The supplementation of sodium bicarbonate (NaHCO3) could increase performance or delay fatigue in intermittent high-intensity exercise. Prolonged tennis matches result in fatigue, which impairs skilled performance. The aim of this study was to investigate the effect of NaHCO3 supplementation on skilled tennis performance after a simulated match. Nine male college tennis players were recruited for this randomized cross-over, placebo-controlled, double-blind study. The participants consumed NaHCO3 (0.3 g. kg-1) or NaCl (0.209 g. kg-1) before the trial. An additional supplementation of 0.1 g. kg-1 NaHCO3 or 0.07 g. kg-1 NaCl was ingested after the third game in the simulated match. The Loughborough Tennis Skill Test was performed before and after the simulated match. Post-match [HCO3-] and base excess were significantly higher in the bicarbonate trial than those in the placebo trial. Blood [lactate] was significantly increased in the placebo (pre: 1.22 ± 0.54; post: 2.17 ± 1.46 mM) and bicarbonate (pre: 1.23 ± 0.41; post: 3.21 ± 1.89 mM) trials. The match-induced change in blood [lactate] was significantly higher in the bicarbonate trial. Blood pH remained unchanged in the placebo trial (pre: 7.37 ± 0.32; post: 7.37 ± 0.14) but was significantly increased in the bicarbonate trial (pre: 7.37 ± 0.26; post: 7.45 ± 0.63), indicating a more alkaline environment. The service and forehand ground stroke consistency scores were declined significantly after the simulated match in the placebo trial, while they were maintained in the bicarbonate trial. The match-induced declines in the consistency scores were significantly larger in the placebo trial than those in the bicarbonate trial. This study suggested that NaHCO3 supplementation could prevent the decline in skilled tennis performance after a simulated match.
Object tracking in wireless sensor networks is to track mobile objects by scattered sensors. These sensors are typically organized into a tree to deliver report messages upon detecting object's move. Existing tree construction algorithms all require a mobility profile that characterizes the movement statistics of the target object. Mobility profiles are generally obtained based on historical running traces. The contribution of this work is twofold. We first show that the problem of finding an optimal message report tree that requires the least amount of report messages is NP-hard. We then propose analytic estimates of mobility profiles based on Markov-chain model. This profiling replaces an otherwise experimental process that generates and analyzes running traces. Simulation results show that the analytic profiling works well and can replace costly statistical profiling without noticeable performance degradation.
Cybersecurity is the biggest threat in the world. More and more people are used to storing personal data on a computer and transmitting it through the Internet. Cybersecurity will be an important issue that everyone continues to pay attention to. One of the most serious problems recently is the prevalence of ransomware, especially crypto-ransomware. Unlike ordinary attacks, crypto-ransomware does not control the victim's computer and steal important data. It focuses on encrypting all data and asking victims to provide ransom to decrypt the data. Currently, many studies focus on various aspects of ransomware, including filebased, behavior-based, and network-based ransomware detection method, and use machine learning to build detection models. In addition to the above research, we found that attackers have begun to develop a new method to encrypt data. It will not only increase the speed of data encryption but also reduce the detection rate in the existing detection system. In any case, we are still facing ransomware dangers, as it is hard to recognize and forestall ransomware executing obscure malicious programs. In other words, user data will be sabotaged as soon as the computer cannot detect the ransomware. To solve the problem, detecting files instead of detecting the executable program might be helpful to establish the backup system immediately before ransomware encrypts all of the user files. We analyze the 22 formats of the encrypted files, extract the specific features and use the Support Vector Machine to distinguish between encrypted and unencrypted files. Conducted analysis results confirm that our method has high performance and can maintain the detection rate of 85.17% (where the detection rate of SVM kernel Trick (Poly) exceeds 92%).
Under the trend of fl ourishing development and rapid transformation in global fi nancial markets, the domestic fi nancial service industry needs to adjust their strategies more actively to accommodate to such a global trend. As these changes can not be implemented without manpower, the importance of human resources is more signifi cant then ever. The human resource management performance plays a vital role in reinforcing a company's competitive advantage. The way to measure human resource performance is an essential part in evaluating competitiveness. Academics and professionals have pay attention to developing the model measuring human resource performance. The purpose of this paper, therefore, is to develop a human resource performance measurement model that is suitable for Taiwan's fi nancial service industry through the collection and summarization of the literatures about human resource management, performance evaluation, balance scorecard and human resource performance, in conjunction with expert survey and analytic hierarchy process. The measurement model in this paper consists of four constructs: fi nance, customer, operation, and learning. Each construct is divided into two or three sub-constructs, and consists of some indices. The corresponding weights for each construct and index are computed by using the method of analytic hierarchy process. The results show that the order of importance for measurement constructs is customer, learning, fi nance, and operation. Among measurement indices, the index of employees' satisfaction on their jobs has the highest weight of importance. The results of this paper can provide some references for the fi nancial service industry while they measure human resource performance.
Virtual reality may provide reliable simulation for object interactions, when embedding virtual objects with artificial attributes to emulated physical and mechanical properties. This paper presents an Attributed Boundary Model (ABM) that emulates realtime and realistic deformations for VR applications. In the ABM, the physical, mechanical, topological and geometrical properties of the solid are extracted and packed into a set of attribute matrices that generate equivalent displacements for its boundary model upon contact with other virtual objects. The attribute matrices are created by training the back propagation artificial neural network with displacement data provided by finite element method (FEM). These matrices are then encoded into the deformation behavioral module for the VR engine to perform realtime interactions in the virtual space. Implementations are built on Virtools using XEON PC with nVIDIA Quadro4 display device. The result shows that the speed of the animation is better than 30 FPS and the deviation from calculation is less than 1%.
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