Tuning Li/Ni mixing by reactive coating to boost the stability of cobalt-free Ni-rich cathode

Cobalt-free cathode materials are attractive for their high capacity and low cost,yet they still encounter issues with structural and surface instability.AlPO_(4),in particular,has garnered attention as an effective stabilizer for bulk and surface.However,the impact of interfacial reactions and elemental interdiffusion between AlPO_(4) and LiNi_(0.95)Mn_(0.05)O_(2) upon sintering on the bulk and surface remains elusive.In this study,we demonstrate that during the heat treatment process,AlPO_(4) decomposes,resulting in Al doping into the bulk of the cathode through elemental interdiffusion.Simultaneously,PO_(4)^(3-)reacts with the surface Li of material to form a Li_3PO_(4) coating,inducing lithium deficiency,thereby increasing Li/Ni mixing.The suitable Li/Ni mixing,previously overlooked in AlPO_(4) modification,plays a pivotal role in stabilizing the bulk and surface,exceeding the synergy of Al doping and Li_3PO_(4) coating.The presence of Ni^(2+)ions in the lithium layers contributes to the stabilization of the delithiated structure via a structural pillar effect.Moreover,suitable Li/Ni mixing can stabilize the lattice oxygen and electrode-electrolyte interface by increasing oxygen removal energy and reducing the overlap between the Ni^(3+/4+)e_g and O^(2-)2p orbitals.These findings offer new perspectives for the design of stable cobalt-free cathode materials.

An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big data.However,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability.In addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced.Based on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data usability.Specifically,we construct a new information loss function based on the information quantity theory.Considering that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss function.In addition,to reduce information loss,we improve K-anonymity in two ways.First,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering algorithm.In addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass.Meanwhile,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information loss.Finally,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.

A blockchain-empowered authentication scheme for worm detection in wireless sensor network

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and self-management.The special structure of WSN brings both convenience and vulnerability.For example,a malicious participant can launch attacks by capturing a physical device.Therefore,node authentication that can resist malicious attacks is very important to network security.Recently,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for WSN.In our scheme,all nodes are managed by utilizing the identity information stored on the blockchain.Besides,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection rate.The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

Development of a target capture sequencing SNP genotyping platform for genetic analysis and genomic breeding in rapeseed

Rapeseed(Brassica napus)is an oil crop grown worldwide,making it a key plant species in molecular breeding research.However,the complexity of its polyploid genome increases sequencing costs and reduces sequencing accuracy.Target capture coupled with high-throughput sequencing is an efficient approach for detecting genetic variation at genomic regions or loci of interest.In this study,588 resequenced accessions of rapeseed were used to develop a target capture sequencing SNP genotyping platform named BnaPan50T.The platform comprised 54,765,with 54,058 resequenced markers from the pan-genome,and 855 variant trait-associated markers for 12 agronomic traits.The capture quality of BnaPan50T was demonstrated well in 12 typical accessions.Compared with a conventional genotyping array,BnaPan50T has a high SNP density and a high proportion of SNPs in unique physical positions and in annotated functional genes,promising wide application.Target capture sequencing and wholegenome resequencing in 90 doubled-haploid lines yielded 60%specificity,78%uniformity within tenfold coverage range,and 93%genotyping accuracy for the platform.BnaPan50T was used to construct a genetic map for quantitative trait loci(QTL)mapping,identify 21 unique QTL,and predict several candidate genes for yield-related traits in multiple environments.A set of 132 core SNP loci was selected from BnaPan50T to construct DNA fingerprints and germplasm identification resources.This study provides genomics resources to support target capture sequencing,genetic analysis and genomic breeding of rapeseed.

Nerve function restoration following targeted muscle reinnervation after varying delayed periods

Targeted muscle reinnervation has been proposed for reconstruction of neuromuscular function in amputees.However,it is unknown whether performing delayed targeted muscle reinnervation after nerve injury will affect restoration of function.In this rat nerve injury study,the median and musculocutaneous nerves of the forelimb were transected.The proximal median nerve stump was sutured to the distal musculocutaneous nerve stump immediately and 2 and 4 weeks after surgery to reinnervate the biceps brachii.After targeted muscle reinnervation,intramuscular myoelectric signals from the biceps brachii were recorded.Signal amplitude gradually increased with time.Biceps brachii myoelectric signals and muscle fiber morphology and grooming behavior did not significantly differ among rats subjected to delayed target muscle innervation for different periods.Targeted muscle reinnervation delayed for 4 weeks can acquire the same nerve function restoration effect as that of immediate reinnervation.

Electrochemically-induced highly reactive PdO^(*) interface on modulated mesoporous MOF-derived Co_(3)O_(4) support for selective ethanol electro-oxidation

Herein,Pd nanoparticles loaded Co_(3)O_(4)catalysts(Pd@Co_(3)O_(4))are constructed from zeolitic imidazolate framework-67(ZIF-67)for the ethanol oxidation reaction(EOR).It is demonstrated for the first time that the electrochemical conversion of Co_(3)O_(4)support would result in the charge distribution alignment at the Pd/Co_(3)O_(4)interface and induce the formation of highly reactive Pd-O species(PdO^(*)),which can further catalyze the consequent reactions of the intermediates of the ethanol oxidation.The catalyst,Pd@Co_(3)O_(4)-450,obtained under the optimized conditions exhibits excellent EOR performance with a high mass activity of 590 mA mg-1,prominent operational stability,and extraordinary capability for the electro-oxidation of acetaldehyde intermediates.Importantly,the detailed mechanism investigation reveals that Pd@Co_(3)O_(4)-450 could be benefit to the C-C bond cleavage to promote the desirable C1 pathway for the ethanol oxidation reaction.The present strategy based on the metal-support interaction of the catalyst might provide valuable inspiration for the design of high-performing catalysts for the ethanol oxidation reaction.

Lightweight Storage Framework for Blockchain-Enabled Internet of Things Under Cloud Computing

Due to its decentralized,tamper-proof,and trust-free characteristics,blockchain is used in the Internet of Things(IoT)to guarantee the reliability of data.However,some technical flaws in blockchain itself prevent the development of these applications,such as the issue with linearly growing storage capacity of blockchain systems.On the other hand,there is a lack of storage resources for sensor devices in IoT,and numerous sensor devices will generate massive data at ultra-high speed,which makes the storage problem of the IoT enabled by blockchain more prominent.There are various solutions to reduce the storage burden by modifying the blockchain’s storage policy,but most of them do not consider the willingness of peers.In attempt to make the blockchain more compatible with the IoT,this paper proposes a storage optimization scheme that revisits the system data storage problem from amore practically oriented standpoint.Peers will only store transactional data that they are directly involved in.In addition,a transaction verification model is developed to enable peers to undertake transaction verification with the aid of cloud computing,and an incentive mechanism is premised on the storage optimization scheme to assure data integrity.The results of the simulation experiments demonstrate the proposed scheme’s advantage in terms of storage and throughput.

Vertical Federated Learning Based on Consortium Blockchain for Data Sharing in Mobile Edge Computing

The data in Mobile Edge Computing(MEC)contains tremendousmarket value,and data sharing canmaximize the usefulness of the data.However,certain data is quite sensitive,and sharing it directly may violate privacy.Vertical Federated Learning(VFL)is a secure distributed machine learning framework that completes joint model training by passing encryptedmodel parameters rather than raw data,so there is no data privacy leakage during the training process.Therefore,the VFL can build a bridge between data demander and owner to realize data sharing while protecting data privacy.Typically,the VFL requires a third party for key distribution and decryption of training results.In this article,we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in MEC.More specifically,we propose a V-Raft consensus algorithm based on Verifiable Random Functions(VRFs),which is a variant of the Raft.The VRaft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by VFL.Moreover,we apply secret sharing todistribute the private key to avoid the situationwhere the training result cannot be decrypted if the leader crashes.Finally,we analyzed the performance of the V-Raft and carried out simulation experiments,and the results show that compared with Raft,the V-Raft has higher efficiency and better scalability.

An Effective Security Comparison Protocol in Cloud Computing

With the development of cloud computing technology,more and more data owners upload their local data to the public cloud server for storage and calculation.While this can save customers’operating costs,it also poses privacy and security challenges.Such challenges can be solved using secure multi-party computation(SMPC),but this still exposes more security issues.In cloud computing using SMPC,clients need to process their data and submit the processed data to the cloud server,which then performs the calculation and returns the results to each client.Each client and server must be honest.If there is cooperation or dishonest behavior between clients,some clients may profit from it or even disclose the private data of other clients.This paper proposes the SMPC based on a Partially-Homomorphic Encryption(PHE)scheme in which an addition homomorphic encryption algorithm with a lower computational cost is used to ensure data comparability and Zero-Knowledge Proof(ZKP)is used to limit the client’s malicious behavior.In addition,the introduction of Oblivious Transfer(OT)technology also ensures that the semi-honest cloud server knows nothing about private data,so that the cloud server of this scheme can calculate the correct data in the case of malicious participant models and safely return the calculation results to each client.Finally,the security analysis shows that the scheme not only ensures the privacy of participants,but also ensures the fairness of the comparison protocol data.

A New Anti-Quantum Proxy Blind Signature for Blockchain-Enabled Internet of Things

Blockchain technology has become a research hotspot in recent years with the prominent characteristics as public,distributed and decentration.And blockchain-enabled internet of things(BIoT)has a tendency to make a revolutionary change for the internet of things(IoT)which requires distributed trustless consensus.However,the scalability and security issues become particularly important with the dramatically increasing number of IoT devices.Especially,with the development of quantum computing,many extant cryptographic algorithms applied in blockchain or BIoT systems are vulnerable to the quantum attacks.In this paper,an anti-quantum proxy blind signature scheme based on the lattice cryptography has been proposed,which can provide user anonymity and untraceability in the distributed applications of BIoT.Then,the security proof of the proposed scheme can derive that it is secure in random oracle model,and the efficiency analysis can indicate it is efficient than other similar literatures.

Antidiarrheal properties of different extracts of Chinese herbal medicine formula Bao-Xie-Ning

OBJECTIVE： Bao-Xie-Ning （BXN）, a traditional Chinese herbal medicine （CHM） formula composed of Fructus Evodiae, Flos Caryophylli and Cortex Cinnamomi, and used for the treatment of infant diarrheal illness, was subject to systematic assessment for its putative multiple pharmacodynamic effects and pharmacological antidiarrheal mechanisms. METHODS： High-performance liquid chromatography-diode array detector-electrospray ionization- mass spectrometric/mass spectrometry was developed and validated for identification and quantification of the main constituents in different extracts of BXN. Male Kunming mice weighing 20 to 25 g were used for detecting the antidiarrheal activity of the extracts. Ethanolic extract （EE）, volatile oil extract （VOE）, and aqueous extract （AE） of BXN were respectively subjected to pharmacodynamic and pharmacological comparison in assessing antidiarrheal effects with senna-induced diarrhea, castor oil-induced diarrhea, acetic acid-induced writhing assay, and isolated duodenum test. RESULTS： The highest yields of three detected components of BXN, rutaecarpine, eugenol and cinnamaldehyde were observed in EE. EE showed the most remarkable antidiarrheal activity in dose-dependent and time-dependent manners in both senna- and castor oil-induced diarrhea models, and presented dose-dependent analgesic activity in acetic acid-induced algesthesia model. In addition, EE extract of BXN also exhibited strong antimobility action on the intestine and strongest depression on spontaneous contraction of isolated duodenum. CONCLUSION： Ethanol extraction is an efficient method to extract the active constituents of BXN. BXN extract demonstrated multiple pharmacological activities affecting the main mechanisms of diarrhea, which validated BXN＇s usage in the comprehensive clinical treatment of diarrhea.

An Asymmetric Controlled Bidirectional Quantum State Transmission Protocol

In this paper,we propose an asymmetric controlled bidirectional transmission protocol.In the protocol,by using the thirteen-qubit entangled state as the quantum channel,Alice can realize the transmission of a two-qubit equatorial state for Bob and Bob can transmit a four-qubit equatorial state for Alice under the control of Charlie.Firstly,we give the construction of the quantum channel,which can be done by performing several H and CNOT operations.Secondly,through implementing the appropriate measurements and the corresponding recovery operations,the desired states can be transmitted simultaneously,securely and deterministically.Finally,we analyze the performance of the protocol,including the efficiency,the necessary operations and the classical communication costs.And then,we describe some comparisons with other protocols.Since our protocol does not require auxiliary particles and additional operations,the classic communication costs less while achieving the multi-particle bidirectional transmission,so the overall performance of the protocol is better.

A Novel Universal Steganalysis Algorithm Based on the IQM and the SRM

The state-of-the-art universal steganalysis method,spatial rich model(SRM),and the steganalysis method using image quality metrics(IQM)are both based on image residuals,while they use 34671 and 10 features respectively.This paper proposes a novel steganalysis scheme that combines their advantages in two ways.First,filters used in the IQM are designed according to the models of the SRM owning to their strong abilities for detecting the content adaptive steganographic methods.In addition,a total variant(TV)filter is also used due to its good performance of preserving image edge properties during filtering.Second,due to each type of these filters having own advantages,the multiple filters are used simultaneously and the features extracted from their outputs are combined together.The whole steganalysis procedure is removing steganographic noise using those filters,then measuring the distances between images and their filtered version with the image quality metrics,and last feeding these metrics as features to build a steganalyzer using either an ensemble classifier or a support vector machine.The scheme can work in two modes,the single filter mode using 9 features,and the multi-filter mode using 639 features.We compared the performance of the proposed method,the SRM and the maxSRMd2.The maxSRMd2 is the improved version of the SRM.The simulated results show that the proposed method that worked in the multi-filter mode was about 10%more accurate than the SRM and maxSRMd2 when the data were globally normalized,and had similar performance with the SRM and maxSRMd2 when the data were locally normalized.

Cipherchain:A Secure and Efficient Ciphertext Blockchain via mPECK

Most existing blockchain schemes are based on the design concept“openness and transparency”to realize data security,which usually require transaction data to be presented in the form of plaintext.However,it inevitably brings the issues with respect to data privacy and operating performance.In this paper,we proposed a novel blockchain scheme called Cipherchain,which can process and maintain transaction data in the form of ciphertext while the characteristics of immutability and auditability are guaranteed.Specifically in our scheme,transactions can be encrypted locally based on a searchable encryption scheme called multi-user public key encryption with conjunctive keyword search(mPECK),and can be accessed by multiple specific participants after appended to the globally consistent distributed ledger.By introducing execution-consensus-update paradigm of transaction flow,Cipherchain cannot only make it possible for transaction data to exist in the form of ciphertext,but also guarantee the overall system performance not greatly affected by cryptographic operations and other local execution work.In addition,Cipherchain is a promising scheme to realize the technology combination of“blockchain+cloud computing”and“permissioned blockchain+public blockchain”.

Message Authentication with a New Quantum Hash Function

To ensure the security during the communication,we often adopt different ways to encrypt the messages to resist various attacks.However,with the computing power improving,the existing encryption and authentication schemes are being faced with big challenges.We take the message authentication as an example into a careful consideration.Then,we proposed a new message authentication scheme with the Advanced Encryption Standard as the encryption function and the new quantum Hash function as the authentication function.Firstly,the Advanced Encryption Standard algorithm is used to encrypt the result of the initial message cascading the corresponding Hash values,which ensures that the initial message can resist eavesdropping attack.Secondly,utilizing the new quantum Hash function with quantum walks can be much more secure than traditional classical Hash functions with keeping the common properties,such as one-wayness,resisting different collisions and easy implementation.Based on these two points,the message authentication scheme can be much more secure than previous ones.Finally,it is a new way to design the message authentication scheme,which provides a new thought for other researchers in the future.Our works will contribute to the study on the new encryption and authentication functions and the combination of quantum computing with traditional cryptology in the future.

An Efficient Steganalysis Model Based on Multi-Scale LTP and Derivative Filters

Local binary pattern(LBP)is one of the most advanced image classification recognition operators and is commonly used in texture detection area.Research indicates that LBP also has a good application prospect in steganalysis.However,the existing LBP-based steganalysis algorithms are only capable to detect the least significant bit(LSB)and the least significant bit matching(LSBM)algorithms.To solve this problem,this paper proposes a steganalysis model called msdeLTP,which is based on multi-scale local ternary patterns(LTP)and derivative filters.The main characteristics of the msdeLTP are as follows:First,to reduce the interference of image content on features,the msdeLTP uses derivative filters to acquire residual images on which subsequent operations are based.Second,instead of LBP features,LTP features are extracted considering that the LTP feature can exhibit multiple variations in the relationship of adjacent pixels.Third,LTP features with multiple scales and modes are combined to show the relationship of neighbor pixels within different radius and along different directions.Analysis and simulation show that the msdeLTP uses only 2592-dimensional features and has similar detection accuracy as the spatial rich model(SRM)at the same time,showing the high steganalysis efficiency of the method.

The Application of Classroom Collaboration and Extracurricular Collaborative Activities to Non-English Majors

This paper talks about the research of application of collaborative learning to non-English majors in Dalian University, First of all, the paper gives a brief review of the theoretical background of collaborative learning. Next, it states the reasons why collaborative learning is ineffective in China and points out the necessity to apply the approach to non-English majors in China. Then, exemplifications of the application of classroom collaboration and extracurricular collaborative activities to non-English majors in our university are given. Finally, research findings are presented.

Oxidized galectin-1 in SLE fails to bind the inhibitory receptor VSTM1 and increases reactive oxygen species levels in neutrophils

Inhibitory immune receptors set thresholds for immune cell activation,and their deficiency predisposes a person to autoimmune responses.However,the agonists of inhibitory immune receptors remain largely unknown,representing untapped sources of treatments for autoimmune diseases.Here,we show that V-set and transmembrane domain-containing 1(VSTM1)is an inhibitory receptor and that its binding by the competent ligand soluble galectin-1(Gal1)is essential for maintaining neutrophil viability mediated by downregulated reactive oxygen species production.However,in patients with systemic lupus erythematosus(SLE),circulating Gal1 is oxidized and cannot be recognized by VSTM1,leading to increased intracellular reactive oxygen species levels and reduced neutrophil viability.Dysregulated neutrophil function or death contributes significantly to the pathogenesis of SLE by providing danger molecules and autoantigens that drive the production of inflammatory cytokines and the activation of autoreactive lymphocytes.Interestingly,serum levels of glutathione,an antioxidant able to convert oxidized Gal1 to its reduced form,were negatively correlated with SLE disease activity.Taken together,our findings reveal failed inhibitory Gal1/VSTM1 pathway activation in patients with SLE and provide important insights for the development of effective targeted therapies.

A Differential Privacy Protection Protocol Based on Location Entropy

A Location-Based Service(LBS)refers to geolocation-based services that bring both convenience and vulnerability.With an increase in the scale and value of data,most existing location privacy protection protocols cannot balance privacy and utility.To solve the revealing problems in LBS,we propose a differential privacy protection protocol based on location entropy.First,we design an algorithm of the best-assisted user selection for constructing anonymity sets.Second,we employ smart contracts to evaluate the credibility of participants,which ensures the honesty of participants.Moreover,we provide a comprehensive experiment;the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge attacks.Generally,our protocol improves data availability.Particularly,it realizes user-controllable privacy protection,which improves privacy protection and strengthens security.

Switchable nanoparticles complexing cisplatin for circumventing glutathione depletion in breast cancer chemotherapy

Cisplatin is broad-spectrum chemotherapeutic agent that has been widely used for the treatment of a variety of malignant tumors including breast cancer.However,the cisplatin chemoresistance,which derives from the inactivation by glutathione(GSH)depletion,remains a scientific issue to solve.Here,we report a novel type of smart disulfide switchable nanoparticles complexing cisplatin(switch NPs-cisplatin)that is rationally designed,and engineered by synthesizing a hyaluronic acid disulfide bonded polyaspartic acid(HA-ss-Pasp)and complexing cisplatin.The results showed that the switch NPs-cisplatin had a nanoscale of particle size(150 nm),higher drug encapsulation efficiency(>90%),and suitable drug release profile.They demonstrated evident pH responsiveness and GSH responsiveness,and targeting effect in the resistant breast cancer cells.Furthermore,they were able to block the cisplatin depletion by GSH in the resistant cancer cells,thereby circumventing the chemoresistance.Consequently,switch NPs-cisplatin displayed a remarkable killing effect in the resistant breast cancer cells in vitro,and in the resistant breast cancer-bearing mice.In conclusion,switch NPs-cisplatin could be used as a smart formulation of cisplatin for overcoming the chemoresistance of breast cancer.The present study also offers a universal drug delivery carrier platform for highly efficient but low systemic toxic chemotherapy.