Simulation of ultrasonic-vibration drawing using the finite element method (FEM)

In ultrasonic-vibration drawing, wires are drawn while ultrasonic vibration is applied to a drawing die. Prior studies provide experimental proof that ultrasonic-vibration drawing reduces drawing resistance, improves lubrication and prevents wire breakage. In the future, ultrasonic-vibration drawing is expected to contribute to the drawing of difficult-to-draw materials and operations, such as shaped wires, ultrafine wires, and the wire drawing operation in semidry or dry condition. However, a detailed analysis and understanding of the mechanism of improvement is not possible on the basis of conventional experimental observations because the ultrasonic-vibration processing phenomenon occurs at high speed. Therefore, we attempted to understand the processing mechanism of ultrasonic-vibration drawing using the finite element method (FEM). ABAQUS was used for the FEM. Drawing force and stress–strain distributions in drawn wires were analyzed. From these studies, we quantitatively clarified the mechanism of improved drawing characteristics, such as decreased drawing force.     

Modelling Intelligent Water Resources Allocation for Multi-users

This study proposes intelligent water resources allocation strategies for multiple users through hybrid artificial intelligence techniques implemented for reservoir operation optimization and water shortage rate estimation. A two-fold scheme is developed for (1) knowledge acquisition through searching input–output patterns of optimal reservoir operation by optimization methods and (2) the inference system through mapping the current input pattern to estimate the water shortage rate by artificial neural networks (ANNs). The Shihmen Reservoir in northern Taiwan is the study case. We first design nine possible water demand conditions by investigating the changes in historical water supply. With the nine designed conditions and 44-year historical 10-day reservoir inflow data collected during the growth season (3 months) of the first paddy crop, we first conduct the optimization search of reservoir operation by using the non-dominated sorting genetic algorithm-II (NSGA-II) in consideration of agricultural and public water demands simultaneously. The simulation method is used as a comparative model to the NSGA-II. Results demonstrate that the NSGA-II can suitably search the optimal water allocation series and obtain much lower seasonal water shortage rates than those of the simulation method. Then seasonal water shortage rates in response to future water demands for both sectors are estimated by using the adaptive network fuzzy inference system (ANFIS). The back-propagation neural network (BPNN) is adopted as a comparative model to the ANFIS. During model construction, future water demands, predicted monthly inflows (or seasonal inflow) of the reservoir in the next coming quarter and historical initial reservoir storages configure the input patterns while the optimal seasonal water shortage rates obtained from the NSGA-II serve as output targets (training targets) for both neural networks. Results indicate that the ANFIS and the BPNN models produce almost equally good performance in estimating water shortage rates, yet the ANFIS model produces even better stability. The reliability of the proposed scheme is further examined by scenario analysis. The scenario analysis indicates that an increase in public water demand or a decrease in agricultural water demand would bring more impacts of water supply on agricultural sectors than public sectors. Similarly, a bigger decrease in inflow amount would obviously bring more influence on agricultural sectors than public one. Consequently, given predicted inflow, decision makers can pre-experience the possible outcomes in response to competing water demands through the estimation models in order to determine adequate water supply as well as preparedness measures, if needed, for drought mitigation.     

Determination of production run time and warranty length under system maintenance and trade credits

Manufacturers offer a warranty period within which they will fix failed products at no cost to customers. Manufacturers also perform system maintenance when a system is in an out-of-control state. Suppliers provide a credit period to settle the payment to manufacturers. This study considers manufacturer's production and warranty decisions for an imperfect production system under system maintenance and trade credit. Specifically, this study uses the economic production quantity to model the decisions under system maintenance and trade credit. These decisions involve how long the production run time and warranty length should be to maximise total profit. This study provides lemmas for the conditions of optimality and develops a theorem and an algorithm for solving the problems described. Numerical examples illustrate the solution procedures and provide a variety of managerial implications. Results show that simultaneously determining production and warranty decisions is superior to only determining production. This study also discusses the effects of the related parameters on manufacturer's decisions and profits. The results of this study are a useful reference for managerial decision-making and administration.     

Database to dynamically aid probe design for virus identification.

Viral infection poses a major problem for public health, horticulture, and animal husbandry, possibly causing severe health crises and economic losses. Viral infections can be identified by the specific detection of viral sequences in many ways. The microarray approach not only tolerates sequence variations of newly evolved virus strains, but can also simultaneously diagnose many viral sequences. Many chips have so far been designed for clinical use. Most are designed for special purposes, such as typing enterovirus infection, and compare fewer than 30 different viral sequences. None considers primer design, increasing the likelihood of cross hybridization to similar sequences from other viruses. To prevent this possibility, this work establishes a platform and database that provides users with specific probes of all known viral genome sequences to facilitate the design of diagnostic chips. This work develops a system for designing probes online. A user can select any number of different viruses and set the experimental conditions such as melting temperature and length of probe. The system then returns the optimal sequences from the database. We have also developed a heuristic algorithm to calculate the probe correctness and show the correctness of the algorithm. (The system that supports probe design for identifying viruses has been published on our web page http://bioinfo.csie.ncu.edu.tw/.)     

Trihalomethane Species Forecast Using Optimization Methods: Genetic Algorithms and Simulated Annealing

Chlorination is an effective method for disinfection of drinking water. Yet chlorine is a strong oxidizing agent and easily reacts with both organic and inorganic materials. Trihalomethanes (THMs), formed as a by-product of chlorination, are carcinogenic to humans. Models can be derived from linear and nonlinear multiregression analyses to predict the THM species concentration of empirical reaction kinetic equations. The main objective of this study is to predict the concentrations of THM species by minimizing the nonlinear function, representing the errors between the measured and calculated THM concentrations, using the genetic algorithm (GA) and simulated annealing (SA). Additionally, two modifications of SA are employed. The solutions obtained from GA and SA are compared with the measured values and those obtained from a generalized reduced gradient method (GRG2). The results indicate that the proposed heuristic methods are capable of optimizing the nonlinear problem. The predicted concentrations may provide useful information for controlling the chlorination dosage necessary to assure the safety of water drinking.     

Fatigue response of APC-2 composite laminates at elevated temperatures

The response of thermoplastic AS-4/PEEK composite laminates of two lay-ups, such as cross-ply and quasi-isotropy, subjected to tension–tension (T–T) fatigue loading at elevated temperatures was investigated. It is found that the ultimate strength of cross-ply laminate is higher than that of quasi-isotropic laminate at various temperatures, so does the fatigue strength. However, the slope of normalized stress vs. cycles curves in the quasi-isotropic laminates is higher than that of the cross-ply laminates at elevated temperatures. Finally, the simple semi-empirical predictive models in statistical analysis and multiple regressions are proposed and provided for design and application purposes.     

Search for an optimal rapid charging pattern for lithium-ion batteries using ant colony system algorithm

Commercial lithium-ion batteries are playing important roles as supplies for mobile phones, laptop computers, and other electronics. In order to maximize the performance of lithium-ion batteries, an advanced rapid charging pattern is required. In this paper, an Ant-Colony-System (ACS)-based algorithm is presented. The proposed ACS-based algorithm can be integrated into a commercially available battery tester to search for the optimal rapid charging pattern. Experimental results show that the obtained rapid charging pattern is capable of charging the lithium-ion batteries to 70% capacity in 30 min. The obtained pattern also provides 25% more cycle life than the conventional constant current-constant voltage method.     

INTRAPARTICLE DIFFUSION-CONTROLLED KINETICS OF ADSORPTION OF PHENOL ON ION EXCHANGE RESINS

A multi-section method was proposed to describe the batch adsorption system under intraparticle diffusion-controlled kinetics with a nonlinear adsorption isotherm. The intraparticle diffusivity of phenol on the anion exchange resins determined by using the proposed method was compared with the values determined from D-K and modified D-K methods. It was found that the intraparticle diffusivity determined in this study was reasonable.