Cost-effectiveness Of Sailcloth Selection For Different Classes, Types And Species Of Sailing Boats

Results over of theoretical analysis for the selection of corresponding basic material of sail clothes fabric for a different classes, types and views of sailing boats are given in the article.Sailing boats needs the proper of yachting equipment, that goes both with the manipulation of boats and rigging (in accordance of views, classes and types of sailing boats). The material for sailing is elected by participants of competitions depending of the area of navigation, the strength of wind, the whole distances and boat\u27s possibilities, coming from technical criteria and parameters of the boats.Full analysis of economic efficiency is conducted and price politics of different material\u27s types for sails in accordance with its chemical composition is analyzed. The most effective type of factory fabric is certain for sewing of sails for racing boats. For a deep scientific analysis and practical realization of the investigated process an accent is done on the exposure of the most popular sailing sailcloth materials used only on the famous world regattas.These results are very important and needed foremost for the first-class skippers of modern world racing boats, to identify most practical and super persistent to the wind, the sun, salt water materials that use in sewing of racing sails. Necessary analyses and descriptions of scientific article concerning sewing of racing sails also will be useful to the yachtsmen that only begin the way in the world of cruise yachting and sporting yachting races. Importance of faithful direction at the choice of material for sails is done not only for the decision element of victories in races and regattas, but also for an answer on many questions, constrained concerning speed of boat, norm of coverage of sails and, in the end, with maneuverability, boat speed

Equine digital tendons show breed‐specific differences in their mechanical properties that may relate to athletic ability and predisposition to injury

Background Throughout the ages, human subjects have selected horse breeds for their locomotor capacities. Concurrently, tissue properties may have diversified because of specific requirements of different disciplines. Objectives The aim of this study was to compare the biomechanical properties of tendons with different functions between equine breeds traditionally selected for racing or sport. Study design This study used ex vivo tendons and compared the mechanical properties of the common digital extensor tendon (CDET) and superficial digital flexor tendon (SDFT) between racehorses (Thoroughbred [TB]) and sports horses (Friesian Horse [FH], Warmblood [WB]). Methods The SDFT and CDET of FH (n = 12), WBs (n = 12) and TBs (n = 8) aged 3-12 years were harvested. The cross sectional area (cm(2)), maximal load (N), ultimate strain (%), ultimate stress (MPa) and elastic modulus (MPa) were determined and tested for significant differences between the breeds (P<0.05). Results The SDFT from WB horses had a significantly lower elastic modulus than TB horses and failed at a higher strain and load than both FHs and TBs. The mechanical properties of the CDET did not differ between breeds. In agreement with previous studies, the CDET failed at a higher stress and had a higher elastic modulus than the SDFT and, for the WB group of horses only, failed at a significantly lower strain. Interestingly, the mode of failure differed between breeds, particularly with respect to the FHs. Main limitations The exercise history of horses used in this study was unknown and the age-range was relatively large; both these factors may have influenced the absolute properties reported in this study. Conclusions This study shows for the first time that mechanical properties of the SDFT differ between breeds. These properties are likely to be related to selection for high-speed vs. an extravagant elastic gait and may be an important indicator of performance ability. The is available in Spanish - see Supporting Informatio

On performance and perceived effort in trail runners using sensor control to generate biosynchronous music

Music has been shown to be capable of improving runners’ performance in treadmill and laboratory-based experiments. This paper evaluates a generative music system, namely HEARTBEATS, designed to create biosignal synchronous music in real-time according to an individual athlete’s heartrate or cadence (steps per minute). The tempo, melody, and timbral features of the generated music are modulated according to biosensor input from each runner using a combination of PPG (Photoplethysmography) and GPS (Global Positioning System) from a wearable sensor, synchronized via Bluetooth. We compare the relative performance of athletes listening to music with heartrate and cadence synchronous tempos, across a randomized trial (N = 54) on a trail course with 76 ft of elevation. Participants were instructed to continue until their self-reported perceived effort went beyond an 18 using the Borg rating of perceived exertion. We found that cadence-synchronous music improved performance and decreased perceived effort in male runners. For female runners, cadence synchronous music improved performance but it was heartrate synchronous music which significantly reduced perceived effort and allowed them to run the longest of all groups tested. This work has implications for the future design and implementation of novel portable music systems and in music-assisted coaching

Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

Efficient learning methods to tune algorithm parameters

This thesis focuses on the algorithm configuration problem. In particular, three efficient learning configurators are introduced to tune parameters offline. The first looks into metaoptimization, where the algorithm is expected to solve similar problem instances within varying computational budgets. Standard meta-optimization techniques have to be repeated whenever the available computational budget changes, as the parameters that work well for small budgets, may not be suitable for larger ones. The proposed Flexible Budget method can, in a single run, identify the best parameter setting for all possible computational budgets less than a specified maximum, without compromising solution quality. Hence, a lot of time is saved. This will be shown experimentally. The second regards Racing algorithms which often do not fully utilize the available computational budget to find the best parameter setting, as they may terminate whenever a single parameter remains in the race. The proposed Racing with reset can overcome this issue, and at the same time adapt Racing’s hyper-parameter α online. Experiments will show that such adaptation enables the algorithm to achieve significantly lower failure rates, compared to any fixed α set by the user. The third extends on Racing with reset by allowing it to utilize all the information gathered previously when it adapts α, it also permits Racing algorithms in general to intelligently allocate the budget in each iteration, as opposed to equally allocating it. All developed Racing algorithms are compared to two budget allocators from the Simulation Optimization literature, OCBA and CBA, and to equal allocation to demonstrate under which conditions each performs best in terms of minimizing the probability of incorrect selection

Relative Income Position and Performance: An Empirical Panel Analysis

Many studies have established that people care a great deal about their relative economic position and not solely, as standard economic theory assumes, about their absolute economic position. However, behavioral evidence is rare. This paper provides an empirical analysis on how individuals’ relative income position affects their performance. Using a unique data set for 1114 soccer players over a period of eight seasons (2833 observations), our analysis suggests that the larger the income differences within a team, the worse the performance of the soccer players is. The more the players are integrated in a particular social environment (their team), the more evident this negative effect is.Relative Income, Positional Concerns, Envy, Performance, Social Integration

COST-EFFECTIVENESS OF SAILCLOTH SELECTION FOR DIFFERENT CLASSES, TYPES AND SPECIES OF SAILING BOATS

Results over of theoretical analysis for the selection of corresponding basic material of sail clothes fabric for a different classes, types and views of sailing boats are given in the article. Sailing boats needs the proper of yachting equipment, that goes both with the manipulation of boats and rigging (in accordance of views, classes and types of sailing boats). The material for sailing is elected by participants of competitions depending of the area of navigation, the strength of wind, the whole distances and boat’s possibilities, coming from technical criteria and parameters of the boats. Full analysis of economic efficiency is conducted and price politics of different material’s types for sails in accordance with its chemical composition is analyzed. The most effective type of factory fabric is certain for sewing of sails for racing boats. For a deep scientific analysis and practical realization of the investigated process an accent is done on the exposure of the most popular sailing sailcloth materials used only on the famous world regattas. These results are very important and needed foremost for the first-class skippers of modern world racing boats, to identify most practical and super persistent to the wind, the sun, salt water materials that use in sewing of racing sails. Necessary analyses and descriptions of scientific article concerning sewing of racing sails also will be useful to the yachtsmen that only begin the way in the world of cruise yachting and sporting yachting races. Importance of faithful direction at the choice of material for sails is done not only for the decision element of victories in races and regattas, but also for an answer on many questions, constrained concerning speed of boat, norm of coverage of sails and, in the end, with maneuverability, boat speed

Transfer learning in ECG classification from human to horse using a novel parallel neural network architecture

Automatic or semi-automatic analysis of the equine electrocardiogram (eECG) is currently not possible because human or small animal ECG analysis software is unreliable due to a different ECG morphology in horses resulting from a different cardiac innervation. Both filtering, beat detection to classification for eECGs are currently poorly or not described in the literature. There are also no public databases available for eECGs as is the case for human ECGs. In this paper we propose the use of wavelet transforms for both filtering and QRS detection in eECGs. In addition, we propose a novel robust deep neural network using a parallel convolutional neural network architecture for ECG beat classification. The network was trained and tested using both the MIT-BIH arrhythmia and an own made eECG dataset with 26.440 beats on 4 classes: normal, premature ventricular contraction, premature atrial contraction and noise. The network was optimized using a genetic algorithm and an accuracy of 97.7% and 92.6% was achieved for the MIT-BIH and eECG database respectively. Afterwards, transfer learning from the MIT-BIH dataset to the eECG database was applied after which the average accuracy, recall, positive predictive value and F1 score of the network increased with an accuracy of 97.1%