THE MANAGEMENT OF SPORTS RESOURCES IN HOCHIMINH NATIONAL UNIVERSITY, VIETNAM

Governance plays a very important role in most social organizations. For any organization, company, or a country, a community, the role of governance becomes more important than ever. For schools, the term management is somewhat strange to some people, especially the term “sports management”, which attracts little interest from the leadership. But sport in school is also one of the school's organizations to meet the goal of comprehensive human education. Therefore, the organization of the school requires good governance and management, which is considered to be an essential part to the success of education. Good governance will help the physical training and sport activities to improve the quality of physical training and sports, enhance the movement of physical training and sports activities and especially find the sports talent for the country.  Article visualizations

How to manage business ethics effectively?

The main purpose of this paper is exploring ways to manage business ethics effectively. To achieve this, first of all, the paper reviews the concepts and importance of business ethics together with its components, such as corporate ethics codes and corporate social responsibility (CSR). Next, five ways to manage business ethics efficiently are revealed. Based on these suggested ways, the paper recommends four practical actions for managers to have good management skills in this field. These recommendations are setting up an effective corporate ethics code, acting and behaving ethically in any circumstances, setting up rules and regulations, and advancing CSR in a very wise way. The paper concludes with two issues for future researchers: whether corporations need a business ethics manager/ specialist, and how companies motivate their employees to act ethicall

Seed priming with sodium nitroprusside enhances the growth of peanuts (Arachis hypogaea L.) under drought stress

Peanuts are a nutrient-dense legume with high lipid, protein, vitamin and mineral content. Peanut development is harmed by drought stress, particularly during the germination and seedling stages. Finding ways to mitigate the impacts of drought stress will have positive effects on peanut production. Seed priming, a short-gun strategy for modulating the impact of abiotic stressors on agricultural plants, has lately piqued the attention of researchers to instill drought tolerance in important crops. In this study, peanut seeds (VD01-2 cultivar) were used as material to investigate the role of priming with sodium nitroprusside at different concentrations (10, 15, 20 and 25 mg L-1) in preventing the damage of peanuts triggered by drought stress. Morphological, physiological and biochemical changes during the development of peanuts in the drought stress condition were analyzed. The results show that moderate drought stress (60% of field capacity) reduced germination and seedling growth. Drought stress reduced relative water content, photosynthesis, and the content of chlorophyll and starch significantly over the control. Seed priming with 20 mg L-1 sodium nitroprusside was effective in increasing these above mentioned growth parameters. Further, the priming of 20 mg L-1 sodium nitroprusside enhanced respiration rate and carotenoid, soluble sugar and proline content compared to the control

Monitoring Heart Rate Variability Based on Self-powered ECG Sensor Tag

This paper proposes a batteryless sensing and computational device to collect and process electrocardiography (ECG) signals for monitoring heart rate variability (HRV). The proposed system comprises of a passive UHF radio frequency identification (RFID) tag, an extreme low power microcontroller, a low-power ECG circuit, and a radio frequency (RF) energy harvester. The microcontroller and ECG circuits consume less power of only ~30 µA and ~3 mA, respectively. Therefore, the proposed RF harvester operating at frequency band of 902 MHz ~ 928 MHz can sufficiently collect available energy from the RFID reader to supply power to the system within a maximum distance of ~2 m. To extract R-peak of the ECG signal, a robust algorithm that consumes less time processing is also developed. The information of R-peaks is stored into an Electronic Product Code (EPC) Class 1st Generation 1st compliant ID of the tag and read by the reader. This reader is functioned to collected the R-peak data with sampling rate of 100ms; therefore, the user application can monitor fully range of HRV. The performance of the proposed system shows that this study can provide a good solution in paving the way to new classes of healthcare applications

The state of the art of battery charging infrastructure for electrical vehicles: Topologies, power control strategies, and future trend

Electric vehicle battery (EVB) charger topologies play a vital role to increase the penetration of EVs. This paper reviews the status quo of EV battery (EVB) chargers in term of converter topologies, operation modes, and power control strategies for EVs. EVB Chargers are classified based on their power levels and power flow direction. Referring to power ratings, EV chargers can be divided into Level 1, Level 2 and Level 3. Level 1 and Level 2 are normally compatible with on-board chargers while Level 3 is used for an off-board charger. Unidirectional/bidirectional power flow can be obtained at all power levels. However, bidirectional power flow is usually designed for Level 3 chargers as it can provide the huge benefit of transferring power back to grid when needed. Moreover, the different operation modes of an EVB charger are also presented. There are two main modes: Grid-to-Vehicle (V1G or G2V) and Vehicle-to-Grid (V2G). The V2G mode helps bring EV batteries to become active distributed sources in smart grids and is the crucial solution for a high EV penetration. Future trend and authors\u27 recommendations with preliminary simulation and experimental results are demonstrated in this paper

A neurodynamic approach for a class of pseudoconvex semivectorial bilevel optimization problem

The article proposes an exact approach to find the global solution of a nonconvex semivectorial bilevel optimization problem, where the objective functions at each level are pseudoconvex, and the constraints are quasiconvex. Due to its non-convexity, this problem is challenging, but it attracts more and more interest because of its practical applications. The algorithm is developed based on monotonic optimization combined with a recent neurodynamic approach, where the solution set of the lower-level problem is inner approximated by copolyblocks in outcome space. From that, the upper-level problem is solved using the branch-and-bound method. Finding the bounds is converted to pseudoconvex programming problems, which are solved using the neurodynamic method. The algorithm's convergence is proved, and computational experiments are implemented to demonstrate the accuracy of the proposed approach

Damage detection for a cable-stayed Bridge under the effect of moving loads using Transmissibility and Artificial Neural Network

Artificial Neural Network (ANN) has been widely used for Structural Health Monitoring (SHM) in the last decades. To detect damage in the structure, ANN often uses input data consisting of natural frequencies or mode shapes. However, this data is not sensitive enough to accurately identify minor structural defects. Therefore, in this study, we propose to use transmissibility to generate input data for the input layer of ANN. Transmissibility uses output signals exclusively to preserve structural dynamic properties and is sensitive to damage characteristics. To evaluate the efficiency of the proposed approach, a cable-stayed bridge with a wide variety of damage scenarios is employed. The results show that the combination of transmissibility and ANN not only accurately detect damages but also outperforms natural frequencies-based ANN in terms of accuracy and computational cost

Damage detection for a cable-stayed Bridge under the effect of moving loads using Transmissibility and Artificial Neural Network

Artificial Neural Network (ANN) has been widely used for Structural Health Monitoring (SHM) in the last decades. To detect damage in the structure, ANN often uses input data consisting of natural frequencies or mode shapes. However, this data is not sensitive enough to accurately identify minor structural defects. Therefore, in this study, we propose to use transmissibility to generate input data for the input layer of ANN. Transmissibility uses output signals exclusively to preserve structural dynamic properties and is sensitive to damage characteristics. To evaluate the efficiency of the proposed approach, a cable-stayed bridge with a wide variety of damage scenarios is employed. The results show that the combination of transmissibility and ANN not only accurately detect damages but also outperforms natural frequencies-based ANN in terms of accuracy and computational cost

NITROGEN AMMONIA REMOVAL FROM GROUNDWATER BY NITRIFICATION-DENITRIFICATION PROCESS WITH A NOVEL ACRYL BIOFILM CARRIER MATERIAL

Joint Research on Environmental Science and Technology for the Eart