Biomechanical Loads and Their Effects on Player Performance in NCAA D-I Male Basketball Games

Basketball games and training sessions are characterized by quick actions and many scoring attempts, which pose biomechanical loads on the bodies of the players. Inertial Measurement Units (IMUs) capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis (IMA) and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and training with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modeled: (i) relations between team-level game stats and the win/loss probabilities of the team, (ii) associations between the player-level training and game loads and their game stats, and (iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses, therefore, included total points [PTS], two-point field goals, and defensive rebounds (DEF REB) that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular, increased loads 2 days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads 2 days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in loads of players and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sports scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.

Performance Evaluation of the Priority Multi-Server System MMAP/PH/M/N Using Machine Learning Methods

In this paper, we present the results of a study of a priority multi-server queuing system with heterogeneous customers arriving according to a marked Markovian arrival process (MMAP), phase-type service times (PH), and a queue with finite capacity. Priority traffic classes differ in PH distributions of the service time and the probability of joining the queue, which depends on the current length of the queue. If the queue is full, the customer does not enter the system. An analytical model has been developed and studied for a particular case of a queueing system with two priority classes. We present an algorithm for calculating stationary probabilities of the system state, loss probabilities, the average number of customers in the queue, and other performance characteristics for this particular case. For the general case with K priority classes, a new method for assessing the performance characteristics of complex priority systems has been developed, based on a combination of machine learning and simulation methods. We demonstrate the high efficiency of the new method by providing numerical examples.

Investigation of the Flow-Based Fast Reroute Model with Implementation of a Multimedia Quality Protection Scheme

In this paper, a tensor flow-based fast reroute model with multimedia quality protection is proposed. In the model, the conditions for implementing a multipath routing strategy and flow conservation are introduced taking into account possible packet loss at the network nodes and preventing overloading communication links both when using the primary and backup routes. At the same time, the novelty of the proposed solution is the formalization of the conditions of protection of the Quality of Experience level in terms of multimedia quality along the primary and backup routes. These conditions have been obtained during the tensor formalization of the network, which made it possible to calculate the quality of service indicators: packet loss probabilities, as well as the average end-to-end delay for audio and video flows transmitted in the multimedia session using the primary and backup routes, respectively. As a criterion for the optimality of the obtained solutions, a condition has been selected related to maximizing the overall performance of the infocommunication network. The results of the research of the proposed model confirm the adequacy of the numerical research results obtained for solving the problem of fast rerouting with link/node protection.

Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability

Water stress is one of the primary environmental factors that limits terrestrial ecosystems’ productivity. Hense, the way to quantify gobal vegetation productivity’s vulnerability under water stress and reveal its seasonal dynamics in response to drought is of great significance in mitigating and adapting to global changes. Here, we estimated monthly gross primary productivity (GPP) first based on light-use efficiency (LUE) models for 1982–2015. GPP’s response time to water availability can be determined by correlating the monthly GPP series with the multiple timescale Standardized Precipitation Evapotranspiration Index (SPEI). Thereafter, we developed an optimal bivariate probabilistic model to derive the vegetation productivity loss probabilities under different drought scenarios using the copula method. The results showed that LUE models have a good fit and estimate GPP well (R2 exceeded 0.7). GPP is expected to decrease in 71.91% of the global land vegetation area because of increases in radiation and temperature and decreases in soil moisture during drought periods. Largely, we found that vegetation productivity and water availability are correlated positively globally. The vegetation productivity in arid and semiarid areas depends considerably upon water availability compared to that in humid and semi-humid areas. Weak drought resistance often characterizes the land cover types that water availability influences more. In addition, under the scenario of the same level of GPP damage with different drought degrees, as droughts increase in severity, GPP loss probabilities increase as well. Further, under the same drought severity with different levels of GPP damage, drought’s effect on GPP loss probabilities weaken gradually as the GPP damage level increaes. Similar patterns were observed in different seasons. Our results showed that arid and semiarid areas have higher conditional probabilities of vegetation productivity losses under different drought scenarios.

Responses to losses in high-deductible health insurance: persistence, emotions, and rationality

There have been few empirical studies on how consumers respond to their loss experience over time when choosing between high- and low-deductible health insurance plans. To address this question, we designed a ten-period web-based experiment to explore how subjects respond to the presence or absence of illness-related costs in a given period when making their future health insurance choices when they are explicitly informed that future premiums or loss probabilities will not change over time. A sizable minority of the respondents who initially chose high-deductible plans switched after a loss, and switching is more likely if they self-report negative emotional responses to experiencing an uninsured loss. Switching from low- to high-deductible plans is less likely and less responsive to loss experience. The study reveals that many individuals make their health insurance choices by considering factors not included in classical economic models of choice.

Risk Assessment Under Perceptual Ambiguity and its Impact on Category Learning

Proper item categorization often requires prolonged learning of the value (gain or loss probabilities) associated with different items. We tested individual differences in learning strategies where the need to discriminate between two perceptually similar categories co- occurred with the need to learn the mean value of items within each category. In the primary experimental condition, participants had to learn that items in one category were always associated with a positive monetary value (+7¢ in 100% of the trials), whereas items in another, “risky” category were associated either with a larger monetary gain (+10¢ in 75% of the trials) or with a substantial loss (-27¢ in 25% of the trials). We found that even after hundreds of learning trials, participants often preferred an item from the second, risky category over an item from the always-positive-value category, despite a respective mean-value ratio of approximately 1:9 between the two categories. On the other hand, in the two other experimental conditions, where the value of the items in one category was always higher than the value of the items in the other category, participants quickly learned to identify which category of items had greater mean value, and preferred those items, as expected. The substantial overvaluing of items from a risky category (with lower mean item-value) over the always-positive-value category (with much higher mean item-value) indicates that under conditions that involve risk-taking and the transfer of knowledge between tasks, experience-based categorization decisions may substantially deviate from Bayesian inference driven by experience.

Design and Modeling of Self-Adapting MAC (SaMAC) Protocol with Inconstant Contention Loss Probabilities

Networks based on IEEE 802.11 standard are one of the main options for deployment in industrial environment. Degradation of throughput in congested networks and short-term unfairness are well-known drawbacks of 802.11 DCF and similar MAC protocols. Those shortcomings represent significant limitation in forecasted growth of wireless usage. This is especially important in industrial wireless networks (IWN) where the scalability of wireless MAC is one of the main requirements. In this paper, a novel self-adapting MAC protocol (SaMAC) is defined and mathematically modeled. SaMAC employs constrained countdown freezing enhanced with shifted window mechanism. As a result, the protocol outperforms 802.11 DCF standard as well as shifted contention window (SCW) and constrained countdown freezing (CPCF) protocols in achieved throughput, fairness, and jitter, while keeping simple implementation. Despite protocol’s simple design, it is shown that its mathematical model is extremely complex. For proposed protocol, the assumption of constant contention loss probability, which is normally used for modeling of MAC schemes, does not hold. In the presented multidimensional Markov chain model, a unique iterative method for determining contention loss probability is developed as well as a method for throughput calculation based on such a chain. Accuracy of the presented model is verified in several network scenarios. Considering the performance of the proposed protocol, authors believe that it could be of benefit to deploy it in heavily loaded wireless networks with timing constraints, such as IWNs.