Multidisciplinary design optimization of a dual-spin guided vehicle

In this research,a Multidisciplinary Design Optimization approach is proposed for the dual-spin guided flying projectile design considering external and internal parts of the body as design variables.In this way,a parametric formulation is developed.All related disciplines,including structure,aerodynamics,guidance,and control are considered.Minimum total mass,maximum aerodynamic control effectiveness,minimum miss distance,maximum yield stress in all subsystems,controllability and gyroscopic stability constraints are some of objectives/constraints taken into account.The problem is formulated in All-At-Ones Multidisciplinary Design Optimization approach structure and solved by Simulated Annealing and minimax algorithms.The optimal configurations are evaluated in various aspects.The resulted optimal configurations have met all design objectives and constraints.

Dialogue on standards:directions and difficulties of standardization discipline construction

To probe into the directions and difficulties of standardization discipline construction in China,China Standardization Press organized a pertinent dialogue on July 10.During the event,Yu Xinli,President of China Association for Standardization,was invited as the moderator.Several standardization experts including Song Mingshun,Mai Lyubo,Qiang Yi,Cao Lili,and Huang Manxue from the China Standardization Expert Committee and renowned universities and research institutes in the country were invited as guests to give their insightful views.

Value and Measures of Integrating Local Red Culture into the Ideological and Political Course of Environmental Disciplines under the OBE Concept

Based on output-oriented education,the OBE(Outcome-Based Education)concept integrates local red culture into the ideological and political course of environmental disciplines,and is an important part of training applied talents of environmental disciplines in the new era.This educational model makes an innovation on the traditional educational and teaching concepts and centers on students.This paper analyzes the value of integrating local red culture into the ideological and political course under the OBE concept,and puts forward an effective implementation path.

Does Financial Globalization Discipline Macroeconomic Policies

Using the unbalanced panel data of 160 countries from 1970 to 2007,we employ inflation and the budget deficit as proxies for monetary policy and fiscal policy,respectively,and study whether financial globalization has discipline effects on these macroeconomic policies.The empirical results in our study suggest a significant discipline effect of financial globalization on monetary policy during the entire sample period,which is robust both to de jure and to de facto measures of financial openness.Our sub-sample investigations demonstrate that financial globalization reduces inflation only in higher-middle-income and high-income countries,and when financial globalization is scaled by the proportion of a country’s foreign assets and liabilities to its GDP,the discipline is evident only after 1988.Nevertheless,we do not demonstrate any evidence of financial globalization’s discipline effect on fiscal policy.The empirical results indicate that financial globalization even increases the budget deficit in certain countries and periods.

Construction and Development of the National Experimental Teaching Demonstration Center for Drama and Film and Television Disciplines

Taking the National Experimental Teaching Demonstration Center of the Central Academy of Drama as an example,this article explores the construction and development of the National Experimental Teaching Demonstration Center for the practical teaching of drama and film and television disciplines.The article first introduces the background and importance of the demonstration center and then analyzes in depth the construction content and achievements of the demonstration center from four aspects:practical teaching philosophy,practical teaching system,practical teaching resources,and practical teaching effects.Finally,the article summarizes the experience and shortcomings of the demonstration center and proposes future development directions and suggestions.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

联合物理层与MAC层的multi-TRP上行重叠传输处理机制

针对非理想回程下现有协议难以有效处理多传输接收节点(multi-TRP)场景中多定时提前(multi-TA)导致的严重上行链路(UL)重叠传输问题,联合改进物理层复用技术和介质访问控制(MAC)令牌桶技术,提出了一种新型的UL重叠传输处理机制。该新型机制通过改进物理层重叠信道识别流程、复用要求及复用规则,将物理层复用信息与重叠信息反馈至MAC层,并对MAC层令牌桶技术进行优化。通过仿真实验对所提机制与现有协议机制进行对比,结果表明,在逻辑时隙不可重叠和可重叠2种情形下,物理上行控制信道(PUCCH)实际复用数量性能平均提升了57.58%和49.40%,物理上行共享信道(PUSCH)实际可用资源数量性能平均提升了12.09%和26.03%;优先级最高逻辑信道实际占用资源数量性能平均提升了33.33%和45.48%。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

Research on Integrating Data Assets Into Accounting Discipline System

As data is incorporated into production factors,the Accounting discipline should intensively study the new data asset.Starting from the analysis of the value attribute,right attribute,and relationship attribute of the data,it is found that under certain conditions,the data has complete Accounting attributes and can be included in the assets.The Accounting discipline shall establish the research direction of Data Asset Accounting and focus on the research,including the recognition and measurement of data assets,the value evaluation of data assets,information disclosure,and Data Asset Accounting standards.Data assets are facing the major challenge of integrating into the Accounting discipline.We can carry out the Accounting professional reform and textbook construction facing the practice of data assets management from the aspects of theoretical construction,talent training,and industry research cooperation.

Top-level modeling theory of multi-discipline virtual prototype

Multi-disciplinary virtual prototypes of complex products are increasingly and widely used in modern advanced manufactur- ing. How to effectively address the problems of unified modeling, composition and reuse based on the multi-disciplinary heteroge- neous models has brought great challenges to the modeling and simulation （M＆S） science and technology. This paper presents a top-level modeling theory based on the meta modeling framework （M2F） of the COllaborative SIMulation （COSlM） theory of virtual prototyping to solve the problems. Firstly the fundamental prin- ciples of the top-level modeling theory are decribed to expound the premise, assumptions, basic conventions and special require- ments in the description of complex heterogeneous systems. Next the formalized definitions for each factor in top level modeling are proposed and the hierarchical nature of them is illustrated. After demonstrating that they are self-closing, this paper divides the top- level modeling into two views, static structural graph and dynamic behavioral graph. Finally, a case study is discussed to demon- strate the feasibility of the theory.

International Politics of Western Literary Theory:Literary Theory as Doctrine and as Discipline

European literary criticism was rapidly transformed into literary theory through the United States after Rene Wellek and Austin Warren’s textbook Theory of Literary Theory by Harcourt,Brace and Co.(1949).Within a short period of time,American new criticism,European psychoanalysis,semiotics,structuralism,hermeneutics,deconstructivism,and so on,as well as the western Marxism,feminism,new historicism,post-colonial criticism,and cultural studies,all the critical methods little referenced to postwar Western ideology and Soviet Ideology,and are even known for their profound criticism of some problems in capitalist society.These intuitively scientific or neutral literary theories flourished during the Cold War years that contained the Soviet Union.During this period,“political correctness”not only influenced the direction of social development in the West,but also directly influenced the academic pattern and the development trend of universities at that time,so that“literary criticism”,which was originally only based on personal experience,benefited and became“literary theory”in the discipline and curriculum system.As a result,the discipline of literary theory,which benefited from the Cold War pattern,is often in conflict with the specific literary theory that transcends ideology.However,the Cold War ended with the failure of the Soviet Union and its socialist realism theory system,and the western literary theory unexpectedly gained full legitimacy like Fukuyama’s“the end of history”.In the post-Cold War era,Western literary critics reasonably expanded the controversial theories and this has become the most concerned topic for 30 years.Thus,the international politics of the disguised Western literary theory was highlighted.Such Western literary theory has been generalized in China in the last 40 years,because Chinese academic yet has not understood the difference between theory as discipline and theory as doctrine,even anti-western in recent years.

Multi Location Field Evaluation of BC1F2 Sorghum Populations for Striga Resistance in Niger

In Niger, a landlocked country, sorghum is the second staple food cultivated over the country by smallholder farmer. The crop is important for human and animal consumption. Despite its importance, the crop is affected by biotic and abiotic constraints. Among those constraints, striga has a high impact on yield. In fact, to survive, farmers are growing their local preferred sorghum varieties wish is highly sensible to the weed. Striga management is a challenge that requires a permanent solution. In addition, the development of high-yielding Striga resistant genotypes will be appreciated by farmers. The development of striga resistance will be based on the breeding population performances under farmer’s diverse environmental conditions adaptation. The main objective of this study is to evaluate two breeding populations for striga resistance in two different environments at Boulke and Dibissou in Tahoua region, to identify the early and high-yielding striga tolerant genotypes under natural infestation.

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.

Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological condition.It disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and balance.Traditional methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this problem.This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy.This paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat over-fitting.We use gene expression data for MS research in the GEO GSE17048 dataset.The dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the dataset.Meanwhile,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

Multi-scale data joint inversion of minerals and porosity in altered igneous reservoirs—A case study in the South China Sea

There are abundant igneous gas reservoirs in the South China Sea with significant value of research,and lithology classification,mineral analysis and porosity inversion are important links in reservoir evaluation.However,affected by the diverse lithology,complicated mineral and widespread alteration,conventional logging lithology classification and mineral inversion become considerably difficult.At the same time,owing to the limitation of the wireline log response equation,the quantity and accuracy of minerals can hardly meet the exploration requirements of igneous formations.To overcome those issues,this study takes the South China Sea as an example,and combines multi-scale data such as micro rock slices,petrophysical experiments,wireline log and element cutting log to establish a set of joint inversion methods for minerals and porosity of altered igneous rocks.Specifically,we define the lithology and mineral characteristics through core slices and mineral data,and establish an igneous multi-mineral volumetric model.Then we determine element cutting log correction method based on core element data,and combine wireline log and corrected element cutting log to perform the lithology classification and joint inversion of minerals and porosity.However,it is always difficult to determine the elemental eigenvalues of different minerals in inversion.This paper uses multiple linear regression methods to solve this problem.Finally,an integrated inversion technique for altered igneous formations was developed.The results show that the corrected element cutting log are in good agreement with the core element data,and the mineral and porosity results obtained from the joint inversion based on the wireline log and corrected element cutting log are also in good agreement with the core data from X-ray diffraction.The results demonstrate that the inversion technique is applicable and this study provides a new direction for the mineral inversion research of altered igneous formations.

Correlates of Parental Choice of Child Discipline Methods in Ghana: A Multilevel Modeling Approach

This study applied multilevel modeling to investigate the impact of observed predictors and different levels or groups that households belong, on parents’ choice of discipline methods using data from 8156 households derived from a nationwide survey by the Ghana Statistical Service (GSS) in 2011. The aim of the study is to provide in-depth information on why parents choose particular discipline methods as corrective measures to reduce unwanted child behaviour in the present and to increase desirable ones in the future. The results of the study show that, religion and age-group of household heads have significant effect on household’s likelihood to choose physical discipline methods whereas the wealth index of a household and ethnicity of the household head, have significant effect on households’ likelihood to choose non-physical and psychological aggression methods. The results further show significant contextual effect on the differences in choices of parents at the household and regional levels. The choice of physical discipline methods by parents was consistent across households and regional levels unlike non-physical and psychological aggression methods whose application varied across the regions. Households in the Northern, Eastern and Volta regions mostly chose to apply physical discipline methods whereas in the Upper West, Western and Northern regions the most chosen discipline methods were non-physical discipline methods. Psychological aggression discipline methods were predominantly applied in the Upper East, Central and Northern regions of the country.

Improved Scatter Search Algorithm for Multi-skilled Personnel Scheduling of Ship Block Painting

This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.

Transmission Control under Multi-Service Disciplines in Wireless Sensor Networks

The wireless sensor network(WSN),as the terminal data acquisition system of the 5G network,has attracted attention due to advantages such as low cost and easy deployment.Its development is mainly restricted by energy.The traditional transmission control scheme is not suitable for WSNs due to the significant information interaction.A switchable transmission control scheme for WSNs based on a queuing game(SQGTC)is proposed to improve network performance.Considering that sensor nodes compete for the resources of sink nodes to realize data transmission,the competitive relationship between nodes is described from the perspective of a game.Different types of sensor node requests require a sink node to provide different service disciplines.Mathematical models of social welfare are established for a sink node under the service disciplines of first-come,first-served(FCFS),egalitarian processor sharing(EPS),and shortest service first(SSF).The optimal service strategies are obtained by maximizing social welfare.The sensor nodes provide the expected benefits and satisfy the service requirements of the requests,and the sink node switches the transmission control strategy for the service.Simulation results show that the proposed scheme improves the data transmission efficiency of WSNs and achieves the optimal allocation of resources.

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.