mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Advanced metal-organic frameworks for aqueous sodium-ion rechargeable batteries

Inexpensive and abundant sodium resources make energy storage systems using sodium chemistry promising replacements for typical lithium-ion rechargeable batteries (LIBs). Fortuitously, aqueous sodium-ion rechargeable batteries (ASIBs), which operate in aqueous electrolytes, are cheaper, safer, and more ionically conductive than batteries that operate in conventional organic electrolytes; furthermore, they are suitable for grid-scale energy storage applications. As electrode materials for storing Na+ ions in ASIBs, a variety of multifunctional metal-organic frameworks (MOFs) have demonstrated great potential in terms of having porous 3D crystal structures, compatibility with aqueous solutions, long cycle lives (-1000 cycles), and ease of synthesis. The present review describes MOF-derived technologies for the successful application of MOFs to ASIBs and suggests future challenges in this area of research based on the current understanding.
Robust S-doped TiO_2@N,S-codoped carbon nanotube arrays as free-binder anodes for efficient sodium storage

Titanium dioxide (TiO_2) has been investigated broadly as a stable, safe, and cheap anode material for sodium-ion batteries in recent years. However, the poor electronic conductivity and inherent sluggish sodium ion diffusion hinder its practical applications. Herein, a self-template and in situ vulcanization strategy is developed to synthesize self-supported hybrid nanotube arrays composed of nitrogen/ sulfur-codoped carbon coated sulfur-doped TiO_2 nanotubes (S-TiO_2@NS-C) starting from H2Ti2O5·H_2O nanoarrays. The S-TiO_2@NS-C composite with one-dimensional nano-sized subunits integrates several merits. Specifically, sulfur doping strongly improves the Na~+ storage ability of TiO_2@C-N nanotubes by narrowing the bandgap of original TiO_2. Originating from the nanoarrays structures built from hollow nanotubes, carbon layer and sulfur doping, the sluggish Na~+ insertion/extraction kinetics is effectively improved and the volume variation of the electrode material is significantly alleviated. As a result, the S-TiO_2@NS-C nanoarrays present efficient sodium storage properties. The greatly improved sodium storage performances of S-TiO_2@NS-C nanoarrays confirm the importance of rational engineering and synthesis of hollow array architectures with higher complexity.
Designing N-doped graphene/ReSe_2/Ti_3C_2 MXene heterostructure frameworks as promising anodes for high-rate potassium-ion batteries

Developing high-performance anodes for potassium ion batteries (KIBs) is of paramount significance but remains challenging. In the normal sense, electrode materials are prepared by ubiquitous wet chemical routes, which otherwise might not be versatile enough to create desired heterostructures and/or form clean interfacial areas for fast transport of K-ions and electrons. Along this line, rate capability/cycling stability of resulting KIBs are greatly handicapped. Herein we present an all-chemical vapor deposition approach to harness the direct synthesis of nitrogen-doped graphene (NG)/rhenium diselenide (ReSe2) hybrids over three-dimensional MXene supports as superior heterostructure anode material for KIBs. In such an innovative design, 1T0-ReSe_2 nanoparticles are sandwiched in between the NG coatings and MXene frameworks via strong interfacial interactions, thereby affording facile K+ diffusion, enhancing overall conductivity, boosting high-power performance and reinforcing structural stability of electrodes. Thus-constructed anode delivers an excellent rate performance of 138 mAh g~(-1) at 10.0 A g~(-1) and a high reversible capacity of 90 mAh g~(-1) at 5 A g~(-1) after 300 cycles. Furthermore, the potassium storage mechanism has been systematically probed by advanced in situ/ex situ characterization techniques in combination with first principles computations.
Heat transfer characterization and improvement in an external catalyst cooler fluidized bed

Gas-solid fluidized beds have been widely used in heat transfer processes,and so there have been many studies focused on increasing heat transfer in such units.In the present work,a pilot scale cold mode experimental rig was constructed to assess the effects of hydrodynamics on bed-to-wall heat transfer and to investigate various means of enhancing heat transfer in a dense gas-solid fluidized bed with external solid circulation.The experimental results show that heat transfer in the dense region played a dominant role in total bed-to-wall heat transfer,accounting for more than 88% of the total heat transfer load.Heat transfer could be lowered as a result of solids bypass that occurred because of external solids circulation,but this effect was weakened by the radial mixing of particles.The heat transfer characteristics identified in this study indicate that a specially designed baffle can be used to enhance bed-to-wall heat transfer.After installing such baffles in the fluidized bed test structure,a 70% increase in the total heat transfer coefficient was obtained.
Queue estimation for isolated signalized intersections in intelligent vehicle-infrastructure cooperation systems

Dear editor, Traffic jams have become an important issue in urban networks, particularly for arterial roads [1].The need for more efficient traffic control techniques has become critical.One good solution is the so-called intelligent vehicleinfrastructure cooperation systems(i-VICS), which assumes autonomous vehicles in the traffic flow(i.e., a mixed traffic flow).The aim of i-VICS is to achieve cooperative vehicle intersection control, i.e., to enable cooperation between vehicles and traffic signals, for safe and efficient intersection operations.
Fault detection for a class of linear systems with integral measurements

This paper discusses the parity space-based fault detection(FD) method for a class of linear discrete-time systems with integral measurements.The integral measurements are functions of the system states over a given time window.We establish a novel parity relation to tackle integral measurements.The parameters of the FD unit are redesigned such that the generated residual signal is simultaneously decoupled from initial states, robust against disturbances, and sensitive to the faults.We employ the singular value decomposition algorithm to calculate parity space matrices.Finally, an experiment is presented to show the effectiveness of the proposed method.
Bi_2O_2Se/BP van der Waals heterojunction for high performance broadband photodetector

Broadband photodetector has wide applications in the field of remote sensing, health monitoring and medical imaging.Two-dimensional(2D) materials with narrow bandgaps have shown enormous potential in broadband photodetection.However, the device performance is often restricted by the high dark currents.Herein, we demonstrate a high performance broadband photodetector by constructing Bi_2O_2Se/BP van der Waals heterojunction.The device exhibits a p-n diode behavior with a current rectification ratio of ~20.Benifited from the low dark current of the heterojunction and the effective carrier separation, the device achieves the responsivity(R) of ~ 500 A/W, ~ 4.3 A/W and ~ 2.3 A/W at 700 nm, 1310 nm and 1550 nm, respectively.The specific detectivity(D*) is up to ~ 2.8×10~(11) Jones(700 nm), ~ 2.4×10~9 Jones(1310 nm) and ~ 1.3×10~9 Jones(1550 nm).Moreover, the response time is ~ 9 ms, which is more than 20 times faster than that of individual BP(~ 190 ms) and Bi_2O_2Se(~ 180 ms) devices.
清醒俯卧位通气策略在低氧性呼吸衰竭患者中的应用进展

俯卧位(PP)作为一种可能改善通气的方式最早于20世纪70年代被提出,此后历经40余年的发展,大样本的随机对照研究证实俯卧位对机械通气的重度急性呼吸窘迫综合征(ARDS)患者能改善氧合及预后。2010年Diaz等在针对重度ARDS患者的“六步法”管理策略中纳入俯卧位通气。2017年美国胸科协会/欧洲重症医学会/美国重症医学会在成人ARDS的机械通气指南中提出对于重度ARDS患者,强烈推荐每日俯卧位通气时间>12 h。然而,俯卧位通气在清醒的低氧性呼吸衰竭患者中的疗效尚缺乏足够的证据,研究多局限于病例报道和小样本量回顾性观察研究,直到新型冠状病毒肺炎(简称新冠肺炎)在全球爆发流行,由于医疗卫生资源的紧缺及专业人员的不堪重负,同时气管插管等易产生气溶胶的操作有引起疾病传播的风险,以及鉴于进行气管插管有创通气的呼吸衰竭患者的高死亡率,清醒俯卧位通气治疗在部分低氧性呼吸衰竭、甚至ARDS的新冠肺炎患者中得到了尝试。清醒俯卧位通气治疗低氧性呼吸衰竭在新冠肺炎患者的可行性得以证实,同时发现其对于改善患者氧合有一定作用。最近,清醒俯卧位通气治疗联合经鼻高流量氧疗(HFNC)或无创通气(NIV)用于治疗氧合指数(PaO_2/FiO_2)低于200 mm Hg(1 mm Hg=0.133 kPa)的新冠肺炎也写入我国《新型冠状病毒肺炎诊疗方案(试行第八版)》。本文就清醒俯卧位通气策略在低氧性呼吸衰竭患者中的应用进行综述。
内镜超声引导下胆道引流治疗肝包虫病相关胆管良性狭窄一例

内镜超声下胆道引流多用于胆道恶性狭窄的治疗。本文报道了1例肝包虫病相关肝门部胆管良性狭窄病例,经胃胆道金属支架置入术后症状缓解快,无并发症发生,诊疗结局良好。
人工流产术后停经适时宫腔镜检查对宫腔粘连的预防价值

目的:探讨早期妊娠人工流产术后停经适时宫腔镜检查对宫腔粘连的预防价值。方法:选择德州市妇幼保健院2017年1月至2020年6月因人工流产术后超过42天月经仍未来潮者进行宫腔镜检查的患者262例。按停经时间分为4组:人工流产术后42~50天月经未来潮者73例(A组), 人工流产术后51 ~ 60天未来潮者84例(B组), 人工流产术后61 ~ 70天未来潮者55例(C组), 人工流产术后> 70天未来潮者50例(D组) 。比较4组的年龄、孕产次、粘连程度等情况。结果: 4组患者的年龄、孕产次比较,差异无统计学意义(P>0.05) 。人工流产术后停经时间不同,宫腔粘连发生程度不同,差异有统计学意义(P<0.05), 随着人工流产术后停经时间的延长,宫腔粘连程度有增加趋势(P<0.05) 。结论:早期妊娠人工流产术后停经时长与粘连的程度具有相关性,粘连的程度可能随着病程延长而加重。及早行宫腔镜检查,可能有助于减少中重度IUA的发生,从而改善患者生殖预后。
宁夏社区2型糖尿病患者血脂异常情况及影响因素分析

目的了解宁夏社区2型糖尿病(T2DM)患者血脂异常现状及其影响因素,为T2DM患者血脂异常预防控制提供建议。方法于2018年12月—2019年4月,采取多阶段分层整群随机抽样方法,以宁夏回族自治区银川市兴庆区、吴忠市所辖青铜峡市、中卫市沙坡头区和石嘴山市平罗县部分社区的1 788例25~75岁T2DM患者为研究对象,进行问卷调查,体格检查和实验室检测。采用SPSS 25.0统计软件进行t检验、方差分析、χ~2检验和秩和检验,采用多因素二元logistic回归对血脂异常影响因素进行分析。结果宁夏社区T2DM患者血脂异常患病率为78.0%(1 394/1 788),高胆固醇(TC)血症、高甘油三酯(TG)血症、低高密度脂蛋白胆固醇(HDL-C)血症和高低密度脂蛋白胆固醇(LDL-C)血症患病率分别为11.1%、40.0%、37.0%和12.1%。多因素logistic回归分析显示,女性(OR=1.520)、较高年龄(45岁~OR=1.487,55岁~ OR=1.417)、高糖化血红蛋白(HbA1C)水平(≥7%OR=1.308)、腰围(81.43~、88.00~、93.50~ cm OR值分别为1.651、2.032和2.137)、尿酸(OR=1.004)、γ-谷氨酰转肽酶(OR=1.020)、总蛋白(OR=1.048)与血脂异常高风险相关,天冬氨酸氨基转移酶/丙氨酸氨基转移酶(OR=0.764)与血脂异常低风险相关,均有统计学意义(P<0.05,P<0.01)。结论宁夏社区T2DM患者的血脂异常患病率较高,以高TG血症和低HDL-C血症为主。高龄女性T2DM患者为血脂异常的高发人群,在控制HbA1C的同时需注意防控中心性肥胖,还需定期监测尿酸、γ-谷氨酰转肽酶、总蛋白等指标以早期发现血脂异常。
胃食管反流病从肾论治

肾主气化、司开阖,是调节人体水液平衡的中心环节。立足于肾藏真阴、真阳的生理功能及其与肝、脾胃的关系,认为胃食管反流病的主要病机为水寒木郁、上焦结热,命门火衰、虚火上灼,肾精亏虚、湿热内蕴,分别以乌梅汤清上温下、顺接阴阳,金匮肾气丸合交泰丸阴中求阳、引火归元,加味三才封髓丹健脾坚肾、益气养阴、芳化湿浊。
2020/2021年度冬季气象条件对农业生产的影响

2020/2021年度冬季平均气温为1961年以来第8个高值,仍属暖冬年,季内气温起伏显著,前冬冷后冬暖。平均降水量较常年同期偏少25%。平均日照接近常年同期,其中四川盆地东部、江南中西部以及华南较常年同期偏多。北方冬麦区大部气温偏高,多次强降温过程对小麦无明显不利影响,2月西北地区东南部、华北南部、黄淮大部出现降水,降水过程平缓、持续时间长,土壤蓄墒好,水热条件利于冬小麦安全越冬及返青生长。南方大部冬季光热充足,后冬的降水缓解了前期的旱情,利于油菜、小麦、冬种作物以及经济林果等生长发育。2020年12月、2021年1月中东部地区遭遇4次大范围寒潮天气过程,导致南方部分露地蔬菜、经济林果遭受不同程度寒冻害。新疆北部、东北、内蒙古东部多大风降温降雪天气,不利农牧业生产。
The biological fate of the polymer nanocarrier material monomethoxy poly(ethylene glycol)-block-poly(D,L-lactic acid)in rat

Monomethoxy poly(ethylene glycol)-block-poly(D,L-lactic acid)(PEG-PLA)is a typical amphiphilic di-block copolymer widely used as a nanoparticle carrier (nanocarrier)in drug delivery. Understanding the in vivo fate of PEG-PLA is required to evaluate its overall safety and promote the development of PEG-PLA-based nanocarrier drug delivery systems. However,acquiring such understanding is limited by the lack of a suitable analytical method for the bioassay of PEG-PLA. In this study,the pharmacokinetics,biodistribution,metabolism and excretion of PEG-PLA were investigated in rat after intravenous administration. The results show that unchanged PEG-PLA is mainly distributed to spleen,liver,and kidney before being eliminated in urine over 48 h mainly (>80%)in the form of its PEG metabolite. Our study provides a clear and comprehensive picture of the in vivo fate of PEG-PLA which we anticipate will facilitate the scientific design and safety evaluation of PEG-PLA-based nanocarrier drug delivery systems and thereby enhance their clinical development.
Distributed Optimal Power Flow of DC Microgrids: A Penalty Based ADMM Approach

The popularity of direct current (DC) networks have made their optimal power flow (OPF) problem a hot topic. With the proliferation of distributed generation, the many problems of centralized optimization methods, such as single point failure and slow response speed, have led to utilization of measures such as distributed OPF methods. The OPF problem is non-convex, which makes it difficult to obtain an optimal solution. The second-order cone programming (SOCP) relaxation method is widely utilized to make the OPF problem convex. It is difficult to guarantee its exactness, especially when line constraints are considered. This paper proposes a penalty based ADMM approach using difference-of-convex programming (DCP) to solve the non-convex OPF problem in a distributed manner. The algorithm is composed of distributed x iteration, z iteration and λ, µ iteration. Specifically, in the distributed z iteration, the active power flow injection equation of each line is formulated as a difference of two convex functions, and then the SOCP relaxation is given in a different form. If the SOCP relaxation is inexact, a penalty item is added to drive the solution to be feasible. Then, an optimal solution can be obtained using a local nonlinear programming method. Finally, simulations on a 14-bus system and the IEEE 123-bus system validate the effectiveness of the proposed approach.
Simultaneous Placement of Tie-lines and Distributed Generations to Optimize Distribution System Post-outage Operations and Minimize Energy Losses

This study proposes a new method which aims to optimally install tie-lines and distributed generations simultaneously. This is done to optimize the post-outage reconfiguration and minimize energy losses and energy not supplied of distribution systems. The number and location of tie-lines, as well as the number, size, and location of DGs, are pinpointed through teaching the learning-based optimization (TLBO) method. The objective function in the current research is to minimize the costs pertaining to the investment, operation, energy losses, and energies not supplied. In addition to the normal operational condition, fault operational condition is also evaluated. Therefore, the optimal post-fault reconfigurations for fault occurrences in all lines are established. Moreover, the operational constraints such as the voltage and line current limits are taken into account in both normal and post-fault operational modes. Finally, the modified IEEE 33-bus and 69-bus distribution test systems are selected and tested to demonstrate the effectiveness of the simultaneous placement of DGs and tie-line technique proposed in this paper.
Data-driven Decision-making Strategies for Electricity Retailers: A Deep Reinforcement Learning Approach

With the continuous development of the electricity market, the electricity retailers, as the intermediaries between producers and consumers, have emerged in some of the liberalized electricity markets. Meanwhile, the electricity retailer faces many increasingly significant challenges from the complexities and uncertainties in both the supply and consumption sides. This paper applies a data-driven decision-making strategy via Advantage Actor-Critic (A2C) and Deep Q-Learning (DQN) for the electricity retailers. The retailers' profits and consumers' costs are both taken into account. This study verifies that the applied data-driven methods can handle the decision-making problem as well as promote the profitability of retailers in the electricity market. Furthermore, A2C is more appropriate than DQN in our simulation. The effectiveness of the applied datadriven methods is validated by using real-world data.
Multi-stage Dynamic Post-disaster Recovery Strategy for Distribution Networks Considering Integrated Energy and Transportation Networks

To improve the resilience of distribution networks (DNs), a multi-stage dynamic recovery strategy is proposed in this paper, which is designed for post-disaster DN considering an integrated energy system (IES) and transportation network (TN). First, the emergency response quickly increases the output of gas turbines (GTs) in the natural gas network (NGN), and responsively reconfigures the DN in microgrids, to maximize the amount of loads to be restored. The single-commodity flow model is adopted to construct spanning tree constraints. Then, in the second stage of energy storage recovery, mobile energy storage systems (MESSs) are deployed to cover the shortages of power demands, i.e., to further restore the loads after evaluating the load recovery situation. The Floyd algorithm based dynamic traffic assignment (DTA) is selected to obtain the optimal path of the MESSs. In the third stage, the outputs of various post-disaster recovery measures are adjusted to achieve an economically optimized operation. Case studies demonstrate the effectiveness of the proposed dynamic post-disaster recovery strategy.
A partition approach for robust gait recognition based on gait template fusion

Gait recognition has significant potential for remote human identification, but it is easily influenced by identityunrelated factors such as clothing, carrying conditions, and view angles. Many gait templates have been presented that can effectively represent gait features. Each gait template has its advantages and can represent different prominent information. In this paper, gait template fusion is proposed to improve the classical representative gait template (such as a gait energy image) which represents incomplete information that is sensitive to changes in contour. We also present a partition method to reflect the different gait habits of different body parts of each pedestrian. The fused template is cropped into three parts (head, trunk, and leg regions) depending on the human body, and the three parts are then sent into the convolutional neural network to learn merged features. We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones. The results show good accuracy and robustness of the proposed method for gait recognition.
Deep Learning in Power Systems Research: A Review

With the rapid growth of power systems measurements in terms of size and complexity, discovering statistical patterns for a large variety of real-world applications such as renewable energy prediction, demand response, energy disaggregation, and state estimation is considered a crucial challenge. In recent years, deep learning has emerged as a novel class of machine learning algorithms that represents power systems data via a large hypothesis space that leads to the state-of-the-art performance compared to most recent data-driven algorithms. This study explores the theoretical advantages of deep representation learning in power systems research. We review deep learning methodologies presented and applied in a wide range of supervised, unsupervised, and semi-supervised applications as well as reinforcement learning tasks. We discuss various settings of problems solved by discriminative deep models including stacked autoencoders and convolutional neural networks as well as generative deep architectures such as deep belief networks and variational autoencoders. The theoretical and experimental analysis of deep neural networks in this study motivates long-term research on optimizing this cutting-edge class of models to achieve significant improvements in the future power systems research.
Reliability assessment of three-dimensional bearing capacity of shallow foundation using fuzzy set theory

The aim of this study is to investigate the applicability of reliability theory on surface square/rectangular footing against bearing capacity failure using fuzzy set theory in conjunction with the finite element method.Soil is modeled as a three-dimensional spatially varying medium,where its parameters (cohesion,friction angle,unit weight,etc.) are considered as fuzzy variables that maintain some membership functions.Soil is idealized as an elastic-perfectly plastic material obeying the Mohr-Coulomb failure criterion,where both associated and non-associated flow rules are considered in estimating the ultimate bearing capacity of the footing.The spatial variability of the soil is incorporated for both isotropic and anisotropic fields,which are determined by the values of scales of fluctuation in both the horizontal and vertical directions.A new parameter namely,limiting applied pressure at zero failure probability is proposed,and it indirectly predicts the failure probability of the footing.The effect of the coefficient of variation of the friction angle of the soil on the probability of failure is analyzed,and it is observed that the effect is significant.Furthermore,the effect of the scale of fluctuation on the probability of failure is investigated,and the necessity for considering spatial variability in the reliability analysis is well proven.
Analysis of stiffness and flexural strength of a reinforced concrete beam using an invented reinforcement system

In this study,we conducted experimental tests on two specimens of reinforced concrete beams using a three-point bending test to optimize the flexure and stiffness designs.The first specimen is a reinforced concrete beam with an ordinary reinforcement,and the second specimen has an invented reinforcement system that consists of an ordinary reinforcement in addition to three additional bracings using steel bars and steel plates.The results of the flexure test were collected and analyzed,and the flexural strength,the rate of damage during bending,and the stiffness were determined.Finite element modeling was applied for both specimens using the LS-DYNA program,and the simulation results of the flexure test for the same outputs were determined.The results of the experimental tests showed that the flexural strength of the invented reinforcement system was significantly enhanced by 15.5% compared to the ordinary system.Moreover,the flexural cracks decreased to a significant extent,manifesting extremely small and narrow cracks in the flexure spread along the bottom face of the concrete.In addition,the maximum deflection for the invented reinforced concrete beam decreased to 1/3 compared to that of an ordinary reinforced concrete beam.The results were verified through numerical simulations,which demonstrated excellent similarities between the flexural failure and the stiffness of the beam.The invented reinforcement system exhibited a high capability in boosting the flexure design and stiffness.
Evaluation of the situational awareness effects for smart distribution networks under the novel design of indicator framework and hybrid weighting method

As a key application of smart grid technologies, the smart distribution network (SDN) is expected to have a high diversity of equipment and complexity of operation patterns. Situational awareness (SA),which aims to provide a critical visibility of the SDN, will enable a significant assurance for stable SDN operations. However, the lack of systematic evaluation through the three stages of perception, comprehensive, and prediction may prevent the SA technique from effectively achieving the performance necessary to monitor and respond to events in SDN. To analyze the feasibility and effectiveness of the SA technique for the SDN, a comprehensive evaluation framework with specific performance indicators and systematic weighting methods is proposed in this paper. Besides, to implement the indicator framework while addressing the key issues of human expert scoring ambiguity and the lack of data in specific SDN areas, an improved interval-based analytic hierarchy process-based subjective weighting and a multi-objective programming method-based objective weighting are developed to evaluate the SDN SA performance. In addition, a case study in a real distribution network of Tianjin, China is conducted whose outcomes verify the practicality and effectiveness of the proposed SA technique for SDN operating security.