Building Individual Player Performance Profiles According to Pre-Game Expectations and Goal Difference in Soccer.

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players' energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder-Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87±61.42 and root mean squared error (RMSE) of 520.69±88.66 achieved by our model, as opposed to the B1 MAE of 429.04±84.87 and RMSE of 581.34±185.84, and B2 MAE of 421.57±95.96 and RMSE of 613.47±300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players' responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics.

Motion Control for Autonomous Heterogeneous Multiagent Area Search in Uncertain Conditions.

Using multiple mobile robots in search missions offers a lot of benefits, but one needs a suitable and competent motion control algorithm that is able to consider sensor characteristics, the uncertainty of target detection, and complexity of needed maneuvers in order to make a multiagent search autonomous. This article provides a methodology for an autonomous 2-D search using multiple unmanned (aerial or possibly other) vehicles. The proposed methodology relies on an accurate calculation of target occurrence probability distribution based on the initial estimated target distribution and continuous action of spatial variant search agent sensors. The core of the autonomous search process is a high-level motion control for multiple search agents which utilizes the probabilistic model of target occurrence via a heat equation-driven area coverage (HEDAC) method. This centralized motion control algorithm is tailored for handling a group of search agents that are heterogeneous in both motion and sensing characteristics. The motion of agents is directed by the gradient of the potential field which provides a near-ergodic exploration of the search space. The proposed method is tested on three realistic search mission simulations and compared with three alternative methods, where HEDAC outperforms all alternatives in all tests. Conventional search strategies need about double the time to achieve the proportionate detection rate when compared to HEDAC controlled search. The scalability test showed that increasing the number of an HEDAC controlled search agents, although somewhat deteriorating the search efficiency, provides needed speed-up of the search. This study shows the flexibility and competence of the proposed method and gives a strong foundation for possible real-world applications.

Search strategy in a complex and dynamic environment: the MH370 case.

Search and detection of objects on the ocean surface is a challenging task due to the complexity of the drift dynamics and lack of known optimal solutions for the path of the search agents. This challenge was highlighted by the unsuccessful search for Malaysian Flight 370 (MH370) which disappeared on March 8, 2014. In this paper, we propose an improvement of a search algorithm rooted in the ergodic theory of dynamical systems which can accommodate complex geometries and uncertainties of the drifting search areas on the ocean surface. We illustrate the effectiveness of this algorithm in a computational replication of the conducted search for MH370. We compare the algorithms using many realizations with random initial positions, and analyze the influence of the stochastic drift on the search success. In comparison to conventional search methods, the proposed algorithm leads to an order of magnitude improvement in success rate over the time period of the actual search operation. Simulations of the proposed search control also indicate that the initial success rate of finding debris increases in the event of delayed search commencement. This is due to the existence of convergence zones in the search area which leads to local aggregation of debris in those zones and hence reduction of the effective size of the area to be searched.

Ergodicity-Based Cooperative Multiagent Area Coverage via a Potential Field.

This paper considers a problem of area coverage where the objective is to achieve given coverage density by use of multiple mobile agents. We present an ergodicity-based coverage algorithm which enables a centralized feedback control for multiagent system based on radial basis function (RBF) representation of the ergodicity problem and a solution of an appropriately designed stationary heat equation for the potential field. The heat equation uses a source term that depends on the difference between the given goal density distribution and the current coverage density (time average of RBFs along trajectories). The agent movement is directed using the gradient of that potential field. The heat equation driven area coverage has a built-in cooperative behavior of agents which includes collision avoidance and coverage coordination. The algorithm is robust, scalable, and computationally inexpensive.

Dynamic autoinoculation and the microbial ecology of a deep water hydrocarbon irruption.

The irruption of gas and oil into the Gulf of Mexico during the Deepwater Horizon event fed a deep sea bacterial bloom that consumed hydrocarbons in the affected waters, formed a regional oxygen anomaly, and altered the microbiology of the region. In this work, we develop a coupled physical-metabolic model to assess the impact of mixing processes on these deep ocean bacterial communities and their capacity for hydrocarbon and oxygen use. We find that observed biodegradation patterns are well-described by exponential growth of bacteria from seed populations present at low abundance and that current oscillation and mixing processes played a critical role in distributing hydrocarbons and associated bacterial blooms within the northeast Gulf of Mexico. Mixing processes also accelerated hydrocarbon degradation through an autoinoculation effect, where water masses, in which the hydrocarbon irruption had caused blooms, later returned to the spill site with hydrocarbon-degrading bacteria persisting at elevated abundance. Interestingly, although the initial irruption of hydrocarbons fed successive blooms of different bacterial types, subsequent irruptions promoted consistency in the structure of the bacterial community. These results highlight an impact of mixing and circulation processes on biodegradation activity of bacteria during the Deepwater Horizon event and suggest an important role for mixing processes in the microbial ecology of deep ocean environments.