Cellular changes of adrenal under the acute stress of O-chlorobenzylidene malononitrile (CS).

This investigation describes histological and cytometrical changes of cortical and medullary tissue of adrenal in rats under the acute stress of O-chlorobenzylidene malononitrile (10 mg/kg and 20 mg/kg). It has been observed that after injection of CS, the adrenal gland showed histological changes both in the cortical and medullary region.     

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.     

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.     

Solar and lunar observations at Istanbul in the 1570s

From the early ninth century until about eight centuries later, the Middle East witnessed a series of both simple and systematic astronomical observations for the purpose of testing contemporary astronomical tables and deriving the fundamental solar, lunar, and planetary parameters. Of them, the extensive observations of lunar eclipses available before 1000 AD for testing the ephemeredes computed from the astronomical tables are in a relatively sharp contrast to the twelve lunar observations that are pertained to the four extant accounts of the measurements of the basic parameters of Ptolemaic lunar model. The last of them are Taqī al-Dīn Mu?ammad b. Ma‘rūf’s (1526–1585) trio of lunar eclipses observed from Istanbul, Cairo, and Thessalonica in 1576–1577 and documented in chapter 2 of book 5 of his famous work, Sidrat muntaha al-afkar fī malakūt al-falak al-dawwār (The Lotus Tree in the Seventh Heaven of Reflection). In this article, we provide a detailed analysis of the accuracy of his solar (1577–1579) and lunar observations.     

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.     

Intrusion Detection System Using Voting-Based Neural Network

Several security solutions have been proposed to detect network abnormal behavior. However, successful attacks is still a big concern in computer society. Lots of security breaches, like Distributed Denial of Service(DDoS),botnets, spam, phishing, and so on, are reported every day, while the number of attacks are still increasing. In this paper, a novel voting-based deep learning framework, called VNN, is proposed to take the advantage of any kinds of deep learning structures. Considering several models created by different aspects of data and various deep learning structures, VNN provides the ability to aggregate the best models in order to create more accurate and robust results. Therefore, VNN helps the security specialists to detect more complicated attacks. Experimental results over KDDCUP'99 and CTU-13, as two well known and more widely employed datasets in computer network area, revealed the voting procedure was highly effective to increase the system performance, where the false alarms were reduced up to 75% in comparison with the original deep learning models, including Deep Neural Network(DNN), Convolutional Neural Network(CNN), Long Short-Term Memory(LSTM), and Gated Recurrent Unit(GRU).     

A Markovian approach to determining process means with dual quality characteristics

This paper studies a production system where products are produced continuously and whose specification limits are specified for screening inspection. In this paper, we consider dual quality characteristics and different costs associated with each quality characteristic that falls below a lower specification limit or above an upper specification limit. Due to these different costs, the expected total profit will greatly depend on the process parameters, especially a process mean. This paper develops a Markovian-based model for determining the optimum process means with the consideration of dual quality characteristics in a single-stage system. The proposed model is then illustrated through a numerical example and sensitivity analysis is performed to validate the model. The results showed that the optimum process mean for both quality characteristics have a significant effect on the performance of the system. Since the literature survey shows that dealing with multi-quality characteristics is extremely limited, the proposed model, coupled with the Markovian approach, provides a unique contribution to this field.     

QUADRATIC REPRESENTATION FOR ROADWAY PROFILE THAT MINIMIZES EARTHWORK COST

Roadway design usually involves choices regarding grade selection and earthwork (transportation) that can be solved using linear programming. Previous work considered the road profile as series of interconnected linear segments. In these models, constraints are included in the linear programming formulation to insure continuity of the road, which cause sharp connectivity points at the intersection of the linear segments. This sharp connectivity needs to be smoothed out after l;he linear programming solution is found and the earth in the smoothed portion of the roadway has to be moved to the landfill. In previous research, the smoothing issue is dealt with after an optimal solution is found. This increases the work required by the design engineer and consequently increases the construction cost; furthermore, the optimal solution is violated by this smoothing operation. In this paper, the issue of sharp connectivity points is resolved by representing the road profile by a quadratic function. The continuit     

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.