Leadership Practice for Building Trust of Followers: Decisive Factors of Organizational Performance

Purpose - Leadership plays a decisive role in key organizational outcomes. To investigate the effects of leadership and its mechanism on organizational performance, this paper examined how transformational leadership impacts followers’ trust for improving operational and financial performance in the case of Vietnamese firms. Design/Methodology - This study used the Structural Equation Modeling to assess the correlation among the constructs based on using survey data of 285 employees at 60 manufacturing and service companies. Findings - The findings revealed that employees' trust in leadership acts as a mediating mechanism in the relationship between transformational leadership and organizational performance. The transformational leadership style of leaders has greater effects on financial performance, while employee’s trust has larger effects on operational performance. This study calls attention to the importance of raising employee trust to link transformational leadership and organizational performance. Practical and theoretical implications - From a practical point of view, the paper brings more knowledge and insights for CEO/managers on the right pathway to enhance organizational performance. The paper also provides theoretical initiatives on the leadership theory and the new pathway to promote operational and financial performance

Synthesis of Hybrid Fuzzy Logic Law for Stable Control of Magnetic Levitation System

In this paper, we present a method to design a hybrid fuzzy logic controller (FLC) for a magnetic levitation system (MLS) based on the linear feedforward control method combined with FLC. MLS has many applications in industry, transportation, but the system is strongly nonlinear and unstable at equilibrium. The fast response linear control law ensures that the ball is kept at the desired point, but does not remain stable at that point in the presence of noise or deviation from the desired position. The controller that combines linear feedforward control and FLC is designed to ensure ball stability and increase the system's fast-response when deviating from equilibrium and improve control quality. Simulation results in the presence of noise show that the proposed control law has a fast and stable effect on external noise. The advantages of the proposed controller are shown through the comparison results with conventional PID and FLC control laws

THE QUALITY WATER ENVIRONMENT HAPPENING OF THE HUONG RIVER IN THE HUE CITY, PERIOD OF 2003-2006

Joint Research on Environmental Science and Technology for the Eart

Effects of drought stress on growth and flavonoid accumulation of fish mint (Houttuynia cordata Thumb.)

Fish mint (Houttuynia cordata Thumb.) is a popular medicinal plant grown primarily because of its pharmacological values. Drought stress has on the relationship between growth and physio-biochemical changes, especially flavonoid content. The impacts of various drought stress conditions on the fish mint development were investigated, including 85% of field capacity (FC), 75% FC, 65% FC and 55% FC in 14, 21 and 28 days. Agronomic, physiological and biochemical parameters during the growth of fish mint plants under drought stress conditions were assessed. According to the results of variance analysis, drought stress results in a considerable drop in the measured parameters (shoot height, leaf number, leaf area and fresh weight). Similarly, all of the above-mentioned parameters were also decreased with increasing the number of drought days. Furthermore, drought period and level caused a drop in respiration, photosynthetic rate, chlorophyll and starch content. The concentration of carotenoids and flavonoids, on the other hand, increased dramatically as drought stress periods and levels increased. In comparison to the control, the drought treatment (65% FC) in 7 days maintained the growth rate and increased flavonoid accumulation from 2.42 mg to 3.04 mg. These findings might give a scientific foundation for growing fish mint plants under drought stress to boost flavonoid content

Improving Pareto Front Learning via Multi-Sample Hypernetworks

Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front.Comment: Accepted to AAAI-2

A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications

Pareto Front Learning (PFL) was recently introduced as an efficient method for approximating the entire Pareto front, the set of all optimal solutions to a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping between a preference vector and a Pareto optimal solution is still ambiguous, rendering its results. This study demonstrates the convergence and completion aspects of solving MOO with pseudoconvex scalarization functions and combines them into Hypernetwork in order to offer a comprehensive framework for PFL, called Controllable Pareto Front Learning. Extensive experiments demonstrate that our approach is highly accurate and significantly less computationally expensive than prior methods in term of inference time.Comment: Under Review at Neural Networks Journa