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In recent years, the need for emergency resources has dramatically increased and it has caused an overcrowding problem for the emergency department (ED). Solving this problem by increasing medical resources is either impractical or infeasible. Thus, this manuscript develops a multi-objective mathematical model to allocate medical resources for the emergency department (ED). The optimal resource allocation is exploited by using some meta-heuristic algorithms, i.e., fast and elitism non-dominated sorting genetic algorithm (NSGA II), non-dominated sorting particle swarm algorithm (NSPSO), and non-dominated sorting differential evolution (NSDE). Thereafter, a dynamic simulation model, which embeds the solutions from the resource allocation model in the simulation process, is constructed. Each feasible solution from the three meta-heuristic algorithms is simulated to estimate the performance of the resources allocated in terms of the average service level and staff utilization. The results show that the performance of the NSGAII, where the average service level and staff utilization for the current resources are 0.844 and 0.751, respectively, is better than those of NSPSO and NSDE. Besides, the number of medical staff gives a significant effect on the service level and utilization while the number of beds only impacts staff utilization. The simulation model can find out that the best combination of the number of staff and the number of beds is from 1 to 10 staffs and 1–6 beds to maximize the utilization and service level. [ABSTRACT FROM AUTHOR]

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The offshore basins in the East Asian Continental Shelf have undergone distinct tectono-geomorphic-sedimentary evolution since the Neogene, driven by complex interactions between tectonic and surface Earth processes. However, the contribution of the latter to the evolution of the offshore basins has been less studied. The landscape simulation software Badlands serves as an ideal tool to investigate the impacts of tectonic and surface processes on sedimentation as it simulates the sediment routing in a source-to-sink context. Focusing on the southern East China Sea Basin (ECSB) and the northern South China Sea Shelf Basin (SCSSB), two key components of the East Asian Continental Shelf, we used Badlands to reconstruct the tectono-geomorphic-sedimentary evolution since the Neogene. It revealed two-phase evolutionary processes: (1) the southern ECSB and the northern SCSSB were in the unified shelf environment and represented as an integral during the Early Miocene, though the northern SCSSB experienced anomalous subsidence due to the southward jump of the SCS mid-ocean ridge. The integrated southern ECSB and northern SCSSB experienced uplift and denudation since the Middle Miocene, under the influence of the Philippine Sea Plate extrusion. (2) Subsequently, the southern ECSB and the northern SCSSB were separated into two independent basins in the Late Miocene, when Taiwan Island was significantly uplifted due to the Philippine Sea Plate extrusion and the arc-continental collision. Then Taiwan Island experienced a multi-stage uplift characterized by southward and westward migration. It concluded that tectonics (i.e. Taiwan Orogeny and the subduction of the Philippine Sea Plate) primarily dominated the differential evolution of the southern ECSB and the northern SCSSB. Surface Earth processes (i.e. the intensification of the East Asian Monsoon and the sea-level fluctuation) are the secondary contributors. • The southern ECSB and the northern SCSSB were an integral during the early Miocene. • The integral was divided into two independent basins due to Taiwan Orogeny. • Taiwan orogeny is characterized by diachronous westward and southward migration. [ABSTRACT FROM AUTHOR]

Background: A country's ability to become a prominent knowledge economy is tied closely to its ability to acquire skilled people who can compete internationally while resolving challenges of the future. To equip students with competence that can only by gained by being immersed in a foreign environment, outbound mobility is vital. Methods: To analyze outbound student mobility in Taiwan using time series methods, this study aims to propose a hybrid approach FSDESVR which combines feature selection (FS) and support vector regression (SVR) with differential evolution (DE). FS and a DE algorithm were used for selecting reliable input features and determining the optimal initial parameters of SVR, respectively, to achieve high forecast accuracy. Results: The proposed approach was examined using a dataset of outbound Taiwanese student mobility to ten countries between 1998 and 2018. Without the requirements of any special conditions for the proprieties of the objective function and constraints, the FSDESVR model retained the advantage of FS, SVR, and DE. A comparison of the FSDESVR model and other forecasting models revealed that FSDESVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) results for all the analyzed nations. The experimental results indicate that FSDESVR achieved higher forecasting accuracy than the compared models from the literature. Conclusion: With the recognition of outbound values, key findings of Taiwanese outbound student mobility, and accurate application of the FSDESVR model, education administration units are exposed to a more in-depth view of future student mobility, which enables the implement of a more accurate education curriculum. [ABSTRACT FROM AUTHOR]

Parameter structure identification is formulated in terms of solving an inverse problem, which allows for a determination of an appropriate level of parameter structure complexity, and the identification of its pattern and the associated parameter values. With the increasing complexity of parameter structure identification in groundwater modeling, demand for robust, fast, and accurate optimizers is on the rise among researchers from groundwater hydrology fields. A novel global optimizer, differential evolution (DE), has been proposed to solve the parameter-structure-identification problem. The Voronoi tessellation is adopted for the automatic parameterization. The stepwise regression method and the error covariance matrix are used to determine the optimal structure complexity. Numerical experiments with a continuous hydraulic conductivity distribution are conducted to demonstrate the proposed methodology. The results indicate that the DE can identify the global optimum effectively and efficiently. A sensitivity analysis of the control parameters and mutation schemes implemented in the DE is employed to examine their influence on the objective function. The comparison between DE and genetic algorithm shows the advantage of DE in terms of robustness and efficiency. The proposed methodology is also applied to a real groundwater system, Pingtung Plain in Taiwan, and the properties of aquifers are successfully identified. [ABSTRACT FROM AUTHOR]

Abstract: In construction industry, cost escalation is a major cause of project failure. The reason is that construction prices fluctuate over time and they are influenced by numerous factors. This study establishes a hybrid intelligence system, named as ELSVM, for modeling construction price variations quantified by the construction cost index (CCI). The proposed method is developed based on the fusion of Least Squares Support Vector Machine (LS-SVM) and Differential Evolution (DE). LS-SVM is used for mining the mapping function between CCI and its causative input factors. Meanwhile, DE is employed to optimize LS-SVM tuning parameters. In this research, a database consisting of 122 historical cases was collected to establish the intelligence system. Based on experimental results, ELSVM can successfully model CCI fluctuations since it has achieved a comparatively low MAPE which is less than 1%. Thus, the proposed system can be a promising tool to assist decision-makers in construction management. [Copyright &y& Elsevier]

Forecasting is important in the decision-making process. Therefore, among many forecasting algorithms that have been developed, fuzzy neural network (FNN) has been widely applied. The FNN structure is commonly trained using a back-propagation algorithm. However, this algorithm is sensitive to the initial weights and high computation, especially for complex problems. Thus, this study intended to overcome these drawbacks by applying the gradient evolution (GE) algorithm to train the intuitionistic FNN (IFNN). The proposed algorithm, GEIFNN, was verified using ten benchmark datasets. The results were compared with some other metaheuristic-based IFNN algorithms, such as genetic algorithm, particle swarm optimization algorithm, and differential evolution algorithm. The computational results show that the proposed GEIFNN relatively outperformed other tested algorithms, especially in training error. Furthermore, the proposed algorithm was applied to forecast medical costs for the treatment of acute hepatitis in Taiwan. The result also shows that GEIFNN can obtain the smallest training error. • Improve backpropagation on intuitionistic fuzzy neural network using a gradient evolution algorithm. • Verify the proposed algorithm with ten benchmark datasets. • Computational results show that the proposed algorithm gives promising results. • Apply the proposed algorithm to forecast medical cost for acute hepatitis treatment in Taiwan. • The contributions are developing GEIFNN algorithm and solving a real problem. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a variable scaling hybrid differential evolution (VSHDE) for solving the economic dispatch (ED) problem in large-scale systems. Different from the hybrid differential evolution (HDE), the concept of a variable scaling factor is used in the VSHDE method. The variable scaling factor based on 1/5 success rule of evolution strategies (ESs) is used to overcome the drawback of the fixed and random scaling factor used in the HDE method. One 15-unit system from the literature and one practical economic dispatch system of Taiwan power company (TPC) are used to compare the performance of the proposed method with HDE, genetic algorithms (GAs) and simulated annealing (SA). Numerical results show that the performance of the proposed method is better than the other methods. [Copyright &y& Elsevier]

The high penetration of distributed generation in distributed energy systems causes the variation of power loss and makes the power grid become more complicated, so this paper takes various types of optimal algorithms into account and simulates the feeder reconfiguration on the IEEE-33 system as well as the Taiwan power system. The simulation verifies linear population size reduction of successful history-based adaptive differential evolution (L-SHADE) and particle swarm optimization (PSO) fitness in different systems and provides the recommended location of distributed energy. The proposed method keeps the voltage bound of 0.95 to 1.03 p.u. of Taiwan regulation. In the IEEE-33 system, we achieved a 52.57% power loss reduction after feeder reconfiguration, and a 70.55% power loss reduction after the distributed generator was implemented and feeder reconfiguration. Under the variation of load demand and power generation of the Taiwan power system, we establish the system models by forecasting one-day load demand. Then, we propose a one-day feeder switch operation strategy by considering the switches' operation frequency with the reduction of 83.3% manual operation and recommend feeder automation to achieve feeder power loss reduction, voltage profile improvement and get regional power grid resilient configuration. [ABSTRACT FROM AUTHOR]