Competitive Problem Research Articles

Minimizing the misinformation concern over social networks

People’s opinions are often affected by their social network, and the associated misinformation on the online social networks can easily mislead people’s judgment and decision-making process, leading people to take unconventional or even radical behaviors. People’s decision-making behavior is influenced by their concern to the misinformation they receive. Building on this, we explore the competitive concern minimization problem of leveraging agents who post correct information to minimize users’ concern to misinformation. First, considering users’ concern to misinformation, this paper constructs a concern-critical competitive model and introduces the Coulomb’s law to quantify the dynamic evolution of users’ concern in information diffusion. Second, we prove hardness results for the competitive concern minimization problem and discuss the modularity of the objective function. Then, to optimize the non-submodular objective function, a two-stage approximate projected subgradient algorithm with data-dependent approximation ratio is developed using Lovász extension and convex envelope. Finally, the experimental simulations on three real networks highlight the efficiency of the approaches proposed in this paper, which is at least 9.71% better than other baselines in reducing misinformation concern.

축구 코치의 코치 입문 경험에 관한 연구

Objectives This study aims to explore the process and experiences of soccer coaches who engage in sports coaching activities targeting elementary, middle, and high school student-athletes becoming a coach. The pur-pose of this study is to suggest the direction of future coach education by analyzing in-depth changes in the per-ception of soccer coaches, environmental factors, and social factors.
 Methods To achieve the purpose of the study, eight soccer coaches were selected as research participants. Research participants were selected as coaches with more than 10 years of experiences as a soccer player and more than 10 years of experiences as a soccer coach. Data were collected through in-depth interviews, and the collected data were analyzed by applying open coding and paradigm models based on grounded theory methods. By classifying information through open coding, categories of information about the phenomenon being studied were formed. Afterwards, axial coding was performed to connect subcategories around the central axis of the category.
 Results As a result of the study, a total of three top categories were derived: ‘Motivation for choosing a coach as a career’, ‘Influence of athlete experiences’, and ‘A gap between ideal and reality’. The sub-categories of ‘motiva-tion to be a leader as a career’ were ‘an interest in coaching activities’, ‘mind for nurturing the next generation’, ‘a challenge to advance to a higher league’, and ‘recommendation from a teacher’. The subcategories of ‘influence of athlete experiences’ were ‘use of experiential knowledge’, ‘pride of athlete experiences’, and ‘narrow career consciousness’. The subcategories of ‘the gap between ideals and reality’ were ‘trial and error in coaching meth-ods’, ‘formal coach education’, and ‘essential problems of competition’.
 Conclusions Based on the research results, the role and qualifications of athletics coaches as educators for stu-dent athletes were presented. Structural problems were pointed out in the coaching introductory process and the employment system of sports coaches to perform their roles simply as conveyors of exercise skills.

Sequential customers’ decisions in facility location with comparison-shopping

In the competitive facility location problem, a newcomer firm optimally locates its facilities and offers a product that is a substitute for those of the already located competitors, aiming at maximizing its market. Usual facility location models assume single-stop or single-facility trips. However, when products are non-homogeneous substitutes, customers may want to compare the products before making a decision (comparison-shopping). Moreover, decisions are often made sequentially as facilities are visited. We model for the first time, sequential customers’ decisions in multi-stop trips, represented using a nested logit rule. Customers make decisions at the place of origin (which facility to visit first), at the first visited facility (whether to buy, or not, or to continue comparing), and at the second facility visited (whether to purchase or not and in which facility). To adequately represent the drive to compare, we include in the customers’ utility the value of the gathered information, and the disutility of visiting facilities without purchasing. We propose a linear formulation with an efficient branch-and-cut approach. We analyze the location patterns in randomly generated instances, as well as in a case study on municipalities in Spain. The three-level nested, two-level nested, and multinomial logit rules are compared.

Dynamic pricing and competition of container liner shipping services in a duopoly spot market

With the application of online booking technology in the spot market of container liner shipping, dynamic pricing is considered an important tool and development trend to balance container shipping supply and demand from the spot market. This study examines the dynamic pricing and competition of container liner shipping services in a duopoly spot market where two shipping companies compete on liner routes with multiple overlapping origin–destination (O-D) pairs. The demand within the spot market for one shipping company is sensitive to both the freight rate set by the shipping company and its competitor. To tackle this complex challange, a non-linear mixed-integer generalized Nash equilibrium optimization model was formulated for the dynamic pricing and competition problem to maximize the profits of both shipping companies. To effectively reach the optimal solution for the dynamic pricing and competition problem, the proposed model is reformulated as a linear complementary problem with a Karush-Kuhn-Tucker (KKT) conditional system. An optimal response decomposition algorithm is developed to obtain the global optimal solution of the model. Numerical experiments with the Trans-Pacifc shipping network of two oligarchic shipping companies were conducted to demonstrate the efficiency of the solution algorithm and the applicability of the proposed model in improving the two companies’ profits from the spot market.

Solutions of the Ortvay Rudolf International Competition in Physics: Can One See One’s Own Back Around a Black Hole? (1991/12 Problem)

This paper is the fifth in a series of published solutions1–4 discussing problems of the Ortvay Rudolf international competition. The problem treated below is an exercise in general relativity and geometrical optics. Specifically, we check and interpret an often-heard statement, roughly of the form: “If you were to orbit a black hole around its photon sphere, you would see the back of your own head”. As the calculation involves general relativity, the full treatment of the problem as given in this article is appropriate for university students in advanced classes. However, by omitting the relativistic calculations and only referring to the results, one can easily formulate the problem as an exercise in geometric optics in a way that could be appropriate for introductionary level.

Competitive Environment Problems and Challenges in the Pre-Pandemic and Post-Pandemic Society

Abstract Competition and a competitive environment serve as a crucial foundation for the effective use of resources and the growth of economy, pushing enterprises to be more efficient, limit costs, and spend more in inventing and developing new goods. Customers may obtain more advantages at a lesser cost and of better quality when there is a strong competition. The competitive climate makes the state more appealing to investors, increasing the possibility of attracting local or international investment. Consumer protection and competition are critical for achieving economic growth and decreasing poverty. The jurisdiction and economic environment in which a enterprise works have a considerable impact on the quality of competition. The goal of the state and associated institutions is to encourage competition and to avoid any activities that place market participants and consumers in an unfair situation. It is also critical that the state’s activities and appropriate regulatory standards do not impose a significant burden on the business sector, since both business and consumer rights must be respected.

Additional Constraints for Dynamic Competitive Facility Location Problem

Additional Constraints for Dynamic Competitive Facility Location Problem

Effect of graphene on soybean root colonization by Bradyrhizobium strains.

Legume crops such as soybean obtain a large portion of their nitrogen nutrition through symbiotic nitrogen fixation by diazotrophic rhizobia bacteria in root nodules. However, nodule occupancy by low-capacity nitrogen-fixing rhizobia can lead to lower-than-optimal levels of nitrogen fixation. Seed/root coating with engineered materials such as graphene-carrying biomolecules that may promote specific attraction/attachment of desirable bacterial strains is a potential strategy that can help overcome this rhizobia competition problem. As a first step towards this goal, we assessed the impact of graphene on soybean and Bradyrhizobium using a set of growth, biochemical, and physiological assays. Three different concentrations of graphene were tested for toxicity in soybean (50, 250, and 1,000 mg/l) and Bradyrhizobia (25, 50, and 100 mg/l). Higher graphene concentrations (250 mg/l and 1,000 mg/l) promoted seed germination but slightly delayed plant development. Spectrometric and microscopy assays for hydrogen peroxide and superoxide anion suggested that specific concentrations of graphene led to higher levels of reactive oxygen species in the roots. In agreement, these roots also showed higher activities of antioxidant enzymes, catalase, and ascorbate peroxidase. Conversely, no toxic effects were detected on Bradyrhizobia treated with graphene, and neither did they have higher levels of reactive oxygen species. Graphene treatments at 250 mg/l and 1,000 mg/l significantly reduced the number of nodules, but rhizobia infection and the overall nitrogenase activity were not affected. Our results show that graphene can be used as a potential vehicle for seed/root treatment.

Agents Attraction Competition in an Extended Friedkin-Johnsen Social Network

Unlike many complex social networks investigated in the existing literatures concerning about the communication, consensus, or cooperative behaviors, this paper is devoted to an agents attraction competition problem in an extended Friedkin-Johnsen network. In this problem, two strategic agents compete to attract the nonstrategic agents to adopt opinions as closer to theirs as possible, by connecting with these nonstrategic agents and allocating their strengths on these connections. Therefore a 2-person zero-sum competitive game is characterized. We develop mathematical tools to provide some useful properties of the game, including the partial derivatives and positive definiteness of the payoff functions, based on which the Nash equilibrium of the game is analyzed. We rigorously examine that each player will allocate their strengths according to the susceptibilities, the intrinsic opinions and the costs of the nonstrategic agents, and the weighted column-sums of the row-stochastic matrix which describes the interpersonal influences of the network, if the network satisfies a condition. Otherwise, a Nash equilibrium seeking algorithm is proposed with simplex constraints, by which each strategic agent can asymptotically learn its optimal strategy. Finally, numerical examples are presented to illustrate the validity of the obtained results.

Cooperative Game-Based Resource Allocation Scheme for Heterogeneous Networks with eICIC Technology

Heterogeneous network (HetNet) is considered to be the most promising approach for increasing communication capacity. However, HetNet control problems are difficult due to their intertier interference. Recently, the enhanced intercell interference coordination (eICIC) technology is introduced to offer several benefits, including a more equitable traffic load distribution across the macro and embedded small cells. In this paper, we design a new resource allocation scheme for the eICIC-based HetNet. Our proposed scheme is formulated as a joint cooperative game to handle conflicting requirements. By adopting the ideas of Kalai and Smorodinsky solution (KSS), multicriteria Kalai and Smorodinsky solution (MCKSS), and sequential Raiffa solution (SRS), we develop a hybrid control algorithm for an adaptive resource sharing between different base stations. To effectively adjust the eICIC fraction rates, the concepts of MCKSS and SRS are applied in an interactive manner. For mobile devices in the HetNet, the assigned resource is distributed by using the idea of KSS. The key insight of our algorithm is to translate the originally competitive problem into a hierarchical cooperative problem to reach a socially optimal outcome. The main novelty of our approach is its flexibility to reach a reciprocal consensus under dynamic HetNet environments. Exhaustive system simulations illustrate the performance gains along different dimensions, such as system throughput, device payoff, and fairness among devices. The superiority of our proposed scheme is fully demonstrated in comparison with three other existing eICIC control protocols.

End-to-End Approach for Autonomous Driving: A Supervised Learning Method Using Computer Vision Algorithms for Dataset Creation

This paper presents a solution for an autonomously driven vehicle (a robotic car) based on artificial intelligence using a supervised learning method. A scaled-down robotic car containing only one camera as a sensor was developed to participate in the RoboCup Portuguese Open Autonomous Driving League competition. This study is based solely on the development of this robotic car, and the results presented are only from this competition. Teams usually solve the competition problem by relying on computer vision algorithms, and no research could be found on neural network model-based assistance for vehicle control. This technique is commonly used in general autonomous driving, and the amount of research is increasing. To train a neural network, a large number of labelled images is necessary; however, these are difficult to obtain. In order to address this problem, a graphical simulator was used with an environment containing the track and the robot/car to extract images for the dataset. A classical computer vision algorithm developed by the authors processes the image data to extract relevant information about the environment and uses it to determine the optimal direction for the vehicle to follow on the track, which is then associated with the respective image-grab. Several trainings were carried out with the created dataset to reach the final neural network model; tests were performed within a simulator, and the effectiveness of the proposed approach was additionally demonstrated through experimental results in two real robotics cars, which performed better than expected. This system proved to be very successful in steering the robotic car on a road-like track, and the agent’s performance increased with the use of supervised learning methods. With computer vision algorithms, the system performed an average of 23 complete laps around the track before going off-track, whereas with assistance from the neural network model the system never went off the track.

Problems of Retailers in the Cut Flower Sector and a Proposal for the Sustainability of the Sector: The Case of Turkey

The aim of this study was to identify the problems faced by retailers operating in the cut flower sector and to propose solutions to problems related to the sustainability of the sector. The study material consisted of data obtained by conducting a survey among 56 cut flower retailers in İzmir. In the study, factor analysis was used to determine the main problems that cut flower retailers encounter in the industry. Factor scores obtained through the factor analysis were divided into three clusters using cluster analysis. According to the results of the research, it was determined that the retailers included in the first cluster had the problem of obstacles to sales, the retailers in the second cluster had the problem of competition and cost increases, and finally, the retailers in the third cluster had the problem of quality. In light of these data, suggestions are made about what should be achieved for the sustainability of the retail cut flower sector.

Computation Resource Optimization for Large-Scale Intelligent Urban Rail Transit: A Mean-Field Game Approach

Offloading tasks in smart devices (SDs) to an edge intelligence-empowered service centre (EISC) is a promising solution to support burgeoning intelligent applications in large-scale intelligent urban rail transits (URTs). However, dynamic computation resource allocation is still a crucial challenge, facing that various large-scale SDs share the EISC computation resources and the reality that the allocated computation resource for an SD is coupled with the offloading rate of all SDs. This paper proposes a joint dynamic offloading rate control and computation resource optimization method for large-scale intelligent URTs. Firstly, we model the large-scale multi-agent computation resource competition problem by a multi-player differential game (MPDG) and prove that the Nash equilibrium (NE) based optimal solution exists for each SDs. Then, we transform the MPDG model into a mean-field game (MFG). By introducing the mean-field into the game, we can solve the multi-agent optimization problem with a single-agent optimization method. We illustrate the rationality of the MFG model and propose an iterative solution method based on the finite difference method to derive the solution. Finally, we propose a z-transforming-based control method to dynamically reschedule computation resources among intelligent applications to achieve a satisfactory quality of service (QoS). Extensive simulation results show that our proposed scheme can significantly improve the performance of intelligent URTs.

Fractional grey unequal-interval time-varying Lotka-Volterra model and its application for microbial communities in compost

Aerobic compost is an effective method for the treatment of livestock manure, which is usually accompanied by complex interspecific competition. Describing these competitive relationships through mathematical models can help understand the interaction of microorganisms and analyze the effect of exogenous additive to regulate the composting process. The common model for analyzing competition problem is the Lotka-Volterra model. However, the fixed parameters of the Lotka-Volterra model are not suitable to reflect the dynamic variations of the competitive relationship when the environmental conditions change during composting process. Therefore, this paper establishes a novel fractional grey unequal-interval time-varying Lotka-Volterra model. Firstly, a fractional grey derivate operator is proposed on the basis of the unequal interval of composting data and historical dependence of microbial growth. Secondly, considering the influence of temperature, a time-varying parameter matrix is defined to reflect the variation of competitive relationship at different composting phases, and it is estimated by forgetting factor recursive least squares. Thirdly, the optimal coefficients are optimized by grey prediction evolution algorithm. Finally, the proposed model is employed to analyze the chicken manure composting experiment. The results show that the proposed model has lower error criteria and more accurate trend of fitting curve than the other five existing models. The parameter matrix describes the dynamical variation of microbial competitive relationship in two taxonomic levels and reveals that effect of the exogenous additive is principally reacted in the thermophilic phase and the competitive advantage is shifted from Bacteroidota to Firmicutes after treatment with the exogenous additive.

On the Improvement of the Heuristic Algorithm for Packing Circles into a Circle of Minimum Radius

The article is devoted to investigation of a heuristic algorithm for solving the competitive problem "Circles Dense packing into a circle of minimum radius" and development of its improved version using Shor's r-algorithm with step dichotomy. The heuristic algorithm was developed by Bohdan Zadorozhnyi, a third-year student of Uzhhorod National University. The program implemented on its basis took second place in the competition and used less time than the maximum time, which, according to the competition, was allocated for a one-time launch of the program for 50 competitive tasks. The article describes the research results of how the Shor r-algorithm can be used to improve efficiency of the heuristic algorithm. The algorithm for improving the heuristic solution is implemented according to the following scheme. We start from the best arrangement of circles found by the heuristic algorithm. Then, we perform the following four stages of the algorithm: Stage 1. We set a rather large value of the initial step (we determine it depending on the input data or simply choose it as a large step); Stage 2. We run the r-algorithm from the starting point and find the local minimum of the corresponding nonlinear programming problem; Stage 3. If the local minimum found guarantees a smaller radius of the outer circle, then the local minimum point becomes the starting point; the local minimum found does not guarantee a smaller radius of the outer circle, then the size of the step h is reduced by half. Stage 4. If the step value is greater than the specified value, then we proceed to Stage 2. Otherwise, we finish work of the algorithm with solution improvement. Python 3.9.5 programming language and NumPy 1.24.2 library were used for software implementation of both algorithms. For 50 competitive tests and different number of iterations of the heuristic algorithm (10, 20, 30, 40 and 50 iterations), the analysis of the obtained results was conducted according to three parameters: the number of points obtained, program execution time and the number of exchanges of circles in one iteration of the heuristic algorithm. For most of the competitive tests, the refined algorithm allows you to get better results in points, in particular, in most cases the improvement is up to 2 points, and in some cases – from 3 to 6 points. The biggest improvements (6 points) are seen in tests, where the circles have the same or close radii. Here, the heuristic algorithm works not so effectively, therefore, for such tests, due to the algorithm with refinement, it is possible to significantly improve the results of competitive tasks in terms of points. Keywords: circle packing, heuristics, r-algorithm, Python 3.9.5, NumPy 1.24.2.

Consensus for cooperative–competitive network systems: An impulsive perspective

AbstractUsually, hypotheses involving cooperative interactions and continuous interplay among interacting agents may result in somewhat conservatism, drastically hurdling potential about interpretations of prevalent aggregation phenomena arising in both nature and practical application. Of interest uncovered in this article is the cooperative–competitive consensus problems for multi‐agent systems by leverage of impulsive‐based control perspective. We first discuss the possibility of quantifying interaction relation of the agents and the description of cooperative–competitive phenomena that capture massive interest recently. Specifically, the former is done by algebraic graph theory, following the same research avenue as the convention; while the cooperative–competitive information is described by some nonzero scaling parameter. We shall pursue the tie that enables to anchor the consensus of participating agents, without depending on the signed graph theory. Subsequently, a manner relying on the local information is carried out to induce the consensus error to circumvent the global information. Then, consensus condition, in connection with the features of both continuous and impulsiveߚbased dynamics, is obtained, as well as several typical circumstances are also elaborated. Both the proposed consensus algorithms and the derived results are underpinned via a numerical study eventually.

Mandating interoperability of interpersonal communications services in an ecosystems competition context

With the adoption of Article 7 of the Digital Markets Act, the EU has taken a progressive approach to mandating horizontal interoperability of number-independent interpersonal communications services. This legislative measure addresses the structural competition challenge posed by direct network effects, which tend to lock users into established messaging platforms. However, it is not entirely clear how, if at all, the Commission should implement this interoperability mandate. To shed light on this matter, we present an ecosystems competition context where these messaging services transition into digital ecosystems to create value from user-generated data and attention. Within this context, we find interoperability mandates like Article 7 a plausible solution to the structural competition problem, although much is yet to be done to sharpen these mandates against the intricate inter-ecosystem dynamics and to empower multi-homing users in a post-interoperability world. Achieving this goal requires collaborative efforts not only from sector regulators responsible for overseeing these mandates but also from antitrust and data protection authorities.

Reinsurance games with variance-premium reinsurers: from tree to chain

AbstractThis paper studies dynamic reinsurance contracting and competition problems under model ambiguity in a reinsurance market with one primary insurer and n reinsurers, who apply the variance premium principle and who are distinguished by their levels of ambiguity aversion. The insurer negotiates reinsurance policies with all reinsurers simultaneously, which leads to a reinsurance tree structure with full competition among the reinsurers. We model the reinsurance contracting problems between the insurer and reinsurers by Stackelberg differential games and the competition among the reinsurers by a non-cooperative Nash game. We derive equilibrium strategies in semi-closed form for all the companies, whose objective is to maximize their expected surpluses penalized by a squared-error divergence term that measures their ambiguity. We find that, in equilibrium, the insurer purchases a positive amount of proportional reinsurance from each reinsurer. We further show that the insurer always prefers the tree structure to the chain structure, in which the risk of the insurer is shared sequentially among all reinsurers.

Solving non-linear optimization problems by a trajectory approach

Abstract Accepted by: Konstantinos Nikolopoulos We propose solving non-linear optimization problems by a trajectory method. A parameter is introduced into the optimization problem. For example, a variable in the original formulation is replaced by its squared value. The parameter is the power at which the variable is raised. For a particular value of the parameter (power of 2), the optimal solution is easily obtained. The original optimization problem is defined for another value of the parameter (power of 1). As another example, the means and standard deviations of a function based on a set of variables can be calculated. We multiply the standard deviations by a factor (the parameter) between 0 and 1. Suppose that the problem is easily solvable for zero standard deviations (factor of 0). If we ‘slowly’ increase the factor, the solution moves to the desired solution for a factor of 1. A trajectory connects the easily obtained solution to the desired solution. We trace the trajectory and the solution for the optimization problem is at the end of the trajectory. The procedure is applied for solving the single facility Weber location problem, and a competitive location problem with good results.

Prediction and Conservation Recommendations for the Effects of Diversified Drought on Plant Community Species

Based on the analysis of plant community data and climate data, this paper established a plant community competition prediction model that includes weather and environmental factors by utilizing species competition theory and the Lotka-Volterra model. During the model construction process, the actual data was pre-processed and summarized for analysis, which ultimately determined the parameters of species quantity, growth rate, and precipitation, improving the accuracy and reliability of the model. Meanwhile, using the Lotka-Volterra model, a relationship model between biomass and time was established, and through the simulated annealing algorithm, two optimal species quantity changes were obtained. Finally, this paper optimized the multi-factor plant community Lotka-Volterra model through the genetic algorithm and obtained the optimal solution: the plant community can achieve the most beneficial competitive state with four species. This further proves the rationality and robustness of the model and also provides new methods and ideas for the management and protection of ecosystems. In summary, the results of this paper are of great significance for the stability and sustainable development of ecosystems and provide useful references for solving the problem of plant community competition and ecosystem stability.