Improved Quantum-Inspired Evolutionary Algorithm for Engineering Design Optimization

An improved quantum-inspired evolutionary algorithm is proposed for solving mixed discrete-continuous nonlinear problems in engineering design. The proposed Latin square quantum-inspired evolutionary algorithm (LSQEA) combines Latin squares and quantum-inspired genetic algorithm (QGA). The novel contribution of the proposed LSQEA is the use of a QGA to explore the optimal feasible region in macrospace and the use of a systematic reasoning mechanism of the Latin square to exploit the better solution in microspace. By combining the advantages of exploration and exploitation, the LSQEA provides higher computational efficiency and robustness compared to QGA and real-coded GA when solving global numerical optimization problems with continuous variables. Additionally, the proposed LSQEA approach effectively solves mixed discrete-continuous nonlinear design optimization problems in which the design variables are integers, discrete values, and continuous values. The computational experiments show that the proposed LSQEA approach obtains better results compared to existing methods reported in the literature

Hybrid Taguchi-Differential Evolution Algorithm for Parameter Estimation of Differential Equation Models with Application to HIV Dynamics

This work emphasizes solving the problem of parameter estimation for a human immunodeficiency virus (HIV) dynamical model by using an improved differential evolution, which is called the hybrid Taguchi-differential evolution (HTDE). The HTDE, used to estimate parameters of an HIV dynamical model, can provide robust optimal solutions. In this work, the HTDE approach is effectively applied to solve the problem of parameter estimation for an HIV dynamical model and is also compared with the traditional differential evolution (DE) approach and the numerical methods presented in the literature. An illustrative example shows that the proposed HTDE gives an effective and robust way for obtaining optimal solution, and can get better results than the traditional DE approach and the numerical methods presented in the literature for an HIV dynamical model

Effects of job rotation and role stress among nurses on job satisfaction and organizational commitment

<p>Abstract</p> <p>Background</p> <p>The motivation for this study was to investigate how role stress among nurses could affect their job satisfaction and organizational commitment, and whether the job rotation system might encourage nurses to understand, relate to and share the vision of the organization, consequently increasing their job satisfaction and stimulating them to willingly remain in their jobs and commit themselves to the organization. Despite the fact that there have been plenty of studies on job satisfaction, none was specifically addressed to integrate the relational model of job rotation, role stress, job satisfaction, and organizational commitment among nurses.</p> <p>Methods</p> <p>With top managerial hospital administration's consent, questionnaires were only distributed to those nurses who had had job rotation experience. 650 copies of the questionnaire in two large and influential hospitals in southern Taiwan were distributed, among which 532 valid copies were retrieved with a response rate of 81.8%. Finally, the SPSS 11.0 and LISREL 8.54 (Linear Structural Relationship Model) statistical software packages were used for data analysis and processing.</p> <p>Results</p> <p>According to the nurses' views, the findings are as follows: (1) job rotation among nurses could have an effect on their job satisfaction; (2) job rotation could have an effect on organizational commitment; (3) job satisfaction could have a positive effect on organizational commitment; (4) role stress among nurses could have a negative effect on their job satisfaction; and (5) role stress could have a negative effect on their organizational commitment.</p> <p>Conclusion</p> <p>As a practical and excellent strategy for manpower utilization, a hospital could promote the benefits of job rotation to both individuals and the hospital while implementing job rotation periodically and fairly. And when a medical organization attempts to enhance nurses' commitment to the organization, the findings suggest that reduction of role ambiguity in role stress has the best effect on enhancing nurses' organizational commitment. The ultimate goal is to increase nurses' job satisfaction and encourage them to stay in their career. This would avoid the vicious circle of high turnover, which is wasteful of the organization's valuable human resources.</p

Randomized Comparative Study of the Effects of Treatment with Once-Daily, Niacin Extended-Release/Lovastatin and with Simvastatin on Lipid Profile and Fibrinolytic Parameters in Taiwan

Hyperlipidemia can be effectively treated either with niacin or HMG-CoA reductase inhibitor (statin), or a combination of both. Few reports showed the effects of the combination regimen with niacin and statin on hemostatic functions. We conducted a single-center, double-blind, double-dummy, randomized, two-arm study to assess the effects of the niacin extended-release/lovastatin therapy in a fixed-dose formulation and of simvastatin on lipid lowering and two fibrinolytic parameters, fibrinogen and d-dimer. All patients were enrolled according to NCEP-ATP III guidelines and underwent a placebo run-in period of 4 weeks before being randomized to either niacin extended-release/lovastatin tablets (500/20 mg) once daily (n = 36) or simvastatin capsule (20 mg) once daily (n = 34). After 16 weeks of treatment, both groups of patients showed significantly reduced low-density lipoprotein cholesterol and total cholesterol (LDL-C, p < 0.001 and < 0.001, respectively, p = 0.159 between the groups; TC, p < 0.001 and < 0.001, respectively, p = 0.018 between the groups). Both drugs were well tolerated. Only in the group treated with niacin extended-release/lovastatin was fibrinogen concentration significantly reduced after treatment (2.48 ± 0.65 to 1.99 ± 0.62 g/L, p = 0.008). No difference was found with d-dimer in either group. This study shows that both niacin extended-release/ lovastatin and simvastatin are effective and well-tolerated lipid-lowering drugs in Taiwanese patients with dyslipidemia. A combinational treatment with niacin extended-release/lovastatin may provide additional benefit in fibrinolysis

Comparison of Classification Algorithms with Wrapper-Based Feature Selection for Predicting Osteoporosis Outcome Based on Genetic Factors in a Taiwanese Women Population

An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data

Long-Term Prediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving Average Model

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation

First Confirmed Detection of a Bipolar Molecular Outflow from a Young Brown Dwarf

Studying the earliest stages in the birth of stars is crucial for understanding how they form. Brown dwarfs with masses between that of stars and planets are not massive enough to maintain stable hydrogen-burning fusion reactions during most of their lifetime. Their origins are subject to much debate in recent literature because their masses are far below the typical mass where core collapse is expected to occur. We present the first confirmed evidence that brown dwarfs undergo a phase of molecular outflow that is typical of young stars. Using the Submillimeter Array, we have obtained a map of a bipolar molecular outflow from a young brown dwarf. We estimate an outflow mass of 1.6 x 10^-4 M_Sun and a mass-loss rate of 1.4 x 10^-9 M_Sun. These values are over two orders of magnitude smaller than the typical ones for T Tauri stars. From our millimiter continuum data and our own analysis of Spitzer infrared photometry, we estimate that the brown dwarf has a disk with a mass of 8 x 10^-3 M_Sun and an outer disk radius of 80 AU. Our results demonstrate that the bipolar molecular outflow operates down to planetary masses, occurring in brown dwarfs as a scaled-down version of the universal process seen in young stars.Comment: accepted by ApJ Letter

Performance Improvements of Selective Emitters by Laser Openings on Large-Area Multicrystalline Si Solar Cells

This study focuses on the laser opening technique used to form a selective emitter (SE) structure on multicrystalline silicon (mc-Si). This technique can be used in the large-area (156 × 156 mm2) solar cells. SE process of this investigation was performed using 3 samples SE1–SE3. Laser fluences can vary in range of 2–5 J/cm2. The optimal conversion efficiency of 15.95% is obtained with the SE3 (2 J/cm2 fluence) after laser opening with optimization of heavy and light dopant, which yields a gain of 0.48%abs compared with that of a reference cell (without fluence). In addition, this optimal SE3 cell displays improved characteristics compared with other cells with a higher average value of external quantum efficiency (EQEavg = 68.6%) and a lower average value of power loss (Ploss = 2.33 mW/cm2). For the fabrication of solar cells, the laser opening process comprises fewer steps than traditional photolithography does. Furthermore, the laser opening process decreases consumption of chemical materials; therefore, the laser opening process decreases both time and cost. Therefore, SE process is simple, cheap, and suitable for commercialization. Moreover, the prominent features of the process render it effective means to promote overall performance in the photovoltaic industry