mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Should we recycle the graphite from spent lithium-ion batteries? The untold story of graphite with the importance of recycling

Demand for graphite in the forthcoming years to develop Li-ion batteries (LIBs) with the goal of driving electric vehicles (EV) and its requirement in multifarious energy storage applications as an electrode. The emerging sector of LIB-based EVs, along with portable electronics, produces an inevitable volume of batteries in the e-waste stream. The main reason for the lower percentage of recycling (at present, <5%) is due to the recovery of economically rich metals like Li, Ni, and Co. However, complete recycling technologies, including the strategic material graphite, which is available in a massive amount of spent LIBs, are urgently needed to be updated to ensure the reuse of all components. This approach lifts the recycling process to develop an economic one besides the geostrategic and environmental policy aspects. Here, we summarize the importance of graphite and its demand and specify the reasons to recycle the graphite from spent LIBs along with its development as an anode in detail. Additionally, the approach of the current recycling process of graphite in lab-scale and industries for various applications, including energy storage, are discussed with the highlights of future progress.
Vision for energy material design: A roadmap for integrated data-driven modeling

Increasing energy demand has led to a diverse suite of advances in the field of energy materials (e.g., electrodes, catalysts) [1]. Recently, theoretical and computational techniques, such as density functional theory (DFT), have become increasingly essential for interpreting experimental phenomena and providing the mechanistic understanding necessary to design more effective energy materials [2]. These techniques can predict the important properties of an arbitrary compound that are closely related to energy science (e.g., ionic conductivity, reaction energy barrier) before the compound has been fabricated in a laboratory; this has wideranging applications such as in material design for energy storage and harvesting [3]. However, high-precision theoretical and computational techniques incur high computational expenses. One recent development is the construction of structure-activity relationships by using machine-learned multi-dimensional fitting in massive material databases [3,4]. As shown in Fig. 1, machine learning (ML) techniques form an important hub that drives databases, attributes, and applications of energy materials, bringing material design to the next stage of rapid development. Zhou et al. summarized the latest advances in applying ML to material attribute prediction for energy applications, including batteries, catalysis, and etc., which could shed new light on the design of energy materials [5,6]. Current approaches for energy material design includes material data expansions, such as the Materials Project [7], where ML techniques have been rapidly developed. Nevertheless, an in-depth discussion of appropriate ML principles for modeling datasets with various availabilities is lacking.
Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization

Vacuum gas oil (VGO) is the most important feedstock for hydrocracking processes in refineries, but its molecular composition cannot be fully acquired by current analysis techniques owing to its complexity. In order to build an accurate and reliable molecular-level kinetic model for reactor design and process optimization, the molecular composition of VGO has to be reconstructed based on limited measurements. In this study, a modified stochastic reconstruction-entropy maximization (SR-REM) algorithm was applied to reconstruct VGOs, with generation of a general molecule library once and for all via the SR method at the first step and adjustment of the molecular abundance of various VGOs via the REM method at the second step. The universality of the molecule library and the effectiveness of the modified SR-REM method were validated by fifteen VGOs (three from the literature) from different geographic regions of the world and with different properties. The simulated properties (density, elemental composition, paraffin-naphthene-aromatics distribution, boiling point distribution, detailed composition of naphthenes and aromatics in terms of ring number as well as composition of S-heterocycles) are in good agreement with the measured counterparts, showing average absolute relative errors of below 10% for each property.
Investigation of heavy ion irradiation effects on 650-V p-GaN normally-off HEMTs

In this study,we investigate heavy ion irradiation effects on commercial 650 V p-GaN normallyoff HEMTs.Ge and Cl ions are used to irradiate the GaN devices in the experiments.Ge and Cl ion beam irradiation have little impact on the output characteristics of GaN devices.After heavy ion irradiation,the leakage currents between source and drain electrodes increase significantly under off-state,decreasing the breakdown voltage (BVDS) sharply.Additionally,Ge and Cl ion irradiation have little effect on the trap states under the gate electrode;thus,the gate leakage currents increase slightly.Many line-shaped crystal defects extending from the surface to the GaN buffer layer can be captured using a transmission electron microscope after Ge/Cl ion irradiation.The buffer layers of the irradiated devices were damaged,and the leakage path was generated in the buffer layer.Defect percolation process in buffer layer is the dominant factor of irradiated high-voltage GaN device failure.
Reversible Data Hiding Based on Greedy Pairing Prediction-Error Expansion

Reversible data hiding (RDH) is a method to embed messages into an image that human eyes are difficult to recognize the differences between the original image and the embedded image. The method needs to make sure that the original image and the embedded information can be exactly recovered. The prediction-error expansion (PEE) is a successful way to realize RDH. However, it is fixed when pairing the conventional twodimensional prediction-error histogram (2D-PEH). So, the embedding capacity (EC) and embedding distortion (ED) are not satisfactory. In this study, we propose a method called greedy pairing prediction-error expansion (GPPEE) based on pairwise RDH and demonstrate GPPEE can achieve a more efficient embedding goal and reduce ED.
Characteristics, Types, and Prospects of Geological Sweet Sections in Giant Continental Shale Oil Provinces in China

“Sweet sections” in giant shale oil provinces are preferential fields that primarily support China to increase the reserves and production of continental shale oil. Based on the study of the geological conditions of shale oil in the continental basins in China, it was found that the shale stratum in major oil generation windows generally has higher degrees of oil and gas accumulation, and mostly contains oil. Hydrocarbon generation and reservoir capacities are the two key parameters for evaluating and optimizing favorable shale oil provinces. The evaluation index (volume of shale stratum multiplied by total organic carbon (TOC) multiplied by total porosity) for the giant continental shale oil provinces is also proposed. It is optimized that the Upper Triassic Chang 7 Member in the southcentral Ordos Basin, Lower Cretaceous Qing 1 Member in the Gulong-Changling Sag in the Songliao Basin, Middle-Lower Permian in the Junggar Basin, Da'anzhai Member of the Ziliujing Formation of Lower Jurassic in the central and northern Sichuan Basin, and Paleogene oil-rich sag in the Bohai Bay Basin are the five giant continental shale oil provinces. The word “geological sweet sections” in continental shale oil provinces of China refers to favorable shale intervals which are relatively rich in oil, with superior physical properties, and more easily modified and developed commercially under applicable economic and technological conditions. After evaluation, there are mainly two types of “geological sweet sections” of giant continental shale oil developed onshore in China. One type of “geological sweet sections” is generally mudstone with optimal physical properties or a thin tight reservoir, to which the shale oil migrates a short distance. They are medium-to-high-mature zones with a thin sandy shale stratum in the Chang 7 Member in the Ordos Basin, mixed shale stratum in the mediummature Lucaogou Formation in the Jimsar Sag, and multi-layered mixed Paleogene shale stratum in the Bohai Bay Basin. The other type of “geological sweet sections” is generally shale oil residing in various shale reservoir spaces. This type was developed in the Qing 1 Member in the Gulong Sag and Da'anzhai Member in the north-central Sichuan Basin. Free shale oil mainly occurs in shale, sandycarbonate lamina, micro-lamella structure, and micro-fractures. Layers with lamina, lamination, and micro-fractures are generally shale oil “geological sweet sections.” Starting from field tests and the construction of the “geological sweet sections” in giant continental shale oil provinces, the shale oil industry has been rapidly developing and will become an important supplement to domestic oil production in China.
Mask R-CNN and multifeature clustering model for catenary insulator recognition and defect detection

Rod insulators are vital parts of the catenary of high speed railways(HSRs). There are many different catenary insulators, and the background of the insulator image is complicated. It is difficult to recognise insulators and detect defects automatically. In this paper, we propose a catenary intelligent defect detection algorithm based on Mask region-convolutional neural network(R-CNN) and an image processing model. Vertical projection technology is used to achieve single shed positioning and precise cutting of the insulator. Gradient, texture, and gray feature fusion(GTGFF) and a K-means clustering analysis model(KCAM) are proposed to detect broken insulators, dirt, foreign bodies, and flashover. Using this model, insulator recognition and defect detection can achieve a high recall rate and accuracy, and generalized defect detection. The algorithm is tested and verified on a dataset of realistic insulator images, and the accuracy and reliability of the algorithm satisfy current requirements for HSR catenary automatic inspection and intelligent maintenance.
Flow resistance adjustments of channel and bars in the middle reaches of the Yangtze River in response to the operation of the Three Gorges Dam

Since the Three Gorges Dam (TGD) was put into operation,the flood water level at an identical discharge rate has not displayed a decreasing trend along the middle reaches of the Yangtze River (MYR).The flow resistance variations of the channel and bars in response to the operation of the TGD remain poorly understood,despite the importance of understanding these for water disaster mitigation and water environment regulation.Herein,the impacts of the TGD on the downstream flow resistance of the channel and bars in the MYR were analyzed using systematic surveys of hydrological datasets,cross- sectional profiles,sediment datasets,and remote sensing images,during different periods.Under the actual natural conditions in the MYR,a modified semi-empirical formula,which considered the grain,dune resistance,as well as the topographic features of the riverbed,was proposed to predict the channel resistance.Furthermore,the effect of various dam-control flow and sediment elements on the variation in different flow resistance components,and the corresponding relationships among them were investigated.Results showed a decline in the comprehensive,channel,and bar resistances as the discharge increased,whereas there was a slight increase when reaching the bank-full discharges.Notably,the bar resistance occupied 65%,while the channel resistance,in which dune resistance was much larger than grain resistance,contributed 35% to the comprehensive resistance.In addition,while flow resistance rose over time,there was a decline as the distance from the TGD increased.In conclusion,the increased dune and bar resistances,interpreted by the fluctuated channel longitudinal profile and growing vegetated area on bars,were the dominant factors preventing the flood water level from dropping.
电池储能系统在节能型酒店中的应用分析

近年来,随着高温天数的增加,居民的用电负荷日渐增大,为避免出现停电的情况,我国多地出台了错峰用电等相关政策,例如近日四川发布的限电令新规定。预计在未来的一段时间,我国多地还会不间断地开启限电模式,由此可见,我国目前的电力能源形势不容乐观。随着各行各业对于节能环保理念的日渐重视,酒店服务业也在日常管理中采取了尽可能地降低成本预算、加大促进节能环保工作力度的策略,在节能型酒店管理的各个层面书写低碳绿色的新篇章。
航空航天电源专辑主编述评

1970年4月24日,我国第一颗人造地球卫星 “东方红一号”由“长征一号”运载火箭发射成功,标志着中国进入了空间时代。随着我国航空航天技术的快速发展,我国在通信导航、载人航天、深空探测和大飞机等领域取得了举世瞩目的成就。
B细胞淋巴瘤引起巨脾的乙肝肝硬化1例报告

我国目前慢性乙肝病毒(HBV)感染者约有7000万。HBV慢性感染不仅可以引起肝硬化,也可以使多种肿瘤的发生率增加,包括肝细胞癌、胃癌、肝内外胆管细胞癌、胰腺癌等。门静脉高压是乙型肝炎肝硬化的常见并发症,可以表现为脾大、脾功能亢进、食管胃底静脉曲张、腹水等。脾大常见于乙肝肝硬化患者。脾大的原因包括急慢性感染、淤血(肝硬化患者因门静脉高压导致脾静脉回流受限)、血液病(包括淋巴瘤等)、结缔组织病、脾脏肿瘤等。乙肝肝硬化患者出现巨脾多与门静脉高压有关,但也有可能是其它少见原因。本文报告1例同时患有乙型肝炎肝硬化、甲状腺功能减退及B细胞淋巴瘤等疾病的病例。
The near-inertial waves observed east of the Philippines

Based on mooring observations from Aug. 1, 2016 to Dec. 14, 2017, the characteristics and underlying mechanisms of near-inertial waves(NIWs)observed east of the Philippines were studied. Three strong NIW events were investigated in detail. The NIWs in Event Ⅰ were induced by typhoon Lan and had the strongest magnitudes of 0.35 m/s. The maximum near-inertial kinetic energy(NIKE)was shown at the ocean surface. The NIW in Event Ⅱ was stimulated by a moderate cyclonic wind with the extreme NIKE located at about 110-m depth. The existence of a cyclonic eddy during Events Ⅰ and Ⅱ led to a blue shift of near-inertial frequencies. For Event Ⅲ, the surface near-inertial signals were also induced by local weak wind, whereas the real generation mechanisms for the subsurface NIWs remain unclear. In particular, during Event Ⅲ, there was a nonlinear wave-wave interaction between NIWs and semidiurnal(D_2)tides, which further induced strong D_2±f waves. Overall, the NIWs in the three events exhibited distinct vertical structures. The NIWs in Events Ⅰ and Ⅱ were dominated by lower modes with elevated NIKE well confined to the upper 250 m and 270 m, respectively. In contrast, the NIW Event Ⅲ was dominated by higher modes and the NIWs penetrated downward beyond 360 m. Such deep penetration of NIWs could be attributed to the weak wind stress curl and positive sea level anomalies associated with an anticyclonic eddy. In addition, the three NIW events had e-folding timescales of less than 7 days.
Screening of nanobody against Microcystis from a human phage display nanobody library

Microcystis species identification is essential for ecological studies and water bloom control. Immunoassays are more specific and convenient and several approaches have been used to develop for diagnosing harmful red tide algae. However, investigations on Microcystis identification using immunological approaches are still in the initial stage. In this study, Microcystis aeruginosa PCC7806 lysates were utilized as coated antigens to enrich and screen specific Microcystis nanobodies from a human domain antibody display library. After three rounds of enrichment, 10 positive monoclonal particles were isolated from the library and the most two positive nanobodies(DAb2 and Dab3)were effectively produced in Escherichia coli BL21. Finally, the DAb2 showed specific immune binding to different Microcystis by the immuno-dot blot assay. This antibody could be used to establish an immunological method to identify Microcystis.
Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data

Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.
Microorganism-derived biological macromolecules for tissue engineering

According to literature, certain microorganism productions mediate biological effects. However, their beneficial characteristics remain unclear. Nowadays, scientists concentrate on obtaining natural materials from live creatures as new sources to produce innovative smart biomaterials for increasing tissue reconstruction in tissue engineering and regenerative medicine. The present review aims to introduce microorganism-derived biological macromolecules, such as pullulan, alginate, dextran, curdlan, and hyaluronic acid, and their available sources for tissue engineering. Growing evidence indicates that these materials can be used as biological material in scaffolds to enhance regeneration in damaged tissues and contribute to cosmetic and dermatological applications. These natural-based materials are attractive in pharmaceutical, regenerative medicine, and biomedical applications. This study provides a detailed overview of natural-based biomaterials, their chemical and physical properties, and new directions for future research and therapeutic applications.
WWP2 ameliorates oxidative stress and inflammation in atherosclerotic mice through regulation of PDCD4/HO-1 pathway

WWP2 is a HECT-type E3 ubiquitin ligase that regulates various physiological and pathological activities by binding to different substrates, but its role in atherosclerosis (AS) remains largely unknown. The objective of the present study is to investigate the role and underlying molecular mechanisms of WWP2 in endothelial injury. We found that WWP2 expression is significantly decreased in Apolipoprotein E (ApoE)~(-/-) mice. Overexpression of WWP2 attenuates oxidative stress and inflammation in AS mice, while knockdown of WWP2 has opposite effects. WWP2 over-expression alleviates oxidized low-density lipoprotein (ox-LDL)-induced human umbilical vein endothelial cell (HUVEC) injury, evidenced by the decreased oxidative stress levels and the secretion of inflammatory cytokines. Programmed cell death 4 (PDCD4) is identified as a potential substrate of WWP2. Co-immunoprecipitation (Co-IP) further demonstrates that WWP2 interacts with PDCD4, which is enhanced by ox-LDL treatment. Furthermore, the level of PDCD4 ubiquitination is significantly increased by WWP2 overexpression under the condition of MG132 treatment, while WWP2 knockdown shows opposite results. Subsequently, rescue experiments demonstrate that WWP2 knockdown further aggravates oxidative stress and inflammation in ox-LDL-treated HUVECs, while knockdown of PDCD4 alleviates this effect. Moreover, the use of sn-protoporphyrin (SnPP), an inhibitor of HO-1 pathway, confirms that PDCD4 enhances endothelial injury induced by ox-LDL through inhibiting HO-1 pathway. In conclusion, our results suggest that WWP2 protects against atherosclerosis progression via the PDCD4/HO-1 pathway, which may provide a novel treatment strategy for atherosclerosis.
Engineering of triterpene metabolism and overexpression of the lignin biosynthesis gene PAL promotes ginsenoside Rg3 accumulation in ginseng plant chassis

The ginsenoside Rg3 found in Panax species has extensive pharmacological properties, in particular anti-cancer effects. However, its natural yield in Panax plants is limited. Here, we report a multimodular strategy to improve yields of Rg3 in a Panax ginseng chassis, combining engineering of triterpene metabolism and overexpression of a lignin biosynthesis gene, phenylalanine ammonia lyase (PAL). We first performed semi-rational design and site mutagenesis to improve the enzymatic efficiency of Pq3-O-UGT2, a glycosyltransferase that directly catalyzes the biosynthesis of Rg3 from Rh2. Next, we used clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) gene editing to knock down the branch pathway of protopanaxatriol-type ginsenoside biosynthesis to enhance the metabolic flux of the protopanaxadiol-type ginsenoside Rg3. Overexpression of PAL accelerated the formation of the xylem structure, significantly improving ginsenoside Rg3 accumulation (to 6.19-fold higher than in the control). We combined overexpression of the ginsenoside aglycon synthetic genes squalene epoxidase, Pq3-O-UGT2, and PAL with CRISPR/Cas9-based knockdown of CYP716A53v2 to improve ginsenoside Rg3 accumulation. Finally, we produced ginsenoside Rg3 at a yield of 83.6 mg/L in a shake flask (7.0mg/g dry weight, 21.12-fold higher than with wild-type cultures). The highproduction system established in this study could be a potential platform to produce the ginsenoside Rg3 commercially for pharmaceutical use.
Quartz luminescence dating of a centennial coastal foredune from Changle in Fujian Province, East China Sea

The Aeolian foredunes in the southern coast of China are records of paleoenvironmental changes for marine monsoon region. Intensive studies have been conducted to decipher their formation and paleoenvironmental implications in which chronology played a key role. However, robust dating results are still limited due to the scarcity of organic materials for radiocarbon and the limited application of quartz optically stimulated luminescence(OSL). Here, we collected 11 OSL samples and 140 grainsize samples from the Changle section with a thickness of 7 m from a coastal foredune in Fujian Province. The dating results indicated that the section spanned a time period from 0.32±0.04 ka(depth of 700 cm)to 0.18±0.02 ka(depth of 80 cm), after removal of age outliers. According to the OSL ages and sedimentary structural characteristics, the section could be divided into two stages, i.e. the stable accumulation period with an age range of 0.32-0.18 ka from the depth of 700 cm to 80 cm, and the activation period above the depth of 60 cm, at which the dune erosion was retained. The foredune development due to increased transport of dune sediment at the top part of the section was the result of recent human activities. Inside the foredunes, normal cross-bedding was generated by seasonal changes in wind direction. Seasonal changes in the wind direction were the primary cause for various sedimentary bedding within the dunes.