Production planning problem with pricing under random yield: CVaR criterion

In this paper, we address a basic production planning problem with price dependent demand and stochastic yield of production. We use price and target quantity as decision variables to lower the risk of low yield. The value of risk control becomes more important especially for products with short life cycle. This is because, the profit implications of low yield might be unbearable in the short run. We apply Conditional Value at Risk （CVaR） to model the, risk. CVaR measure is a coherent risk measure and thereby having nice conceptual and mathematical underpinnings. It is also widely used in practice. We consider the problem under general demand function and general distribution function of yield and find sufficient conditions under which the problem has a unique local maximum. We also both analytically and numerically analyze the impact of parameter change on the optimal solution. Among our results, we analytically show that with increasing risk aversion, the optimal price increases. This relation is opposite to that of in Newsvendor problem where the uncertainty lies in demand side.     

Forecasting stock returns an examination of stock market trading in the presence of transaction costs

The paper presents new evidence on the predictability of excess returns on common stocks for the Standard and Poor's 500 and the Dow Jones Industrial portfolios at the monthly, quarterly, and annual frequencies. It shows that recursive predictions obtained on the basis of the excess returns regressions are capable of correctly predicting a statistically significant proportion of the signs of the actual returns. The paper also shows that the switching portfolios constructed on the basis of the signs of the recursive predictions mean-variance dominate the respective market portfolios when trading takes place on a quarterly or annual basis. This result holds even under a high transaction cost scenario. However, due to the larger number of transactions at the monthly frequency the monthly switching portfolios only mean-variance dominate the respective market portfolios when transaction costs are zero or low.     

Application of SSM in tackling problematical situations from academicians’ viewpoints

In recent years, a wide variety of studies have applied soft systems methodology (SSM) to address the complexities of the messy situations. This paper seeks to identify 1) application areas and 2) application levels of the use of SSM in dealing with problematical situations. To do so, 149 articles published between 2000 and 2015 were studied. The findings revealed the application of SSM in new application areas such as sustainable development, knowledge management and project management. The results of this study also indicated that researchers applied SSM at three levels: SSM as a process of inquiry, SSM as an action-oriented methodology, and the hybrid use of SSM. Based on the findings, SSM at the level of a single methodology for bringing out changes has been less welcomed by researchers, but the application levels of inquiry processes and hybrid approaches have been more utilized. This suggests that in the correspondence of SSM with the nature or situation of problems, a sort of incommensurability is witnessed. Factors arising from the nature of the problem, inadequate mastery of the researcher over SSM or the limitations of SSM can exert an influence on this incommensurability.     

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Objective： To develop a new bioinformatic tool based on a data-mining approach for extraction of the most informative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods： Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surfaceenhanced laser desorption/ionization time-of-flight mass spectrometry （SELDI-TOF MS）. The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality reduction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results： From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion： The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.     

Fabrication and characterization of nanocrystalline Al/Al12（Fe,V）3Si alloys by consolidation of mechanically alloyed powders

The aim of this study was to produce bulk nanocrystalline Al/Al12（Fe,V）3Si alloys by mechanical alloying （MA） and subsequent hot pressing （HP） of elemental powders. A nanostructured Al-based solid solution was formed by MA of elemental powders for 60 h. After HP of the as-milled powders at 550℃ for 20 min, the Al12（Fe,V）3Si phase was precipitated in a nanocrystalline Al matrix. Scanning electron microscopy （SEM） images of the bulk samples represented a homogeneous and uniform microstructure that was superior to those previously obtained by rapid solidification-powder metallurgy （RS-PM）. Nanostructured Al-8.5Fe-1.3V-1.7Si and Al-11.6Fe-1.3V-2.3Si alloys ex-hibited high HV hardness values of～205 and～254, respectively, which are significantly higher than those reported for the RS-PM counter-parts.     

CT Brain Image: Abnormalities Recognition and Segmentation

In order to develop an automated segmentation system for Computed Tomography (CT) brain images, a new approach which consists of several unsupervised segmentation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid (CSF), and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy c-means (FCM) methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions, In the second phase segmentation, the modified FCM with population-diameter independent (PDI) segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.     

The FN3 and BRCT motifs in the exomer component Chs5p define a conserved module that is necessary and sufficient for its function

Chs5p is a component of the exomer, a coat complex required to transport the chitin synthase Chs3p from the trans-Golgi network to the plasma membrane. The Chs5p N-terminal region exhibits fibronectin type III (FN3) and BRCT domains. FN3 domains are present in proteins that mediate adhesion processes, whereas BRCT domains are involved in DNA repair. Several fungi—including Schizosaccharomyces pombe, which has no detectable amounts of chitin—have proteins similar to Chs5p. Here we show that the FN3 and BRCT motifs in Chs5p behave as a module that is necessary and sufficient for Chs5p localization and for cargo delivery. The N-terminal regions of S. cerevisiae Chs5p and S. pombe Cfr1p are interchangeable in terms of Golgi localization, but not in terms of exomer assembly, showing that the conserved function of this module is protein retention in this organelle and that the interaction between the exomer components is organism-specific.     

Prediction of α‐stable GARCH and ARMA‐GARCH‐M models

The best prediction of generalized autoregressive conditional heteroskedasticity (GARCH) models with α‐stable innovations, α‐stable power‐GARCH models and autoregressive moving average (ARMA) models with GARCH in mean effects (ARMA‐GARCH‐M) are proposed. We present a sufficient condition for stationarity of α‐stable GARCH models. The prediction methods are easy to implement in practice. The proposed prediction methods are applied for predicting future values of the daily SP500 stock market and wind speed data.     

Optimal Predictive Impedance Control in the Presence of Uncertainty for a Lower Limb Rehabilitation Robot

As an innovative concept, an optimal predictive impedance controller(OPIC) is introduced here to control a lower limb rehabilitation robot(LLRR) in the presence of uncertainty. The desired impedance law is considered to propose a conventional model-based impedance controller for the LLRR. However, external disturbances, model imperfection, and parameters uncertainties reduce the performance of the controller in practice. In order to cope with these uncertainties, an optimal predictive compensator is introduced as a solution for a proposed convex optimization problem, which is performed on a forward finite-length horizon. As a result, the LLRR has the desired behavior even in an uncertain environment. The performance and efficiency of the proposed controller are verified by the simulation results.