Piezoelectric actuators (PEAs) have been widely used in micro- and nanopositioning applications due to their fine resolution, fast responses, and large actuating forces. However, the existence of nonlinearities such as hysteresis makes modeling and control of PEAs challenging. This paper reviews the recent achievements in modeling and control of piezoelectric actuators. Specifically, various methods for modeling linear and nonlinear behaviors of PEAs, including vibration dynamics, hysteresis, and creep, are examined; and the issues involved are identified. In the control of PEAs as applied to positioning, a review of various control schemes of both model-based and non-model-based is presented along with their limitations. The challenges associated with the control problem are also discussed. This paper is concluded with the emerging issues identified in modeling and control of PEAs for future research.

In hot-dip galvanizing lines, undesirable vibration of the moving steel strip occurs due to the impingement of the high-speed turbulence jet, which leads to non-uniformity of zinc thickness, as well as splash of the liquid zinc. In this paper, the turbulent jet flow field is firstly numerically obtained using the CFD method. Then, the influence of the turbulent jet flow on the steel strip is simplified as a harmonic force at the impingement line, and the response of the steel strip is obtained by means of finite element analysis for different strip lengths, thickness and pretension forces. Influences of impingement distance, air knife slot gap and jet pressure, on vibration of the steel strip are also analyzed. The results will provide theoretical basis for the reduction of steel strip vibration in continuous hot-dip galvanizing process.

Most of the human genetic variations are single nucleotide polymorphisms (SNPs), and among them, non-synonymous SNPs, also known as SAPs, attract extensive interest. SAPs can be neural or disease associated. Many studies have been done to distinguish deleterious SAPs from neutral ones. Since many previous studies were based on both structural and sequence features of the SAP, these methods are not applicable when protein structures are not available. In the current paper, we developed a method based on UMDA and SVM using protein sequence information to predict SAP’s disease association. We extracted a set of features that are independent of protein structure for each SAP. Then a SVM-based machine-learning classifier that used grid search to tune parameters was applied to predict the possible disease associa-tion of SAPs. The SVM method reaches good prediction accuracy. Since the input data of SVM contain irrelevant and noisy features and parameters of SVM also affect the prediction performance, we introduced UMDA-based wrapper approach to search for the ‘best’ solution. The UMDA-based method greatly improved prediction performance. Com-pared with current method, our method achieved better performance.

We employ uncertain programming to investigate the competitive logistics distribution center location problem in uncertain environment, in which the demands of customers and the setup costs of new distribution centers are uncertain variables. This research was studied with the assumption that customers patronize the nearest distribution center to satisfy their full demands. Within the framework of uncertainty theory, we construct the expected value model to maximize the expected profit of the new distribution center. In order to seek for the optimal solution, this model can be transformed into its deterministic form by taking advantage of the operational law of uncertain variables. Then we can use mathematical software to obtain the optimal location. In addition, a numerical example is presented to illustrate the effectiveness of the presented model.