The Basics of Developing an Alternative Concept for Commercial and Military Vehicle Operation (Random Strategy)

The efficiency of commercial automobiles and wheeled military vehicles mainly depends on the choice of maintenance (M) and current repair (CR) concept. In the paper the difficulties of adapting the (M) and (CR) planning strategies to the structural characteristics of modern transport facilities are pointed out. The advantages of using the (M) and (CR) random strategy for transport facilities based on the stochastic nature of failures and malfunctions are substantiated. Considering the failures and malfunctions as random values and identifying the patterns of their distribution based on γ percentage resources, it is proposed to develop a list of regulated maintenance and repair work, periodicity and labor intensity based on a random strategy, which will increase the efficiency of preserving the technical resource of the rolling stock throughout the entire life cycle of the vehicle.

Rock-Paper-Scissors Play: Beyond the Win-Stay/Lose-Change Strategy

This research studied the strategies that players use in sequential adversarial games. We took the Rock-Paper-Scissors (RPS) game as an example and ran players in two experiments. The first experiment involved two humans, who played the RPS together for 100 times. Importantly, our payoff design in the RPS allowed us to differentiate between participants who used a random strategy from those who used a Nash strategy. We found that participants did not play in agreement with the Nash strategy, but rather, their behavior was closer to random. Moreover, the analyses of the participants’ sequential actions indicated heterogeneous cycle-based behaviors: some participants’ actions were independent of their past outcomes, some followed a well-known win-stay/lose-change strategy, and others exhibited the win-change/lose-stay behavior. To understand the sequential patterns of outcome-dependent actions, we designed probabilistic computer algorithms involving specific change actions (i.e., to downgrade or upgrade according to the immediate past outcome): the Win-Downgrade/Lose-Stay (WDLS) or Win-Stay/Lose-Upgrade (WSLU) strategies. Experiment 2 used these strategies against a human player. Our findings show that participants followed a win-stay strategy against the WDLS algorithm and a lose-change strategy against the WSLU algorithm, while they had difficulty in using an upgrade/downgrade direction, suggesting humans’ limited ability to detect and counter the actions of the algorithm. Taken together, our two experiments showed a large diversity of sequential strategies, where the win-stay/lose-change strategy did not describe the majority of human players’ dynamic behaviors in this adversarial situation.

A prediction model of urban counterterrorism based on stochastic strategy

With ever increasing complexity and intelligence of the modern cities, protecting key public facilities and important targets from any damage is a major challenge for the security sector. In all types of anti-terrorism prediction models, the prediction of attack behaviour is indispensable. Therefore, the attack behaviour model plays an important role in the anti-terrorism security system. This paper builds the attacker’s behaviour model, and carries out the prediction about the possible attack behaviour by the attacker model based on random strategy. According to the attack strategies, analysis and construction of the attack tree and attack graph are carried out based on the state-based stochastic model. The paper describes the security system in detail taking use of the state-based stochastic model method, so as to clarify the state distribution and the transfer relationship between the states of various security resources after threatened by attacks. At the same time, this paper applies the state-based stochastic model to establish the attacker model through the impact of attack on the security system.

Improved NSGA-III with Second-Order Difference Random Strategy for Dynamic Multi-Objective Optimization

Most real-world problems that have two or three objectives are dynamic, and the environment of the problems may change as time goes on. For the purpose of solving dynamic multi-objective problems better, two proposed strategies (second-order difference strategy and random strategy) were incorporated with NSGA-III, namely SDNSGA-III. When the environment changes in SDNSGA-III, the second-order difference strategy and random strategy are first used to improve the individuals in the next generation population, then NSGA-III is employed to optimize the individuals to obtain optimal solutions. Our experiments were conducted with two primary objectives. The first was to test the values of the metrics mean inverted generational distance (MIGD), mean generational distance (MGD), and mean hyper volume (MHV) on the test functions (Fun1 to Fun6) via the proposed algorithm and the four state-of-the-art algorithms. The second aim was to compare the metrics’ value of NSGA-III with single strategy and SDNSGA-III, proving the efficiency of the two strategies in SDNSGA-III. The comparative data obtained from the experiments demonstrate that SDNSGA-III has good convergence and diversity compared with four other evolutionary algorithms. What is more, the efficiency of second-order difference strategy and random strategy was also analyzed in this paper.

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

Knowledge of Brazilian genral people on risk factors for stroke

Background: Stroke is one of the main causes of death in the Brazil and worldwide. The level of lay people’s knowledge on risk factors for stroke have yet to be fully described. Objectives: To assess the level of information on risk factors for stroke among lay people Design and setting: Cross-sectional study applying a survey to people in public spaces in cities of Brazil’s Northeast, in 2020. Methods: Individuals who were passing by in such places were invited in a quasi- random strategy, to be interviewed. We exposed the volunteers to a typical case of a stroke presentation and asked what risk factors for it. We did not provide alternatives for the question. Ethical principles were followed. Results: We enrolled 1,477 individuals (52.5% female, 36.2±14.8yo). Stroke was identified for 1,226/1,477 (83.0%) individuals. From these, 423/1,226 (34.5%) did not mention any risk factors for stroke; and 393/1,226 (32.1%) reported two or more factors. The often-indicated risk factors were hypertension (418, 34.1%), stress (201, 16.4%), inappropriate diet (125, 10.2%), diabetes (124, 10.1%) and sedentary lifestyle (109, 8.9%). There was not differences on mention or not risk factors in relation to sex (p=.290) and age (p=.085), but those who known had more years of formal education (p <.001) and more frequently had health insurance (p=.010). Conclusion: The knowledge of general population on risk factors for stroke is poor. It is necessary to provide public campaigns in order to improve knowledge on this theme and to encourage screening in individuals at risk.

Pengaruh Perceived Desirability, Perceived Feasibility, dan Propensity to Act Terhadap Intensi Berwirausaha

The points of this consider deciding the impact of perceived desirability, perceived feasibility, and propensity to act on intentions of entrepreneurial. This ponders utilizes clear examination investigate sort with a quantitative approach, that have four factors such we perceived desirability, perceived feasibility and propensity to act on intentions of entrepreneurial as free factors, the intention of entrepreneurial as subordinate factors. The tests of this consider comprise 395 understudies of Universitas Pendidikan Indonesia, This investigation utilizes a sampling-random strategy, utilizing questionnares as an information collection technic. The instrument legitimacy was evaluated by product-moment relationship and the unwavering quality by Cronbach’s Alpha. Information examination utilizing numerous straight relapse with IBM SPSS 25. The comes about of this consider demonstrating that Perceived Desirability, Perceived Feasibility, and Propensity to act have a positive and critical impact on the Intention of Entrepreneurial. Theory of Entrepreneurial Event gives a positive commitment to entrepreneurial inquire about, so it is fundamental to create a inquire about demonstrate by considering other components that moreover play a part in empowering entrepreneurial.

Intelligent Data Fusion and Multi-Agent Coordination for Target Allocation

This paper addresses the fusion processing techniques for multi-sensor data perceived through the infrared sensors of military surveillance robots, and proposes their decision-theoretic coordination to effectively monitor multiple targets. To combine the multi-sensor data from the distributed battlefield robots, a set of fusion rules are used to formulate a combined prediction from the multi-source data. The possible type of a target is estimated through the fusion rules. For the identification of targets, agents need to keep track of targets for continuous situation awareness. The coordination of the agents with limited range of surveillance is indispensable for their successful monitoring of multiple targets. For dynamic and flexible coordination, our agents follow the decision-theoretic approach. We implement a military simulator to compare the capabilities of fusion processing and those of coordination, and conduct experiments with our framework in distributed and uncertain battlefield environments. The experimental results show that the fusion process of multi-sensor data from military robots can improve the performance of estimation of the type of a target, and our coordinated agents outperform agents using random strategy for their target selection in various military scenarios.

A Two-Way Parallel Slime Mold Algorithm by Flow and Distance for the Travelling Salesman Problem

In order to solve the problem of poor local optimization of the Slime Mold Algorithm (SMA) in the Travelling Salesman Problem (TSP), a Two-way Parallel Slime Mold Algorithm by Flow and Distance (TPSMA) is proposed in this paper. Firstly, the flow between each path point is calculated by the “critical pipeline and critical culture” model of SMA; then, according to the two indexes of flow and distance, the set of path points to be selected is obtained; finally, the optimization principle with a flow index is improved with two indexes of flow and distance and added random strategy. Hence, a two-way parallel optimization method is realized and the local optimal problem is solved effectively. Through the simulation of Traveling Salesman Problem Library (TSPLIB) on ulysses16, city31, eil51, gr96, and bier127, the results of TPSMA were improved by 24.56, 36.10, 41.88, 49.83, and 52.93%, respectively, compared to SMA. Furthermore, the number of path points is more and the optimization ability of TPSMA is better. At the same time, TPSMA is closer to the current optimal result than other algorithms by multiple sets of tests, and its time complexity is obviously better than others. Therefore, the superiority of TPSMA is adequately proven.

RANDOM STRATEGY VERSUS TECHNICAL ANALYSIS STRATEGY: THE CASE OF EUR/USD INTRADAY TRADING

This paper provides a comparison between the strategy based on technical analysis and the strategy based on random trading on a highly liquid EUR/USD foreign exchange market. The authors analyze three years of data, and in every intraday trading session. Technical analysis strategy uses essential indicators such as moving averages (MA). Every trading position will have the risk-reward ratio (RRR) 3 to 1. In addition, another trading positions on the EUR/USD currency pair will be opened at the same time each day, without technical analysis. The time of entry into position will be indicated by past high liquidity on a given currency pair at a given time with a similar risk-reward ratio (RRR) 3 to 1. This paper aims to compare the strategy of technical analysis and the random strategy in intraday trading concerning the profitability of these trades.