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The source of oil and gas in the Tarim Basin's platform has always been controversial. The Lower Cambrian Yurtusi Formation and the Xidashan Formation–Xishanbulak Formation argillaceous source rocks are an important set of oil source series in the basin. Based on the principle of conservation of matter, this paper calculates the hydrocarbon expulsion amount of the source rock. Using a phase state hydrocarbon expulsion amount characterization model, the paper reconstructs the evolution process of the hydrocarbon expulsion phase state of the source rock. According to the characterization model of hydrocarbon expulsion dynamics, the relative contribution of each force to hydrocarbon expulsion is evaluated. The hydrocarbon expulsion geologic model of argillaceous source rocks in the Lower Cambrian in the Tarim Basin is established. The results show that this set of source rocks can serve the main source rocks in the basin. The hydrocarbon expulsion of the Lower Cambrian argillaceous source rocks in the Tarim Basin can be divided into three stages. In the first stage, the hydrocarbon expulsion phase is dominated by the water‐soluble and diffusion phases. The conversion of clay minerals into dehydration and diffusion forces is the main driving force for the hydrocarbon expulsion. In the second stage, the oil and gas expulsion present the characteristics of multiphase and multidynamic coexistence. In the third stage, the phase state of oil and gas expulsion is mainly free phase. The capillary force difference and the thermal expansion force of the fluid rock are the main driving forces for oil and gas expulsion. The research results of this paper can deepen the understanding of the hydrocarbon expulsion mechanism of the set of source rocks, and then guide oil and gas exploration. [ABSTRACT FROM AUTHOR]

In order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision‐making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper. [ABSTRACT FROM AUTHOR]

Objective: This systematic review aimed to analyse the effect of early weight bearing versus late weight bearing on rehabilitation outcomes after ankle fractures, which primarily include ankle function scores, time to return to work/daily life and complication rates. Methods: The China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, China Science and Technology Journal, Web of Science, PubMed, Embase and Cochrane Library databases were searched. The focus was on identifying randomised controlled trials centred on early weight‐bearing interventions for post‐operative ankle fracture rehabilitation. All databases were searched for eligible studies published within the period from database inception to 20 June 2023. The eligible studies were screened according to the inclusion criteria. Study quality was evaluated using the methodology recommended by the Cochrane Handbook for the Systematic Evaluation of Interventions. Two authors independently performed the literature search and data extraction. Eligible studies were subjected to meta‐analyses using Review Manager 5.3. Based on the time points at which post‐operative ankle function was reported in the studies included in this paper, we decided to perform a meta‐analysis of ankle function scores at 6 weeks post‐operatively, 12 weeks post‐operatively, 24–26 weeks post‐operatively and 1 year post‐operatively. Results: A total of 11 papers, comprising 862 patients, were included. Meta‐analysis indicated that patients receiving early weight‐bearing interventions, which referred to weight‐bearing for 6 weeks post‐operatively, experienced enhancements in ankle function scores (Olerud–Molander score, AOFAS score or Baird–Jackson score) at various post‐operative milestones: 6 weeks (SMD = 0.69, 95% CI: 0.49–0.88 and p < 0.01), 12 weeks (SMD = 0.57, 95% CI: 0.22–0.92 and p < 0.01) and the 24–26 weeks range (SMD = 0.52, 95% CI: 0.20–0.85 and p < 0.01). The results of subgroup analyses revealed that the effects of early weight‐bearing interventions were influenced by ankle range‐of‐motion exercises. Additionally, early weight bearing allows patients to return to daily life and work earlier, which was evaluated by time when they resumed their preinjury activities (MD = −2.74, 95% CI: −3.46 to −2.02 and p < 0.01), with no distinct elevation in the incidence of complications (RR = 1.49, 95% CI: 0.85–2.61 and p > 0.05). Conclusion: The results showed that early weight bearing is effective in improving ankle function among post‐operative ankle fracture patients and allows patients to return to daily life earlier. Significantly, the safety profile of early weight bearing remains favourable, with no higher risk of complications than late weight bearing. [ABSTRACT FROM AUTHOR]

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

This paper investigates whether and how export growth to China varies following shocks in bilateral political relations between China and six Southeast Asian countries—Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam—between 2000 and 2019. High‐frequency news data is used to quantify bilateral political relations between each of the six countries and China. Then, a net cooperation index, plus separate indices of political cooperation and conflict, are placed in an augmented gravity model of trade situated in a vector autoregression framework alongside real exchange rate and industrial production variables to examine the short‐ (months) and long‐run (years) effects of shocks to bilateral political relations on each countryʼs exports growth to China. The results reveal that political relations with China played a role in Thailandʼs and Vietnamʼs exports growth to China but not in Indonesiaʼs, Malaysiaʼs, the Philippinesʼ and Singaporeʼs, and contribute new findings to the literature on politics and trade. Code and data for the analysis is available at: https://github.com/tradepolsrepository/tradepols.git. [ABSTRACT FROM AUTHOR]

Background: For adults with inflammatory bowel disease (IBD), they experience many challenges in dietary decision‐making. Thus, this study examined the perspective and experiences of adults with IBD in dietary decision‐making. Objective: This study aimed to explore the perception and consideration of people with IBD in their daily dietary decisions through monitoring, interpretation and action during the decision‐making process. Design: A qualitative study of individuals affected by IBD was conducted through semistructured interviews. Results: Twenty patients were recruited from four tertiary hospitals in Nanjing, China, and each participant completed a semistructured interview. The majority of participants reported on the process and experience of dietary decision‐making. Key themes were categorised into three stages: (1) assessing needs, preferences and food cues (monitor); (2) moving from experience to expertise (interpret) and (3) balancing expectations amidst limitations (act). The majority of participants reported that their decisions were shaped by assessing current disease status and food cues. Those interviewed with IBD were willing to make tradeoffs for bowel stability, but their decisions were also influenced by past dietary experiences and traditional Chinese beliefs. The lack of awareness of dietary guidelines was a significant barrier to healthy eating decisions. Positive or negative feelings accompanied dietary decisions. Conclusion: Although most people with IBD change their diet after diagnosis, the changes made are often inconsistent with existing dietary recommendations. Several factors can influence the dietary decision‐making process. This study will help assess the experiences of people with IBD in dietary decision‐making to encourage the formation of targeted dietary health and well‐being interventions. Knowledge of nutrition and diet should be provided in education and training programmes for IBD management. Patient or Public Contribution: The first three authors of this paper were the lead researchers in this study's design. These authors were mentored by patient researchers who also contributed to the manuscript, and the research process was co‐lead and directed by other patient participants and consultants. The results of this paper were directly obtained from patient participants. [ABSTRACT FROM AUTHOR]

Bayesian network (BN) is an important tool in probabilistic seismic risk analysis (PSRA) due to its holistic nature and powerful probabilistic inference capabilities. However, while the information that can be stored by BN includes variable values, probability distributions, and relationships between variables, it does not involve event quantities. The results obtained from PSRA and probabilistic seismic hazard analysis (PSHA) are commonly frequency‐related (the number of occurrences per unit time), fundamentally describing event numbers. BN assumes a fixed event number relationship between nodes, but the aftershock counts vary for different mainshock magnitudes. This limitation further restricts its effectiveness in lifespan PSRA for structures. Therefore, this study aims to propose an enhanced BN (EBN) for modeling aftershock PSHA (APSHA) considering frequency information. This paper first introduces the modeling method using BN, including PSHA and a simplified model of APSHA using Båth's law. Next, an EBN that can effectively address frequency issues between the mainshock and aftershocks is presented for modeling APSHA considering the modified Omori's law, while the mathematical principles of both forward and backward inferences for the EBN are elaborated in detail. Then, the article carefully analyzes the utilization of the EBN for APSHA modeling under different scenarios where the information regarding the mainshock is known to varying extents, followed by the extension of the EBN in a time domain to obtain time‐dependent aftershock hazard. Finally, an example of APSHA in a specific location in China is illustrated. [ABSTRACT FROM AUTHOR]

As urban population increases, integrated underground–aboveground complexes are being constructed at growing paces in major cities. The seismic analysis of such complexes is crucial for the safety and functionality in the threat of potential earthquake disasters. However, fine‐grained numerical modeling and analysis of such large and complex structures are still inefficient due to the consideration of the soil–structure interaction (SSI). To address this challenge, an efficient approach for numerical modeling of large‐scale seismic SSI analysis is presented in this paper to overcome the limitations of existing finite element analysis (FEA) software. Moreover, a multi‐graphic processing unit (GPU) parallel explicit algorithm is implemented for the nonlinear dynamic SSI problems to further increase the computational efficiency. A large underground–aboveground complex project in China is used as an example to demonstrate the capability of the integrated method. The accuracy and reliability of the multi‐GPU parallel explicit finite element algorithm for SSI analysis (GFEA‐SSIA) are verified through a comparative analysis of the linear‐elastic and nonlinear dynamic response of the building calculated by GFEA‐SSIA and common FEA software. Finally, the structural response and structural damage of the underground–aboveground complex are analyzed under multidirectional seismic motions, and the damage distributions of the structures are provided. [ABSTRACT FROM AUTHOR]

Aim: To investigate the seizure course of PCDH19 clustering epilepsy (PCDH19‐CE) in a cohort of female children in China. Method: This ambidirectional cohort study examined 113 female patients with PCDH19‐CE through multicentre collaboration. Prognostic factors for seizure freedom were evaluated by multivariate Cox regression analysis. Results: The median seizure course period from seizure onset was 6 years 6 months. Of 113 patients, 78% and 56% experienced seizure freedom for at least 1 year and at least 2 years respectively. In patients younger than 5 years (n = 30), 5 to 10 years (n = 52), and older than 10 years (n = 31), 57%, 81%, and 94% experienced at least 1 year of seizure freedom, and 32%, 52%, and 84% experienced at least 2 years of seizure freedom, respectively. However, 58% (65 out of 113) relapsed at least once after more than 1 year of seizure freedom without trigger exposure (40%) or because of common triggers, including fever (43%) and antiseizure medication (ASM) reduction (29%). There was an 84% risk of seizure relapse after ASM reduction attempts. The likelihood of seizure freedom decreased with early age at seizure onset and developmental delay. Interpretation: Patients with PCDH19‐CE exhibit increasing seizure freedom with age, but there is a risk of relapse. ASM reduction in children younger than 10 years old requires caution. Patients with early seizure onset and developmental delay have a reduced chance of seizure freedom. What this paper adds: The seizure freedom rate in PCDH19 clustering epilepsy gradually increases with age.The disease course is characterized by relapsing–remitting seizures.Antiseizure medication reduction requires caution for patients younger than 10 years of age.Patients with early seizure onset and developmental delay are less likely to achieve seizure freedom. [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality will be important policies to constrain China's economic and social development in the future. Based on the theory of computable general equilibrium, CGE model of carbon emission was constructed in this paper to simulate the influence of different scenarios of carbon emission reduction on economic and social development. The major contribution is to subdivide the energy sector according to primary energy and secondary energy, and construct the macro SAM table and micro SAM table of 21 energy sectors in 2017 to quantitatively simulate the impact of carbon emission reduction policies on China's economy and society, and put forward some policy suggestions based on this. [ABSTRACT FROM AUTHOR]

More than 50 earthquakes occurred in the Luding region, including a 6.8‐magnitude earthquake on September 5, 2022, which induced huge amounts of geological disasters. As the main triggered factors for the geological disasters, the vegetation, precipitation and temperature show regular changes on the time axis. Thus, this paper based on the spatial–temporal evolution of the precipitation, temperature and normalized difference vegetation index (NDVI), focuses on the susceptibility of the Luding area using the weighted information content method (ICM) and analytic hierarchy process (AHP) for the post‐earthquake regional planning and reconstruction. In the Luding area, it was found that there were 18 debris flow gullies, and 470 geological disasters scattered in the debris flow gullies. The NDVI of the study area has been declining in recent years, with only about 12% of the NDVI increasing from 2015 to 2020. From 2011 to 2015, the annual cumulative precipitation has been dropping overall, while the July average temperature has increasing quadratic functions after decreasing trend. Furthermore, the studied area's geological hazards susceptibility was classified into five categories, with extremely low susceptibility accounting for 5.82%, low susceptibility accounting for 13.61%, moderate susceptibility accounting for 38.54%, high susceptibility accounting for 39.96% and extremely high susceptibility accounting for 2.08%. The geological disasters in moderate, high and extremely high susceptibility accounted for approximately 95.08%, and the covered area of geological disasters in moderate, high and extremely high susceptibility accounted for 88.06%. Through the receiver operating characteristic (ROC) curve of the model verification found the AUC = 0.743. The geological hazards susceptibility study results show that the approach is useful for the analysis and prevention of geological disasters. [ABSTRACT FROM AUTHOR]

The Dunhuang Basin, situated in western China along the Altyn Tagh Fault (ATF) zone, intersects the Tethys and Paleo‐Asian Ocean tectonic domains. Influenced by both the ATF system and the far‐field effects of the Qiangtang‐Lhasa‐Eurasia collision during the Mesozoic, the mechanism of the Jurassic sedimentary migration of the basin in response to tectonic movements is unclear so far. The paper uses a comprehensive approach, including field geological surveys, lithologic and lithofacies discern, stratigraphic relationships analysis and 2D seismic profile interpretation, to examine the distribution of Jurassic residual strata in the basin. The comprehensive results of our study suggest that the Dunhuang Basin exists as an isolated block with unique tectonic and sedimentary evolution characteristics. In the Early Jurassic, the Dunhuang Basin underwent initial rifting, leading to the formation of small segmented intermontane sags. This phase was marked by the coarse particle sedimentary system of alluvial fans and braided rivers, represented the near source rapid deposition in the initial formation period of the basin. Stratigraphic distribution was primarily influenced by pre‐Jurassic basement topography and was not significantly constrained by faulting during this period. The formation of these isolated discontinuous small intermontane sags indicates the segmented activities of the ATF in the Early Jurassic period. In the Middle Jurassic period, influenced by the ATF dextral strike‐slip faulting, sedimentation extended eastward and the depocenter migrated clockwise compared with the distribution of the Middle Jurassic strata within the Dunhuang Basin. This period witnessed the development of coal measure strata at the basin's margins and lacustrine fine‐grained clastic deposition in the centre. The segmented fracture of the ATF gradually initiated a unified dextral strike‐slip tectonic movement. In the Late Jurassic period, sedimentary strata were locally present in the Wanyao Sag but absent in other sags. The depocenter migrated counterclockwise compared with the distribution of Middle Jurassic strata within the Dunhuang Basin, due to regional uplift accompanied by the ATF sinistral strike‐slip faulting caused by the collision between the Lhasa Block and the Eurasia Plate. The depocenter migration of the Dunhuang Basin constrained within the ATF system from the Early to Middle and Late Jurassic can be attributed to the transition of the ATF strike‐slip faulting in context of the stress relaxation and compression between the collision of the Qiangtang and Lhasa blocks to the Eurasia Plate, respectively. [ABSTRACT FROM AUTHOR]

All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently. [ABSTRACT FROM AUTHOR]

In the chemical flooding process, the premature breakthrough of chemical agents in production wells results in a large waste of chemical agents and increases the volume and processing difficulty of the produced fluids. The early warning method of chemical agent channeling can predict the strength of agent channeling in advance. The practice of chemical flooding shows that the production performance can be used for early warning of chemical agent channeling. In this paper, we analyze the relationship between cumulative oil production and cumulative polymer production of production wells in polymer–surfactant flooding. Three types of curves according to the enhanced oil production characteristics of chemical flooding, including convex type, S‐type, and concave type. We use the drop speed of the water–oil ratio and the rapid‐decline speed of water cut as early warning indicators to predict the channeling coefficient. A Latin hypercube experimental design method is used to design a polymer–surfactant flooding scheme with the main control factors of the channeling coefficient and early warning indicators. Numerical simulation is used to calculate samples of the channeling coefficient and early warning indicators under various conditions. The drop speed of the water–oil ratio reference value model and the rapid‐decline speed of the water cut reference value model are determined with a multiple regression method. A prediction model for the chemical agent channeling coefficient is established using the deviation coefficient of an early warning index. The method is applied in the Ng54‐61 polymer–surfactant flooding pilot area in the west of the Gudong Seventh District, Shengli Oilfield, China, and the error between the predicted result and the actual value is less than 10%. This research is helpful in taking effective antichanneling measures and improving the oil recovery degree of chemical flooding reservoirs. [ABSTRACT FROM AUTHOR]

This study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction. [ABSTRACT FROM AUTHOR]

This paper explores the impact of the political hierarchies of cities in China from different perspectives. First, we examine the economic disparities between prefectural cities and municipalities. Furthermore, this paper draws upon a quasi‐ experiment to analyze the impact of upgrading Chongqing to a municipality in 1997 using the synthetic control method. The city‐upgrading policy significantly increased Chongqing's gross domestic product (GDP) in the following 4 years. Finally, we find that the policy increased GDP in treated cities within 1200 km of Chongqing by about 10%–13% relative to the control cities. [ABSTRACT FROM AUTHOR]

Using data from the 2015 China Household Financial Survey (CHFS) this paper examines the effect of culture on the gender gap in financial literacy. We exploit geographical differences in culture in China, comparing outcomes between rural and urban areas and between areas in the east and west (Shanghai and Chongqing). Using the Blinder‐Oaxaca decomposition we show that, nationally, the gender gap in financial literacy is entirely the product of differences in the way men and women acquire financial literacy. It is a result consistent with cultural effects. When considering just women in Shanghai and Chongqing we observe a raw financial literacy differential of 13% (favoring Shanghai). This gap is also the product of differences in the way financial literacy is acquired. It provides additional evidence as to the importance of culture when it comes to understanding financial literacy. [ABSTRACT FROM AUTHOR]

The high incidence of adolescent depression has become the focus of social and academic attention. Exercise is an important method to improve adolescent depression, but its intervention effect is still controversial. This study first compares and analyzes the relevant studies at home and abroad and finds that exercise prescription in adolescent depression intervention is not accurate enough. A meta‐analysis was conducted to develop a precise exercise intervention strategy for adolescent depression. Firstly, this thesis identified how to optimize five elements (exercise intensity, exercise frequency, exercise time, exercise cycle, and exercise type) of exercise prescription to improve depression in adolescents. This is the problem. Furthermore, the concept of "precision exercise" was proposed, and a precision exercise intervention strategy (moderate‐intensity aerobic exercise for 8–10 weeks, 3 times/week, 45–50 min/time) was constructed to improve adolescent depression. This paper also presents research that strengthens the cross‐sectional research and empirical research on adolescent depression and establishes a precision exercise prescription database for adolescent depression in China. In conclusion, this study not only puts forward the concept of "precision exercise" but also constructs a precision exercise intervention strategy for adolescent depression, which has important theoretical and practical significance for improving the high incidence of adolescent depression. [ABSTRACT FROM AUTHOR]

Background: Kenny–Caffey syndrome type 2 (KCS2) is an extremely rare inherited disorder characterized by proportionate short stature, skeletal defects, ocular and dental abnormalities, and transient hypocalcemia. It is caused by variants in FAM111A gene. Diagnosis of KCS2 can be challenging because of its similarities to other syndromes, the absence of clear hallmarks and the deficient number of genetically confirmed cases. Here, we aimed to further delineate and summarize the genotype and phenotype of KCS2, in order to get a better understanding of this rare disorder, and promote early diagnosis and intervention. Methods: We present clinical and genetic characteristics of eight newly affected individuals with KCS2 from six families, including one family with three individuals found to be a father‐to‐daughter transmission, adding to the limited literature. Furthermore, we performed a review of genetically confirmed KCS2 cases in PubMed, MEDLINE and CNKI databases. Results: There were six females and two males in our cohort. All the patients presented with short stature (100.0%). Clinical manifestations included ocular defects such as hypermetropia (5/8), dental problems such as defective dentition (3/8) and dental caries (3/8), skeletal and brain anomalies such as delayed closure of anterior fontanelle (6/8), cerebral calcification (3/8), cortical thickening (3/8) and medullary stenosis (4/8) of tubular bones. Endocrinologic abnormalities included hypoparathyroidism (5/8) and hypocalcemia (3/8). One male patient had micropenis and microorchidism. All cases harboured missense variants of FAM111A, and nucleotides c.1706 arose as a mutational hotspot, with seven individuals harbouring a c.1706G>A (p.Arg569His) variant, and one child harbouring a c.1531T>C (p.Tyr511His) variant. Literature review yielded a total of 46 patients from 20 papers. Data analysis showed that short stature, hypoparathyroidism and hypocalcemia, ocular and dental defects, skeletal features including cortical thickening and medullary stenosis of tubular bones, and seizures/spasms were present in more than 70% of the reported KCS2 cases. Conclusion: We provide detailed characteristics of the largest KCS2 group in China and present the first genetically confirmed instance of father‐to‐daughter transmission of KCS2. Our study confirms that Arg569His is the hot spot variant and summarizes the typical phenotypes of KCS2, which would help early diagnosis and intervention. [ABSTRACT FROM AUTHOR]

In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF). [ABSTRACT FROM AUTHOR]

From core observations of shales of the Niutitang formation in the northern part of Guizhou Province, China, calcite was often found to act as a natural fracture filler and affect the extension of fractures in hydraulic fracturing. Therefore, it is crucial to understand the tensile mechanical behavior of shales by calcite veins. In this paper, by computed tomography scanning of shales containing calcite veins of different dip angles from the Niutitang formation, a three‐dimensional numerical model reflecting the internal fine structure of the shale was constructed. Direct tensile numerical simulations were carried out to investigate the effect of calcite veins at different angles on the fine‐scale damage process and mechanical properties of shale. The experimental results show that the tensile capacity of the shale increases with the increase in calcite veins. Depending on the dip angle of the calcite vein, the damage pattern of the shale is divided into three types of damage: pull‐off damage along the calcite vein, jagged damage, and horizontal damage perpendicular to the loading method. At high dip angles, the shale damage is more intense and the fracture network more complex. The temporal and spatial characteristics of the acoustic emissions provide a good indication of the microscopic behavior of the shale specimens during the damage process. The box dimension method was used to calculate the fractal dimension of the shale specimens at the final damage, and it was found that the damage was more intense and the fracture network was more complex at high dip angles, and there was an angular threshold. [ABSTRACT FROM AUTHOR]

Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future. Plain Language Summary: This research proposes a new method for downscaling using deep learning. The method uses a specific type of neural network called UNR‐Net, which combines attention mechanisms, residual modules, and terrain information. The performance of UNR‐Net is compared to two other methods: U‐Net and LR. In the study, UNR‐Net shows promise in performing a 10× downscaling of the daily 2‐m temperature in North China. The UNR‐Net demonstrates the best overall performance among all the comprehensive indicators (NSE, pattern correlation coefficient, root mean square error, and structural similarity index metric). Errors in the predictions are mainly found in complex terrain areas like the Taihang Mountains, but UNR‐Net shows noticeable improvements in these regions. The study also proposes a 12 components‐based error decomposition method to analyze the error sources of different models. All in all, it is found that the smallest errors are observed during the summer season and the main source error is the sequence error component. Additionally, when considering lead times of 1–7 days, UNR‐Net consistently shows the lowest errors among all 12 error components. Based on these findings, combining numerical weather prediction models with deep learning methods holds great promise for generating high‐resolution temperature forecasts. Key Points: This paper presents a novel deep learning downscaling method, UNR‐Net, capable of downscaling daily 2‐m temperature by a factor of 10The overall performance of the UNR‐Net method surpasses the U‐Net method and linear regression methodThe 12 components‐based error decomposition method is proposed to analyze the error source of different models [ABSTRACT FROM AUTHOR]

A PDE‐constrained model predictive control (MPC) algorithm for open‐channel systems based on the Saint‐Vevant(S‐V) equations is investigated in this paper. The S‐V equations, which precisely model the dynamics of open‐channel systems, are quasi‐linear hyperbolic partial differential equations (PDEs) without analytical solutions. Directly applying the S‐V equations to an MPC controller design becomes sophisticated. In this work, the calculus of variation is used to obtain the adjoint equations and the adjoint analysis method is utilized to deduce the gradients of the MPC optimization problem. Particularly, the physical constraints involving both the state and control variables are also considered. A gradient‐based optimization algorithm in combination with the numerical computation of Preissmann implicit scheme is proposed to solve the constrained MPC optimization problem. The control performances of the developed PDE‐constrained MPC algorithm with respect to the controlled water levels and gate openings are compared with those of the MPC controller designed for the linearized model. All the simulation tests are carried out on an aqueduct reach in Yehe Irrigation District in Hebei Province, China. The results show that the proposed PDE‐constrained MPC algorithm is a promising method in dealing with the constraints in terms of hyperbolic PDEs, control variables and state variables simultaneously. [ABSTRACT FROM AUTHOR]

The Shanghai Soft X-ray Free-Electron Laser (SXFEL) is the first X-ray freeelectron laser facility in China. The SASE beamline, which consists of a pinkbeam branch and a mono-beam branch, is one of the two beamlines in the Phase-I construction. The pink-beam branch opened for users in 2023 after successful first-round beamline commissioning. In this paper, the design of the beamline is presented and the performance of the pink-beam branch is reported. The measured energy-resolving power of the online spectrometer is over 6000 @ 400 eV. The focusing spot size of the pink beam is less than 3 μm in both the horizontal and vertical at the endstation. [ABSTRACT FROM AUTHOR]

Organic‐rich shale in saline lacustrine basins holds significant importance as a source rock for conventional hydrocarbon exploration and has emerged as a prominent target for unconventional hydrocarbon exploration and development in recent years. Based on saline lacustrine basins, this paper provides a summary of the organic matter enrichment mechanisms in saline lacustrine basins, considering sedimentary characteristics, biological activities, factors for the organic matter enrichment and consumption, and hydrocarbon generation. The implications for these factors are discussed in relation to the distribution prediction of high‐quality lacustrine shale district settings and the exploration and development of shale oil. Saline lacustrine basins undergo distinct evolutionary stages, each corresponding to different sedimentary stages involving carbonate minerals, sulphate minerals, and alkaline minerals. Moreover, these basins exhibit diverse biological types and experience extensive biological activities. The prosperity of organisms and the accumulation of sedimentary organic matter are ensured by halophilic organisms. Organic matter enrichment in saline lacustrine basins is influenced by two main aspects: the primary productivity of organic matter, which is promoted by the proliferation of halophilic organisms, and the efficient preservation of organic matter facilitated by the strong reducing environment resulting from promoted water salinity stratification. The organic matter consumption in saline lacustrine basins involves bacterial sulphate reduction (BSR) in the early stages, thermochemical sulphate reduction (TSR) in the late stages, and dilution of salt minerals with higher depositional rates. of the presence of salt beds and saline minerals positively influences hydrocarbon generation and expulsion in organic‐rich shale within saline lacustrine deposition. Consequently, continental saline lacustrine basins in China offer favourable conditions for the formation of organic‐rich shale and present broad prospects for the exploration of shale oil and gas resources. [ABSTRACT FROM AUTHOR]

Prospecting for and exploiting buried mineral deposits is currently challenging. Given their high precision and resolution, reflection seismic methods might be useful in such applications involving deep mineral deposits. However, there are few open seismic datasets available from mineral deposit exploration, especially in hardrock environments. The world‐class Caosiyao porphyry Molybdenum deposit (1.76 Mt) in the Jining area of Inner Mongolia, China, is largely covered by loess layers, which poses challenges to its exploration. Seismic reflection surveys were conducted to help delineate the deep granite porphyry intrusions and associated orebodies. This paper presents the raw seismic reflection dataset from three profiles on the Caosiyao deposit area, which can be used as a standard dataset for reflection seismic processing in shallow coverage and hardrock areas. Situated at the juxtaposition of the Khondalite Belt and the Trans‐North China Orogen in the northern North China Craton, the Jining region hosts considerable known porphyry Mo deposits. As such, this open dataset can assist in research on deep geological structures and hence increase prospecting efficiency in geologically similar areas. [ABSTRACT FROM AUTHOR]

This paper examines the impacts of a government‐led e‐commerce expansion project on household income. We find that the project has led to increases in rural household income. The positive impacts are more prominent for lower‐income rural households. The income‐increasing effects are driven by the reduction in information search costs and transportation costs. The provision of complementary interventions for e‐commerce, such as promoting the internet‐related skills of farmers and the expansion of formal financial credit support is also important for the program's success. The results provide policy implications for the design and implementation of government‐led e‐commerce policies in developing economies. [ABSTRACT FROM AUTHOR]