Organic cathode materials for rechargeable magnesium-ion batteries:Fundamentals, recent advances, and approaches to optimization

Rechargeable magnesium-ion batteries(MIBs) are favorable substitutes for conventional lithium-ion batteries(LIBs) because of abundant magnesium reserves, a high theoretical energy density, and great inherent safety. Organic electrode materials with excellent structural tunability,unique coordination reaction mechanisms, and environmental friendliness offer great potential to promote the electrochemical performance of MIBs. However, research on organic magnesium battery cathode materials is still preliminary with many significant challenges to be resolved including low electrical conductivity and unwanted but severe dissolution in useful electrolytes. Herein, we provide a detailed overview of reported organic cathode materials for MIBs. We begin with basic properties such as charge storage mechanisms(e.g., n-, p-, and bipolartype), moving to recent advances in various types of organic cathodes including carbonyl-, nitrogen-, and sulfur-based materials. To shed light on the diverse strategies targeting high-performance Mg-organic batteries, elaborate summaries of various approaches are presented.Generally, these strategies include molecular design, polymerization, mixing with carbon, nanosizing and electrolyte/separator optimization.This review provides insights on exploring high-performance organic cathodes in rechargeable MIBs.

Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

ON APPROXIMATE EFFICIENCY FOR NONSMOOTH ROBUST VECTOR OPTIMIZATION PROBLEMS

In this article,we use the robust optimization approach(also called the worst-case approach)for findingε-efficient solutions of the robust multiobjective optimization problem defined as a robust(worst-case)counterpart for the considered nonsmooth multiobjective programming problem with the uncertainty in both the objective and constraint functions.Namely,we establish both necessary and sufficient optimality conditions for a feasible solution to be anε-efficient solution(an approximate efficient solution)of the considered robust multiobjective optimization problem.We also use a scalarizing method in proving these optimality conditions.

The Optimized Way of Commercial Bank Management under the Background of Big Data

With the advent of the era of big data,the commercial management activities of commercial banks are facing both new challenges and new development opportunities.In the increasingly fierce market competition,commercial banks must pay attention to enhance their core competitiveness,only in this way can they occupy a certain advantage in the fierce market environment.Therefore,commercial banks must accurately grasp the development opportunities brought by big data,and further strengthen the optimization of business management.The author explores and analyzes the impact of big data on commercial banks,and puts forward an effective way for commercial banks to optimize business management in the context of big data,hoping to contribute to the long-term development of commercial banks.

Optimization of a Segmented Filter with a New Error Diffusion Approach

The segmented filters, based on spectral cutting, proved their efficiency for the multi-correlation. In this article we propose an optimisation of this cutting according to a new error diffusion method.

Communication security of autonomous ground vehicles based on networked control systems:The optimized LMI approach

The paper presents a study of networked control systems(NCSs)that are subjected to periodic denial-of-service(DoS)attacks of varying intensity.The use of appropriate Lyapunov-Krasovskii functionals(LKFs)help to reduce the constraints of the basic conditions and lower the conservatism of the criteria.An optimization problem with constraints is formulated to select the trigger threshold,which is solved using the gradient descent algorithm(GDA)to improve resource utilization.An intelligent secure event-triggered controller(ISETC)is designed to ensure the safe operation of the system under DoS attacks.The approach is validated through experiments with an autonomous ground vehicle(AGV)system based on the Simulink platform.The proposed method offers the potential for developing effective defense mechanisms against DoS attacks in NCSs.

An improved equivalent beam model of large periodic beam-like space truss structures

Space truss structures are essential components for space-based remote sensing loads with high spatial and temporal resolutions.To achieve high-precision vibration control,an accurate and efficient dynamics model is essential.In addition to the current equivalent beam model(EBM)based on the classical continuum theory,an improved equivalent beam model(IEBM)is proposed that considers the impact of the distinction between trusses and beams on torsional and shear deformations,as well as the impact of shear deformation on flexural rigidity.According to the displacement expressions of spatial beams,torsional,shear,and bending correction coefficients are introduced to derive expressions of strain energy and kinetic energy.The energy equivalence principle is then utilized to calculate the elasticity and inertia matrices,and dynamics equations are established using the finite element method.Subsequently,an IEBM is constructed by employing the particle swarm optimization approach to determine the correction coefficients with the truss natural frequency as the optimization target.The natural vibration characteristics of the structure are estimated for various material properties.Compared with the full-scale finite element model,the EBM reaches a maximum error of 80%for a low modulus of elasticity,while the maximum error of the IEBM is less than 2%for any given parameters,indicating its superior accuracy to the EBM.

Enhancing performance of oppositional BBO using the current optimum(COOBBO)for TSP problems

Purpose-The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum(COOBBO)algorithm.Design/methodology/approach-The improvement measures tested in this paper include different initialization approaches,crossover approaches,local optimization approaches,and greedy approaches.Eight well-known traveling salesman problems(TSP)are employed for performance verification.Four comparison criteria are recoded and compared to analyze the contribution of each modified method.Findings-Experiment results illustrate that the combination model of“25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy”may be the best choice of all when considering both the overall algorithm performance and computation overhead.Originality/value-When solving TSP with varying scales,these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees.And an appropriate combination model may make the fullest possible contribution.

Estimation of hydraulic parameters from pumping tests in a multiaquifer system

The hydrogeologic conditions in Tianjin,China,are characterized by a system of multiple alternating aquifers and aquitards and a high groundwater level.Generally,the groundwater system to a depth of 70 m includes one phreatic aquifer and three semiconfined aquifers(AqI–AqIII).The distribution of aquifers varies greatly.Accurate estimates of aquifer parameters from pumping tests are essential in geotechnical engineering practice.In this study,the applicability of different models for estimating hydraulic parameters in a multiaquifer system is analyzed.To avoid errors in the graphic-analytical process and to enhance the efficiency and accuracy,a hybrid algorithm,called GALMA(a genetic algorithm(GA)combined with the Levenberg–Marquardt(LM)algorithm),is used with the Neuman and Witherspoon model and ratio method.The estimated values in the second and third semiconfined aquifers(AqII and AqIII)from various solutions are compared with the measured data.Further validations based on the prediction of the drawdown values are performed via a three dimensional numerical simulation.