Study on metal recovery process and kinetics of oxidative leaching from spent LiFePO_(4)Li-batteries

A green environmental protection and enhanced leaching process was proposed to recover all elements from spent lithium iron phosphate(LiFePO_(4)) lithium batteries.In order to reduce the influence of Al impurity in the recovery process,NaOH was used to remove impurity.After impurity removal,the spent LiFePO_(4) cathode material was used as raw material under the H_(2)SO_(4) system,and the pressure oxidation leaching process was adopted to achieve the preferential leaching of lithium.The E-pH diagram of the Fe-P-Al-H_(2)O system can determine the stable region of each element in the recovery process of spent LiFePO_(4)Li-batteries.Under the optimal conditions(500 r·min^(-1),15 h,363.15 K,0.4 MPa,the liquid-solid ratio was 4:1 ml·g^(-1)and the acid-material ratio was 0.29),the leaching rate of Li was 99.24%,Fe,Al,and Ti were 0.10%,2.07%,and 0.03%,respectively.The Fe and P were precipitated and recovered as FePO_(4)·2H_(2)O.The kinetic analysis shows that the process of high-pressure acid leaching of spent LiFePO_(4) materials depends on the surface chemical reaction.Through the life cycle assessment(LCA)of the spent LiFePO_(4) whole recovery process,eight midpoint impact categories were selected to assess the impact of recovery process.The results can provide basic environmental information on production process for recycling industry.

新高考志愿推荐算法研究

新高考政策的推行和逐步落实,使志愿填报由以往院校为主体且志愿可服从调剂、变为“专业+院校”组合更突出专业的模式。目前实行新高考的省份可填志愿数为80个、96个或112个,而且志愿间不再存在服从调剂选项。针对新高考志愿填报要求,以考生分数能否被院校专业录取作为评价标准,结合专业类型、选科要求、院校属性、学费等多维数据,使用长短时记忆网络算法,可以预测出该分数在当年录取位次,再根据一定报考规则形成志愿填报方案。使用考生报考当年前3年的专业录取位次和当年录取位次作为输入,得到当年考生被各专业的录取概率,并基于该录取概率为考生进行志愿推荐。在河北省2021年新高考中,使用该算法进行志愿填报实践,结果表明:基于长短时记忆网络的新高考预测算法,为新高考考生志愿填报提供了有效支持,既帮助其进行专业选择,又能很好地发挥其考分的价值,做到科学填报。该研究可以为新高考考生个性化填报提供有益帮助,帮助实行新高考省份的学校对学生进行科学填报指导。

A Knowledge-Enriched and Span-Based Network for Joint Entity and Relation Extraction

The joint extraction of entities and their relations from certain texts plays a significant role in most natural language processes.For entity and relation extraction in a specific domain,we propose a hybrid neural framework consisting of two parts:a span-based model and a graph-based model.The span-based model can tackle overlapping problems compared with BILOU methods,whereas the graph-based model treats relation prediction as graph classification.Our main contribution is to incorporate external lexical and syntactic knowledge of a specific domain,such as domain dictionaries and dependency structures from texts,into end-to-end neural models.We conducted extensive experiments on a Chinese military entity and relation extraction corpus.The results show that the proposed framework outperforms the baselines with better performance in terms of entity and relation prediction.The proposed method provides insight into problems with the joint extraction of entities and their relations.

HBXIP blocks myosin-ⅡA assembly by phosphorylating and interacting with NMHC-ⅡA in breast cancer metastasis

Tumor metastasis depends on the dynamic balance of the actomyosin cytoskeleton.As a key component of actomyosin filaments,non-muscle myosin-ⅡA disassembly contributes to tumor cell spreading and migration.However,its regulatory mechanism in tumor migration and invasion is poorly understood.Here,we found that oncoprotein hepatitis B X-interacting protein(HBXIP) blocked the myosin-ⅡA assemble state promoting breast cancer cell migration.Mechanistically,mass spectrometry analysis,co-immunoprecipitation assay and GST-pull down assay proved that HBXIP directly interacted with the assembly-competent domain(ACD) of non-muscle heavy chain myosin-ⅡA(NMHC-ⅡA).The interaction was enhanced by NMHC-ⅡA S1916 phosphorylation via HBXIP-recruited protein kinase PKCβⅡ.Moreover,HBXIP induced the transcription of PRKCB,encoding PKCβⅡ,by coactivating Sp1,and triggered PKCβⅡ kinase activity.Interestingly,RNA sequencing and mouse metastasis model indicated that the anti-hyperlipidemic drug bezafibrate(BZF) suppressed breast cancer metastasis via inhibiting PKCβⅡ-mediated NMHC-ⅡA phosphorylation in vitro and in vivo.We reveal a novel mechanism by which HBXIP promotes myosin-ⅡA disassembly via interacting and phosphorylating NMHC-ⅡA,and BZF can serve as an effective anti-metastatic drug in breast cancer.

EV713C protease cleaves host anti-viral factor OAS3 and enhances virus replication

The global spread of enteroviruses(EVs)has become more frequent,severe and life-threatening.Intereron(IFN)I has been proved to control EVs by regulating IFN-stimulated genes(ISG)expression.20-50-oligoadenylate synthetases 3(OAS3)is an important ISG in the OAS/RNase L antiviral system.The relationship between OAS3 and EVs is still unclear.Here,we reveal that OAS3,superior to OAS1 and OAS2,significantly inhibited EV71 replication in vitro.However,EV71 utilized autologous 3C protease(3C^(pro))to cleave intracellular OAS3 and enhance viral replication.Rupintrivir,a human rhinovirus 3C protease inhibitor,completely abolished the cleavage of EV713C^(pro)on OAS3.And the proteolytically deficient mutants H40G,E71A,and C147G of EV713C^(pro)also lost the ability of OAS3 cleavage.Mechanistically,the Q982-G983 motif in C-terminal of OAS3 was identified as a crucial 3C^(pro)cutting site.Further investigation indicated that OAS3 inhibited not only EV71 but also Coxsackievirus B3(CVB3),Coxsackievirus A16(CA16),Enterovirus D68(EVD68),and Coxsackievirus A6(CA6)subtypes.Notably,unlike other four subtypes,CA163C^(pro)could not cleave OAS3.Two key amino acids variation Ile36 and Val86 in CA163C^(pro)might result in weak and delayed virus replication of CA16 because of failure of OAS and 3AB cleavage.Our works elucidate the broad anti-EVs function of OAS3,and illuminate a novel mechanism by which EV71 use 3C^(pro)to escape the antiviral effect of OAS3.These findings can be an important entry point for developing novel therapeutic strategies for multiple EVs infection.

IFN-β1b induces OAS3 to inhibit EV71 via IFN-β1b/JAK/STAT1 pathway

Enterovirus 71(EV71) caused hand, foot and mouth disease(HFMD) is a serious threat to the health of young children. Although type I interferon(IFN-I) has been proven to control EV71 replication, the key downstream IFNstimulated gene(ISG) remains to be clarified and investigated. Recently, we found that 2’-5’-oligoadenylate synthetases 3(OAS3), as one of ISG of IFN-β1b, was antagonized by EV71 3C protein. Here, we confirm that OAS3is the major determinant of IFN-β1b-mediated EV71 inhibition, which depends on the downstream constitutive RNase L activation. 2’-5’-oligoadenylate(2-5A) synthesis activity deficient mutations of OAS3 D816A, D818A,D888A, and K950A lost resistance to EV71 because they could not activate downstream RNase L. Further investigation proved that EV71 infection induced OAS3 but not RNase L expression by IFN pathway. Mechanically, EV71 or IFN-β1b-induced phosphorylation of STAT1, but not STAT3, initiated the transcription of OAS3 by directly binding to the OAS3 promoter. Our works elucidate the immune regulatory mechanism of the host OAS3/RNase L system against EV71 replication.

From one to crowd： a survey on crowdsourcing-based wireless indoor localization

Wireless indoor localization has attracted growing research interest in the mobile computing community for the last decade. Various available indoor signals, including radio frequency, ambient, visual, and motion signals, are extensively exploited for location estimation in indoor environments. The physical measurements of these signals, however, are still limited by both the resolution of devices and the spatial-temporal variability of the signals. One type of noisy signal complemented by another type of signal can benefit the wireless indoor localization in many ways, since these signals are related in their physics and independent in noise. In this article, we survey the new trend of integrating multiple chaotic signals to facilitate the creation of a crowdsourced localization system. Specifically, we first present a three-layer framework for crowdsourcing-based indoor localization by integrating multiple signals, and illustrate the basic methodology for making use of the available signals. Next, we study the mainstream signals involved in indoor localization approaches in terms of their characteristics and typical usages. Furthermore, considering multiple different outputs from different signals, we present significant insights to integrate them together, to achieve localizability in different sce- narios.

A Cumulative Prospect Theory Based Counterterrorism Resource Allocation Method under Interval Values

Strategic resource allocation into decision-making model plays a valuable role for the defender in mitigating damage and improving efficiency in military environments.In this paper,we develop a defensive resource allocation model based on cumulative prospect theory (CPT),which considers terrorists' psychological factors of decision-making in reality.More specifically,we extend existing models in the presence of multiple attributes and terrorists' deviations from rationality using a multi-attribute cumulative prospect theory.In addition,interval values are used to cope with uncertainties regarding gain and loss.Comparative studies are also carried out to demonstrate the differences among minmax,Nash equilibrium (NE),and traditional probability risk analysis (PRA) strategies.Results show that the defender's optimal defensive resource allocation will change along with terrorists' behaviors and the proposed model makes more sense compared with other traditional resource allocation strategies.

Compound graph based hybrid data center topologies

In large-scale data centers, many servers are in- terconnected via a dedicated networking structure, so as to satisfy specific design goals, such as the low equipment cost, the high network capacity, and the incremental expansion. The topological properties of a networking structure are criti- cal factors that dominate the performance of the entire data center. The existing networking structures are either fully random or completely structured. Although such networking structures exhibit advantages on given aspects, they suffer ob- vious shortcomings in other essential fields. In this paper, we aim to design a hybrid topology, called R3, which is the com- pound graph of structured and random topology. It employs random regular graph as a unit duster and connects many such clusters by means of a structured topology, i.e., the gen- eralized hypercube. Consequently, the hybrid topology com- bines the advantages of structured as well as random topolo- gies seamlessly. Meanwhile, a coloring-based algorithm is proposed for R3 to enable fast and accurate routing. R3 pos- sesses many attractive characteristics, such as the modularity and expansibility at the cost of only increasing the degree of any node by one. Comprehensive evaluation results show that our hybrid topology possesses excellent topology properties and network performance.

ATP1B3 Restricts Hepatitis B Virus Replication Via Reducing the Expression of the Envelope Proteins

Our recent study reported that ATP1B3 inhibits hepatitis B virus(HBV)replication via inducing NF-κB activation.However,ATP1B3 mutants which were defective in NF-κB activation still maintained the moderate degree of suppression on HBV replication,suggesting that another uncharacterized mechanism is also responsible for ATP1B3-mediated HBV suppression.Here,we demonstrated that ATP1B3 reduced the expression of HBV envelope proteins LHBs,MHBs and SHBs,but had no effect on intracellular HBV DNA,RNA levels as well as HBV promoter activities.Further investigation showed that proteasome inhibitor MG132 rescued ATP1B3-mediated envelope proteins degradation,demonstrating that proteasome-dependent pathway is involved in ATP1B3-induced degradation of envelope proteins.Co-IP showed that ATP1B3 interacts with LHBs and MHBs and induces LHBs and MHBs polyubiquitination.Immunofluorescence colocalization analysis confirmed LHBs and MHBs colocalized with ATP1B3 together.Our work provides important information for targeting ATP1B3 as a potential therapeutic molecule for HBV infection.

Passive-Event-Assisted Approach for the Localizability of Large-Scale Randomly Deployed Wireless Sensor Network

Localizability in large-scale, randomly deployed Wireless Sensor Networks(WSNs) is a classic but challenging issue. To become localizable, WSNs normally require extensive adjustments or additional mobile nodes. To address this issue, we utilize occasional passive events to ease the burden of localization-oriented network adjustment. We prove the sufficient condition for node and network localizability and design corresponding algorithms to minimize the number of nodes for adjustment. The upper bound of the number of adjusted nodes is limited to the number of articulation nodes in a connected graph. The results of extensive simulations show that our approach greatly reduces the cost required for network adjustment and can thus provide better support for the localization of large-scale sparse networks than other approaches.

Enabling entity discovery in indoor commercial environments without pre-deployed infrastructure

Finding entities of interest in indoor commercial places, such as the merchandise in supermarkets and shopping malls, is an essential issue for customers, especially when they are unfamiliar with an ad hoc indoor environment. This type of location-based indoor service requires comprehensive knowledge of indoor entities, including locations as well as their semantic information. However, the existing indoor localization approaches fail to directly localize these general entities without dedicated devices. This paper first focuses on the problem of discovering large-scale general entities of interest in indoor commercial spaces without pre-deployed infrastructure. We present a unique entity localization approach that leverages the localization results from multiple independent users to accurately determine the location of corresponding entities. Our key idea is to exploit the short-distance estimation with dead reckoning to guarantee the accuracy of entity localization. We develop a prototype system based on the crowdsourcing method, iScan, and test it in one of the biggest supermarkets in Changsha, China, to validate the performance of our design. Extensive experimental results show that our approach can achieve meter-level accuracy in a single day with 70 participants. Moreover, in a monthly evaluation with 500 effective participants, iScan discovered more than 200 entities and localized approximately 75% of them within 2 m.