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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]

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]