mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Galvanic replacement reaction for in situ fabrication of litchi-shaped heterogeneous liquid metal-Au nano-composite for radio-photothermal cancer therapy

With tremendous research advances in biomedical application, liquid metals (LM) also offer fantastic chemistry for synthesis of novel nano-composites. Herein, as a pioneering trial, litchi-shaped heterogeneous eutectic gallium indium-Au nanoparticles (EGaIn-Au NPs), served as effective radiosensitizer and photothermal agent for radio-photothermal cancer therapy, have been successfully prepared using in situ interfacial galvanic replacement reaction. The enhanced photothermal conversion efficiency and boosted radio-sensitization effect could be achieved with the reduction of Au nanodots onto the eutectic gallium indium (EGaIn) NPs surface. Most importantly, the growth of tumor could be effectively inhibited under the combined radio-photothermal therapy mediated by EGaIn-Au NPs. Inspired by this approach, in situ interfacial galvanic replacement reaction may open a novel strategy to fabricate LM-based nano-composite with advanced multi-functionalities.
核苷(酸)类似物初治慢性乙型肝炎患者发生部分病毒学应答的影响因素分析

目的探讨核苷(酸)类似物[Nucleos(t)ide analogs,NAs]初治的慢性乙型肝炎(Chronic hepatitis B,CHB)患者发生部分病毒学应答(Partial virological response,PVR)的影响因素,并分析PVR发生过程中病毒学及肝功能指标的动态变化。方法回顾性分析2019年9月至2021年4月南昌大学第一附属医院感染性疾病科门诊接受NAs治疗至少48周的84例初治CHB患者临床资料。根据治疗期间的HBV DNA水平,将患者分为完全病毒学应答(Complete virological response,CVR)组(n = 60)和PVR组(n =24)。采用多因素Logistic回归分析PVR发生的危险因素,应用受试者工作曲线(Receiver operating characteristic curve,ROC)评估各指标预测发生PVR的效能。结果多因素Logistic回归分析显示,基线HBsAg、丙氨酸转氨酶(Alanine aminotransferase, ALT)及治疗12周HBV DNA是NAs治疗初治CHB患者发生PVR的独立影响因素(P <0.05)。基线HBsAg、治疗12周HBV DNA和基线ALT均能预测PVR的发生(P <0.05或<0.01)。基线ALT对PVR发生的预测价值低于治疗12周HBV DNA(Z =3.436,P<0.001)和基线HBsAg(Z = 2.436,P = 0.040);而治疗12周HBV DNA与基线HBsAg的预测价值比较,差异无统计学意义(Z =0.815,P =0.390)。治疗前后PVR组HBsAg及HBV DNA均高于CVR组(F = 26.468和101.433, P < 0.05)。结论治疗前高HBsAg水平、治疗12周高HBV DNA载量及治疗前低ALT水平的NAs初治CHB患者更易发生PVR。动态监测此类人群的HBsAg、HBV DNA及ALT水平有重要意义。
基于多源遥感数据的物种多样性及陆表植被恢复力监测研究

生物多样性和生态系统恢复力是生态系统健康的关键因素和重要指标。物种多样性指一定区域内物种数量的总和,物种多样性监测是生物多样性监测与保护的核心部分。作为生态系统恢复力的重要组成部分,植被恢复力是陆表生态系统服务和生态系统功能的重要指标,直接决定了陆表植被变化、陆表生态系统稳定性等关键生态过程和生态参量。目前,受到观测手段的限制,物种多样性与陆表植被恢复力监测普遍空间范围小,时间范围不连续,其中热带地区植物物种多样性变化对植被影响效果争议较大。论文基于长时间序列高精度遥感数据,从动物物种多样性遥感监测、植物物种多样性变化对热带雨林影响的遥感评估、遥感植被恢复力监测三方面出发开展相关工作。
Recent Status of Taiji Program in China

The Taiji-1 satellite is a pilot satellite mission of Taiji program, which is used to verify Taiji's key technology and also to testify the feasibility of Taiji roadmap. Taiji-1 was launched on 31 August 2019 and its designed mission was successfully completed. The in-orbit scientific achievements of Taiji-1 satellite in the first stage have been published and now it has entered the extended task phase. Taiji-2 will prepare all the technology needed by Taiji-3, and remove all the technical obstacles faced by Taiji-3.
Recent Progress in Space Science and Applications of China's Space Station in 2020-2022

China scheduled to complete the assembly of the T-shaped Tiangong Space Station in 2022, and will enter a new stage of utilization. There are more than 20 experiment racks inside the modules, and more than 50 external onboard payloads mounting spaces, which will support large-scale science and technology experiments during the operation. The development of internal experiment racks and external research accommodations approved during the construction has been completed, of which 4 racks in Tianhe core module, including High Microgravity Level research Rack (HMLR) and Container-less Materials Processing Rack (CMPR), have finished on-orbit tests; while other racks in Wentian and Mengtian experiment modules are under comprehensive ground tests. The Chinese Space Survey Telescope (CSST) has advanced much in the last two years with 24 pre-launch research projects funded and 4 joint science center built in preparation for CSST's future scientific observations and operations. The systematic research planning for China's Space Station (CSS) during 2022 -2032 is updated with the researches classified into four important areas: space life sciences and human research, microgravity physical sciences, space astronomy and Earth science, and new space technologies and applications. According to the planning, more than 1000 experiments are expected to perform in CSS during the operating period. Overall, the CSS utilization missions are proceeding as planned, which will contribute to the major scientific or application output and have a positive impact on the quality of life on Earth.
甲状腺全切除术后甲状旁腺激素及血钙的变化

选取70例行甲状腺全切除术的患者为研究对象,结果表明,入院时、术后第1、2、3天血清甲状旁腺激素(PTH)分别为(51.6±3.1)、(16.7±1.7)、(19.5±2.1)、(20.8±2.3) pg/ml,血清钙分别为(2.381±0.013)、(2.105+0.023)、(2.071±0.020)、(2.074±0.020) mmol/L。术后血清PTH明显低于入院时,术后第1天下降百分比>82.00%时出现低钙血症,下降百分比>88.35%时出现低钙症状。PTH在术后第3天开始恢复,而血钙的恢复时间滞后于血清PTH。术后第1天血清PTH下降程度可作为术后出现低钙血症或低钙症状的预测指标,为术后是否需要补钙治疗提供参考。
运动对慢性肾脏病患者生存质量改善的意义

慢性肾脏病(CKD)发病率和患病率日益增加,已成为全球十大死亡原因之一。如何改善CKD患者的预后,提高生存率和生活质量,是肾脏病工作者义不容辞的使命。运动是所有生命体的永恒话题,既往有关运动在肾脏疾病患者中的作用一直存在争议,现在越来越多的证据表明适当运动对CKD患者有益。
Natural Stibnite for Lithium-/Sodium-Ion Batteries:Carbon Dots Evoked High Initial Coulombic Efficiency

The application of Sb_2S_3 with marvelous theoretical capacity for alkali metal-ion batteries is seriously limited by its poor electrical conductivity and low initial coulombic efficiency (ICE).In this work,natural stibnite modified by carbon dots (Sb_2S_3@xCDs) is elaborately designed with high ICE.Greatly,chemical processes of local oxidation-partial reduction-deep coupling for stibnite reduction of CDs are clearly demonstrated,confirmed with in situ high-temperature X-ray diffraction.More impressively,the ICE for lithium-ion batteries (LIBs) is enhanced to 85%,through the effect of oxygen-rich carbon matrix on C-S bonds which inhibit the conversion of sulfur to sulfite,well supported by X-ray photoelectron spectroscopy characterization of solid electrolyte interphase layers helped with density functional theory calculations.Not than less,it is found that Sb-O-C bonds existed in the interface effectively promote the electronic conductivity and expedite ion transmission by reducing the bandgap and restraining the slip of the dislocation.As a result,the optimal sample delivers a tremendous reversible capacity of 660 mAh g~(-1) in LIBs at a high current rate of 5 A g~(-1).This work provides a new methodology for enhancing the electrochemical energy storage performance of metal sulfides,especially for improving the ICE.
Tailoring Nitrogen Terminals on MXene Enables Fast Charging and Stable Cycling Na-Ion Batteries at Low Temperature

Sodium-ion batteries stand a chance of enabling fast charging ability and long lifespan while operating at low temperature (low-T).However,sluggish kinetics and aggravated dendrites present two major challenges for anodes to achieve the goal at low-T.Herein,we propose an interlayer confined strategy for tailoring nitrogen terminals on Ti_3C_2 MXene (Ti_3C_2-N_(funct)) to address these issues.The introduction of nitrogen terminals endows Ti_3C_2-N_(funct) with large interlayer space and charge redistribution,improved conductivity and sufficient adsorption sites for Na~+,which improves the possibility of Ti_3C_2 for accommodating more Na atoms,further enhancing the Na~+ storage capability of Ti_3C_2.As revealed,Ti_3C_2-N_(funct) not only possesses a lower Na-ion diffusion energy barrier and charge transfer activation energy,but also exhibits Na~+-solvent co-intercalation behavior to circumvent a high de-solvation energy barrier at low-T.Besides,the solid electrolyte interface dominated by inorganic compounds is more beneficial for the Na~+ transfer at the electrode/electrolyte interface.Compared with of the unmodified sample,Ti_3C_2-N_(funct) exhibits a twofold capacity (201 mAh g~(-1)),fast-charging ability (18 min at 80% capacity retention),and great superiority in cycle life (80.9%@5000 cycles) at - 25 °C.When coupling with Na_3V_2(PO_4)_2F_3 cathode,the Ti_3C_2-N_(funct)//NVPF exhibits high energy density and cycle stability at - 25 °C.
Designing Evolutionary Wavelet Neural Network for Estimating Foaming Slag Quality in Electric Arc Furnace Using Power Quality Indices

In the present study, a novel approach based on an evolutionary wavelet neural network (EWNN) is proposed to estimate the slag quality in an electric arc furnace (EAF) employing power quality indices. In the EWNN, an evolutionary method is applied to train the parameters for a combination of neural networks and wavelets. For this purpose, all of the electrical parameters for six melting processes are measured with a power quality analyzer, attached to the secondary component of an EAF transformer at a Saba steel complex, to estimate the foaming slag quality. Experimental results on various combinations of measured electrical parameters, applying the designed EWNN estimator, demonstrate that utilizing five leading indicators leads to the highest precision. The obtained 99% accuracy for estimating the foaming slag quality by EWNN compared to the other methods illustrates the proposed method's efficiency.
Remaining Useful Life Prediction for a Multi-stack Solid Oxide Fuel Cell System with Degradation Interactions

Due to the constraints of manufacturing and materials, high-power plants cannot rely on only one solid oxide fuel cell stack. A multi-stack system is a solution for a high-power system, which consists of multiple fuel cell stacks. A short lifetime is one of the main challenges for the fuel cell before large-scale commercial applications, and prognostic is an important method to improve the reliability of fuel cells. Different from the traditional prognostic approaches applied to single-stack fuel cell systems, the key problem in multi-stack prediction is how to solve the correlation of multi-stack degradation, which can directly affect the accuracy of prediction. In response to this difficulty, a standard Brownian motion is added to the traditional Wiener process to model the degradation of each stack, and then the probability density function of the remaining useful life (RUL) of each stack is calculated. Furthermore, a Copula function is adopted to reflect the dependence between life distributions, so as to obtain the remaining useful life for the whole multi-stack system. The simulation results show that compared with the traditional prediction model, the proposed approach has a higher prediction accuracy for multi-stack fuel cell systems.
Circuit Model with Current Interruption for Hybrid High Voltage DC Circuit Breakers to Achieve Precise Current Experiments

The current interruption test based on an LC resonance circuit for hybrid DC circuit breakers (HVDC CBs) is widely employed to characterize the current interruption capability of CBs. In order to ensure a high-fidelity replica of the fault current in a high voltage application, this paper first proposes an equivalent model of the test circuit, where not only parasitic resistances but also the threshold voltages and on-state resistances of various semiconductor devices are considered. Moreover, the analytical formula of the test current is derived by including the working principle of the HVDC CB. Secondly, the parameter extraction method, which combines finite element analysis and measurements by an impedance analyzer, is given in this paper. The extracted result implies that, in current interruption transients, equivalent resistances of 500 kV CB are as large as 535 mΩ, which have a significant influence on current waveforms. Thirdly, the 34 kV/25 kA current interruption test for the 500 kV CB is conducted. The measured results are proved to be consistent with the analytical results obtained from the proposed model, and the relative error is less than 2%.
Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology

Defect factors and their relevant rules can be analyzed in depth by processing defect records which are often expressed in the form of text data. However, considering that defect text consists of both structured and unstructured data, it is necessary to excavate structured information from unstructured data. In this paper, a text mining method based on semantic framework technology is introduced to transform unstructured defect description into structured information such as components and defect attributes. Then, a deep analyzing model of a power equipment defect is established, which provides a scheme of defect mining based on historical defect texts. Case studies prove that the proposed deep analysis method has a guiding significance for equipment upgrading, selection and maintenance.
Effective Controlled Islanding Method for Power Grids Solving a Sequence of Optimization Problems

Controlled islanding is an important approach to prevent instability in power grids. In this paper, a novel approach is proposed for power system separation, which consists of two steps: 1) Finding multiple islanding scenarios; 2) Choosing the best option to obtain the most desirable island. In the first step, different islanding solutions are determined by a proposed hierarchical clustering method. In this algorithm, which is based on a minimum active power flow disruption objective function, the generator coherency constraints are considered in the clustering process. In the second step, the best separation scenario is chosen based on an arbitrary objective function. Particularly, in this paper, the amount of load shedding and the voltage profile deviation after separation are considered as the final criteria to select the best solution among available options. In so doing, the degree of load importance is also taken into account. The proposed two-step method is applied on an IEEE 9-bus test system and it is also evaluated on an IEEE 39-bus grid. The simulation results on the IEEE 39-bus grid and the comparative analysis with a state-of-the-art method confirm that the final islanding solution is more optimized based on the secondary criteria, which have not been addressed in the existing approaches. Moreover, the proposed method is computationally efficient and can be employed in real-scale power grids.
Active Power Correction Strategies Based on Deep Reinforcement Learning-Part II: A Distributed Solution for Adaptability

This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning. In Part II,we consider the renewable energy scenarios plugged into the large-scale power grid and provide an adaptive algorithmic implementation to maintain power grid stability. Based on the robustness method in Part I,a distributed deep reinforcement learning method is proposed to overcome the influence of the increasing renewable energy penetration. A multi-agent system is implemented in multiple control areas of the power system, which conducts a fully cooperative stochastic game. Based on the Monte Carlo tree search mentioned in Part I,we select practical actions in each sub-control area to search the Nash equilibrium of the game. Based on the QMIX method,a structure of offline centralized training and online distributed execution is proposed to employ better practical actions in the active power correction control. Our proposed method is evaluated in the modified global competition scenario cases of "2020 Learning to Run a Power Network - Neurips Track 2".
Load Shedding Control Strategy in Power Grid Emergency State Based on Deep Reinforcement Learning

In viewing the power grid for large-scale new energy integration and power electrification of power grid equipment, the impact of power system faults is increased, and the ability of anti-disturbance is decreased, which makes the power system fault clearance more difficult. In this paper, a load shedding control strategy based on artificial intelligence is proposed, this action strategy of load shedding, which is selected by deep reinforcement learning, can support autonomous voltage control. First, the power system operation data is used as the basic data to construct the network training dataset, and then a novel reward function for voltage is established. This value function, which conforms to the power grid operation characteristics, will act as the reward value for deep reinforcement learning, and the Deep Deterministic Policy Gradient algorithm (DDPG) algorithm, with the continuous action strategy, will be adopted. Finally, the deep reinforcement learning network is continuously trained, and the load shedding strategy concerning the grid voltage control problem will be obtained in the power system emergency control situation, and this strategy action is input into the Pypower module for simulation verification, thereby realizing the joint drive of data and model. According to the numerical simulation analysis, it shows that this method can effectively determine the accurate action selection of load shedding, and improve the stable operational ability of the power system.
KPI-based Real-time Situational Awareness for Power Systems with a High Proportion of Renewable Energy Sources

With the increasing complexity of power systems and the widespread penetration of renewable energy sources (RES), real-time situational awareness for power systems is of great significance for operational scheduling. Considering the impact of RES on power system operations, a situational awareness key performance index (KPI) system for power systems with a high proportion of RES is proposed in this paper, which consists of reserve capacity abundance, ramp resource abundance, center of inertia (COI) frequency deviation, interface power flow margin, synthesized voltage stability, and angle stability margin. Then, the KPIs are synthesized and visualized by the decision tree method and radar chart method, respectively, for monitoring the operation states (i.e., normal, alert, and emergency states) of power systems with a high proportion of RES. Numerical simulations are conducted in a revised New England 16-machine 68-bus power system and an actual CEPRI-RE power system in the northwest region of China with a high proportion of RES. The results show that the proposed KPI-based situational awareness method is able to accurately monitor the real-time state of power systems with a high proportion of RES, and can assist power dispatchers to make effective decisions.
Amodified YOLOv4detectionmethod for a vision-based underwater garbage cleaning robot

To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. We propose a garbage detection method based on a modified YOLOv4, allowing high-speed and high-precision object detection. Specifically, the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection accuracy, YOLOv4 is transformed into a four-scale detection method. To improve the detection speed, model pruning is applied to the new model. By virtue of the improved detection methods, the robot can collect garbage autonomously. The detection speed is up to 66.67 frames/s with a mean average precision (mAP) of 95.099%, and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.
A joint call for actions to advance taxonomy in China

Taxonomy plays an important role in understanding the origin,evolution,and ecological functionality of biodiversity.There are large number of unknown species yet to be described by taxonomists,which together with their ecosystem services cannot be effectively protected prior to description.Despite this,taxonomy has been increasingly underrated insufficient funds and permanent positions to retain young talents.Further,the impact factordriven evaluation systems in China exacerbate this downward trend,so alternative evaluation metrics are urgently necessary.When the current generation of outstanding taxonomists retires,there will be too few remaining taxonomists left to train the next generation.In light of these challenges,all co-authors worked together on this paper to analyze the current situation of taxonomy and put out a joint call for immediate actions to advance taxonomy in China.
Functional trait profiles and diversity of trees regenerating in disturbed tropical forests and agroforests in Indonesia

A main question in restoration of degraded forests and forest landscapes recovering from logging and fire is what to expect from natural regeneration through surviving propagules in the soil or seed sources and associated dispersal agents from the surrounding landscape mosaic, as alternative to tree planting. Tree diversity in secondary forests may be high, but based on newcomer species of low wood density and long-distance, abiotic dispersal modes. We compiled and analyzed three pairs of case studies (totaling 815 plots and 11.8 ha) of secondary forests recovering from logging, fire and conversion to agroforest in Sumatra and Kalimantan (Indonesia) on mineral soils. Data on tree species diversity, wood density frequency distribution (indicative of successional status) and dispersal modes were compared with those of less disturbed comparator forests in the same landscapes. Relatively undisturbed lowland dipterocarp forest in Kalimantan had close to 200 species of trees (>10 cm diameter) at a 1-ha sample scale (and 450 at a 10-ha scale). After repeated fires a sample area of 2 ha was needed to reach the same species richness. Regulation-based logging had little impact on tree species richness. In rubber agroforest with low-intensity management beyond rubber planting, 50 tree species were found at a 1-ha scale and close to 100 species in 3 ha. The Kalimantan forest after repeated fires had a markedly higher fraction of low-wood-density trees (40%), but otherwise, all forests sampled were similar in overall wood density profiles. Selectively logged forest managed by a local community (village forest) and rubber agroforest in Sumatra contained larger fractions of heavy-wood-density trees. The majority of trees (50%-70%) had birds, bats and primates as dispersal agents in all sites. Selectively logged forests had higher fractions of autochorous species (15%) compared to other sites. Anemochorous (wind-dispersed) species, especially Macaranga lowii, were most common (20%) in lowland dipterocarp forest. Comparison between secondary forests and agroforests showed the influence of farmer selection regarding what is allowed to grow beyond the pole stage. Wood density and seed dispersal profiles can be used as degradation indicators of species assemblages across various disturbance levels and types, as they reflect the habitat quality of the surrounding landscape mosaics.
Posterior lamellar tarsorrhaphy in the early treatment of severe eyelid trauma/burns

In patients with lagophthalmos due to eyelid trauma, it is imperative to ensure adequate closure of the eye to protect the ocular surface. Sometimes it is difficult to surgically close eyelids by conventional methods of tarsorrhaphy, such as intermarginal suture tarsorrhaphy, Week's tarsorrhaphy, and tongue-in-groove tarsorrhaphy. We need intact eyelid tissues to perform these procedures and might not be possible in patients with severe lagophthalmos due to lost/ damaged eyelid tissues either due to deficient length of tissues or increased tension at the wound site, which can result in surgical failure.