Match-Referee Matching Model in Football

Referees are assigned to football matches in Turkey by a three-person subcommittee formed by Central Referee Committee, a subsidiary of the Turkish Football Federation, in a non-transparent way and this procedure makes manipulation possible. Therefore, referee appointments are among the topics discussed in the football world. It is a major deficiency that the matching theory, which has been successful in regulating many problematic markets, has not been used to date in football for referee appointments. In this study, a match-referee matching model, using the Gale-Shapley algorithm, for the football matches played in Turkish professional football leagues is proposed. The match-referee matching result offered by this algorithm is the best possible stable matching result for the clubs. This matching result is produced in line with the preferences of the clubs and clubs' satisfaction is at the forefront. Most importantly, it is obtained through a transparent, clear, fair and manipulation-free match-referee matching system

Landing in binary asteroids: A global map of feasible descend opportunities for unpowered spacecraft

Asteroid surface science provides the necessary “ground-truth” to validate and enhance remote sensing from orbiting spacecraft. Yet, due to uncertainties associated with the dynamical environment near asteroids, it is generally prudent for the main spacecraft to remain at a safe distance. Instead, small landers could be used much more daringly. This paper explores the potential for ballistic landing opportunities in binary asteroid systems. The dynamics near a binary asteroid are modelled by means of the Circular Restricted Three Body Problem, which provides a reasonable representation of a standard binary system. Natural landing trajectories are sought that allow for deployment from safe distances and touchdown with minimum local-vertical velocity. The necessary coefficient of restitution to ensure a successful landing and the effects of navigation and deployment errors are also analysed. Assuming deployment errors in the order of 10 meters and 1 cm/s (1-sigma), the results show that ballistic descent landing operations are likely to be successful if targeting near equatorial regions with longitude within 320o to 20o in the secondary of the binary system

THE RELATION OF RELIGIOUS ATTITUDES AND BEHAVIOURS WITH DEPRESSION IN BOARDING QURAN COURSE STUDENTS

Background: Boarding Quran courses are religious institutions where course attendees spend large part of the year. Depression is an ever-increasing health problem. So, it is worth to study on the effects of religion concept and religious belief and behaviours’ that religion concept brings, on depression. The main purpose of this study, is to analyse the effect of religious attitudes and behaviours on depression in Quran course / hafiz students. Subjects and methods: The study is a cross sectional, case-control survey research. Boarding Quran courses and high schools were visited in Samsun city. A total of 956 participants enrolled between June 2015 and December 2015 were included into study from Samsun city of Turkey. Volunteers, 13 years and over ones without any psychiatric disorders were included in the study. Religious attitude-behaviour inventory and Beck’s depression inventory were used in the study. Results: Median point of case group attitude scale was 49, control group’s was 57 and difference among both has a statistical meaning (p<0.001). Beck’s depression score average of case group is 12.93±9.33, its control group’s average is 13.74±11.14 and difference between them is not important. Median score of both groups are 11. When scores of attitude and depression scales compared with each other in terms of demographic parameters, there is a difference among group, gender, age and education parameters (p<0.001). Conclusions: It was seen that religious attitudes and behaviours can be protective for boarding Quran course students but it cannot be enough by itself

Analysis of natural landing trajectories for passive landers in binary asteroids: A case study for (65803) 1996GT didymos

Binary asteroids are believed to constitute about 15% percent of the near-Earth asteroid (NEA) population. Their abundance and yet-to-be-resolved formation mechanism make them scientifically interesting, but they can also be exploited as a test bed for kinetic impactors, as the Asteroid Impact and Deflection Assessment (AIDA) joint mission proposal suggested. In addition to impactor spacecraft of AIDA, i.e. DART, the observation spacecraft, called Asteroid Impact Mission (AIM) (whose future is now uncertain) is to characterize Didymos, including pre- and post-impact variations. Due to the highly perturbed dynamical environment around asteroids, large, and generally expensive missions are preferred to be operated from a safe distance from target asteroid. Even if advanced remote sensing techniques provide the finest details of the target, surface agents can obtain higher resolution and ground truth data. Lander solutions for small body exploration have already been suggested in various missions/proposals. The most recent example is the AIM proposal, which envisage to deploy MASCOT lander on the surface of Didymoon. Additionally, AIM proposed to carry two CubeSats on board. A team led by Royal Observatory of Belgium (ROB) proposed Asteroid Geophysical Explorer (AGEX) CubeSat to land on Didymoon. CubeSats can be employed much more daringly in small body environments due to their versatile character and low development cost. Nevertheless, they possess only limited AOCS capabilities because of their size, and in most cases they are passive. This research offers novel landing trajectories by exploiting the natural dynamics of binary systems. The framework of Circular Restricted Three-Body Problem is used for this purpose, in which two asteroids orbit each other around their common center of mass, while third body (CubeSat) move under their gravitational field. Landing trajectories are propagated backwards in time; from each latitude-longitude points in densely meshed surface through the low energy gate at L2. A newly developed bisection algorithm ensures to generate the lowest energy trajectory for landing point under given constraints. The results suggest that landing speeds less than 8 cm/s are possible, while coefficient restitutions of over 0.9 for spherical asteroids would ensure a successful landing. Robustness of trajectories is also investigated. Uncertainties in deployment mechanism and GNC errors of mothership are considered. Trajectories that are obtained in backwards time propagation are added pseudo-random errors, then propagated forward to the surface in a Monte Carlo simulation, in which 1000 trajectories are propagated. The deployment altitude is found to be severely degrading the success rate. The GNC velocity errors are also found to be more effective than their position counterparts. The success rate over 99.7% (3) can be achieved, though extra requirements might need to be considered for mothership design

Volume XCIII, Number 5, October 26, 1973

WOS: 000346148200006Objective: The goal of this study was to determine the influence of various carbon sources on the citric and isocitric acid production by various Yarrowia lipolytica strains. Methods: The yeasts used in our study were first investigated for organic acid production using screening media. Then, the effect of several complex carbon sources on the citric and isocitric acid production of selected yeast strains was investigated. The amount of citric and isocitric acid production was determined via enzymatic reactions. Results: In this study, 22 Y. lipolytica strains were investigated for the organic acid production. Among these strains, 2 strains (TEM YL 3 and TEM YL 20) were found to be highest organic-acid producer. Taken all results together, the highest amounts of citric acid (66.2 g/L for TEM YL 3, 50.0 g/L for TEM YL 20) were observed in the production medium containing sunflower oil. Conclusion: Citric acid consumption, and thus, the need for it are constantly rising in our country, which imports citric acid. Therefore, in order to meet this need, further studies which will yield to the maximum citric acid production should be performed by utilizing waste carbon sources and by using new low- cost but high citric acid-producing strains.Scientific Research Unit of Ege UniversityEge University [12 FEN 006]This study was financially supported by Scientific Research Unit of Ege University (Project number: 12 FEN 006). This study was presented at the 21st National Biology Congress (3 - 7 September 2012) in Izmir, Turkey

MP3: Movement Primitive-Based (Re-)Planning Policy

We introduce a novel deep reinforcement learning (RL) approach called Movement Prmitive-based Planning Policy (MP3). By integrating movement primitives (MPs) into the deep RL framework, MP3 enables the generation of smooth trajectories throughout the whole learning process while effectively learning from sparse and non-Markovian rewards. Additionally, MP3 maintains the capability to adapt to changes in the environment during execution. Although many early successes in robot RL have been achieved by combining RL with MPs, these approaches are often limited to learning single stroke-based motions, lacking the ability to adapt to task variations or adjust motions during execution. Building upon our previous work, which introduced an episode-based RL method for the non-linear adaptation of MP parameters to different task variations, this paper extends the approach to incorporating replanning strategies. This allows adaptation of the MP parameters throughout motion execution, addressing the lack of online motion adaptation in stochastic domains requiring feedback. We compared our approach against state-of-the-art deep RL and RL with MPs methods. The results demonstrated improved performance in sophisticated, sparse reward settings and in domains requiring replanning.Comment: The video demonstration can be accessed at https://intuitive-robots.github.io/mp3_website/. arXiv admin note: text overlap with arXiv:2210.0962

Curriculum-Based Imitation of Versatile Skills

Learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelihood given the demonstrations. Yet, human demonstrations are often multi-modal, i.e., the same task is solved in multiple ways which is a major challenge for most imitation learning methods that are based on such a maximum likelihood (ML) objective. The ML objective forces the model to cover all data, it prevents specialization in the context space and can cause mode-averaging in the behavior space, leading to suboptimal or potentially catastrophic behavior. Here, we alleviate those issues by introducing a curriculum using a weight for each data point, allowing the model to specialize on data it can represent while incentivizing it to cover as much data as possible by an entropy bonus. We extend our algorithm to a Mixture of (linear) Experts (MoE) such that the single components can specialize on local context regions, while the MoE covers all data points. We evaluate our approach in complex simulated and real robot control tasks and show it learns from versatile human demonstrations and significantly outperforms current SOTA methods. A reference implementation can be found at https://github.com/intuitive-robots/ml-cu

Curriculum-Based Imitation of Versatile Skills

Learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelihood given the demonstrations. Yet, human demonstrations are often multi-modal, i.e., the same task is solved in multiple ways which is a major challenge for most imitation learning methods that are based on such a maximum likelihood (ML) objective. The ML objective forces the model to cover all data, it prevents specialization in the context space and can cause mode-averaging in the behavior space, leading to suboptimal or potentially catastrophic behavior. Here, we alleviate those issues by introducing a curriculum using a weight for each data point, allowing the model to specialize on data it can represent while incentivizing it to cover as much data as possible by an entropy bonus. We extend our algorithm to a Mixture of (linear) Experts (MoE) such that the single components can specialize on local context regions, while the MoE covers all data points. We evaluate our approach in complex simulated and real robot control tasks and show it learns from versatile human demonstrations and significantly outperforms current SOTA methods. A reference implementation can be found at https://github.com/intuitive-robots/ml-cu