mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Cassava MeRS40 is required for the regulation of plant salt tolerance

Soil salinity affects the expression of serine/arginine-rich(SR)genes and isoforms by alternative splicing, which in turn regulates the adaptation of plants to stress. We previously identified the cassava spliceosomal component 35 like(SCL)and SR subfamilies, belonging to the SR protein family, which are extensively involved in responses to abiotic stresses. However, the post-transcriptional regulatory mechanism of cassava arginine/serine-rich(RS)subfamily in response to salt stress remains to be explored. In the current study, we identified 37 genes of the RS subfamily from 11 plant species and systematically investigated the transcript levels of the RS40 and RS31 genes under diverse abiotic stress conditions. Subsequently, an analysis of the conserved protein domains revealed that plant RS subfamily genes were likely to preserve their conserved molecular functions and played critical functional roles in responses to abiotic stresses. Importantly, we found that overexpression of MeRS40 in Arabidopsis enhanced salt tolerance by maintaining reactive oxygen species homeostasis and up-regulating the salt-responsive genes. However, overexpression of MeRS40 gene in cassava reduced salt tolerance due to the depression of its endogenous gene expression by negative autoregulation of its own pre-mRNA. Moreover, the MeRS40 protein interacted with MeU1- 70Ks(MeU1-70Ka and MeU1-70Kb)in vivo and in vitro, respectively. Therefore, our findings highlight the critical role of cassava SR proteins in responses to salt stress in plants.
A Self -calibration Bundle Adjustment Algorithm Based on Block Matrix Cholesky Decomposition Technology

In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment. The innovation points are reflected in the following aspects: ① The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear; ② The complexities of time and space tend to O(n) in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly; ③ The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes; ④ Some measures are employed to improve the optimization effects. Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth. This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.
Dual-mode Switching Fault Ride-through Control Strategy for Self-synchronous Wind Turbines

The installed capacity of renewable energy generation has continued to grow rapidly in recent years along with the global energy transition towards a 100% renewable-based power system. At the same time, the grid-connected large-scale renewable energy brings significant challenges to the safe and stable operation of the power system due to the loss of synchronous machines. Therefore, self-synchronous wind turbines have attracted wide attention from both academia and industry. However, the understanding of the physical operation mechanisms of self-synchronous wind turbines is not clear. In particular, the transient characteristics and dynamic processes of wind turbines are fuzzy in the presence of grid disturbances. Furthermore, it is difficult to design an adaptive fault ride-through(FRT)control strategy. Thus, a dual-mode switching FRT control strategy for self-synchronous wind turbines is developed. Two FRT control strategies are used. In one strategy, the amplitude and phase of the internal potential are directly calculated according to the voltage drop when a minor grid fault occurs. The other dual-mode switching control strategy in the presence of a deep grid fault includes three parts: vector control during the grid fault, fault recovery vector control, and self-synchronous control. The proposed control strategy can significantly mitigate transient overvoltage, overcurrent, and multifrequency oscillation, thereby resulting in enhanced transient stability. Finally, simulation results are provided to validate the proposed control strategy.
Phasor Analytical Model of Non-isolated DC/ DC Converter Based on Modular Multilevel Converter for DC Transmission Grids

Non-isolated DC/DC converter based on modular multilevel converter(MMC)technology is expected to play an important role in future DC transmission grids. This paper presents a phasor analytical model for this new family of converters which is suitable for a range of studies like DC grid power flow or DC/DC parametric design. The 30th-order phasor model is derived in 3 coordinate frames: zero sequence(DC), fundamental frequency(dq), and double frequency(d2q2). The second-harmonic current suppression control is included as an option. Additionally, an estimation of the required control signals is presented, and a closed-loop model is developed which facilitates direct calculation of all variables and fast parametric studies. The accuracy of the proposed models is verified against a detailed PSCAD model for a wide range of parameters. The studies illustrate the importance of the second-harmonic components on the model accuracy. Finally, the impact of the converter parameters on the performance is studied, and a basic eigenvalue stability analysis is given.
Reliability Assessment of Interconnected Power Systems with HVDC Links Considering Frequency Regulation Process

With various components and complex topologies, the applications of high-voltage direct current(HVDC)links bring new challenges to the interconnected power systems in the aspect of frequency security, which further influence their reliability performances. Consequently, this paper presents an approach to evaluate the impacts of the HVDC link outage on the reliability of interconnected power system considering the frequency regulation process during system contingencies. Firstly, a multi-state model of an HVDC link with different available loading rates(ALRs)is established based on its reliability network. Then, dynamic frequency response models of the interconnected power system are presented and integrated with a novel frequency regulation scheme enabled by the HVDC link. The proposed scheme exploits the temporary overload capability of normal converters to compensate for the imbalanced power during system contingencies. Moreover, it offers frequency support that enables the frequency regulation reserves of the sending-end and receiving-end power systems to be mutually available. Several indices are established to measure the system reliability based on the given models in terms of abnormal frequency duration, frequency deviation, and energy losses of the frequency regulation process during system contingencies. Finally, a modified two-area reliability test system(RTS)with an HVDC link is adopted to verify the proposed approach.
非编码RNA对血管平滑肌细胞功能调控的研究进展

血管内膜增生(IH)是血管损伤后继发的一种结构性改变,是糖尿病血管病变、高血压、动脉粥样硬化等疾病的重要病理特征,同时也是血管介入手术、动静脉内瘘、血管旁路移植术后再狭窄的病理基础。血管平滑肌细胞(VSMC)在IH过程中扮演着决定性作用。越来越多的研究表明非编码RNA通过调控VSMC功能参与IH过程。该文主要从非编码RNA调控VSMC表型转换、增殖、迁移、凋亡等角度综述其在IH过程中的机制。
张小萍教授治疗溃疡性结肠炎经验撷菁

溃疡性结肠炎是消化系统高发病,临床症状多变,往往迁延难愈,反复发作,给患者的生活质量带来很大影响。张小萍教授认为溃疡性结肠炎病因病机虽复杂,但总结归纳为湿、郁、瘀、虚四大病因所致,其中湿是重要致病因素,横贯疾病发展始终,郁、瘀是影响疾病进展预后的关键,而虚则是发病的基础和久病的根源,临床治疗需抓住湿、郁、瘀、虚四大核心病因,采取祛湿、解郁、散瘀、补虚等治法,疗效显著。
从一则教学查房病例谈高血压全科医学理念的培养

高血压一直被归于心内科专科研究领域,多年来其患病率一直居高不下,引起了政府卫生部门和全社会的高度重视。本文以一例普通高血压的常规教学查房为契机,尝试从全科医学视角对高血压进行重新认识、解读和防治,或许有助于降低高血压的发病率。
肿瘤专业质量控制指标(2023年版):食管癌、胃癌、肝癌、结直肠癌

1.定义:首次治疗前完成临床TNM分期诊断的食管癌患者数占接受首次治疗的食管癌患者总数的比例。2.计算公式:食管癌患者首次治疗前临床TNM分期诊断率=(首次治疗前完成临床TNM分期诊断的食管癌患者数/同期接受首次治疗的食管癌患者总数)×100% 3.意义:治疗前全面评价病情是肿瘤规范化治疗的基础。4.说明:(1)首次治疗指针对肿瘤开展的手术、放疗、化疗、靶向治疗、免疫治疗等治疗,不包括为明确诊断或病情而采取的穿刺、活检、检查等诊疗措施。下同。(2)分期方法参见国家卫生健康委发布的《食管癌诊疗指南(2022年版)》。下同。
Identifying the uneven distribution of health and education services in China using open geospatial data

Growing attention has been directed to the use of satellite imagery and open geospatial data to understand largescale sustainable development outcomes.Health and education are critical domains of the Unites Nations' Sustainable Development Goals (SDGs),yet existing research on the accessibility of corresponding services focused mainly on detailed but small-scale studies.This means that such studies lack accessibility metrics for large-scale quantitative evaluations.To address this deficiency,we evaluated the accessibility of health and education services in mainland China in 2021 using point-of-interest data,OpenStreetMap road data,land cover data,and WorldPop spatial demographic data.The accessibility metrics used were the least time costs of reaching hospital and school services and population coverage with a time cost of less than 1 h.On the basis of the road network and land cover information,the overall average time costs of reaching hospital and school were 20 and 22 min,respectively.In terms of population coverage,94.7% and 92.5% of the population in China has a time cost of less than 1 h in obtaining hospital and school services,respectively.Counties with low accessibility to hospitals and schools were highly coupled with poor areas and ecological function regions,with the time cost incurred in these areas being more than twice that experienced in non-poor and non-ecological areas.Furthermore,the cumulative time cost incurred by the bottom 20% of counties (by GDP) from access to hospital and school services reached approximately 80% of the national total.Low-GDP counties were compelled to suffer disproportionately increased time costs to acquire health and education services compared with high-GDP counties.The accessibility metrics proposed in this study are highly related to SDGs 3 and 4,and they can serve as auxiliary data that can be used to enhance the evaluation of SDG outcomes.The analysis of the uneven distribution of health and education services in China can help identify areas with backward public services and may contribute to targeted and efficient policy interventions.
面向高分辨率遥感影像大范围道路提取的深度学习方法研究

高分辨率遥感影像具有空间信息丰富、获取技术成熟、影像分辨率高、覆盖范围广等优势,已成为道路提取的主要数据来源。面对海量遥感数据,目前使用最为广泛的是基于深度学习的方法,深度学习的出现使自学习特征成为现实,开启了数据驱动的"表示学习"范式,避免了传统方法依赖于专家知识设计特征,自动化程度低,人工干预大的问题。深度学习方法依赖于海量数据来训练模型,而高分辨率遥感影像空间分辨率高,影像信息高度细节化,会给道路识别带来很多干扰。例如:(1)高分辨率遥感影像上道路提取面临道路面分割、中心线提取和边界探测3个典型任务,目前深度学习方法未能有效实现3个任务的统一表达;(2)复杂高分辨率遥感影像上的建筑物、树木等其他地物会给道路带来遮挡、阴影等干扰,造成道路识别结果不连续;(3)在大范围道路提取应用时,由于不同地区影像数据分布差异大,深度学习模型的泛化能力十分有限,导致模型输出结果中存在严重的道路缺失现象。
A practical method for identifying key factors in the distribution and formation of heavy metal pollution at a smelting site

Smelting activities are the main pathway for the anthropogenic release of heavy metals (HMs) into the soil-groundwater environment.It is vital to identify the factors affecting HMs pollution to better prevent and manage soil pollution.The present study conducted a comprehensive investigation of HMs in soil from a large abandoned Zn smelting site.An integrated approach was proposed to classify and quantify the factors affecting HMs pollution in the site.Besides,the quantitative relationship between hydrogeological characteristics,pollution transmission pathways,smelting activities and HMs pollution was established.Results showed that the soils were highly contaminated by HMs with a pollution index trend of As >Zn >Cd >Pb >Hg.In identifying the pollution hotspots,we conclude that the pollution hotspots of Pb,As,Cd,and Hg present a concentrated distribution pattern.Geo-detector method results showed that the dominant driving factors for HMs distribution and accumulation were the potential pollution source and soil permeability.Additionally,the main drivers are variable for different HMs,and the interaction among factors also enhanced soil HMs contamination.Our analysis illustrates how the confounding influences from complex environmental factors can be distilled to identify key factors in pollution formation to guide future remediation strategies.
The feasibility for natural graphite to replace artificial graphite in organic electrolyte with different film-forming additives

The feasibility for natural graphite (NG) to replace artificial graphite (AG) in organic electrolytes with different additives are investigated.Although the strong film-forming additives contributes to form robust solid electrolyte interphase (SEI) film on graphite particle surface,great differences in gas evolution,lithium inventory loss and other side reactions are observed.Lithium bis(oxalato)borate (LiBOB) and fluoroethylene carbonate (FEC) are found more effective and the combination shows to be more promising.In the optimized electrolyte,natural graphite anode exhibits excellent long-term cycling capability.After 800 cycles at high temperature,the capacity retention is comparable to that using artificial graphite.The mechanisms for the capacity-fading of the full cells with AG and NG anode are investigated by ICP,SEM and polarization studies.The results shows that NG electrode consumes more active lithium due to the rough surface and larger volume expansion.The rapid capacity-fading in the initial 100 cycles is related to the instability of the SEI film aroused from large volume expansion.The systematic analysis is inspiriting for the development of high performance lithium ion batteries with reduced cost.
Pathway for high-energy density LiMnFePO_4 cathodes

Polyanion cathodes are credited for its thermal stability and better safety,no matter in lithium ion batteries or sodium ion batteries.Polyanion oxides with phosphate groups came to the public's attention in 1997,and the representative material is LiFePO_4,which has been widely applied and plays a huge role in the field of powder batteries and energy storage system.However,owing to the low lithiation potentials and storage sites,the energy densities of polyanion cathodes have been restricted,resulting of low-endurance and limited application scenarios.Accordingly,here,we use cheap and environmental friendly raw materials as precursors to synthesis high energy density LiMn0.6Fe0.4PO_4@C cathode by a simple spray-drying and high temperature calcination process.The self-designed liquid polyacrylonitrile (LPAN) is added for the intention of nanoparticle coating,conductive network construction and particle granulation.The low-cost and carbon-coated LiMn0.6Fe0.4PO_4 cathode exhibits excellent reversible capacity,low electrochemical polarization and excellent rate capacity,which maintains 93.5% capacity retention after cycling 1000 times at 5C.The work introduces a new avenue to fabricate olivine structure cathodes with outstanding electrochemical performance for the high energy density lithium ion batteries.
A Hybrid Entropy and Blockchain Approach for Network Security Defense in SDN-Based IIoT

In the industrial Internet of things(IIoT), various applications generate a large number of interactions and are vulnerable to various attacks, which are difficult to be monitored in a sophisticated way by traditional network architectures. Therefore, deploying software-defined network(SDN)in IIoT is essential to defend against various attacks. However, SDN has a drawback: there is a security problem of distributed denial-ofservice(DDoS)attacks at the control layer. This paper proposes an effective solution: DDoS detection within the domain using tri-entropy in information theory. The detected attacks are then uploaded to a smart contract in the blockchain, so that the attacks can be quickly cut off even if the same attack occurs in different domains. Experimental validation was conducted under different attack strengths and multiple identical attacks, and the results show that the method has better detection ability under different attack strengths and can quickly block the same attacks.
Numerical analysis of matrix swelling and its effect on microstructure of digital coal and its associated permeability during CO_2-ECBM process based on X-ray CT data

Matrix swelling effect will cause the change of microstructure of coal reservoir and its permeability, which is the key factor affecting the engineering effect of CO_2-ECBM technology. The Sihe and Yuwu collieries are taken as research objects. Firstly, visualization reconstruction of coal reservoir is realized. Secondly, the evolution of the pore/fracture structures under different swelling contents is discussed. Then, the influence of matrix phase with different swelling contents on permeability is discussed. Finally, the mechanism of swelling effect during the CO_2-ECBM process is further discussed. The results show that the intra-matrix pores and matrix-edge fractures are the focus of this study, and the contacting area between matrix and pore/fracture is the core area of matrix swelling. The number of matrix particles decreases with the increase of size, and the distribution of which is isolated with small size and interconnected with large size. The swelling effect of matrix particles with larger size has a great influence on the pore/fracture structures. The number of connected pores/fractures is limited and only interconnected in a certain direction. With the increase of matrix swelling content, the number, porosity, width, fractal dimension, surface area and volume of pores/fractures decrease, and their negative contribution to absolute permeability increases from 0.368% to 0.633% and 0.868%-1.404%, respectively. With the increase of swelling content, the number of intra-matrix pores gradually decreases and the pore radius becomes shorter during the CO_2-ECBM process. The matrix continuously expands to the connected fractures, and the width of connected fractures gradually shorten. Under the influence of matrix swelling, the bending degree of fluid flow increases gradually, so the resistance of fluid migration increases and the permeability gradually decreases. This study shows that the matrix swelling effect is the key factor affecting CBM recovery, and the application of this effect in CO_2-ECBM process can be discussed.
Reclassification and distribution patterns of discovered oils in the Dongying Depression, Bohai Bay Basin, China

The Dongying Depression is an important petrolifeous province, with diverse source rocks and complex petroleum distribution patterns. A total of 32 crude oils were analyzed by the gas chromatographyemass spectrometry and isotopic compositions to better understanding the petroleum systems in the study area. Three oil types were classified by hierarchical cluster analyses. Type I and II oils have closely correlation with the discovered source rocks, which have been confirmed to be mainly derived from the lower third and upper forth member of the Eocene Shahejie Formation source rocks (Es3L and Es4U), respectively. Obviously, type III oils contain abundant gammacerane, tricyclic terpanes and C29 steranes and have lower values of δ~(13)C than type I and II oils, indicating a completely different source rock and biological origins. Until recently, type III oils fail to match any of the discovered source rock, which contains main contribution of aquatic organism or/and bacteria inputs. In addition, the spacial distribution of these three oil types were discussed. Type I oils mainly distributed in the Es3 and Es4 reservoirs that closed to the generative kitchens. Type II oils occurred in the Es4 reservoirs in the sourthern slope of the depression, which probably caused by lateral migration along the horizontal fractures and sandstone layers within the Es4 interval. Differently, type III oils in the sourthern slope of the depression were mainly discovered in the Eocene Kongdian or Ordocician reservoirs, which suggests great exploration potential of deep underlying strata.
A systematic machine learning method for reservoir identification and production prediction

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development. Machine learning (ML) methods are used for petroleum-related studies, but have not been applied to reservoir identification and production prediction based on reservoir identification. Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production. In this paper, a systematic ML method was developed using classification models for reservoir identification, and regression models for production prediction. The production models are based on the reservoir identification results. To realize the reservoir identification, seven optimized ML methods were used: four typical single ML methods and three ensemble ML methods. These methods classify the reservoir into five types of layers: water, dry and three levels of oil (I oil layer, II oil layer, III oil layer). The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification. The XGBoost produced the model with the highest accuracy; up to 99%. The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production. Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness. To validate the superiority of the ML methods, reference models using overall reservoir thickness were built for comparison. The models based on effective thickness outperformed the reference models in every evaluation metric. The prediction accuracy of the ML models using effective thickness were 10% higher than that of reference model. Without the personal error or data distortion existing in traditional methods, this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges. The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.
Rock mechanical properties of coal in cryogenic condition

Liquid nitrogen (LN2) fracturing is a kind of non-aqueous fracturing technology, which is expected to provide a new and efficient way for coalbed methane (CBM) development. The mechanical properties of coal under LN2 freezing are very important for studying the mechanism of LN2 fracturing. However, most of the current research is limited to studying mechanical properties of rocks after being frozen by LN2 and returned to room temperature. In this paper, the effect of LN2 freezing on the mechanical properties of coal was studied. Uniaxial strength tests and Brazil tests were carried out for dry and water-saturated coal samples with different types and bedding directions. In addition, standard electron microscopy (standard SEM) and cryo-electron microscopy (Cryo-SEM) were used to compare the fracture morphology of coal samples at room temperature and LN2 temperature. The results showed that LN2 freezing can damage and improve the mechanical properties of coal simultaneously. The strength of saturated coal under freezing is higher than that of dry coal, and the filling of ice can enhance the mechanical strength of coal. In addition, the mechanical properties of coal with higher porosity are enhanced more than that of coal with lower porosity under LN2 freezing. The main findings of this study are the keys to the research of LN2 fracturing mechanisms in CBM reservoirs.