A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Roles of N-cadherin in cerebral cortical development:cooperation with membrane trafficking and actin cytoskeletal regulation

Cell adhesion plays pivotal roles in the morphogenesis of multicellular organisms.Epithelial cells form several types of cell-to-cell adhesion,including zonula occludens(tight junctions),zonula adhaerens(adherens junctions),and macula adhaerens(desmosomes).Although these adhesion complexes are basically observed only in epithelial cells,cadherins,which are the major cell adhesion molecules of adherens junctions,are expressed in both epithelial and non-epithelial tissues,including neural tissues(Kawauchi,2012).The cadherin superfamily consists of more than 100 members,but classic cadherins.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Discreet investors exert greater influence on cooperation in the public goods game

This paper studies the evolutionary process of cooperative behavior in a public goods game model with heterogeneous investment strategies in square lattices.In the proposed model,players are divided into defectors,cooperators and discreet investors.Among these,defectors do not participate in investing,discreet investors make heterogeneous investments based on the investment behavior and cooperation value of their neighbors,and cooperators invest equally in each neighbor.In real life,heterogeneous investment is often accompanied by time or economic costs.The discreet investors in this paper pay a certain price to obtain their neighbors'investment behavior and cooperation value,which quantifies the time and economic costs of the heterogeneous investment process.The results of Monte Carlo simulation experiments in this study show that discreet investors can effectively resist the invasion of the defectors,form a stable cooperative group and expand the cooperative advantage in evolution.However,when discreet investors pay too high a price,they lose their strategic advantage.The results in this paper help us understand the role of heterogeneous investment in promoting and maintaining human social cooperation.

Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall

A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation.In this paper,a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task.Firstly,the physical human-robot cooperation model,including the role factor is built.Then,a reinforcement learningmodel that can adjust the role factor in real time is established,and a reward and actionmodel is designed.The role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot’s Jerk during the repeated installation.Finally,the roles adjustment rule established above continuously improves the comprehensive performance.Experiments of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been verified.The results show that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot’s Jerk.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Resource Allocation for IRS Assistedmm Wave Wireless Powered Sensor Networks with User Cooperation

In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.

Numerical and theoretical study of large-scale failure of strata overlying sublevel caving mines with steeply dipping discontinuities

The deformation and fracture evolution mechanisms of the strata overlying mines mined using sublevel caving were studied via numerical simulations.Moreover,an expression for the normal force acting on the side face of a steeply dipping superimposed cantilever beam in the surrounding rock was deduced based on limit equilibrium theory.The results show the following:(1)surface displacement above metal mines with steeply dipping discontinuities shows significant step characteristics,and(2)the behavior of the strata as they fail exhibits superimposition characteristics.Generally,failure first occurs in certain superimposed strata slightly far from the goaf.Subsequently,with the constant downward excavation of the orebody,the superimposed strata become damaged both upwards away from and downwards toward the goaf.This process continues until the deep part of the steeply dipping superimposed strata forms a large-scale deep fracture plane that connects with the goaf.The deep fracture plane generally makes an angle of 12°-20°with the normal to the steeply dipping discontinuities.The effect of the constant outward transfer of strata movement due to the constant outward failure of the superimposed strata in the metal mines with steeply dipping discontinuities causes the scope of the strata movement in these mines to be larger than expected.The strata in the metal mines with steeply dipping discontinuities mainly show flexural toppling failure.However,the steeply dipping structural strata near the goaf mainly exhibit shear slipping failure,in which case the mechanical model used to describe them can be simplified by treating them as steeply dipping superimposed cantilever beams.By taking the steeply dipping superimposed cantilever beam that first experiences failure as the key stratum,the failure scope of the strata(and criteria for the stability of metal mines with steeply dipping discontinuities mined using sublevel caving)can be obtained via iterative computations from the key stratum,moving downward toward and upwards away from the goaf.

Establishing a mechanism for international cooperation for Fukushima nuclear-contaminated water monitoring

The Japanese government’s unilateral decision to discharge the nuclear-contaminated water from the Fukushima nuclear power plant into the ocean has caused immense nuclear safety risks.Monitoring the unclear contaminated water is a starting point to combat these risks and seek remedies for the rights and interests of all concerned parties.The establishment of a mechanism for international cooperation in this respect is necessary to handle the risks of the Fukushima nuclear-contaminated water and to lay the foundation of a framework for tackling any future disposal of nuclear-contaminated water following Japan’s example.At present,the international legal systems in the spheres of nuclear safety and security,marine environmental protection,and other areas,as well as the questioning of the monitoring reports of the International Atomic Energy Agency(IAEA)by the relevant parties,the monitoring practices of historical nuclear accidents,and numerous radioactivity monitoring mechanisms have provided the institutional and practical basis for constructing such a mechanism.The mechanism can be promoted by the IAEA through its existing mechanisms or be jointly initiated by China,the Russian Federation,the Republic of Korea,the Democratic People’s Republic of Korea,and the Pacific Island countries,among other stakeholders.Specifically,this mechanism should consist of three levels:first,the framework of the basic legal system,including the cooperative principles of national sovereignty,interest-relatedness,and procedural fairness,and the signing of the Framework Convention on the Monitoring of Fukushima’s nuclear-contaminated water and its Optional Protocol;second,the organizational structure and its responsibilities,which may include the Conference of Parties as the decision-making body,the Secretariat as the central coordinating body,and the monitoring committees in various fields as specific implementing agencies;and third,specific administrative arrangements,which involve the standardization of monitoring,the management system of monitoring networks and stations,the rules for monitoring procedures,and the rules for the utilization of the monitoring data,etc.With the urgent need for the scientific and fair monitoring of Fukushima’s nuclear-contaminated water,China,as a stakeholder country,can promote the establishment of such a mechanism for monitoring nuclear-contaminated water through the following paths:①It is necessary to clarify the factors affecting the construction of an international cooperation mechanism for monitoring nuclear-contaminated water so as to ascertain the standpoints of the stakeholders,claims of their interests,contents of their cooperation,and the relevant international relations.②On the basis of existing practices,China should consider improving the monitoring mechanism to cope with the risks of the discharge of Fukushima’s nuclear-contaminated water by formulating targeted policies and systems,setting up specialized monitoring institutions,and establishing a systematic monitoring network system.③This is an effective way for China to actively promote the participation of stakeholders in the construction of an international cooperation mechanism for monitoring nuclear-contaminated water in Fukushima by further innovating the dissemination mechanism to address the risk of Fukushima’s nuclear-contaminated water discharging into the sea and facilitating the identification of issues for international cooperation in monitoring Fukushima’s nuclear-contaminated water based on the concept of a community with a shared future for mankind.

"Building the Best Platform for Capacity Cooperation Between China and Malaysia"

Kuantan,the capital of Pahang State on the east coast of Malaysia,is a quiet town,where the Kuantan River winds and flows into the sea.The Malaysia-China Kuantan Industry Park,a major project of the Belt and Road Initiative and a demonstration base for cross-border international capacity cooperation,is located here.Since the official opening of the park in 2013,it has created an innovative model of economic cooperation between the two countries.In 2023,on the 10th Anniversary of the Kuantan Industrial Park,a new development plan was launched again-the 3rd plot of the Malaysia-China Kuantan Industry Park,known as Malaysia-China Kuantan International Logistics Park,was officially started.

Logical Framework for Deepening International Energy Cooperation from the Perspective of Project Practice

Energy cooperation is a key area in the joint construction of the Belt and Road Initiative.To deepen international energy cooperation,it is advisable to place greater emphasis on whole life cycle and systematic planning in project collaboration,government-led initiatives with businesses playing a central role,alongside coordination with non-governmental international institutions,and comprehensive integration of resources.

China-ASEAN Technological Cooperation Digital and green technologies remain the focus of cooperation

At the 11th Forum on China-ASEAN Technology Transfer and Collaborative Innovation(hereinafter referred to as the China-ASEAN Innovation Forum)in July 2023,ASEAN Secretary-General Kao Kim Hourn highlighted the pivotal role of technology transfer and innovation in driving economic growth.He emphasized that technology transfer can assist China and ASEAN in bypassing traditional developmental stages.

EYE ON EMERGING INDUSTRIES ASEAN(Malaysia)-China Conference on Emerging Industries aims to foster new growth points in China-Malaysia economic and trade cooperation

On June 5,the opening ceremony of the ASEAN(Malaysia)-China Conference on Emerging Industries took place in Kuala Lumpur.The conference was guided and hosted by ASEAN-China Centre(ACC),China International Communications Group(CICG),and Ministry of Investment,Trade,and Industry of Malaysia(MITI),and co-hosted by the CICG Center for Asia-Pacific,National Chamber of Commerce and Industry of Malaysia(NCCIM),Associated Chinese Chambers of Commerce and Industry of Malaysia(ACCCIM),and China Enterprises Chamber of Commerce in Malaysia(CECCM).More than 300 guests from government departments,business communities,and academia of China and Malaysia attended.

Promoting China-U.S.Arctic education cooperation:challenges,opportunities and recommendations

Arctic education refers not only to the teaching,but also to research,communication,dissemination as well as popularization of knowledge related to the Arctic.This article reviews joint efforts between Chinese and American educators and researchers to promote cooperation and understanding in Arctic education and research,and examines the facing challenges of China-U.S.Arctic education cooperation which include current political or economic tensions between the two countries,the differing perspectives and priorities on Arctic policy,the disproportion in Arctic scientific research,different research methodologies and discourse system in social science.This article also argues that there are opportunities for the two countries to cooperate in Arctic education.Common goals and interests in the Arctic,Arctic-dedicated institutions with significant Arctic research capabilities and partnerships around the world provide foundations for Arctic education cooperation.The implementation of a new science-based Arctic treaty of the Arctic Council is an opportunity for China-U.S.Arctic education cooperation.As for future cooperation,it suggests that in addition to promoting the direct bilateral cooperation,cooperation within international cooperation platforms and mechanisms,especially within the Arctic Council also needs to be further promoted.

Boundless Potential China-Africa cooperation in the space sector exemplifies mutually beneficial partnerships

Exports of Chinese satellites,joint construction of space infrastructure,and joint research and development of satellites...The recent years have seen substantial growth in China-Africa cooperation in the space sector,which is bringing tangible benefits to both sides.