Factors associated with job satisfaction among district hospital health workers in Northern Vietnam : a cross-sectional study

Background: In many developing countries, including Vietnam, little is known about job satisfaction among lower level-health staff. The purpose of this study was to assess job satisfaction and its determinants among district hospital health staff. Methods: In a cross-sectional quantitative study, 128 health staff from a rural district hospital in Northern Vietnam were approached for data collection. Regression techniques were adopted to assess factors associated with several types of job satisfaction. Results: Overall job satisfaction was moderately high, ranging from 69% to 91%. Across all dimensions, health workers showed their highest satisfaction with co-worker relationships, while, in comparison, it was much lower for their supervisor's style and relationship. However, they claimed their lowest satisfaction with compensation and benefits. In final multivariate models, females and those satisfied with knowledge, skills and job performance were most likely to be satisfied with relationships with co-workers. Staff who were married, received a low pay, who were not satisfied with supervisor style and relationships and who were not satisfied with staff training, development opportunities were least likely to be satisfied with compensation and benefits. Conclusions: The study findings highlight an important need for designing an intervention program that considers organizational factors. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd

Designingan Adaptive PID Controller for Dissolved Oxygen Control of the Activated Sludge Wastewater Treatment using Hedge Algebras

In this paper, we present the design methodology of the coefficients of adjustment of classical PI controller based on hedge algebras approach (HA - Hedge Algebras) to improve the quality of operation of the system. The adjustment works online with a wide range of adjustment enough around the value calculated by empirical methods Ziegler - Nichols. Subjects selected for trial is the controller method for dissolved oxygen in the wastewater treatment system by activated sludge method. Through system simulation in Matlab or Simulink environment at some different reference values, the results have been evaluated for quality control shows that the response time and overshoot reduce significantly, static deviation level is small. Through these results, it can be tested on the control system for more complex subjects to evaluate the effectiveness of methods and practical applications on the industrial control systems

Combination of Wavelet and MLP Neural Network for Emotion Recognition System

Emotional recognition from the EEG signal is one of the areas in which many scientists around the world have concerned. Two important issues are EEG feature extraction and EEG classification. The wavelet transform method allows the extraction of nonlinear characteristics of the data from which it is possible to derive smaller feature vector than other methods. The MLP neural network has proven to be a very effective classification method. Thus, in this paper, the authors present one method to construct a highly accurate emotional recognition system by combining the two above methods. The results based on Matlab simulations with the standard data from the international scientific community

An Investigation of the Determinants of US FDI in Developed Countries, 1982-2010

The paper uses macro panel data on US FDI in developed countries during 1982-2010 to empirically investigate the influence of host country characteristics on FDI. Differing from earlier panel data studies on FDI determinants which often impose the standard restrictions of the homogeneity of slope coefficients on the observed variables and the homogeneity of the factor loadings on the unobserved common factors in the empirical specification, this paper allows the effects of observed variables and unobserved common factors to vary across countries by using recently-introduced estimators. In this research, the data seem to support the empirical specification allowing for slope heterogeneity across countries rather more than the standard ones imposing the restrictions of slope homogeneity. Empirical results indicate that the stock of US FDI in a given FDI recipient is likely to be significantly determined by market size, lower relative tax rates, and risks in terms of the investment climate, corruption and the legal environment of the host country

An Investigation of the Determinants of US FDI in Developed Countries, 1982-2010

The paper uses macro panel data on US FDI in developed countries during 1982-2010 to empirically investigate the influence of host country characteristics on FDI. Differing from earlier panel data studies on FDI determinants which often impose the standard restrictions of the homogeneity of slope coefficients on the observed variables and the homogeneity of the factor loadings on the unobserved common factors in the empirical specification, this paper allows the effects of observed variables and unobserved common factors to vary across countries by using recently-introduced estimators. In this research, the data seem to support the empirical specification allowing for slope heterogeneity across countries rather more than the standard ones imposing the restrictions of slope homogeneity. Empirical results indicate that the stock of US FDI in a given FDI recipient is likely to be significantly determined by market size, lower relative tax rates, and risks in terms of the investment climate, corruption and the legal environment of the host country

A Study on a Model of Anchovy Solar

In central and southern coastal areas of Vietnam, annual yield of anchovy is enormous that leads the high demand for anchovy drying. Moreover, seafood in generally and anchovy in particularly brings more benefit for fishermen, especially dried anchovy as an exporting product is one of the main their income. The market requires that anchovy product has to be dried before packaging to export. There are many drying methods to process the anchovy but some problems might need to be solved such as the drying efficiency, the low product quality and sanitation, and the environmental annihilation. In order to using the profuse solar energy, a model for experiment investigation the anchovy dryer has been conducted in ThuDuc district, Hochiminh city with the anchovy caught from Kien giang and Baria-Vung tau province, southern Vietnam. The results indicate that solar energy is one of renewable energy which can be completely used for anchovy drying with high drying efficiency. The dried anchovy has good color, high quality, and especially it passes the requirements of food hygiene and environment protecting

Removal of Power Line Interference from Electrocardiograph (ECG) using Proposed Adaptive Filter Algorithm

ECG signals in measurements are contaminated by noises including power line interference. In recent years, adaptive filters with different approaches have been investigated to remove power line interference in ECG.In this paper, an adaptive filter is proposed to cancel power line interference in ECG signals. The proposed algorithm is experimented with MIT-BIH ECG signals data base. The algorithm2019;s results are compared with the results of other adaptive filter algorithms using Least Mean Square (LMS), Normalized Least Mean Square (NLMS) by Signal to Noise (SNR). Theses works are performed by LabVIEW software

Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review

Rainfall-induced landslides threaten lives and properties globally. To address this, researchers have developed various methods and models that forecast the likelihood and behavior of rainfall-induced landslides. These methodologies and models can be broadly classified into three categories: empirical, physical-based, and machine-learning approaches. However, these methods have limitations in terms of data availability, accuracy, and applicability. This paper reviews the current state-of-the-art of rainfall-induced landslide prediction methods, focusing on the methods, models, and challenges involved. The novelty of this study lies in its comprehensive analysis of existing prediction techniques and the identification of their limitations. By synthesizing a vast body of research, it highlights emerging trends and advancements, providing a holistic perspective on the subject matter. The analysis points out that future research opportunities lie in interdisciplinary collaborations, advanced data integration, remote sensing, climate change impact analysis, numerical modeling, real-time monitoring, and machine learning improvements. In conclusion, the prediction of rainfall-induced landslides is a complex and multifaceted challenge, and no single approach is universally superior. Integrating different methods and leveraging emerging technologies offer the best way forward for improving accuracy and reliability in landslide prediction, ultimately enhancing our ability to manage and mitigate this geohazard

Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review

Rainfall-induced landslides threaten lives and properties globally. To address this, researchers have developed various methods and models that forecast the likelihood and behavior of rainfall-induced landslides. These methodologies and models can be broadly classified into three categories: empirical, physical-based, and machine-learning approaches. However, these methods have limitations in terms of data availability, accuracy, and applicability. This paper reviews the current state-of-the-art of rainfall-induced landslide prediction methods, focusing on the methods, models, and challenges involved. The novelty of this study lies in its comprehensive analysis of existing prediction techniques and the identification of their limitations. By synthesizing a vast body of research, it highlights emerging trends and advancements, providing a holistic perspective on the subject matter. The analysis points out that future research opportunities lie in interdisciplinary collaborations, advanced data integration, remote sensing, climate change impact analysis, numerical modeling, real-time monitoring, and machine learning improvements. In conclusion, the prediction of rainfall-induced landslides is a complex and multifaceted challenge, and no single approach is universally superior. Integrating different methods and leveraging emerging technologies offer the best way forward for improving accuracy and reliability in landslide prediction, ultimately enhancing our ability to manage and mitigate this geohazard