Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

From the perspective of experimental practice: High-throughput computational screening in photocatalysis

Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these systems.In uncertain environments,agents must make judicious decisions based on subjective epistemic.To verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems.In addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)formulas.Accordingly,we transform the FCTLK model checking problem into the FCTL model checking.This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads.Finally,we present correctness proofs and complexity analyses of the proposed algorithms.Additionally,we further illustrate the practical application of our approach through an example of a train control system.

Multi-Agent Deep Reinforcement Learning for Efficient Computation Offloading in Mobile Edge Computing

Mobile-edge computing(MEC)is a promising technology for the fifth-generation(5G)and sixth-generation(6G)architectures,which provides resourceful computing capabilities for Internet of Things(IoT)devices,such as virtual reality,mobile devices,and smart cities.In general,these IoT applications always bring higher energy consumption than traditional applications,which are usually energy-constrained.To provide persistent energy,many references have studied the offloading problem to save energy consumption.However,the dynamic environment dramatically increases the optimization difficulty of the offloading decision.In this paper,we aim to minimize the energy consumption of the entireMECsystemunder the latency constraint by fully considering the dynamic environment.UnderMarkov games,we propose amulti-agent deep reinforcement learning approach based on the bi-level actorcritic learning structure to jointly optimize the offloading decision and resource allocation,which can solve the combinatorial optimization problem using an asymmetric method and compute the Stackelberg equilibrium as a better convergence point than Nash equilibrium in terms of Pareto superiority.Our method can better adapt to a dynamic environment during the data transmission than the single-agent strategy and can effectively tackle the coordination problem in the multi-agent environment.The simulation results show that the proposed method could decrease the total computational overhead by 17.8%compared to the actor-critic-based method and reduce the total computational overhead by 31.3%,36.5%,and 44.7%compared with randomoffloading,all local execution,and all offloading execution,respectively.

Optimization of Grouping Evacuation Strategy in High-rise Building Fires Based on Graph Theory and Computational Experiments

It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grouping evacuation strategy is proposed. Occupants escape in groups according to the shortest evacuation route is determined by graph theory. In order to evaluate and find the optimal grouping, computational experiments are performed to design and simulate the evacuation processes. A case study shown the application in detail and quantitative research conclusions is obtained. The thoughts and approaches of this study can be used to guide actual high-rise building evacuation processes in future.

Multi-Agent Reinforcement Learning for Resource Allocation in Io T Networks with Edge Computing

To support popular Internet of Things(IoT)applications such as virtual reality and mobile games,edge computing provides a front-end distributed computing archetype of centralized cloud computing with low latency and distributed data processing.However,it is challenging for multiple users to offload their computation tasks because they are competing for spectrum and computation as well as Radio Access Technologies(RAT)resources.In this paper,we investigate computation offloading mechanism of multiple selfish users with resource allocation in IoT edge computing networks by formulating it as a stochastic game.Each user is a learning agent observing its local network environment to learn optimal decisions on either local computing or edge computing with a goal of minimizing long term system cost by choosing its transmit power level,RAT and sub-channel without knowing any information of the other users.Since users’decisions are coupling at the gateway,we define the reward function of each user by considering the aggregated effect of other users.Therefore,a multi-agent reinforcement learning framework is developed to solve the game with the proposed Independent Learners based Multi-Agent Q-learning(IL-based MA-Q)algorithm.Simulations demonstrate that the proposed IL-based MA-Q algorithm is feasible to solve the formulated problem and is more energy efficient without extra cost on channel estimation at the centralized gateway.Finally,compared with the other three benchmark algorithms,it has better system cost performance and achieves distributed computation offloading.

Collaborative multi-agent reinforcement learning based on experience propagation

For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key research problems. State list extracting means to calculate the optimal shared state path from state trajectories with cycles. A state list extracting algorithm checks cyclic state lists of a current state in the state trajectory, condensing the optimal action set of the current state. By reinforcing the optimal action selected, the action policy of cyclic states is optimized gradually. The state list extracting is repeatedly learned and used as the experience knowledge which is shared by teams. Agents speed up the rate of convergence by experience sharing. Competition games of preys and predators are used for the experiments. The results of experiments prove that the proposed algorithms overcome the lack of experience in the initial stage, speed up learning and improve the performance.

Load Balancing Based on Multi-Agent Framework to Enhance Cloud Environment

According to the advances in users’service requirements,physical hardware accessibility,and speed of resource delivery,Cloud Computing(CC)is an essential technology to be used in many fields.Moreover,the Internet of Things(IoT)is employed for more communication flexibility and richness that are required to obtain fruitful services.A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents.This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources.Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload.Aload balancing algorithm is developed to serve users’requests to improve the solution of workload problems with an efficient distribution.The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability ofCC.Then,the server’s availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance.Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and(low resources and large number)of IoT.

Construction and Practice of Cloud-based Experiment and Innovation Project Supporting Platform for Computer Science Education

Higher and more requirements on experiments and innovation projects of computer science courses is brought forward to promote the "Double First Class". For these requirements, the cloud-based experiment and innovation project supporting platform is introduced in this paper. On the one hand, the platform could satisfy the diversified resource requirements of both teachers and students in course experiments and innovation projects. On the other hand, the feature of "construct once and access anywhere" could achieve higher resource utilization rate and lower costs than the traditional "lab-based" experiment mode. The supporting platform has been applied to practice and gains a lot of positive feedbacks.

A CASE STUDY IN THE APPLICATION OF SUPERSATURATED DESIGNS TO COMPUTER EXPERIMENTS

Supersaturated design is essentially a fractional factorial design in which the number of potential effects is greater than the number of runs. In this article, the supersaturated design is applied to a computer experiment through an example of steady current circuit model problem. A uniform mixed-level supersaturated design and the centered quadratic regression model are used. This example shows that supersaturated design and quadratic regression modeling method are very effective for screening effects and building the predictor. They are not only useful in computer experiments but also in industrial and other scientific experiments.

Computer modeling of high-pressure leaching process of nickel laterite by design of experiments and neural networks

Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process be- came a need and a challenge. In this paper, the design of experiments （DOE） theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching （HPSAL） process for nickel laterite in ＂Rudjinci＂ ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression （MLR） and response surface method （RSM）, together with the two-level and four-factor full factorial central composite design （CCD） plan, were used. The proposed re- gression models have not been proven adequate. Therefore, the artificial neural network （ANN） approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation （BP） learning algorithm and the bipolar sigmoid activation function.

Design and Implementation of Virtual Experiments for Computer Architecture Based on Simulators

In china,many students are unable to do experiments in computer architecture courses,which is very important in helping them to understand many key points.The reason is that the cost of the hardware required is too much.Besides,it is very difficult to do research study in hardware experiments.In our course,we adopted an alternative way to deal with the problem: to use software simulators,and designed a set of virtual experiments based on these simulators,which are described in detail in this paper.

A framework for equipment systems-of-systems effectiveness evaluation using parallel experiments approach

Equipment systems-of-systems （SOS） effectiveness evaluation can provide important reference for construction and optimization of the equipment SoS. After discussing the basic theory and methods of parallel experiments, we depict an SoS effectiveness analysis and evaluation method using parallel expe- riments theory in detail. A case study is carried out which takes the missile defense system as an example. An artificial system of the missile defense system is constructed with the multi-agent modeling method. Then, single factor, multiple factors and defense position deployment computational experiments are carried out and evaluated with the statistical analysis method. Experiment re- sults show that the altitude of the secondary interception missile is not the key factor which affects SoS effectiveness and putting the defense position ahead will increase defense effectiveness. The case study demonstrates the feasibility of the proposed method.

Cavitation Optimization for a Centrifugal Pump Impeller by Using Orthogonal Design of Experiment

Cavitation is one of the most important performance of centrifugal pumps. However, the current optimization works of centrifugal pump are mostly focusing on hydraulic efficiency only, which may result in poor cavitation performance. Therefore, it is necessary to find an appropriate solution to improve cavitation performance with acceptable efficiency. In this paper, to improve the cavitation performance of a centrifugal pump with a vaned diffuser, the influence of impeller geometric parameters on the cavitation of the pump is investigated using the orthogonal design of experiment （DOE） based on computational fluid dynamics. The impeller inlet diameter D1, inlet incidence angle Aft, and blade wrap angle ~0 are selected as the main impeller geometric parameters and the orthogonal experiment of L9（3＂3） is performed. Three-dimensional steady simulations for cavitation are conducted by using constant gas mass fraction model with second-order upwind, and the predicated cavitation performance is validated by laboratory experiment. The optimization results are obtained by the range analysis method to improve cavitation performance without obvious decreasing the efficiency of the centrifugal pump. The internal flow of the pump is analyzed in order to identify the flow behavior that can affect cavitation performance. The results show that D1 has the greatest influence on the pump cavitation and the final optimized impeller provides better flow distribution at blade leading edge. The final optimized impeller accomplishes better cavitation and hydraulic performance and the NPSHR decreases by 0.63m compared with the original one. The presented work supplies a feasible route in engineering practice to optimize a centrifugal pump impeller for better cavitation performance.

Comparison of numerical simulations and experiments in conical gas–solid spouted bed

Flow behavior of gas and particles in conical spouted beds is experimentally studied and simulated using the twofluid gas-solid model with the kinetic theory of granular flow.The bed pressure drop and fountain height are measured in a conical spouted bed of 100 mm I.D.at different gas velocities.The simulation results are compared with measurements of bed pressure drop and fountain height.The comparison shows that the drag coefficient model used in cylindrical beds under-predicted bed pressure drop and fountain height in conical spouted beds due to the partial weight of particles supported by the inclined side walls.It is found that the numerical results using the drag coefficient model proposed based on the conical spouted bed in this study are in good agreement with experimental data.The present study provides a useful basis for further works on the CFD simulation of conical spouted bed.

CFD Simulation and Experimental Study of a New Elastic Blade Wave Energy Converter

Small moving vehicles represent an important category of marine engineering tools and devices(equipment)typically used for ocean resource detection and maintenance of marine rights and interests.The lack of efficient power supply modes is one of the technical bottlenecks restricting the effective utilisation of this type of equipment.In this work,the performance characteristics of a new type of elastic-blade/wave-energy converter(EBWEC)and its core energy conversion component(named wave energy absorber)are comprehensively studied.In particular,computational fluid dynamics(CFD)simulations and experiments have been used to analyze the hydrodynamics and performance characteristics of the EBWEC.The pressure cloud diagrams relating to the surface of the elastic blade were obtained through two-way fluid-solid coupling simulations.The influence of blade thickness and relative speed on the performance characteristics of EBWEC was analyzed accordingly.A prototype of the EBWEC and its bucket test platform were also developed.The power characteristics of the EBWEC were analyzed and studied by using the blade thickness and motion cycle as control variables.The present research shows that the EBWEC can effectively overcome the performance disadvantages related to the transmission shaft torque load and power curve fluctuations of rigid blade wave energy converters(RBWEC).

A study of the relationship between nurses'experience,structural empowerment,and attitudes toward computer use

Objective:This study examined the relationship between structural empowerment and nurses’experience and attitudes toward computer use.Methods:This study was conducted using a cross-sectional quantitative design.A total of 184 registered nurses from four hospitals in Jordan participated in the current study.Data were collected using a demographics questionnaire,the Conditions for Work Effectiveness Questionnaire-II(CWEQ-II),and the Pretest for Attitudes toward Computers in Healthcare(PATCH).Results:The median of experience in years among nurses was 5.0,ranging from one to 26 years.The mean score for the attitudes toward computer use was 61.90±11.38.Almost half of the participants,45.11%,were in the category of“feel comfortable using user-friendly computers.”The participants’mean average of the total structural empowerment was 12.40±2.43,and the values for its four subscales were:opportunity 3.57±0.87,resources 2.83±0.85,information 3.06±0.79,and support 2.95±0.86.The frequencies analysis revealed that most participants had a moderate level of empowerment(n¼127,69.02%).The bivariate correlation between nurses’experience and attitudes toward computer use was significant(r¼0.17,P<0.05).The relationship between the total structural empowerment score and attitudes toward computer use was positive but weak(r¼0.20,P<0.01).Conclusion:The results indicated that more experienced nurses are more reluctant toward computer use.However,creating an empowering work environment can facilitate nurses’attitudes toward computer use.

Design and Application of PCE-Oriented Multi-agent Software Framework and Agent Communication Module

With the improvement of mobile equipment performance and development of Pervasive Computing,interactive computational applications such as Multi-Agent (MA) systems in Pervasive Computing Environments (PCE) become more and more prevalent. Many applications in PCE require Agent communication,manual control,and diversity of devices. Hence system in PCE must be designed flexible,and optimize the use of network,storage and computing resources. However,traditional MA software framework cannot completely adapt to these new features. A new MA software framework and its Agent Communication Modules to solve the problem brought by PCE was proposed. To describe more precisely,it presents Wright/ADL (Architecture Description Language) description of the new framework. Then,it displays an application called AI Eleven based on this new framework. AI Eleven achieves Agent-Agent communication and good collaboration for a task. Two experiments on AI Eleven will demonstrate the new framework's practicability and superiority.

Architectural Teaching and Design Computing： A Didactic Experience in a Brazilian Architecture Course

In the last years, architectural practice has been confronted with a paradigm shift towards the application of digital methods in design activities. In this regard, it is a pedagogic challenge to provide a suitable computational background for architectural students, to improve their ability to apply algorithmic-parametric logic, as well as fabrication and prototyping resources to design problem solving. This challenge is even stronger when considering less favored social and technological contexts, such as in Brazil, for example. In this scenario, this article presents and discusses the procedures and the results from a didactic experience carried out in a design computing-oriented discipline, inserted in the curriculum of a Brazilian architecture course. Hence, this paper shares some design computing teaching experiences and presents some results on computational methods and creative approaches, with a view to contribute to a better understanding about the relations between logical thinking, mathematics and architectural design processes.

Design and flow field analysis for visualization experiment facility of pebble bed based on molten salt reactor

Molten salt pebble bed reactor is one of the sixth-generation Ⅳ reactor types. To investigate the mechanical behavior of the fuel pebbles in the core, a visualization experiment facility of pebble bed(VEFPB) is designed. To obtain a uniform flow field of the core and analyze the influence of the flow field on the structure of the pebble bed, computational fluid dynamics software Fluent is used to simulate the flow field distribution of the core of VEFPB. The simulation results show that the disturbance at the bottom of the pebble bed is proportional to the flow velocity of the inlet pipe, and the flow velocity close to the inlet side is more significant than that in other parts; the design of the cylinder bottom plate with holes of different sizes can effectively reduce the flow velocity and the disturbance at the bottom of the pebble bed. In addition,according to the velocity contours of the core of VEFPB, it is observed that the flow field distribution of the core is considerably uniform except at the bottom of the pebble bed. This ensures the stability of the pebble bed and verifies the rationality of the design of VEFPB. This study provides the technical support and reference for the flow field analysis of the core of molten salt pebble bed reactor.