Northeastern Provinces Of China Research Articles

Epidemiological investigation and diagnostic analysis of osteonecrosis of the femoral head in three northeastern provinces of China

BackgroundIn this retrospective case investigation, we analysed the data of patients with osteonecrosis of the femoral head (ONFH) to reveal demographic and clinical diagnostic features of ONFH in three northeastern provinces of China and provide a reference for its prevention, diagnosis, and treatment.MethodsWe collected data from patients in Beijing Orthopaedic Hospital of Liaoning, focusing on the aetiology and diagnosis of ONFH. Medical records and self-designed questionnaires were used to collect information for statistical analysis, including age, aetiology, reason for glucocorticoid use, hospital level at first visit, and diagnosis.ResultsIn total, 906 patients with complete medical records were included in the analysis. The mean patient age was 47.65 ± 12.12 years. The peak age distribution was in the 40s for men and the 50s for women. Among the total cohort, 72 patients (7.95%; 40 men and 32 women) had traumatic ONFH, 198 (21.85%; 131 men and 67 women) had steroid-induced ONFH, 230 (25.39%; 121 men and 109 women) had idiopathic ONFH, and 406 (44.81%; 397 men and 9 women) had alcohol-induced ONFH. Six hundred and twenty patients were diagnosed with ONFH at the first visit, while 286 patients were misdiagnosed, with a diagnosis rate of 68.43%. The diagnosis rate at the first visit in tertiary hospitals was 76.14%. The diagnosis rate at the first visit in second-class hospitals was 52.07%.ONFH was most likely to be misdiagnosed as lumbar disc herniation.ConclusionsMost patients with ONFH in three northeastern provinces of China were middle-aged, male, and had alcohol-induced ONFH. The misdiagnosis rate of ONFH at the first visit was very high, especially for misdiagnosis of lumbar disc herniation, indicating that the diagnosis of ONFH requires further improvement.

Характеристика демографических процессов на Северо-Востоке Китая на рубеже XX-XXI вв.

The article analyzes the reasons for negative demographic growth in the Northeastern provinces of China. The authors analyze the factors influencing the decline in demographic indicators, the causes and conditions of the excess of the birth rate over the death rate. Natural causes (aging population, disproportion between the male and female population) and socio-economic factors (predominance of the urban population, increasing levels of education, economic recession, job cuts) are identified.

The Verification and Fusion Analysis of Passive Microwave Soil Moisture Products in the Three Northeastern Provinces of China

The utilization of remote sensing soil moisture products in agricultural and hydrological studies is on the rise. Conducting a regional applicability analysis of these soil moisture products is essential as a preliminary step for their effective utilization. The triple collocation (TC) method enables the estimation of the standard deviation of errors in products when true soil moisture values are unavailable. It assesses data uncertainty and mitigates the influence of product errors on fusion, thereby enhancing product accuracy significantly. In this study, the TC uncertainty error analysis was employed to integrate Soil Moisture Active Passive (SMAP), the Advanced Microwave Scanning Radiometer 2 (AMSR-2), and the European Space Agency Climate Change Initiative (ESA CCI) active (ESA CCI A) and passive (ESA CCI P) products, with ground-based measurements serving as a reference. Traditional evaluation metrics, such as the correlation coefficient (R), bias, root mean square error (RMSE), and unin situed root mean square error (ubRMSE), were employed to evaluate the accuracy of the product. The findings indicate that SMAP and ESA CCI P products demonstrate strong spatiotemporal continuity within the research area and exhibit low uncertainty across various land types. The products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) exhibit a high level of temporal and spatial continuity; however, there is a requirement for enhancing their accuracy. The products of ESA CCI A exhibit notable spatiotemporal disjunction, contributing significantly to their elevated level of uncertainty. After fusion with TC analysis, the correlation coefficient (R = 0.7) of the TC-2 product derived from the fusion of SMAP, AMSR-2, and ESA CCI P products is significantly higher than the correlation coefficient of the TC-1 product (R = 0.65) obtained from the fusion of SMAP, AMSR-2, and ESA CCI A products at a 95% confidence level. The integration of data can efficiently mitigate the challenges associated with spatiotemporal gaps and inaccuracies in products, offering a dependable foundation for the subsequent utilization of remote sensing products.

Analysis of the Convergence of Manufacturing Industry Structure in the Three Provinces of Northeast China

At present, China's economic development is in an important stage of transformation towards high-quality development. Realizing industrial structure adjustment, promoting rational regional industrial layout, and collaborative division of labor play an important role in promoting high-quality economic development. Therefore, conducting research on the convergence of regional industrial structure is of great practical significance for promoting sustainable economic development. This article takes the manufacturing industry in the three northeastern provinces as the research object, and analyzes the problem of industrial structure convergence in the manufacturing industry from multiple perspectives. By measuring the similarity coefficient of industrial structure, location entropy, and regional division of labor index, it was found that the manufacturing industry in the three northeastern provinces of China has intensified industrial homogeneity while the convergence of industrial structure is not obvious. Policy suggestions are proposed to address this phenomenon.

Detection and phylogenetic classification of Neoehrlichia mikurensis in rodents from the region of Liupan Mountain, China.

Neoehrlichia mikurensis (N. mikurensis) is an emerging tick-borne pathogen that can cause neoehrlichiosis. Rodents are considered the major host for N. mikurensis. Currently, N. mikurensis has been detected in rodents in several studies from China and other countries. However, no research on N. mikurensis infection in rodents has been reported in the Liupan mountain region. The region of Liupan Mountain, located in northwestern China, is the center of the triangle formed by the cities of Xi'an, Yinchuan, and Lanzhou, with multiple tourist sites in the region. To survey whether there is N. mikurensis in hosts, rodents were captured in this region in September 2020. A nested polymerase chain reaction was used to detect the DNA of N. mikurensis, followed by nucleotide sequencing and phylogenetic analysis. In the region, among 88 rodents, 3 rodents were detected positive for N. mikurensis, a detection rate of 3.4%. Based on phylogenetic analysis of the partial groEL gene sequences, N. mikurensis from rodents in Liupan Mountain clustered in the same evolutionary branch with those found in rodents from Japan, Russia, and northeastern China, and also in ticks and clinical cases from Heilongjiang Province in northeastern China.

The Spatial and Temporal Evolution Pattern and Influencing Factors of Urban Human Settlement Resilience in Three Provinces of Northeast China

It is widely recognized that urban resilience is one of the core goals of urban development. As an important part of a city, the resilience level of urban human settlements directly affects the development trend of urban resilience. However, at present, research results on the resilience of urban human settlements are very rare, are mainly concentrated in the central region of China, and rarely take into account the economically backward northeastern region. Therefore, in order to better improve the anti-risk ability of the urban human settlement environment system in three provinces of Northeast China, fully implement the strategic goal of “Comprehensive Revitalization of Northeast China”, and achieve high-quality urban development, this paper focuses on 34 prefecture-level cities in three provinces of Northeast China and proposes an urban human settlement resilience evaluation system with 36 indicators in five dimensions, namely, the natural system, human system, housing system, supporting system, and social system. Using the entropy weight method, the Dagum Gini coefficient, and a geographical probe model, the changes in the resilience level of each city from 2005 to 2020 were measured, and the urban living environment was assessed in terms of the adaptability and resilience of the development level in each subsystem based on the temporal and spatial evolution law and its influencing factors. The following conclusions were drawn: (1) The development level of urban human settlement resilience in the three provinces in Northeast China showed an N-shaped development trend from 2005 to 2020, but the regional differences were significant, and the overall spatial pattern was “high in the south and low in the north”. (2) In terms of the overall difference, the overall difference in urban human settlement resilience in the three northeastern provinces of China was small: the inter-regional difference was the main source of the difference, and the intra-regional difference was the secondary source. The regional differences were in the order of Heilongjiang Province > Liaoning Province > Jilin Province, indicating that Jilin Province had the smallest difference and that the resilience level of urban human settlements does not show a balanced development trend. In terms of the average Gini coefficient between regions, the order of difference was Liaoning Province–Heilongjiang Province > Jilin Province–Liaoning Province > Jilin Province–Heilongjiang Province, indicating that the difference between Liaoning Province and Heilongjiang Province was the most significant. (3) The “natural system”, “human system”, “living system”, “supporting system”, and “social system” had significant spatial and temporal heterogeneity and significantly affected the resilience level of urban human settlements in the three provinces in Northeast China. Among them, the “social system” has always been the main factor affecting the resilience level of urban human settlements.

The economic hierarchy, linkage, and directions between the three northeastern provinces of China and Russian Far East

AbstractThe economic hierarchy index, economic linkage model, and economic membership model are used to study the economic hierarchy, linkage, and direction of Northeast China and Russian Far East from 2005 to 2020, using the three northeastern provinces of China and 11 Far East federal subjects of Russia as the research objects. The findings are as follows: (1) There is a major polarization effect between developed affluent economic regions and stagnant isolated backward regions in the economic rankings in the 11 Russian Far East federal subjects from 2005 to 2020. (2) The three northeastern provinces of China have more economic linkages with their near Russian neighbors— the east‐oriented Primorsky Territory, the north‐oriented Amur Region, and the north‐oriented Khabarovsk Territory, than with their farther neighbors— the Chukotka Autonomous Area, Magadan Region, and Kamchatka Territory. (3) From 2005 to 2020, the Primorsky Territory has been serving as the focal point of economic linkages between Russian Far East and Northeast China, showing the expanding outward pattern resembling the concentric circles.

CHINA’S NATURAL GAS MARKET AND RUSSIAN EXPORT OUTLOOK

The article analyzes the state of the Chinese gas market and factors in the long-term dynamics of gas demand in China. The author has attempted to assess the expansion of Russia’s presence in the Chinese gas market and provide intermediate recommendations on using the potential for developing energy cooperation between China and Russia. It must be taken into account that the northeastern provinces of China, located near the border with our country, will be supplied with Russian gas thanks to the Power of Siberia gas pipeline. However, the insufficient development of the relevant infrastructure in China – pipeline capacities, gas storage facilities, distribution networks – will not allow Russia to significantly increase exports through this gas pipeline. These limiting factors apply to an even greater extent to the Power of Siberia-2 gas pipeline project. According to the author, the most promising way to increase Russian gas exports to China may be to organize supplies of Russian liquefied natural gas to the coastal regions of China, where a steady increase in gas demand is predicted.

A dataset of JLS-5-20M black soil image in Northeast China

This is a dataset of the surface reflectance data covering some black soil areas in the three northeastern provinces of China, based on the L1 data of the JL1GP satellites’ multispectral imager with a spatial resolution of 5-20 meters. Specifically, we adopted the on-orbit absolute calibration technology based on multiple stable sites to ensure that the on-orbit absolute radiometric calibration accuracy of the JL1GP satellites’ multispectral imagers is no less than 7%. Then, we selected the L1 data of JL1GP satellite multispectral imager that imaged in the three provinces of Northeast China with clear weather and no clouds are as the input image of atmospheric correction process. Finally, the L1-level image data are atmospherically corrected and orthorectified by the atmospheric correction algorithm based on the atmospheric radiative transfer model, to generate the reflectance product dataset covering some black soil areas in the three northeastern provinces of China with an absolute accuracy of no less than 7%. This dataset takes advantage of the domestic JL1GP satellites’ advantages of high spatial resolution, a large number of bands, and stable imaging quality, hence it can provide reliable support for quantitative remote sensing applications in the black soil areas of the three northeastern provinces in China.

The biophysical effects of potential changes in irrigated crops on diurnal land surface temperature in Northeast China

Irrigated crops have experienced a significant global expansion. The biophysical response of climate change to irrigated crop expansion in different regions, particularly in terms of monitoring the influence mechanism of nighttime land surface temperature (LST) change, however, remains insufficiently explored. Taking the three northeastern provinces of China as our study area, we apply window analysis, partial correlation analysis, and geographical detector to quantitatively characterize the spatial and temporal distribution pattern of daytime and nighttime LST (diurnal LST) and biophysical parameters, and the main driving mechanism of diurnal LST change. The results showed that irrigated crop expansion led to asymmetric changes in daytime (−2.11 ± 0.2°C, 97.4%) and nighttime (0.64 ± 0.2°C, 79.9%) LST. ΔLSTDT had a negative correlation with ΔLE (63%), but a positive correlation with ΔSSR and ΔH (91% and 77%). This revealed that the cooling effect caused by the superposition of the output latent heat flux and the absorbed solar shortwave radiation was greater than its heating effect. ΔLSTNT and ΔLE had a positive connection across 69% of the region. ΔLSTNT demonstrated a negative correlation with ΔSSR and ΔH in 82% and 75% of the regions, respectively. At this time, the superposition of latent heat flux and heating potential term produces a greater heating effect. The explanatory power of the single factor (the mean of q<0.50) of biophysical parameters for diurnal LST variation was significantly smaller than that of the interaction factor (the mean of q>0.50, p<0.01). This study shows more detailed dynamic information of diurnal LST and biophysical parameters from 8day scale. The findings highlighted the critical role of asymmetric changes in the diurnal surface thermal environment caused by irrigated crop expansion in the global climate from a land surface hydrothermal energy balance perspective.

Spatial network structure and driving factors of human settlements in three Northeastern provinces of China

IntroductionUrban human settlements' spatial network structures have emerged as crucial determinants impacting their health and sustainability. Understanding the influencing factors is pivotal for enhancing these settlements. This study focuses on 34 prefecture-level cities in Northeastern China from 2005 to 2020. It employs a modified gravitational model to establish spatial relationships among urban human settlements. Social network analysis techniques, including modularity and the quadratic assignment procedure (QAP) regression model, are introduced to analyze the network's characteristics and driving factors.MethodsA modified gravitational model is applied to create the spatial association network of urban human settlements. Social network analysis tools, along with modularity and the QAP regression model, are utilized to investigate the network's attributes and influencing elements. The study evaluates the evolution of spatial correlation, network cohesion, hierarchy, and efficiency.ResultsThroughout the study period, spatial correlation among urban human settlements in Northeastern China progressively intensified. However, the network exhibited relatively low density (0.217675), implying limited interconnectivity among cities. The average network hierarchy was 0.178225, indicating the need for optimization, while the average network efficiency was 0.714025, reflecting fewer redundant relationships. The analysis reveals the emergence of a polycentric network pattern with core and sub-core cities like Shenyang, Dalian, Changchun, Daqing, and Harbin. The urban network configuration has largely stabilized. The spatial association network showcases the intertwining of "small groups" and community organizations. Geographic proximity and merit-based linkages govern feature flow. Measures such as breaking administrative barriers, reducing flow time and distance, boosting resident income, and increasing government investment are identified to foster balanced network development and structural optimization.DiscussionThe research underscores the increasing spatial correlation and evolving network pattern among urban human settlements in Northeastern China. Despite the observed strengthening correlation, challenges related to network cohesion and hierarchy persist. The formation of a polycentric network signifies positive progress in urban development. The study highlights the importance of proximity and merit-based connections for feature flow. The proposed measures offer pathways to enhance network development and optimize structure, promoting holistic urban settlement growth and sustainability.

Coupling coordination of the water‒energy‒carbon system in three provinces of Northeastern China

Water resource consumption, energy consumption, and carbon emissions are key factors that restrict the level of regional development and affect the quality of the regional ecological environment. This study investigates three provinces in Northeastern China, using remote sensing and historical data from the period 2001–2019 to analyze the spatiotemporal trends of water and energy consumption, and carbon emissions. Using the AHP-entropy weight method, comprehensive analysis method, and system dynamics research method, the coupling relationships of the three subsystems (water‒energy‒carbon, WEC) were determined, and the development and changes over the next 20 years were simulated. The results show that: 1) Over the period 2001–2019, the comprehensive index of the WEC system in the three Northeastern provinces ranged from 0.0994 to 0.2314. 2) Over the period 2001–2019, the coupling degree of the WEC system in the three Northeastern provinces ranged from 0.9457 to 0.9985, equating to a high coupling stage level. However, the level of coupling coordination was low, with values ranging from 0.3151 to 0.4780, equating to a state of mild disorder or near disorder. 3) During the study period, water resource consumption, energy consumption, and carbon emissions all showed an increasing trend, with the former having the fastest growth rate. This study is of great significance for promoting the sustainable utilization of resources and green and low-carbon development in the three studied Northeastern provinces of China.

Soybean phenological changes in response to climate warming in three northeastern provinces of China

ContextSoybean plays an important role in agricultural production in three northeastern provinces of China; furthermore, climate change, particularly temperature and rainfall changes, can significantly regulate soybean phenological periods. Research QuestionCurrent research on the spatial and temporal characteristics of soybean phenology and its relationship with climate change in the three northeastern provinces of China is still inadequate. MethodsIn this study, phenological and meteorological data from 26 agrometeorological stations in northeastern China were used to evaluate temporal and spatial changes and their correlations with soybean temperature, rainfall, phenological stage, and growth duration over the long term in three northeastern provinces. ResultsOn average, 22 of the 26 stations showed increases in temperature, and 15 stations showed increases in rainfall during the survey stage. All of soybean growth nodes were delayed, except R8, which advanced by 0.28 days, and all soybean growth durations were shortened except for during the V3-R1 period. In addition, temperature was negatively correlated with growth duration, while rainfall was positively correlated with growth duration. Further, the spatial and temporal distribution of temperature and rainfall showed that an increase in temperature led to an increase in thermal conditions and a significant northward and eastward expansion of the cumulative temperature zone, with the most significant increase in temperature occurring in most areas of Heilongjiang, followed by Liaoning. ConclusionsSoybean planting practices in the three eastern provinces should be adjusted to medium- and late-maturing varieties, which can adapt to climate change and mitigate its effects during the phenological period, due their tolerance to high heat. SignificanceThese findings fill a knowledge gap for understanding the relationship between soybean phenological changes and climate change in the three northeastern provinces.

Research on Regional Financial Risk Identification and Contribution Analysis Based on ANN-RBF Neural Network

Regional financial risk is an issue that cannot be ignored around the world today. Studying financial risk and analyzing the deep-seated influential factors is conducive to reducing the generation of financial crises. This paper aims to study the influencing factors of regional financial risk and their contribution. Firstly, we identify influential factors of regional risk in the three northeastern provinces of China, then based on the artificial neural network-radial basis function (ANN-RBF) algorithm, we collect data from the region for the past 20 years for analysis and calculate the contribution of different risk influential factors to regional risk. The results show that: (1) CPI dominates in influencing the regional financial risk in the three northeastern provinces of China, followed by the growth rate of import and export and the local public finance surplus. (2) When the neuron is 29, the accuracy of the algorithm converges at this point and the model reaches optimum. The research in this paper is conducive to uncovering the influential factors of regional financial risk and reducing the generation of financial crises.

Spatial correlation analysis and prediction of carbon stock of “Production-living-ecological spaces” in the three northeastern provinces, China

The “Production-Living-Ecological Space” (PLES) is a paramount indicator in the filed of territory space development and optimization in China, under the new era. Exploring the driving factors of the PLES'land expansion is of great significance for improving space utilization, mitigating severe climate changes, and promoting green, healthy and sustainable development. In the background of the “Carbon Emissions Peak and Carbon Neutrality” strategy, analyzing and predicting the carbon stock of PLES is effective to boosting the achievement of ‘Dual-Carbon’ vision. Based on the above research questions， this study constructs the PLES based on statistics about land use (Year 1990, 2000, 2010 and 2020) in three northeastern provinces, and reveals the spatial correlation of PLES’ carbon stock in the research area through ArcGIS spatial analysis and InVEST model. Then, the PLUS model was used to clarify the contribution of each driver to the conversion of space land, and to predict the distribution of the PLES pattern and the carbon stock's spatial correlation in 2030 and 2060 under the Natural-Development Scene and Ecological-Protection-Development Scene. Results show that: (1) The PLES in the three northeastern provinces of China is primarily green ecological space (55.71%) and agricultural production space (38.10%), while industrial production space (3.60%) and urban living space (2.76%) expand significantly, and green ecological space (−0.17%) and blue ecological space (−0.89%) are on a recession trend. Besides, 2000–2010 is the most intense period of all kinds of space land transformation in the study area. (2) Population density, proximity to roads at all levels, annual average temperature and elevation are the prime drivers of PLES’ profile within the scope of the study region, but the contribution rate is significant difference. (3) The urban living space decreases and the green and blue ecological space increases significantly in the predicted years under the scene of Natural-Development and Ecological-Protection development; the pattern of PLES is relatively stable in the predicted years under both scenarios. (4) The spatial correlation of carbon stock is closely related to the distribution of PLES, with the high-value significant regions primarily in the distribution region of green ecological space, otherwise, the low-value significant regions mostly concentrated in the region with complex spatial land use types and large spatial development intensity; the overall structure within the scope of the study region shows a layout of high value areas surrounding low value areas. It can show that insisting on the ecological civilization construction is an effective way to achieve sustainable development and practice the “double carbon” strategy; in the future spatial land use control, ecological protection measures should still be adopted to ensure the sustainable development and positive operation of the study area.

A probable ornithopod egg from a historic collection of dinosaur eggs recovered from the Upper Cretaceous of Liaoning Province, China

ABSTRACT Since the first discovery of dinosaur eggs announced by the American Museum of Natural History (central Asiatic expeditions to Mongolia) in 1923, these fossils became widely recognised from Mesozoic strata in Asia and elsewhere. In the 1920s, and actually before 1923, dinosaur eggs had been collected from another part of Asia, specifically Liaoning Province in northeastern China. From 1921 to the late 1920s, five eggs were collected from the Cenomanian Quantou Formation during construction, and thus represent an important historic collection as some of the earliest dinosaur eggs discovered in Asia. Here, we describe one egg from this collection and assign it to Paraspheroolithus irenensis, an egg taxon that likely belonged to hadrosaur dinosaurs. The egg is approximately 7 × 8 cm in diameter, with eggshell that displays typical spheroolithid microstructure and is just over a millimetre in thickness. Paraspheroolithus irenensis is shown to be the most widespread spheroolithid oospecies in China, and our comparative work extends its geographic range to northeastern most China. The Quantou Formation thus has yielded at least three spheroolithid oospecies, indicating the potential for a diversity of hadrosaur or related ornithopod species in the region at that time.

An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using Sentinel-2 imagery

Accurate crop type mapping is essential for crop growth monitoring and yield estimation. Recently, various machine learning methods have been increasingly used for crop type mapping, but they often lose their validity when directly applied to other regions and years due to differences in the distribution of source and target data, that is, domain shift. To address the problem, we developed a deep adaptation crop classification network (DACCN) based on the idea of unsupervised domain adaptation (UDA). The proposed DACCN mainly consists of two parts, a feature extractor that converts the original input into high-level representations, and a domain aligner where the discrepancy between source and target distributions is measured using multiple kernel variant of maximum mean discrepancy (MK-MMD). Four states in the United States (U.S.) Corn Belt and two provinces in northeastern China were used as study areas, where samples used for model building and accuracy evaluation were collected based on time-series Sentinel-2 imagery and reference maps in 2018 and 2019. Then, three experiments were designed to verify the transferability of DACCN across space, time, and space–time, respectively. In each experiment, the proposed DACCN was compared to deep crop classification network (DCCN), a model with a similar structure to DACCN but without the domain adaptation mechanism, and two machine learning methods, random forest (RF) and support vector machines (SVM). The experimental results showed that DACCN outperformed other models in most transfer cases with overall classification accuracies ranging from 0.835 to 0.922. The DACCN also performed better in spatially continuous mapping with its predicted crop type maps more consistent with the reference ones. As an innovative application of transfer learning in crop type mapping, the methodology proposed in this study effectively addressed the problem of missing labels in target domains and alleviated the negative impact of domain shift.

An optimization approach of water-food-energy nexus in agro-forestry-livestock system under uncertain water supply

Plantation, forestry, and livestock are inseparably intertwined and constitute the main sectors of the agricultural system. An integrated analysis of energy flow and material conversion processes of the agro-forestry-livestock system is beneficial for achieving sustainable and coordinated agricultural development. The availability of water resources is sensitive to climatic conditions and human activities, and uncoordinated water allocation affects the combined efficiency of various agricultural sectors. Therefore, an integrated modeling framework, based on the water-energy-food nexus under water supply uncertainty, is developed to maximize the performance of agro-forestry-livestock system. The advantages of the model include (1) systematic analysis of energy flow and material conversion processes in farmland, forest land and livestock; (2) consideration of trade-offs between economic benefits, multiple energy use efficiency (biomass, light energy), environmental impacts, and ecological benefits in the system; and (3) rational allocation of farmland, forest land, and livestock resources under the uncertainty of water supply. Empirical frequency analysis was used to obtain different frequencies of water supply. The model framework is based on a multi-objective programming model, which is solved by an affiliation function approach. The model constructs several objective functions, such as economic benefits, bioenergy productivity, light energy use efficiency, ecological economic benefits, and greenhouse gas emissions of the agro-forestry-livestock system. The concordance index was applied to evaluate the integrated performance of the model. The method was applied to Heilongjiang Province in northeastern China, where plantation, forestry, and livestock are predominant. Results show that livestock manure and urine have the highest energy potential, followed by wood, and straw has the lowest energy production. The main contribution to greenhouse gas emissions from the plantation is N2O, and the main contribution from livestock is CH4. The model had a higher concordance index for the larger water supply, but lower crop yield and photosynthetically active radiation. The proposed modeling framework contributes to the generation of stable and coordinated optimization solutions and provides decision makers methods and perspectives for sustainable agriculture.

Prevalence and genetic diversity of canine coronavirus in northeastern China during 2019–2021

Canine coronavirus (CCoV) is associated with diarrhea in dogs, with a high incidence and sometimes even death. However, there is currently limited information about its prevalence and molecular characterization in northeastern China. Therefore, in this study, we examined 325 canine fecal specimens in four provinces in northeastern China from 2019 to 2021. PCR results revealed that 57 out of 325 (17.5%) samples were found to be positive for CCoV, and the positive rate varies obviously with city, season, age and so on. High incidence (65%) of viral co-infection was detected in the diarrhea samples and mixed infection of distinct CCoV genotypes occurs extensively. More importantly, sequence analysis showed that the S gene has a strong mutation. Phylogenetic analysis demonstrated that CCoV-I and CCoV-II strains has different origins. In particular, we found the CCoV-IIa strains of S gene sequenced and the reference strain B906_ZJ_2019 were highly clustered, and the reference strain was a recombinant strain of CCoV-I and CCoV-II. Our findings provide useful orienting clues for evaluating the pathogenic potential of CCoV in canines, and point out more details on characterization in northeastern China. Further work is required to determine the significance and continuous genetic evolution of CCoV.

ЗНАЧЕНИЕ И РОЛЬ ПРИГРАНИЧНОГО СОТРУДНИЧЕСТВА В РАЗВИТИИ ТОРГОВЫХ ОТНОШЕНИЙ МЕЖДУ РОССИЙСКОЙ ФЕДЕРАЦИЕЙ И КИТАЙСКОЙ НАРОДНОЙ РЕСПУБЛИКОЙ

Currently, the Russian-Chinese relations of strategic partnership have a tendency of progressive and systematic development, the leadership of both countries pays special attention to regional cooperation, mutually beneficial use of comparative advantages of border regions, sustainable economic development of border territories. This paper analyzes the dynamics of trade relations between the Far Eastern Federal District and China. There is a positive trend in the growth of foreign trade between Russia and China. China ranks first in the volume of Russia’s foreign trade turnover. The border territories of Russia and China are traditionally considered the basis of Russian-Chinese relations. The analysis of trade relations between the Far Eastern Federal District and China demonstrates that China has been the most important trading partner of the Far Eastern Federal District for a long period of time. The three northeastern provinces of China have the closest trade ties with Russia. Regional cooperation between Russia and China provides ample opportunities for the development of cross-border economic ties.