Suppress oxygen evolution of lithium-rich manganese-based cathode materials via an integrated strategy

Improving the reversibility of anionic redox and inhibiting irreversible oxygen evolution are the main challenges in the application of high reversible capacity Li-rich Mn-based cathode materials.A facile synchronous lithiation strategy combining the advantages of yttrium doping and LiYO_(2) surface coating is proposed.Yttrium doping effectively suppresses the oxygen evolution during the delithiation process by increasing the energy barrier of oxygen evolution reaction through strong Y–O bond energy.LiYO_(2) nanocoating has the function of structural constraint and protection,that protecting the lattice oxygen exposed to the surface,thus avoiding irreversible oxidation.As an Li^(+) conductor,LiYO_(2) nano-coating can provide a fast Li^(+) transfer channel,which enables the sample to have excellent rate performance.The synergistic effect of Y doping and nano-LiYO_(2) coating integration suppresses the oxygen release from the surface,accelerates the diffusion of Li^(+)from electrolyte to electrode and decreases the interfacial side reactions,enabling the lithium ion batteries to obtain good electrochemical performance.The lithium-ion full cell employing the Y-1 sample(cathode)and commercial graphite(anode)exhibit an excellent specific energy density of 442.9 Wh kg^(-1) at a current density of 0.1C,with very stable safety performance,which can be used in a wide temperature range(60 to-15℃)stable operation.This result illustrates a new integration strategy for advanced cathode materials to achieve high specific energy density.

Electrocatalytic oxygen evolution activities of metal chalcogenides and phosphides:Fundamentals,origins,and future strategies

The development of inexpensive and efficient electrocatalysts is key to commercializing energy-related electrocatalytic techniques such as water electrolyzers and metal-air batteries.In particular,novel oxygen evolution reaction(OER)pre-catalysts,such as transition metal chalcogenides(TMCs)and phosphides(TMPs),have evolved in recent years from traditional stable OER electrocatalysts,which show superior OER electrocatalytic performance compared with transition metal oxides(TMOs)or(oxy)hydroxides(TMOHs).In this feature article,we summarize recent advances in the development of TMCand TMP-based OER electrocatalysts,as well as approaches to improve the OER performance in terms of morphology,structure,composition,surface engineering,lattice-strained and in-situ transformation in the electrolysis process.In particular,the electrochemical stability of TMCs and TMPs in alkaline electrolytes and the evolution of morphology,structure and composition under OER conditions are discussed.In the last section,we discuss the challenges that need to be addressed in this specific area of research and the implications for further research.

Productive functional evolution of rural settlements:analysis of livelihood strategy and land use transition in eastern China

In China, rural settlements are usually multifunctional complex of natural societies and they also play important productive function. This study explored the spatiotemporal evolution of the productive function of three rural settlements, i.e.,Hetaoyuan, Dawengshan, and Gaoqiao, in the Yimeng Mountain in Shandong province of eastern China.Residents lived in this three types of rural settlements were all involved in agricultural production(i.e., grain threshing, drying and storage), which had remained dominant in Hetaoyuan. Dawengshan became an industrial-oriented settlement, while Gaoqiao became a commercial-oriented settlement. This evolution was reflected in the livelihood strategy changes of rural households and the transition of rural settlements changes. The choice of livelihood strategy was affected by the livelihood assets and policies, which resulted in the spatiotemporal evolution of the productive function of rural settlements. This study can provide references for the implementation and adjustment of rural residential land consolidation policies and the improvement of production and the quality of rural life in future.

Strategies on improving the electrocatalytic hydrogen evolution performances of metal phosphides

Among the sustainable energy sources,hydrogen is the one most promising for alleviating the pollution issues related to the usage of conventional fuels,as it can be produced in an efficient and eco-friendly way via electrocatalytic water splitting.The hydrogen evolution reaction(HER,a half-reaction of water splitting)plays a pivotal role in decreasing the price and increasing the catalytic efficiency of hydrogen production and is efficiently promoted by metal phosphides in different electrolytes.Herein,we summarize the recent advances in the development of metal phosphides as HER electrocatalysts,focus on their synthesis(post-treatment,in situ generation,and electrodeposition methods)and the enhancement of their electrocatalytic activity(via elemental doping,interface and vacancy engineering,construction of specific supports and nanostructures,and the design of bior polymetallic phosphides),and highlight the crucial issues and challenges of future development.

Zero-determinant strategy:An underway revolution in game theory

Repeated games describe situations where players interact with each other in a dynamic pattern and make decisions ac- cording to outcomes of previous stage games. Very recently, Press and Dyson have revealed a new class of zero-determinant （ZD） strategies for the repeated games, which can enforce a fixed linear relationship between expected payoffs of two play- ers, indicating that a smart player can control her unwitting co-player＇s payoff in a unilateral way [Proc. Acad. Natl. Sci. USA 109, 10409 （2012）]. The theory of ZD strategies provides a novel viewpoint to depict interactions among players, and fundamentally changes the research paradigm of game theory. In this brief survey, we first introduce the mathematical framework of ZD strategies, and review the properties and constrains of two specifications of ZD strategies, called pinning strategies and extortion strategies. Then we review some representative research progresses, including robustness analysis, cooperative ZD strategy analysis, and evolutionary stability analysis. Finally, we discuss some significant extensions to ZD strategies, including the multi-player ZD strategies, and ZD strategies under noise. Challenges in related research fields are also listed.

Phase control of 2D binary hydroxides nanosheets via controlling-release strategy for enhanced oxygen evolution reaction and supercapacitor performances

An OH^--slow-release strategy was established to controllably tune the( α-and β-) phase of nickel cobalt binary hydroxide in the presence of ammonium chloride. Ammonium chloride is added to the ionic solution to regulate the p H of the solution and slow down the release of OH^-, effectively regulating the phase, nanostructure, interlayer spacing, surface area, thickness, and the performance of binary Ni –Co hydroxide. The ion-slow-release mechanism is conducive to the formation of α-phase with larger interlayer spacing and thinner flakes rather than β-phase. Attributed to the enlarged interlayer spacing, thinner nanosheets, and more exposed active sites, the resultant α-phase hydroxides(NCNS-5.2), displayed much lower over potential of 285 mV with respect to the dense-stacked β-phase hydroxides(362 mV) for OER at 10 mA/cm^2. It also exhibited high specific capacitance of 1474.2 F/g, when tested at 0.5 A/g within a voltage range of 0–0.45 Vvs. Hg/Hg O. This composite was also stable for water oxidation reaction and supercapacitor. The proof-of-concept of using controlled-release agent may provide suggestive insights for the material innovation and a variety of applications.

Route strategy of satellite network in GNSS based on topology evolution law

As each type of satellite network has different link features, its data transmission must be designed based on its link features to improve the efficiency of data transferring. The transmission of navigation integrated services information （NISI） in a global navigation satellite system （GNSS） with inter-satellite links （ISLs） is studied by taking the real situation of inter-satellite communication links into account. An on-demand computing and buffering centralized route strategy is proposed based on dynamic grouping and the topology evolution law of the GNSS network within which the satellite nodes are operated in the manner of dynamic grouping. Dynamic grouping is based on satellites spatial relationships and the group role of the satellite node changes by turns due to its spatial relationships. The route strategy provides significant advantages of high efficiency, low complexity, and flexi- ble configuration, by which the established GNSS can possess the features and capabilities of feasible deployment, efficient transmission, convenient management, structural invulnerability and flexible expansion.

Regional Evolution Features and Coordinated Development Strategies for Northeast China

Northeast China, as the most important production base of agriculture, forestry, and livestock-breeding as well as the old industrial base in the whole country, has been playin a key role in the construction and development of China＇s economy. However, after the policy of reform and open-up was taken in China. the economic development speed and efficiency ofthis area have turned to be evidently lower than those of coastal area and the national average level as well, which is so-called ＇Northeast Phenomenon＇ and ＇Neo-Northeast Phenomenon＇. In terms of those phenomena, this paper firstly reviews the spatial and temporal features of the regional evolution of this area so as to unveil the profound forming causes of ＇Northeast Phenomena＇ and ＇Neo-Northeast Phenomena＇. And then the paper makes a further exploration into the status quo of this region and its forming causes by analyzing its economy gross, industrial structure, product structure, regional eco-categories, etc. At the end of the paper, the authors put forward the basic coordinated development strategies for Northeast China. namely we can revitalize this area by means of adjustment of economic structure, regional coordination, planning urban and rural areas as a whole, institutional innovation, etc.

Precursor-modified strategy to synthesize thin porous amino-rich graphitic carbon nitride with enhanced photocatalytic degradation of RhB and hydrogen evolution performances

The photocatalytic activity of carbon nitride(CN)materials is mainly limited to small specific surface areas,limited solar absorption,and low separation and mobility of photoinduced carriers.In this study,we developed a precursor-modified strategy for the synthesis of graphitic CN with highly efficient photocatalytic performance.The precursor dicyandiamide reformed by different acids undergoes a basic structural change and transforms into diverse new precursors.The thin porous amino-rich HNO_(3)-CN(5H-CN)was calcined by dicyandiamidine nitrate,formed by concentrated nitric acid modified dicyandiamide,and presented the best photocatalytic degradation rate of Rh B,more than 34 times that of bulk graphitic CN.Moreover,the photocatalytic hydrogen evolution rate of 5H-CN significantly improved.The TG-DSC-FTIR analyses indicated that the distinguishing thermal polymerization process of 5H-CN led to its thin porous amino-rich structure,and the theoretical calculations revealed that the negative conduction band potential of 5H-CN was attributed to its amino-rich structure.It is anticipated that the thin porous structure and the negative conduction band position of 5H-CN play important roles in the improvement of the photocatalytic performance.This study demonstrates that precursor modification is a promising project to induce a new thermal polycondensation process for the synthesis of CN with enhanced photocatalytic performance.

Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.

LDAS&ET-AD:Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation

Adversarial distillation(AD)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training.However,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation.Additionally,the reliability of guidance from static teachers diminishes as target models become more robust.This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation(LDAS&ET-AD).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the AD loss.Secondly,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target model.By calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization and robustness abilities is assessed to serve as feedback to fine-tune standard and robust teachers accordingly.Experiments evaluate the performance of LDAS&ET-AD against different adversarial attacks on the CIFAR-10 and CIFAR-100 datasets.The experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAR-10 dataset for ResNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline method.In comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial Distillation,the top-performing method in terms of robustness under AA attack for the CIFAR-10 dataset,with enhancements of 1.40%and 1.43%for ResNet-18 and MobileNet-V2,respectively.These findings demonstrate the effectiveness of our proposed method in enhancing the robustness of deep learning networks(DNNs)against prevalent adversarial attacks when compared to other competing methods.In conclusion,LDAS&ET-AD provides reliable and informative soft labels to one of the most promising defense methods,AT,alleviating the limitations of untrusted teachers and unsuitable AEs in existing AD techniques.We hope this paper promotes the development of DNNs in real-world trust-sensitive fields and helps ensure a more secure and dependable future for artificial intelligence systems.

An Evolution Strategy for the Induction of Fuzzy Finite-State Automata

This paper presents an evolution strategy to induce fuzzy finite-state automata from examples of fuzzy languages. The coding, fitness function of a generated automaton and corresponding mutation operators are given respectively. The application example given at last shows the effectiveness of the proposed evolution strategy for automata induction.

Expansion Strategies and Evolution Paths of Hotel Groups in China

The organizational structure change of enterprises should be subject to the change of strategy,and strategy change is the reaction to the environment change.This paper studied the expansion strategies and evolution paths of hotel groups in China by a survey in 2007,in which the research objects were 91 hotel groups listed by China Tourist Hotels Association.The conclusions are drawn:based on the two dimensions of unification management function scope and the unification management degree,hotel groups can be divided into four strategic types,including strategically concentrated group,close function group,widespread alliance group and loose union group.Simultaneously there are three different expansion paths:stability path,growth path and undulation path.About 72.5% of Chinese hotel groups have a stability or growth expansion path.

Bidding Strategy in Deregulated Power Market Using Differential Evolution Algorithm

The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).

Blind Equalization Based on Evolution Strategies

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Study on the Evolution of Rural Settlement Landscapes on Guanzhong Plain of China

Overall figures of rural settlement landscapes on Guanzhong Plain were summarized,material components of these landscapes were classified into living landscape facilities and production landscape facilities,and then introduced respectively.From the perspective of rural central landscapes,rural yard landscapes,rural cluster landscapes and rural road landscapes,evolution of rural landscape spatial forms on Guanzhong Plain was analyzed.Moreover,problems of present rural landscapes were proposed,necessity and urgency of controlling evolution of rural settlement landscapes were stressed,and a rural landscape control mode in view of regional characteristics of Guanzhong Plain was given.

Epidemiological feature analysis of SVEIR model with control strategy and variant evolution

The complex interactions were performed among non-pharmaceutical interventions,vaccinations,and hosts for all epidemics in China's Mainland during the spread of COVID-19.Specially,the small-scale epidemic in the city described by SVEIR model was less found in the current studies.The SVEIR model with control was established to analyze the dynamical and epidemiological features of two epidemics in Jinzhou City led by Omicron variants before and after Twenty Measures.In this study,the total population(N)of Jinzhou City was divided into five compartments:the susceptible(S),the vaccinated(V),the exposed(E),the infected(I),and the recovered(R).By surveillance data and the SVEIR model,three methods(maximum likelihood method,exponential growth rate method,next generation matrix method)were governed to estimate basic reproduction number,and the results showed that an increasing tendency of basic reproduction number from Omicron BA.5.2 to Omicron BA.2.12.1.Meanwhile,the effective reproduction number for two epidemics were investigated by surveillance data,and the results showed that Jinzhou wave 1 reached the peak on November 1 and was controlled 7 days later,and that Jinzhou wave 2 reached the peak on November 28 and was controlled 5 days later.Moreover,the impacts of non-pharmaceutical interventions(awareness delay,peak delay,control intensity)were discussed extensively,the variations of infection scales for Omicron variant and EG.5 variant were also discussed.Furthermore,the investigations on peaks and infection scales for two epidemics in dynamic zero-COVID policy were operated by the SVEIR model with control.The investigations on public medical requirements of Jinzhou City and Liaoning Province were analyzed by using SVEIR model without control,which provided a possible perspective on variant evolution in the future.