in the context of Shaolin Kung Fu. [ABSTRACT FROM AUTHOR]

This paper is based on the reconstruction project of south section of National Highway No.310 (Xingyang Qiaolou-Cuimiao section) in Zhengzhou of National Highway No.234, which involves the construction of an overpass for National Highway No.234 to cross over Zhengzhou- Xi'an High-speed Railway. The main focus of this paper is to analyze the key and difficult points in the design of the foreign object intrusion monitoring system for the project and propose solutions. This project is the first foreign object intrusion monitoring system design for the operating high-speed railway overpass within the jurisdiction of Zhengzhou Bureau, providing reference and experience for the design and construction of similar projects in Henan and even throughout the whole country of China in the future. [ABSTRACT FROM AUTHOR]

New energy vehicles (NEVs) are the main direction for the development of the global automobile industry. Evaluating and analyzing the competitiveness of new energy vehicle enterprises (NEVEs) is of great significance for promoting their development. In order to explore the current situation of NEVEs in Henan Province, this paper firstly constructs a competitiveness evaluation index system for NEVEs, comprising both quantitative and qualitative indexes. Then, a new definition of consistency, the consistency measure level, and corresponding improvement methods for interval multiplicative preference relations (IMPRs) are proposed. On this basis, fuzzy group decision-making models with IMPRs are constructed to deal with the ambiguity and uncertainty of the decision information, where consistency and consensus are both considered. In our case study, decision results are derived using Lingo 11.0 software. The results of this paper show that the degree of specialization has the greatest impact on the competitiveness of NEVEs, and some NEVEs are deficient in this regard. Related suggestions based on expert evaluation results are also provided. In addition, a comparison with other consistency improvement methods of IMPRs reveals that the methods proposed utilize the original information provided to decision-makers to the utmost degree. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study focuses on the carbon emissions of key industries in Henan Province, employing techniques of seasonal adjustment, frequency transformation, and statistical modeling to construct an industry-level "Electricity–Energy–Carbon" model to aid in the high-frequency monitoring of carbon emissions in the province's industries. Based on relevant data, this research performs high-frequency calculations of carbon emissions from energy consumption in 34 typical industries and from the production processes of 53 typical sub-categories in the industrial sector of Henan. The findings reveal the following: Firstly, industrial energy consumption in Henan accounts for over half of the total provincial energy consumption, with most months seeing proportions around 60%. Industries such as energy, non-ferrous metals, building materials, steel, chemicals, petrochemicals, and paper making contribute to over 80% of the industrial energy consumption's carbon emissions, often nearing 90% in most months. Secondly, among the major industries, such as non-ferrous metals, chemicals, building materials, and steel, there is a dual challenge of being restricted under the "high energy consumption and high emissions" project while also being required to build key industrial bases, leading to fluctuating trends in historical annual carbon emissions data. Thirdly, six sub-categories, namely plastic products, cement, flat glass, steel, ten types of non-ferrous metals, and alumina, have significant carbon emissions in their production processes, accounting for about 72.3% of the total production-related emissions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Civil Engineering & Architecture is the property of Journal of Beijing University of Civil Engineering & Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With rapid urbanization, environmental problems such as soil erosion and resource shortages have emerged. Ecological environmental quality is decreasing, and ecological security issues are becoming increasingly prominent; thus, relevant research is particularly urgent. The ecological security issue is complex due to many influencing factors. The transformation of landscape type is the most important factor affecting ecological security. Therefore, there is an urgent need to optimize and screen for the indicator factors that affect ecological security, carry out a dynamic evaluation of ecological security based on landscape pattern analysis, and analyze the driving forces behind ecological security changes. Song County is located in the ecological core area of the Funiu Mountains in western Henan, with complex topography and geomorphology; large changes in landscape patterns in recent years; frequent geological disasters, which have posed a greater threat to people's life and property safety; and significant ecological security problems. This paper takes Song County as the research area, using the decision tree model to obtain the land use classification results of four periods in Song County in 2005, 2010, 2015, and 2020 based on remote sensing images. Landscape pattern analysis is conducted from two aspects: patch level and landscape level. On this basis, ecological security evaluation indicators are constructed from three levels: pressure, state, and response, and the comprehensive index model is used to obtain the results of four ecological security evaluations. Exploratory spatial data analysis (ESDA) is used to conduct research and prediction on spatiotemporal differentiation. Finally, the spatial heterogeneity relationship between the ecological security level and its driving factors in Song County is quantitatively analyzed using a geographic detector model. The results clearly show that the overall landscape form gradually tends to develop in the direction of complex irregularity. Due to frequent geological disasters and strong human engineering activities near the core areas of the Luhun Reservoir and Yi River basin, as well as Baihejie Village in Baihe Township and Che Village in Muzhijie Township, the landscape pattern is changing considerably. The self-restoration ability of the land's ecosystem is gradually weakening, and the degree of ecological damage is gradually accelerating. The ecological security level is unsafe, the area of unsafe security is gradually increasing, and the ecological security index (ESI) will continue to decrease in the future. To improve ecological security, we recommend paying attention to land conservation and rational utilization while pursuing economic development. [ABSTRACT FROM AUTHOR]

The prefabricated recyclable supporting structure (PRSS) is an innovative support system that integrates a steel skeleton with polymer waterproof technology. Earth berms are extensively adopted to support the PRSS, but there is limited understanding on the factors influencing their behavior in circular excavations. In this paper, a numerical model is first validated with a case history in Henan, China. Afterwards, the geometric parameters of the earth berms, including the height (H), the top width (B1), and the bottom width (B2), on the behavior of the PRSS, are investigated. It is shown that, by increasing the height, top width and bottom width of earth berms, the lateral deflections, and bending moments of supporting piles, as well as the ground surface settlements, tend to decrease. However, the reduction effect of these parameters diminishes as well. Moreover, the raised effective formation level considering the effect of the earth berms on stability and deformation analyses is discussed. The factor of the safety of the excavation is almost doubled when axisymmetric conditions are considered compared to plane strain conditions. In deformation analysis, the raised effective formation level increases with the height of the earth berms until a steady value is reached. [ABSTRACT FROM AUTHOR]

[Objective] Soil organic carbon (SOC) is not only an indicator of soil health but also plays a crucial role in feedback interaction between global warming and carbon stock in terrestrial ecosystems. Its dynamics is modulated by a multitude of interactive abiotic and biotic factors. In this paper, we analyze the spatial distribution of SOC in the plough layer and its correlation with soil texture and other geochemical properties in the region of Zhengzhou in Henan province. [Method] Soil samples were taken from a grid network in the region. SOC, bulk density, soil texture and other geochemical properties in each soil sample were measured. The correlation between SOC and soil nutrients, including nitrogen, phosphorus, potassium and micronutrients in the soil, was analyzed using the GeoChemStudio software. [Result] The calcareous skeletol soil in the region has the highest SOC, reaching 2.307%. The SOC content varied from 0.1%-11.6% across the region, and was normally distributed with a mean of 1.134%. The SOC was positively correlated with N, P2O5, Fe, Mn, Zn, Cu, Mo and B, and their associated correlation coefficients were 0.804 5, 0.223 0, 0.156 3, 0.119 1, 0.223 8, 0.263 4, 0.162 8 and 0.191 1, respectively. Negative correlations were found between SOC and K2O and pH, and their associated correlation coefficients were -0.088 8 and -0.195 7, respectively. The average SOC content in the plough layer in the region was 1.09%. Spatially, SOC was rich in Xinmi, Dengfeng and Gongyi in the southwest of the region. In terms of soil texture, SOC is higher in skeletol soil and calcareous cinnamon soil than in other soils. SOC stored in the topsoil in the whole region was 1 012.87x104 t, with 523.13x104 t stored in the cinnamon soils and calcareous cinnamon soils, accounting for 51.64% of the total topsoil SOC in the region. [ABSTRACT FROM AUTHOR]

This paper refers to a survey conducted in Zhengzhou section of the Yellow River for the purpose of investigating heavy metal pollution characteristics and of exploring the pollution sources. Monitoring was thereby carried on August 29-31, 2022 along the riparian zone collecting samples of the river sediments and analyzing the concentrations of nine kinds of heavy metal, including Cd, Pb, Cu, Zn, As, Hg, Cr, Ni and Mn. Then the Tessier five-step sequential extraction method was used for the morphological analysis of the heavy metals; the ground accumulation index (Igeo) was used to evaluate heavy-metal pollution degree, with Nemero pollution index being applied to the comprehensive assessment of the pollution situation; and the principal component analysis (PCA) and absolute factor analysis-multiple linear regression analysis model (APCS-MLR) were used to analyze the source of the pollutants. Consequently, the survey showed that both of the mean concentrations of Pb and Cd in the sediments of the riverine zone of Zhengzhou section of the Yellow River exceeded the soil background value of heavy-metal in Henan Province. Results of the speciation analysis showed that the environmental activities of Mn, Cd, Pb and Hg were relatively high; the evaluation of geo-accumulation index (Igeo) indicated that Cd had resulted in slight to moderate pollution in the sediments of the River; and the evaluation results of the Nemero pollution index showed that all the above-mentioned heavy-metal elements were on the safety level. Result of the source apportionment suggested that tannery industry, agricultural activities and automobile parts production, as well as the natural source were the major heavy-metal pollution sources in the riparian zone of Zhengzhou section of the Yellow River. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is the release of plant energy during photosynthesis, which is significantly superior to the vegetation index in the characterization of vegetation growth. However, the existing satellite retrieved SIF data have the problems of low spatial resolution and spatial discontinuity. To solve these problems, this paper proposes multiple parameters downscaling method that considers the structural and physiological characteristics of SIF. Multiple linear regression (MLR), random forest (RF), and convolutional neural network (CNN) models were used to construct a downscaling model for the TROPOspheric Monitoring Instrument (TROPOMI) Enhanced SIF (eSIF) data. The theory of spatial scale invariance was applied to invert the 500 m spatial resolution SIF data products for Henan Province from 2012 to 2021 using Moderate-resolution Imaging Spectroradiometer (MODIS) data. The evaluation metrics for assessing downscaling accuracy include the determination coefficient (R²), mean absolute error (MAE), and root mean squared error (RMSE). The experimental results demonstrate that the RF model outperforms others, achieving R², MAE, and RMSE values of 0.935, 0.041mW/m²/nm²/sr, and 0.061mW/m²/nm²/sr, respectively. These results successfully meet the downscaling requirements. The downscaling data products have better fitting effect with eSIF and new Global 'OCO-20 SIF (GOSIF) data both in time and space. The correlation between downscaling SIF data and winter wheat yield is significantly better than that of GOSIF data products and shows strong correlation with Gross Primary Productivity (GPP). By considering the structural and physiological characteristics of SIF, the RF algorithm can effectively retrieve reliable 500 m spatial resolution SIF data, this provides methodological support for the application of SIF data at higher spatial scales. [ABSTRACT FROM AUTHOR]

Safety, progress, and investment risks are correlated during the construction period of large-scale water transfer projects. However, previous studies have only considered individual risk factors, overlooking the potential systemic risk posed to safety, progress, and investment, as well as any underlying common cause failures. Since traditional risk analysis methods are ill-suited to addressing common cause failure, this paper's objective was to establish a comprehensive evaluation index framework and to identify the basic events of common cause failure. To do that, we developed a risk analysis method that models common cause failure based on a Bayesian network for assessing that systemic risk. The Henan Section of the Yangtze-to-Huaihe River Water Diversion Project in China was then used as a case study. The results show that a variety of common cause failure events, such as epidemic disease, design alteration, lagged approval process, heavy rain in the flood season, renewal material and failing equipment, construction accidents, and external interference, can significantly impact the safety, progress, and investment systemic risk. Design alteration poses the greatest risk, with renewal material and failing equipment exerting the strongest influence among all common cause failure events. It is also possible to elucidate the predominant causal chains; specifically, the contributing influence of each basic failure event to the systemic risk can be clarified by adjusting their respective initial state. The failure of renewal material and failing equipment was found to significantly increase the safety risk. This study effectively simulated the complex causal relationships and uncertainties of pertinent risk factors, thereby enhancing our understanding of the systemic risk associated with safety, progress, and investment in large-scale water transfer projects. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) assessment methods relying on physical and empirical models are considered to be the most commonly used method at present, but their estimation efficiency and accuracy are deficient. Although the hybrid model of these two methods can address these issues, a poor coupling mechanism can easily occur. Given that, a PROSAIL model coupling particle swarm optimization (PSO) neural network (NN) algorithm (PSO-NN-PROSAIL model) was introduced to invert the winter wheat LAI (WWLAI) at five distinct growth stages. The Xiangfu District in the east of Kaifeng City, Henan Province was served as the study region. Based on the measured WWLAI data at varying stages and GF-1 WFV satellite images, the initial analysis focused on assessing the PROSAIL model's sensitivity in simulating vegetation canopy reflectance. It then calculated six vegetation index models according to the wavelength reflectance of GF-1 WFV and analysed their correlation with LAI to select the input parameters that could be used in the model. Normalized differential vegetation Index (NDVI) and ratio vegetation Index (RVI) as well as vegetation canopy reflectance were employed as input parameters to invert the WWLAI by adopting the PSO-NN-PROSAIL model. The experimental results showed the following: (1) In the vegetation index model, the determination coefficient (R2) of NDVI and RVI was greater than 0.68, implying that NDVI and RVI might serve as input factors for the proposed model in this paper; (2) LAI and chlorophyll a + b content (Cab) were most sensitive to the PROSAIL model in near-infrared and visible light bands; and (3) the PSO-NN-PROSAIL model possessed better LAI inversion accuracy. In summary, the model proposed in this paper provided a technical reference for rapid and accurate remote sensing monitoring of WWLAI. The optimization technology of particle swarm optimization algorithm is integrated into the neural network, and the weight of the neural network is adjusted by its fitness function transformation and inertia weight. By integrating prior knowledge into the inversion of crop leaf area index constructed by machine learning and radiative transfer model, the ill-posed problem of physical model inversion has been improved. [ABSTRACT FROM AUTHOR]

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions. [ABSTRACT FROM AUTHOR]

While previous studies have primarily focused on the negative effects of stereotype threat on performance, there has been limited attention given to its positive effects—stereotype reactance. This paper aims to address this gap by exploring stereotype reactance and its salience among employees with disabilities. Drawing on stereotype threat literature and the Mood Behavior Model (MBM), we build a three-way interaction model of stereotype threat, perspective-taking, and performance self-esteem on job performance. We collected a sample of 193 employees with disabilities from two manufacturing companies located in Henan and Hebei Province in China and conducted multilevel regression analyses to test our hypotheses. We find that stereotype threat—commonly perceived as a negative influence—actually correlates positively with job performance. This positive association is particularly evident among employees who demonstrate low perspective-taking abilities but have a high sense of performance self-esteem. The current findings shed light on stereotype reactance literature and provide suggestions for promoting inclusivity in the workplace. [ABSTRACT FROM AUTHOR]

Geological hazards in Xinxian County, Xinyang City, Henan Province, are characterized by their small scale, wide distribution, and significant influence from regional tectonics. This study focuses on collapses and landslide hazards within the area, selecting twelve evaluation factors: aspect, slope shape, normalized difference vegetation index (NDVI), topographic relief, distance from geological structure, slope, distance from roads, land use cover type, area of land change (2012–2022), average annual rainfall (2012–2022), and river network density. Utilizing data from historical disaster sites across the region, the information quantity method and hierarchical analysis method are employed to ascertain the information quantity and weight of each factor. Subsequently, a random forest model is applied to perform susceptibility zoning of geological hazards in Xinxian County and to examine the characteristics of these geological disasters. The results show that in the study area, the primary factors influencing the development of geohazards are the distance from roads, rock groups, and distance from geological structure areas. A comparison of the susceptibility results obtained through two methods, the analytic hierarchy process information quantity method and the random forests model, reveals that the former exhibits a higher accuracy. This model categorizes the geohazard susceptibility in the study area into four levels: low, medium, high, and very high. Notably, the areas of very high and high susceptibility together cover 559.17 km2, constituting 35.99% of the study area's total area, and encompass 57 disaster sites, which represent 72.15% of all disaster sites. Geological hazards in Xinxian County frequently manifest on steep canyon inclines, along the curved and concave banks of mountain rivers, within watershed regions, on gully inclines, atop steep cliffs, and on artificially created slopes, among other sites. Areas with very high and high vulnerability to these hazards are mainly concentrated near the county's geological formations. The gneiss formations are widely exposed in Xinxian County, and the gneisses' strength is significantly changed under weathering, which makes the properties of the different degrees of weathering of the rock and soil bodies play a decisive role in the stability of the slopes. This paper provides a basis for evaluating and preventing geologic hazards in the Dabie mountainous area of the South Henan Province, and the spatial planning of the national territory. [ABSTRACT FROM AUTHOR]

Actively encouraging farmers to adopt green control techniques (GCT) is an important measure for achieving sustainable agricultural development and ensuring global food security. Based on microsurvey data covering 912 households in Henan and Shandong Provinces, this paper uses a binary probit model to explore the effect of off-farm employment on the adoption behavior of farmers' GCT and the mechanism of the effect. The study finds that off-farm employment has a capacity accumulation (CAP) effect and a crowding-out (CROWD) effect on farmers' GCT adoption behavior. Additionally, the CROWD effect generally exceeds the CAP effect, making off-farm employment unfavorable for farmers' adoption of GCT. Further analysis finds that off-farm employment has a positive effect on the adoption of GCT by farmers based on agricultural income, and as the distance from off-farm employment increases, the positive effect is greater. In contrast, off-farm employment has a negative effect on rural households whose main income is nonagricultural income, and with the increase in the distance from off-farm employment, its negative effect gradually increases. On this basis, to promote green prevention and control techniques for diseases and insect pests, we should focus on farmers whose main income is agricultural, enhance their CAP effect, and guide farmers whose main income is nonagricultural to transfer out of their land. While enriching research on GCT, this paper also provides an empirical basis and policy reference for how to better promote GCT during the period of rapid development of off-farm employment. [ABSTRACT FROM AUTHOR]

Based on urban flood hydrology processes and hydrodynamic principles, the stormwater management model (SWMM) was improved upon. The coupling and implementation methods of the SWMM and two-dimensional hydrodynamic model are proposed. The improved SWMM was coupled with the hydrodynamic model both from the vertical and horizontal directions. The hydrology and hydrodynamic coupling model (HHDCM) was constructed and verified by using extreme rainstorm data. Taking July.20 extreme rainstorms (from July 17 to July 20, 2021, i.e., July.20 extreme rainstorm) in Zhengzhou city, Henan Province, China, as an example and using the HHDCM model, the flood disaster caused by July.20 extreme rainstorm was simulated. Based on the simulation results, an inundation distribution map was drawn for the urban area. A comparison between the simulated and measured results reveals that the maximum relative error in the simulated results is 12.5%. Therefore, the HHDCM model proposed in this paper has desirable accuracy and reliability for simulating extreme urban rainstorms and flood disasters. [ABSTRACT FROM AUTHOR]

The water supply of water resources allocation under extreme drought is insufficient, and the limited available water resources make it urgent to tap the potential of water supply. In this paper, the Yellow River water supply area in Henan Province is taken as an example to study the multi-water source allocation under extreme drought. According to the Palmer Drought Severity Index (PDSI), the extreme drought years are selected, and the water supply and demand balance in the extreme drought years is analyzed, and the water shortage degree of each water supply area is obtained. In this paper, unconventional water, flood resource utilization and elastic exploitation of groundwater are used as potential water sources. Different water supply scenarios are set up according to different potential tapping measures, and multi-scenario supply increase under extreme drought is explored. A multi-water source allocation model with the goal of minimizing water shortage is constructed, and a multi-scenario supply increase allocation scheme is proposed, which provides a basis for the study of water supply increase allocation to alleviate the drought degree of the the Yellow River Water Supply Area in Henan. Through the Multi-scenario potential tapping of multiple water sources, the existing potential water volume can be maximized, which is conducive to reducing the water supply pressure and water use restrictions of conventional water sources, improving the support capacity and guarantee capacity of water resources, and reducing the economic and social development bottlenecks caused by extreme drought. [ABSTRACT FROM AUTHOR]

Extreme precipitation usually cause grievous losses&casualties, which varies greatly under different scenarios. This paper took Henan province as an example, it innovatively constructed three different extreme precipitation scenarios and built indicators system of social vulnerability from exposure, sensitivity and resilience based on MOVE framework. Social Vulnerability Indexs(SoVI) were then calculated by mathematical models under three different reoccurrence intervals. The results show that SoVI was low in the west and high in the north. High SoVI areas expanded to the middle and south as recurrence intervals increased. SoVI in each area of Henan province increased along with the recurrence intervals at different growth rates. The larger the recurrence interval was, the faster the SoVI increased. The results indicate SoVI is greatly affected by disaster levels, which need to be incorporated into social vulnerability. This study provides not only a new thought for social vulnerability assessment, but also a reference for the policymakers to formulate related risk management policies. [ABSTRACT FROM AUTHOR]

In order to overcome the limitations of traditional clean energy utilization methods, this paper proposed an innovative technical solution for a combined heating system that cleverly integrated solar, wind, and geothermal energy to achieve complementarity and synergized among them, thereby ensuring stable and efficient energy utilization. First, a comprehensive mathematical model was developed for the entire heating system, encompassing solar thermal subsystem, geothermal subsystem, wind power generation subsystem, and a second-stage reheating subsystem. Subsequently, Ebsilon simulation software was utilized to cleverly couple these subsystems together, with corresponding boundary conditions set to ensure the overall efficiency and stability of the system. Based on meteorological data and geothermal resource parameters from a typical heating season in Zhengzhou, Henan Province, China, this paper thoroughly analyzed the variations in key performance indicators such as the photothermal conversion efficiency of solar thermal subsystem and the heating capacity of geothermal subsystem. This provided valuable insight for optimizing the design of heating system. The results indicated that during the daylight hours of the heating season, both the photothermal conversion efficiency and the heat supply from the solar thermal subsystem exhibited an increasing trend as solar radiation increased. Among them, the photothermal conversion efficiency peaked at 76.013%, while the maximum heat supply output reached 40.311 kW. When solar direct radiation was relatively weak, the system primarily relied on the heat release process of the thermal storage tank to maintain heating, with a minimum heat supply of 27.268 kW. During nighttime hours of the heating season, the geothermal subsystem dominated the heating process, with a maximum heat supply of 125.556 kW. Additionally, for every 5 °C increased in geothermal water temperature, the heat supply from the geothermal subsystem increased by an average of 6.553 kW, demonstrating excellent heating response performance. Therefore, the integrated clean heating system that combines solar, geothermal, and wind energy not only significantly improves the utilization efficiency of clean energy but also enhances the heating stability of the integrated clean energy coupling system to a certain extent. The clean heating technical solution proposed in the paper had a theoretical total heating capacity of 19 680 kW during the heating season. When converted, this equates to a substitution of 6.9 tons of standard coal, resulting in a reduction of carbon dioxide emissions by 17.94 tons. This demonstrates the considerable cleanliness and environmental benefits of the proposed heating system. This study provides a valuable reference for the engineering application of renewable energy in the field of clean heating. [ABSTRACT FROM AUTHOR]

In response to a series of problems brought about by rapid economic development, such as global warming and the continuous deterioration of the ecological environment, China has taken the initiative to shoulder the responsibility of a major country and continued to contribute Chinese wisdom and Chinese solutions to the goal of "carbon peak and carbon neutrality" at an early date. In this paper, Henan Province has been selected as the study area, and the changes in land use and carbon storage in Henan Province from 2000 to 2020 have been analyzed spatially and temporally. The PLUS model is used to predict future land use changes under different scenarios, and the InVEST model is used to estimate carbon storage under the corresponding scenarios. The results showed that (1) During 2000–2020, the farmland in Henan Province has been in a decreasing trend, grassland and construction land showed a decreasing trend and then an increasing trend, and woodland showed a decreasing trend. From 2000 to 2020, Henan's overall carbon storage showed a downward trend each year, with storage mainly in the western and southern regions of the province, with a spatial distribution of high storage in the west and low storage in the east. (2) Under the normal development scenario (SSP2-RCP4.5) from 2030 to 2050, the area of farmland and woodland basically showed a continuous downward trend, while construction land showed an upward trend annually, and farmland and construction land showed an increasing trend under the normal development scenario (SSP2-RCP4.5) and economic priority scenario (SSP5-RCP8.5). The decreasing trend of carbon storage was the smallest under the normal development scenario (SSP2-RCP4.5) and the ecological protection scenario (SSP1-RCP2.6). The results provide a basis for decision-making regarding low-carbon and circular developments and rational and optimal land use in Henan. [ABSTRACT FROM AUTHOR]

Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, and the Geodetector model based on Landsat satellite images and an open-surface water body dataset from 1990 to 2020. The results revealed that (1) from 1990 to 2020, SMWs in the Yellow River Basin exhibited an overall pattern of fluctuation reduction. The total area decreased by approximately 1.12 × 105 hm2, with the predominant decline occurring in the 0–1 hm2 and 1–3 hm2 size categories. In terms of spatial distribution, SMWs in Qinghai and Gansu decreased significantly, while the SMWs in Inner Mongolia, Henan, and Shandong gradually increased. (2) From 1990 to 2020, SMWs were mostly converted into grassland and cropland, with some transformed into impervious water surface and barren, and only a small percentage converted into other land types in the Yellow River basin. (3) The alterations in SMWs were influenced by factors, with their interplay exhibiting nonlinear or bilinear enhancement. Among these factors, annual precipitation, elevation, and potential evapotranspiration were the primary natural factors influencing the changes in the distribution of SMWs. On the other hand, land use cover type, gross domestic product (GDP), and road distance were the main anthropogenic factors. [ABSTRACT FROM AUTHOR]

Introduction: Obtaining wheat growth information accurately and efficiently is the key to estimating yields and guiding agricultural development. Methods: This paper takes the precision agriculture demonstration area of Jiaozuo Academy of Agriculture and Forestry in Henan Province as the research area to obtain data on wheat biomass, nitrogen content, chlorophyll content, and leaf area index. By using the coefficient of variation method, a Comprehensive Growth Monitoring Indicator (CGMI) was constructed to perform fractional derivative processing on drone spectral data, and correlation analysis was performed on the fractional derivative spectra with a single indicator and CGMI, respectively. Then, grey correlation analysis was carried out on differential spectral bands with high correlation, the grey correlation coefficients between differential spectral bands were calculated, and spectral bands with high correlation were screened and taken as input variables for the model. Next, ridge regression, random forest, and XGboost models were used to establish a wheat CGMI inversion model, and the coefficient of determination (R2 ) and root mean squared error (RMSE) were adopted for accuracy evaluation to optimize the wheat optimal growth inversion model. Results and discussion: The results of the study show that: using the data of wheat biomass, nitrogen content, chlorophyll content and leaf area index to construct the comprehensive growth monitoring indicators, the correlation between the wheat growth monitoring indicators and the spectra was calculated, and the results showed that the correlation between the comprehensive growth monitoring indicators and the single indicator correlation had different degrees of increase, and the growth rate could reach 82.22%. The correlation coefficient between the comprehensive growth monitoring indexes and the differential spectra reached 0.92 at the flowering stage, and compared with the correlation coefficient with the original spectra at the same period, the correlation coefficients increased to different degrees, which indicated that the differential processing of spectral data could effectively enhance the spectral correlation. The three models of Random Forest, Ridge Regression and XGBoost were used to construct the wheat growth inversion model with the best effect at the flowering stage, and the XGBoost model had the highest inversion accuracy when comparing in the same period, with the training and test sets reaching 0.904 and 0.870, and the RMSEs were 0.050 and 0.079, so that the XGBoost model can be used as an effective method of monitoring the growth of wheat. To sum up, this study demonstrates that the combination of constructing comprehensive growth monitoring indicators and differential processing spectra can effectively improve the accuracy of wheat growth monitoring, bringing new methods for precision agriculture management. [ABSTRACT FROM AUTHOR]

Ammonia emission has attracted considerable attention concerning environmental protection in the world. About 33% of the atmospheric ammonia emission comes from farmland fertilization, so the monitoring of ammonia emissions from farmland is of great significance for controlling atmospheric ammonia pollution. In recent years, tuneable diode laser absorption spectroscopy (TDLAS) has been applied to the measurement of farmland ammonia emission; however, the spectral signal of the open optical path is susceptible to distortion due to temperature, noise, turbulence, etc. Meanwhile, the inversion of the distorted spectrum will result in abnormal concentration values that reduce the detection accuracy. This paper proposes a TDLAS spectral quality evaluation method based on dynamic time warping (DTW) algorithm and support vector data description (SVDD) algorithm. DTW algorithm was used to analyse the similarity between the measured spectrum and the reference spectrum, then SVDD algorithm was used to generate a classification model according to the results of DTW algorithm. DTWSVDD algorithm could effectively avoid obtaining wrong inversion concentration and improve the accuracy of concentration monitoring with identification accuracy of spectral quality reached more than 98%. Applied to the measurement of farmland ammonia emission in Fengqiu, Henan Province, China at the Agricultural Ecological Experiment Station of Chinese Academy of Sciences in November 2021, this method could effectively reduce the dispersion degree of measured concentration trend, as a result, which was better than that of the reported correlation coefficient method. DTW-SVDD algorithm should be of further research value for the application of the analysis of the Big Data of atmospheric composition detection based on grand-based optical remote sensing system. [ABSTRACT FROM AUTHOR]

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The analysis of the influence of human activities and climate change on groundwater is an important basis for formulating groundwater management policies. However, the relationship between climate change, human activities and groundwater system is complex, and the research on the response of groundwater to changing environment is in the initial stage. In this paper, the interactions between groundwater water cycle and climate change and human activities are analyzed, based on climate change data and hydrogeological information from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The MODFLOW model was used to develop a numerical model of shallow groundwater movement in Zhongmu County, Henan province, and to predict the response of groundwater levels to climate change and human activities in three cases from 2016 to 2050. The results show that under the current scenario, the groundwater level will decrease at an average annual rate of 4.24 cm/A from 2016 to 2050. Under the climate change scenario, the precipitation increased by an average of 5.01%, the annual evaporation increased by an average of 17.84% and the annual temperature increased by an average of 1.29 °C from 2016 to 2050 under the three emission cases of RCP2.6, RCP4.5 and RCP8.5, under the climate change–autonomous human activities scenario, when water conservation and South–North Water Transfer Project water supply are implemented simultaneously, the water table will decrease by an average of 5.58 CMA per year under the direct impact scenario and by an average of 4.44 CMA per year under the indirect impact scenario, the water table dropped by 3.21 cm/A. The changing environment will have an important effect on groundwater circulation, and appropriate measures must be taken to deal with the continuous decline of groundwater level. [ABSTRACT FROM AUTHOR]

As the supporting region for ecological barrier in the Yellow River Basin, it is of profound significance for Henan Province to explore the relationship between land use and ecosystem service value (ESV) for promoting ecological protection and high-quality development in the basin. In this paper, the ESV equivalent table proposed by Xie Gaodi et al was revised and the ecological value of construction land was also incorporated into the table. Combined with the FLUS model, the ESV of Henan from 2000 to 2050 was calculated. The historical evolution characteristics of ESV and the impact of different land use scenarios on ESV were analyzed. The results are shown as follows. (1) The ESV of Henan showed a trend of rising, falling and rising again from 2000 to 2020. The spatial distribution pattern was high in the West and low in the East. The distribution range of low value areas and sub-high value areas was enlarged. (2) The ESV in each city of Henan showed an upward trend from 2000 to 2010, and first decreased and then increased from 2010 to 2020. The ESV and its variation in Nanyang, Xinyang and Luoyang were higher, contributing to about 50% ESV in Henan. (3) The ESV ratios of cultivated land, forest land and water area were relatively high. During the study period, the growth of ESV mainly benefited from the expansion of water area and forest land, while the reduction of cultivated land and the disorderly expansion of construction land were the key factors leading to the decline of ESV. (4) From 2025 to 2050, the ESV in Henan will be the highest under the cultivated land protection scenario and the lowest under the natural scenario. It is very important to protect farmland, forest, grass, water area and reduce the disorderly expansion of construction land for maintaining and improving ecosystem service value in the next 30 years. [ABSTRACT FROM AUTHOR]

Introduction: The impact of farm machinery outsourcing services (FMOS) on grain production has been extensively studied in the literature. However, whether FMOS would adjust cropping structures and ensure food security is little known. To address this research gap, we analyzed the impact of FMOS on the cropping structure adjustment and grain production of farmers. Methods: We estimate the impact of FMOS on grain production by using data collected from a survey of 3,863 wheat farmers in China. We employ the Extended Regression Model (ERM) to address selectivity bias. Results and discussion: The empirical results show that FMOS can adjust the cropping structure and increase the proportion of household food cultivation. Among all segments of FMOS, fertilizer application services drive the increase in grain acreage more significantly. We also find that large-scale households prefer to self-purchase farm machinery rather than FMOS, leading to an insignificant impact of FMOS on cropping structure. Therefore, in the process of promoting the development of FMOS, we should focus on the coordinated development of various types of FMOS, strengthen the popularization and application of fertilization services, and formulate a differentiated subsidy policy for heterogeneous farmers in terms of scale to better play the role of agricultural machinery in promoting grain production. [ABSTRACT FROM AUTHOR]

As China's economy progresses towards high-quality development, the construction of science and technology think tanks, with talent construction as the key, has become an important supporting force and occupies a unique position in the national innovation system. In recent years, with the rapid expansion of the number of think tank talents, how to evaluate and manage think tank talents has become a key factor in the development of think tank influence and competitiveness. Based on the experience of talent team construction at home and abroad, Taking the typical science and technology innovation think tanks of Henan Province as an example, this paper analyzes the current situation and status quo of science and technology innovation think tank talents, and finds problems in the talents, Summarize the factors that affect the talent construction and development of science and technology innovation think tanks, From the perspective of six dimensions of competency model, four first-level indicators of talent quantity, talent quality, talent ability and talent contribution of science and technology innovation think tank and 16 subordinate second-level indicators are constructed for talent evaluation of think tank. Ahp is adopted to set questionnaires and collect data with Likert5-level scale, and yaahp software is used to verify the weight verification evaluation effect. Invite experts in the field to score the evaluation indicators. The research results show that the talent evaluation index of science and technology innovation think tank based on competency model pays more attention to the standardization, integration and systematization of the talent evaluation of the think tank, and the system is scientific and reasonable. It can provide reference and reference for promoting the construction, evaluation and development management of scientific and technological innovation think tanks. [ABSTRACT FROM AUTHOR]

The information released by the public on various social media platforms can assist experts in decision-making, thus improving the reliability of the decision-making results and increasing their likelihood of gaining public support. However, the determination of how to fully integrate public opinions with expert opinions and in what aspects to realize the integration is a research topic worthy of attention. Based on this, this paper proposes a multi-subject weighted information fusion method that fully considers public opinion. First, the Bayesian subject model is used to obtain the attribute set of public opinion, and the sentiment analysis technique is used to calculate the reference attribute weights. Second, public opinion is utilized to improve the traditional best–worst method to determine the subjective attribute weights of experts at a faster rate. Third, expert weights are determined using "knowledge" and attribute weights are defined at the aggregate level. Fourth, the cluster weights are determined according to the proximity of the clustered opinions to the public opinions, and the clustered opinions are then summarized to get the overall decision-making information, thus scientifically realizing the synergistic decision-making between the public and experts. Finally, the case of a rainstorm emergency in Henan Province, China, is used to analyze and determine the best plan that matches with the actual situation, and the results are then compared with the findings of existing research to illustrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• The center of gravity of China's EPC is located in Luoyang City, Henan Province. • During the study period, China's EPC in general exhibits an east-high and west-low trend. • During the period 2000–2019, there are significant regional differences in the strength of the spatial linkage of EPC in China. • In 2000–2019, the decoupling status of EPC in china is of two types of decoupling: weak decoupling and expansive coupling. Exploring the evolution of spatial and temporal patterns of electricity power consumption in China is of great significance and value for the promotion of Carbon Peaking and Carbon Neutrality Strategy and the scientific allocation of electricity resources. Therefore, this paper takes China as an example and innovatively applies nighttime light data from a combination of two remote sensing satellites. And using spatial analysis methods to analyze the evolution of spatial and temporal patterns of electricity power consumption over a longer study time series. Meanwhile, this paper also uses the Tapio decoupling model to study the decoupling effect of electricity power consumption and economic growth in China. The results of the study show that, during the period 2000–2019, China's electricity power consumption was in an unbalanced state, with the eastern coastal region dominating. It also formed a gradient distribution from the coastal area to the inland area. In terms of spatial connection, it shows an unbalanced distribution pattern of "low in the west and high in the east". In addition, the spatial linkage of electricity power consumption among provinces is more than 93 % of weak linkage, and gradually increases to 97 %, indicating that the spatial linkage of electricity power consumption among regions is gradually weakening. The decoupling state of electricity power consumption in China is characterized by two types of decoupling: Weak decoupling and Expansive coupling. The decoupling status has experienced a significant change from 84 % of provinces being Expansive coupling to 100 % of provinces being Weak decoupling. [ABSTRACT FROM AUTHOR]

Due to the co-combustion of hydrogen-rich atmosphere and coal in power plants has a great influence on NO X emission, it has been studied by more and more researchers. Therefore, the study of the effect of hydrogen-rich atmosphere on coal pyrolysis mechanism provides a theoretical basis for subsequent application in power plants. In this paper, reaction molecular dynamics simulation is used to investigate the pyrolysis mechanism of Henan anthracite coal within the temperature range of 300–3600 K. The consistency between the activation energy, TG curve, and gas release characteristics derived from the ReaxFF simulation and the experimental results provides evidence for the validity of this method in investigating the pyrolysis mechanism of coal. Furthermore, the pyrolysis behavior of Henan anthracite coal in N 2 , H 2 , and NH 3 atmospheres with varying temperatures are studied. The pyrolysis of Henan anthracite coal can be categorized into three distinct stages under varying atmospheric conditions, as revealed by the simulation findings. Compared with N 2 pyrolysis atmosphere, H 2 and NH 3 pyrolysis atmospheres can reduce the release of CO, reduce the activation energy, promote the degree of pyrolysis and accelerate the pyrolysis process. NH 3 atmosphere has more obvious influence on the coal pyrolysis process. Also, HCN (NO X precursor) generation reactions during pyrolysis are revealed in N 2 , H 2 and NH 3 atmosphere. H 2 and NH 3 pyrolysis atmospheres can increase the number of HCN generation reactions and change the distribution of HCN generation reactions. The analysis of HCN formation reaction shows that CN and H 2 CN are the main precursors of HCN. • Henan coal pyrolysis is studied via ReaxFF MD. • The macromolecular model of Henan coal has been verified. • In NH 3 atmosphere, the activation energy of Henan coal pyrolysis is reduced. • The HCN generation reaction in different atmosphere is proposed. [ABSTRACT FROM AUTHOR]

To meet the needs of professional development promotion through cross-university collaborative construction in the era of Internet + , a new model of cross-university teaching and research organization is proposed by constructing a regional virtual teaching and research room for remote teaching and research activities among grassroots teaching organizations of different universities, utilizing modern information technology. Led by the national first-class specialty construction site of Building Electrical and Intelligent Specialty at Zhengzhou University of Light Industry, a cross-university regional virtual teaching and research room for this specialty is established in collaboration with other universities offering the same specialty in Henan province. This paper outlines the efficient and stable operation mechanism for the regional virtual teaching and research room's multi-university collaborative construction and explores the co-construction path for cross-university regional virtual teaching and research rooms. A high-quality resource sharing platform for multi-university collaborative construction is built within the regional virtual teaching and research room, enabling intercollegiate high-quality resource and outstanding teaching team interconnection, co-construction, and sharing. This approach aims to fully leverage the distinctive features and advantages of the Building Electrical and Intelligent Specialty in each university in the region, promote cooperative development and interdisciplinary integration among partnering universities, and improve the quality of talent cultivation. It may also provide valuable reference and guidance for the collaborative construction of virtual teaching and research rooms across a broader range of universities. [ABSTRACT FROM AUTHOR]

Carbon emission reduction is an important part of regional low-carbon economic development. In this paper, gray correlation analysis, neural network model, Gaussian multi-mode fitting and other methods were used to analyze the relationship between total carbon emissions and regional economic development, industrial structure, and energy consumption in Henan Province. On this basis, the future development of carbon emissions is predicted. The calculation results showed that the correlation between the three industries and carbon emissions in Henan Province is more than .7, among which the secondary industry has the highest correlation (.77). In the secondary industry, the correlation coefficient between coal and carbon emissions is the highest .87, while the correlation coefficient between other energy sources is about .5. In the neural network prediction model, the correlation coefficient between the prediction curve and the actual total carbon emission curve is .989, and the prediction results have a good degree of fit. The carbon emission prediction curve was divided into two parts: a linear decline stage from 2018 to 2024, and a rapid decline stage after 2024.The results showed that more efforts should be made in industrial structure, energy consumption structure and environmental protection to achieve low-carbon development in Henan province. [ABSTRACT FROM AUTHOR]

Since the traditional multivariable grey prediction model has insufficient consideration of the coupling effect of background value and time-delay accumulative term, which leads to the low prediction accuracy of the model. Based on this, we propose a new multivariable time-delay grey prediction model with dynamic background value. The model adds dynamic background value coefficient, time-delay parameters, linear correction term, and grey action quantity term to the traditional GM(1,N) model. First, the delay periods of driver factors are determined by using the grey time-delay correlation analysis method. Second, the parameter estimation method of the model is discussed and the direct solution of the TLDBGM(1,N) time response function is given by defining the derived form of the TLDBGM(1,N) model. Finally, the model background value coefficient and time-delay parameters are identified and optimized based on the differential evolutionary algorithm. The model is applied to the problem of grain yield prediction in Henan Province. Result shows that the simulation and prediction accuracy of TLDBGM(1,N) are better than other multivariable grey prediction models. The model is theoretically more generalized. And it is shown that GM(1,1), GM(1,N), and TLGM(1,N) models are all special forms of the model for different parameter values. [ABSTRACT FROM AUTHOR]

To overcome the limitations of long-term prediction of PM2.5 concentration, a multi-factor information flow causality analysis method is used to screen suitable meteorological and air pollutant-related factors and concatenate them with a PM2.5 sequence as the dataset. A modal decomposition algorithm is used as a module to be integrated into the autoformer (transformer improved with autocorrelation mechanism) model to improve it, and the modal autoformer (empirical modal decomposition combined with autoformer) is proposed. The constructed model decomposes the sequence into several components by using the modal decomposition module and uses the self-correlation mechanism and decomposition structure to decompose and extract features of different components at the time-feature level. Based on the matching method, the model is adjusted for different component features to improve the long-term prediction effect. The model is applied to three cities in Henan Province, Zhengzhou, Luoyang, and Zhumadian, as examples for experiments, and gated neural unit (GRU), informer, autoformer, and modal GRU (empirical modal decomposition combined with GRU model) are constructed for comparative verification. The results show that the modal autoformer can better cope with the complex characteristics of long-term prediction of the PM2.5 time series, has strong spatial adaptability and that its various indicators are optimal for the three cities, with R2 values being all above 0.96, where the highest is 0.987 in Zhengzhou; MAPE (Mean absolute percentage error) values all being less than 10, where the best is 7.602 in Zhumadian; and MAE (Mean absolute error) values all being less than 4. The prediction effect is stable enough, showing its feasibility and adaptability in long-term prediction. [ABSTRACT FROM AUTHOR]

Increased CO2 emissions from urban energy consumption pose a significant challenge to regional carbon mitigation policies. In this paper, we integrated two nighttime light (NTL) data: the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) composite data to estimate the energy carbon emissions from 2000 to 2019. Then the spatiotemporal dynamics of carbon footprint and deficit in the Yellow River Basin were analyzed at the provincial, municipal, and county scales combined with NPP data. The study shows that (1) the total amount of energy consumption CO2 emissions in the Yellow River Basin had increased from 1332 Mt in 2000 to 6469 Mt in 2019, but the average annual growth rate slowed down after 2010 from 11.5 to 5.61%. (2) From 2000 to 2018, the provinces with the highest carbon footprint and carbon deficit were concentrated in Inner Mongolia and Shanxi. In 2018, Inner Mongolia's carbon footprint was 1366.91 × 104 km2, accounting for 22.8% of the total. Cities with high carbon footprint were mainly economic centers and energy-intensive areas of various provinces. High-carbon deficit counties were mainly distributed in the western region. In 2018, 954 counties exhibited carbon deficits. (3) The carbon footprint in the Yellow River Basin at the municipal and county scales have a significant spatial correlation. The H–H clusters of the carbon footprint on the municipal scale were distributed in the middle reaches of the Yellow River Basin. At the county scale, the L-L clusters were mainly in Sichuan and eastern Henan regions. Through the analysis of the spatial and temporal evolution of carbon footprint and carbon deficit in the Yellow River Basin, it is significant to measure the degree of comprehensive coordination of carbon sources and sinks in the basin, to grasp the differences in the level of regional carbon emissions, and to promote synergistic regional governance, assist in the formulation of more precise carbon emission reduction policies, and to promote green and high-quality development. [ABSTRACT FROM AUTHOR]

• A novel joint optimal allocation model of regional water and land resources (JOAMRWLS). • A three-level unit division method of "basic units-computing units-hydrological response units". • Use of a two-layer nested algorithm involving successive approximation and nonlinear programming (SA-NLP). • The model improves the regional economy and water resource utilization efficiency. Water and land resources are indispensable prerequisites for the sustenance and advancement of human civilization. In recent decades, China has faced significant challenges in managing its water and land resources due to the intensifying competition resulting from rapid urbanization, industrialization, and population growth. The joint optimal allocation of water and land resources can effectively address these issues. However, there have been limited accomplishments in investigating the integrated water and land resources allocation system's interaction with social, economic, and environmental development. This paper develops a novel joint optimal allocation model of regional water and land resources (JOAMRWLS). The model integrates water and land resources for integrated allocation, taking into account the mutual feed relationship between them, and thereby achieving comprehensive utilization and coordination of water and land resources within the region. Then a two-layer nesting algorithm based on successive approximation and nonlinear programming (SA-NLP) is proposed to solve the model, obtaining the regional optimal land use pattern and optimal water allocation scheme. Subsequently, this paper uses Luoyang City in Henan Province, China, as a case study to verify the proposed JOAMRWLS. The results indicate that the water volume and area of each land use type tend to stabilize after the fifth iteration of the calculation with a consistent water volume of 18.65 × 108 m3. Compared with the conventional optimization model of water resources (COMWR), the JOAMRWLS encompasses five cities experiencing water scarcity, whereas the latter includes twelve such cities. Furthermore, the economic benefit of the JOAMRWLS is 2.65 × 1011 CNY, surpassing that of the former at 2.56 × 1011 CNY, highlighting its superiority. [ABSTRACT FROM AUTHOR]

To address the issues surrounding incomplete coverage of core dictionaries, limited training corpora, and low precision in Chinese geological text segmentation, a knowledge- and data-driven word segmentation method by combining combination probability and machine learning was proposed in this paper. We extracted mathematical feature information from terms in Chinese geological text to construct a Term Combination Probability Model (TCPM) for Chinese word combinations by integrating the combination features of geological terms and the Chinese writing styles under zero-sample conditions. The TCPM was used to extract geological terms with high combination characteristics as a user-defined dictionary, and then a geological corpus was constructed by using a general domain word segmentation method based on this dictionary. After a small amount of manual review and optimization, the geological corpus was trained with a BiLSTM-CRF model to segment Chinese geological text. The proposed method in this paper was tested using a regional geological survey report set in Henan Province, and the precision, recall, and F1-score of the method are 92.65%, 92.53%, and 92.59%, respectively. The experimental results demonstrated that this method, combined with the inherent knowledge features of geological text and the machine learning method, can assist in expanding the core dictionary for Chinese geological text segmentation based on zero-sample, and can improve the segmentation precision of Chinese geological text compared to simple general word segmentation methods or machine learning methods. • TCPM with no sample can reflect the structural features from professional terms in geological field texts. • Chinese word segmentation with custom dictionary from terms extracted by TCPM is better than general segmentation methods. • Combining TCPM with machine learning can improve the accuracy in word segmentation for Chinese geological text. [ABSTRACT FROM AUTHOR]

Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation. Article highlights: Considering the influence of 5G high-frequency electromagnetic wave on the electrical equipment in the substation, the positioning accuracy of 5G base station antenna in the substation is improved. The large area physical optics method can improve the calculation time and accuracy of radiofrequency radiation field intensity Combining the advantages of particle swarm optimization and interior point method, it can effectively avoid the problem of local optimal solution of location selection The simulation results show that the proposed method not only improves the location accuracy of 5G base station antennas in substations, but also applies to 5G base station antennas in more complex engineering environments [ABSTRACT FROM AUTHOR]

Studying the spatiotemporal evolution characteristics of the coupling coordination of the land–ecology–food system (LEF) aids in promoting green agricultural development and regional resource management. This study enriches food indicators under the dietary structure and uses the coupling coordination degree model to analyze the coupling coordination relationship among the LEF of 18 cities in Henan Province from 2011 to 2020. The gray relational degree model is used to investigate the main influencing factors determining the synergistic development of the system. The results show that the comprehensive development index of the LEF in Henan Province ranges between 0.4 and 0.6. The overall comprehensive evaluation index of various cities is ranked as Southern Henan > Eastern Henan > Central Henan > Northern Henan > Western Henan, with the greatest fluctuation observed in the food subsystem. During the study period, the coupling degree of Henan's LEF ranged from 0.277 to 0.996, indicating stages from low- to high-level coupling. The coupling coordination degree ranged from 0.338 to 0.775, generally bordering on imbalance and barely coordinated. The impact of each subsystem evaluation index on the system's coupling coordination degree can be ranked as food subsystem > ecology subsystem > land subsystem, with the correlation degree of internal indicators of the food and ecology subsystems with the system's coupling coordination degree being over 85%, emphasizing the importance of strict management. In summary, the coupling coordination of the LEF system in Henan Province urgently needs to be improved; especially, the coordination of the agricultural system is particularly important. Clarifying the spatiotemporal pattern of the LEF coupling and its coordination can provide a scientific basis for the coordinated development of land use, agricultural ecology, and grain production in Henan Province. [ABSTRACT FROM AUTHOR]

Gap graded natural sand is widely used in hydraulic structures, foundation treatment and recharge well fillers, and it's also common in geological structures such as landslide deposits. In order to study the influence of grain shape on its pore and permeability characteristics, river sand in Yichang (YS), standard sand in Henan (HS) and standard smooth glass beads (BZ) are selected as test materials, and the grain shape parameters of three materials are analyzed on multiple scales. Then tests on porosity and hydraulic conductivity of gap graded natural sand were conducted. On this basis, a numerical model considering grain size distribution, coarse grain content and grain shape was established. The results showed that the average values of the shape parameters increased or decreased with the increase of grain size, and the coefficients of variation for the parameters were between 0.026 and 0.208. The shape parameters showed a negative skewness distribution, but the absolute value of the skewness coefficient was small (0.254–0.616). Grain size distribution and grain shape are the main factors controlling the porosity and hydraulic conductivity of gap graded natural sand. The minimum values of porosity and hydraulic conductivity appear at about 70 % and 30 % of large grains, respectively. The closer the grains are to the sphere, the closer the material is stacked, and the smaller the porosity value is. The grain size ratio has a significant effect on the minimum porosity of the mixture and the minimum porosity increases with the increase of the grain size ratio. The porosity of gap graded natural sand obtained by numerical method is accurate, and the error is within 10 %. The hydraulic conductivity is obtained by deriving fluid grid information and the results show that the simulated value of hydraulic conductivity is highly matched with the experimental value, and the error range is roughly between 10 % and 30 %. • This paper selects sand materials with different shapes, and analysis the grain shape characteristics on multiple scales. • Porosity and permeability of natural gap graded sand with different shapes and sizes are studied by laboratory tests. • A numerical calculation model reflecting grain shape parameters is established. [ABSTRACT FROM AUTHOR]