mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
六味地黄丸“三泻”药辨疑

六味地黄丸中茯苓、泽泻、丹皮三药的作用存在争议,结合方源从肾之“体阴用阳”进行解释较为明晰。此方源于《金匮要略》之肾气丸,所治5种病证均有水液代谢不利。方中地黄、山茱萸、山药三药用量较大为主药,功在补肾体之阴;桂枝、附子少量使用,温阳化气;茯苓、泽泻、丹皮利水渗湿、祛除水邪以助肾之用属“佐助药”,全方补肾化气利水,八药均从利水着想。宋代钱乙去掉方中桂、附用于小儿以发育迟缓为表现的肾虚证,并无水液代谢不利的症状,但保留了茯苓、泽泻、丹皮三味利水药,后世因袭成为滋阴补肾之名方,对其配伍多以“三补三泻”为解,将“三泻”药误解为防补太过的“反佐药”。临床中但欲补阴益肾、无水湿痰瘀者,“三泻”药则不需保留,若嫌其滋腻可佐陈皮、砂仁、鸡内金等运脾药,若兼有水湿痰瘀者则保留“三泻”药与补肾体之阴的药同用。
关于地质灾害防治需要关注的几个问题

地质灾害防治是一项复杂的系统工程,既具有很强的专业性,又是一项涉及千家万户生命财产安全的社会性工作。党中央高度重视,人民群众密切关注。做好新时期的地质灾害防治工作,责任重大,使命光荣。
乙状结肠系膜巨大副神经节瘤1例

副神经节瘤多表现为无痛性肿块,患者常以高血压为首发症状。若术前未能详细询问病史、完善相关检查及检验进行鉴别诊断,可能导致术前准备不足,从而使手术风险增加。本文报告了1例术前未能确诊的乙状结肠系膜巨大副神经节瘤的病例,通过术中给予酚妥拉明、术后给予去甲肾上腺素及补液等治疗,患者术后7 d好转出院。
第555例水肿一反复肾周出血一多浆膜腔积液一淋巴结肿大

Castleman病是一类罕见的血液系统疾病。本文报道1例以膜性肾病、多浆膜腔积液为主要临床表现的不典型的Castleman病患者,病程中合并反复肾周出血,病情迁延复杂,经多学科协作明确诊断并给予有效治疗。
中国心脏瓣膜病介入治疗:发展迅猛但仍需努力

近年来,心脏瓣膜病的介入治疗发展迅猛,各瓣膜领域的循证医学证据逐步积累、器械研发不断推陈出新。我国瓣膜病介入治疗开展相对较晚,但现开展瓣膜病介入治疗的中心和团队快速增多,对各瓣膜领域的研究热点、当前应用与将要面临的困难及挑战应有足够的认识。该文主要总结了2021年主动脉瓣、二尖瓣、三尖瓣、肺动脉瓣4个领域国外发展的新形势及我国的应用现状。
Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.
Optimization and Configuration of Control Parameters to Enhance Small-signal Stability of Hybrid LCC-MMC HVDC System

This paper investigates the small-signal stability of the hybrid high-voltage direct current (HVDC) transmission system.The system is composed of line commutated converter (LCC) as rectifier and modular multi-level converter (MMC) as inverter under weak AC grid condition.Firstly,the impact of short-circuit ratio (SCR) at inverter side on the system stability is investigated by eigen-analysis,and the key control parameters which have major impact on the dominant mode are identified by the participation factor and sensitivity analysis.Then,considering the quadratic index and damping ratio characteristic,an objective function for evaluating the system stability is developed,and an optimization and configuration method for control parameters is presented by the utilization of Monte Carlo method.The eigenvalue results and the electromagnetic transient (EMT) simulation results show that,with the optimized control parameters,the small-signal stability and the dynamic responses of the hybrid system are greatly improved,and the hybrid system can even operate under weak AC grid condition.
Efficient Wind Turbine Generation Planning for Decreasing Distribution System Company Payments in Real Applications

Wind energy has posed new challenges in both transmission and distribution systems owing to its uncertain nature.The effect of wind turbines (WTs) on the actual payments charged by upstream networks to distribution system companies (DISCOs) is one challenge.Moreover,when the grid-connected inverters of WT operate in the lead or lag modes,WTs absorb or inject reactive power from the system.This paper proposes an approach to assess the importance of operation modes of WTs to minimize the costs by DISCOs in the presence of system uncertainties.Accordingly,an optimization problem is formulated to minimize the costs to DISCO by determining the optimal locations and sizes of WTs in optimally reconfigured distribution systems.In addition,an improved vector-based swarm optimization (IVBSO) algorithm is proposed because it is highly suitable for vector-based problems.Two distribution systems are used in the simulations to evaluate the proposed algorithm.Firstly,the capabilities of the IVBSO algorithm to determine better solutions over other heuristic algorithms are confirmed using the IEEE 33-bus test system.Secondly,the Bijan-Abad distribution system (BDS) is used to demonstrate the effectiveness of the proposed optimization problem.Accordingly,the distribution system model,cumulative distribution function of wind speed,and load profile are all extracted from the actual data of the BijanAbad region.Finally,the optimization problem is applied to BDS in both the lead and lag modes of WTs.Results indicate that the total costs of DISCO are lower when WTs operate in the lag mode than in the lead mode.
Thermal Stability of Supercapacitor for Hybrid Energy Storage System in Lightweight Electric Vehicles:Simulation and Experiments

Recent research findings indicate that the nonmonotonic consumption of energy from lithium-ion (Li-ion) batteries results in a higher heat generation in electrical energy storage systems.During peak demands,a higher heat generation due to high discharging current increases the temperature from 80 °C to 120 °C,thereby resulting in thermal runaway.To address peak demands,an additional electrical energy storage component,namely supercapacitor (SC),is being investigated by various research groups.This paper provides insights into the capability of SCs in lightweight electric vehicles (EVs) to address peak demands using the worldwide harmonized light-duty driving test cycle (WLTC) driving profile in MATLAB/Simulink at different ambient temperatures.Simulation results indicate that temperature imposes a more prominent effect on Li-ion batteries compared with SCs under peak demand conditions.The effect of the discharging rate limit on the Li-ion battery current is studied.The result shows that SCs can accommodate the peak demands for a low discharging current limit on the battery,thereby reducing heat generation.Electrochemical impedance spectroscopy and cyclic voltammetry are performed on SCs to analyze their thermal performance at different temperatures ranging from 0 °C to 75 °C under different bias values of-0.6,0,0.6,and 1 V,respectively.The results indicate a higher specific capacitance of the SC at an optimum operation temperature of 25 °C for the studied bias.This study shows that the hybrid combination of the Li-ion battery and SC for a lightweight EV can address peak demands by reducing thermal stress on the Li-ion battery and increasing the driving range.
Defect-mediated Rashba engineering for optimizing electrical transport in thermoelectric BiTeI

The Rashba effect plays a vital role in electronic structures and related functional properties.The strength of the Rashba effect can be measured by the Rashba parameter α_R;it is desirable to manipulate α_R to control the functional properties.The current work illustrates how α_R can be systematically tuned by doping,taking BiTel as an example.A five-point-spin-texture method is proposed to efficiently screen doped BiTel systems with the Rashba effect.Our results show that α_R in doped BiTel can be manipulated within the range of 0-4.05 eV A by doping different elements.The dopants change α_R by affecting both the spin-orbit coupling strength and band gap.Some dopants with low atomic masses give rise to unexpected and sizable α_R,mainly due to the local strains.The calculated electrical transport properties reveal an optimal α_R range of 2.75-3.55 eVA for maximizing the thermoelectric power factors.α_R thus serves as an effective indicator for high-throughput screening of proper dopants and subsequently reveals a few promising Rashba thermoelectrics.This work demonstrates the feasibility of defect-mediated Rashba engineering for optimizing the thermoelectric properties,as well as for manipulating other spin-related functional properties.
Bidding Strategy for Coordinated Operation of Wind Power Plants and NGG-P2G Units in Electricity Market

The synergies between electricity and other energy resources could promote energy utilization efficiency in electricity market.Within this context,this paper proposes a bi-level stochastic optimization model for the joint operation of a coordinated wind power plant (WPP) and natural gas generating (NGG)-power to gas (P2G) suppliers participating in the day-ahead (DA) market and real-time (RT) market as well as providing real-time auxiliary services.The coordinated supplier's payoff is maximized in the upper level with consideration of the uncertainties of WPP output capacity and RT electricity price,while the social welfare of the grid is maximized in the lower level.Simulation results demonstrate the effectiveness of the proposed bidding model of the coordinated WPPs and NGG-P2G suppliers by examining its bidding behaviors and benefits with comparisons of four other bidding models.
Environmental and biological factors affecting the abundance of Prosopis flexuosa saplings in the central-west Monte of Argentina

Background: Trees and forests in drylands help mitigate the challenges through provision of economic products and vital environmental services such as habitat for biodiversity, prevention of erosion and desertification, regulation of water, microclimate, and soil fertility. The condition and changes in dry forests can be assessed by using ecological indicators able to quantify spatial and temporal changes in vegetation. One of the ways to determine the condition of the forest is to study the dominant tree species and its regeneration. Our study aimed to assess whether the abundance of Prosopis flexuosa saplings is affected by environmental and biological factors. Results: To evaluate the first variables we used data from remote sensing such as satellite images and Aster Global Digital Model (GDEM). The second set of variables was about exotic and native ungulates and we used feces of these animals and camera traps to take data. We found that sapling abundance related positively to sandy sub- strates and negatively to Wetness Index. On the other hand, in relation to biological variables, the abundance of saplings was positively affected by density of adult trees and by number of seeds dispersed by equines, but space use by Lama guanicoe had a negative relationship with saplings. This research shows that P. flexuosa saplings are bene fited from sandy substrates and the conditions around adult trees. In addition to this, we found that exotic ungulates in low densities have neutral (i.e. cattle) or positive (i.e. equines) effects on sapling abundance. Conclusions: Based on these findings, we conclude that regeneration of the population of P. flexuosa in our study area has no major problems. In addition, we corroborated that the presence of exotic and domestic ungulates in low densities does not have deleterious consequences for saplings of the dominant tree, P. flexuosa.
A better carbon-water flux simulation in multiple vegetation types by data assimilation

Background: The accurate estimation of carbon-water flux is critical for understanding the carbon and water cycles of terrestrial ecosystems and further mitigating climate change. Model simulations and observations have been widely used to research water and carbon cycles of terrestrial ecosystems. Given the advantages and limitations of each method, combining simulations and observations through a data assimilation technique has been proven to be highly promising for improving carbon-water flux simulation. However, to the best of our knowledge, few studies have accomplished both parameter optimization and the updating of model state variables through data assimilation for carbon-water flux simulation in multiple vegetation types. And little is known about the variation of the performance of data assimilation for carbon-water flux simulation in different vegetation types. Methods: In this study, we assimilated leaf area index (LAI) time-series observations into a biogeochemical model (Biome-BGC) using different assimilation algorithms (ensemble Kalman filter algorithm (EnKF) and unscented Kalman filter (UKF)) in different vegetation types (deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF) and grassland (GL)) to simulate carbon-water flux. Results: The validation of the results against the eddy covariance measurements indicated that, overall, compared with the original simulation, assimilating the LAI into the Biome-BGC model improved the carbon-water flux simulations (R2 increased by 35%, root mean square error decreased by 10%; the sum of the absolute error decreased by 8%) but more significantly, improved the water flux simulations (R2 increased by 31%, root mean square error decreased by 18%; the sum of the absolute error decreased by 16%). Among the different forest types, the data assimilation techniques (both EnKF and UKF) achieved the best performance towards carbon-water flux in EBF (R2 increased by 44%, root mean square error decreased by 24%; the sum of the absolute error decreased by 28%), and the performances of EnKF and UKF showed slightly different when simulating carbon fluxes. Conclusion: We suggest that to reduce the uncertainty in global carbon-water flux quantification, forthcoming data assimilation treatment should consider the vegetation types where the data assimilation experiments are carried out, the simulated objectives and the assimilation algorithms.
Identification, through transcriptome analysis, of transcription factors that regulate anthocyanin biosynthesis in different parts of red-fleshed apple ‘May' fruit

Anthocyanins, a class of flavonoid compounds that provide pigmentation to plant organs, are beneficial components of the human diet and there is an interest in understanding the regulation of their formation in crop plants. The apple cultivar ‘May' represents an excellent model for the study of anthocyanin metabolism due to the spatial color pattern of its flesh, which transitions sequentially from red to white to red again, from the outside to the inside of the apple fruit. To screen the transcription factors involved in the anthocyanin pathway, transcriptomes from differently colored sections of ‘May' fruits were analyzed by RNA sequencing. Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to analyse the gene expression data. The results suggested differentially expressed genes (DEGs) that are related to phytohormones are involved in regulating anthocyanin biosynthesis. K-means clustering analysis revealed 167 common DEGs between different sections of fruit with the same expression pattern as candidates for regulating anthocyanin synthesis. Further analysis showed that nine of the 167 DEGs were annotated as transcription factors and quantitative real time PCR (qRT-PCR) confirmed that their expression was obviously higher in red regions of the fruit, consistent with their roles as hub genes that regulate anthocyanin synthesis. This study provides valuable results for future studies of anthocyanin synthesis in apple fruit.
Cancer metabolism and dietary interventions

Metabolic remodeling is a key feature of cancer development. Knowledge of cancer metabolism has greatly expanded since the first observation of abnormal metabolism in cancer cells, the so-called Warburg effect. Malignant cells tend to modify cellular metabolism to favor specialized fermentation over the aerobic respiration usually used by most normal cells. Thus, targeted cancer therapies based on reprogramming nutrient or metabolite metabolism have received substantial attention both conceptually and in clinical practice. In particular, the management of nutrient availability is becoming more attractive in cancer treatment. In this review, we discuss recent findings on tumor metabolism and potential dietary interventions based on the specific characteristics of tumor metabolism. First, we present a comprehensive overview of changes in macronutrient metabolism. Carbohydrates, amino acids, and lipids, are rewired in the cancer microenvironment individually or systematically. Second, we summarize recent progress in cancer interventions applying different types of diets and specific nutrient restrictions in pre-clinical research or clinical trials.
Machine learning identification of symmetrized base states of Rydberg atoms

Studying the complex quantum dynamics of interacting many-body systems is one of the most challenging areas in modern physics. Here, we use machine learning (ML) models to identify the symmetrized base states of interacting Rydberg atoms of various atom numbers (up to six) and geometric configurations. To obtain the data set for training the ML classifiers, we generate Rydberg excitation probability profiles that simulate experimental data by utilizing Lindblad equations that incorporate laser intensities and phase noise. Then, we classify the data sets using support vector machines (SVMs) and random forest classifiers (RFCs). With these ML models, we achieve high accuracy of up to 100% for data sets containing only a few hundred samples, especially for the closed atom configurations such as the pentagonal (five atoms) and hexagonal (six atoms) systems. The results demonstrate that computationally cost-effective ML models can be used in the identification of Rydberg atom configurations.
Novel eco-efficient reactive distillation process for dimethyl carbonate production by indirect alcoholysis of urea

Dimethyl carbonate is an eco-friendly essential chemical that can be sustainably produced from CO_2,which is available from carbon capture activities or can even be captured from the air. The rapid increase in dimethyl carbonate demand is driven by the fast growth of polycarbonates, solvent, pharmaceutical, and lithium-ion battery industries. Dimethyl carbonate can be produced from CO_2 through various chemical pathways, but the most convenient route reported is the indirect alcoholysis of urea. Previous research used techniques such as heat integration and reactive distillation to reduce the energy use and costs, but the use of an excess of methanol in the trans-esterification step led to an energy intensive extractive distillation required to break the dimethyl carbonate-methanol azeotrope. This work shows that the production of dimethyl carbonate by indirect alcoholysis of urea can be improved by using an excess of propylene carbonate (instead of an excess of methanol), a neat feat that we showed it requires only 2.64 kW·h·kg~(-1) dimethyl carbonate in a reaction-separation-recycle process, and a reactive distillation column that effectively replaces two conventional distillation columns and the reactor for dimethyl carbonate synthesis. Therefore, less equipment is required, the methanol-dimethyl carbonate azeotrope does not need to be recycled, and the overall savings are higher. Moreover, we propose the use of a reactive distillation column in a heat integrated process to obtain high purity dimethyl carbonate (>99.8 wt-%). The energy requirement is reduced by heat integration to just 1.25 kW·h·kg~(-1) dimethyl carbonate, which is about 52% lower than the reaction-separation-recycle process. To benefit from the energy savings, the dynamics and control of the process are provided for ±10% changes in the nominal rate of 32 ktpy dimethyl carbonate, and for uncertainties in reaction kinetics.
Plant target of rapamycin signaling network: Complexes, conservations, and specificities

Target of rapamycin (TOR) is an evolutionarily conserved protein kinase that functions as a central signaling hub to integrate diverse internal and external cues to precisely orchestrate cellular and organismal physiology. During evolution, TOR both maintains the highly conserved TOR complex compositions, and cellular and molecular functions,but also evolves distinctive roles and strategies to modulate cell growth, proliferation, metabolism,survival, and stress responses in eukaryotes. Here,we review recent discoveries on the plant TOR signaling network. We present an overview of plant TOR complexes, analyze the signaling landscape of the plant TOR signaling network from the upstream signals that regulate plant TOR activation to the downstream effectors involved in various biological processes, and compare their conservation and specificities within different biological contexts. Finally, we summarize the impact of dysregulation of TOR signaling on every stage of plant growth and development, from embryogenesis and seedling growth, to flowering and senescence.
A bi-scale assessing framework for aesthetic ecosystem services of villages in a world heritage site

The relative spatial scale relationship of observers and ecosystem and their aesthetic dynamic interaction process are fundamental to evaluation and optimization of aesthetic ecosystem service (AES). A comprehensive and efficient framework for the assessment of AES is lack in the integration of scale relationship and dynamic process. This study took 9 villages in 4 different developmental stages (traditional, folk, rapidly changed, newly built) in Honghe Hani Rice Terraces, a world heritage site, as the research object. From two scales, viewing from inside and outside, the bi-scale assessing framework was established, which includes the three components of interaction process, connection area (as precondition of interaction), quality (as result of interaction), and influencing factors of quality (elements' characteristics of villages). Among them, the connection areas were evaluated with visual and traffic accessibility along the route. The quality and influencing factors were evaluated through participatory preferences methods by expert group. The influencing factors include 9 characteristics, such as, space size, architecture layout, vegetation species richness, color diversity. The results suggested that villages with high AES quality and low accessibility need to be optimized, and the key influencing factors are space size, architecture layout, color harmony and surrounding sanitation. Therefore, the bi-scale assessing framework can provide important references for decision making and visual protection regulations on the villages.