Microfluidic Synthesis of Ultrasmall Chitosan/Graphene Quantum Dots Particles for Intranasal Delivery in Alzheimer's Disease Treatment

Nanoparticles (NPs) based therapies for Alzheimer's disease (AD) attract interest due to their ability to pass across or bypass the blood-brain barrier. Chitosan (CS) NPs or graphene quantum dots (GQDs) are promising drug carriers with excellent physicochemical and electrical properties. The current study proposes the combination of CS and GQDs in ultrasmall NP form not as drug carriers but as theranostic agents for AD. The microfluidic-based synthesis of the CS/GQD NPs with optimized characteristics makes them ideal for transcellular transfer and brain targeting after intranasal (IN) delivery. The NPs have the ability to enter the cytoplasm of C6 glioma cells in vitro and show dose and time-dependent effects on the viability of the cells. IN administration of the NPs to streptozotocin (STZ) induced AD-like models lead to a significant number of entrances of the treated rats to the target arm in the radial arm water maze (RAWM) test. It shows the positive effect of the NPs on the memory recovery of the treated rats. The NPs are detectable in the brain via in vivo bioimaging due to GQDs as diagnostic markers. The noncytotoxic NPs localize in the myelinated axons of hippocampal neurons. They do not affect the clearance of amyloid β (Aβ) plaques at intercellular space. Moreover, they showed no positive impact on the enhancement of MAP2 and NeuN expression as markers of neural regeneration. The memory improvement in treated AD rats may be due to neuroprotection via the anti-inflammation effect and regulation of the brain tissue microenvironment that needs to be studied.     

Second-order modeling of variability and uncertainty in microbial hazard characterization

This study describes an analytical framework that permits quantitative consideration of variability and uncertainty in microbial hazard characterization. Second-order modeling that used two-dimensional Monte Carlo simulation and stratification into homogeneous population subgroups was applied to integrate uncertainty and variability. Specifically, the bootstrap method was used to simulate sampling error due to the limited sample size in microbial dose-response modeling. A data set from human feeding trials with Campylobacter jejuni was fitted to the log-logistic dose-response model, and results from the analysis of FoodNet surveillance data provided further information on variability and uncertainty in Campylobacter susceptibility due to the effect of age. Results of our analyses indicate that uncertainty associated with dose-response modeling has a dominating influence on the analytical outcome. In contrast, inclusion of the age factor has a limited impact. While the advocacy of more closely modeling variability in hazard characterization is warranted, the characterization of key sources of uncertainties and their consistent propagation throughout a microbial risk assessment actually appear of greater importance.     

Prediction of polymer mixture compatibility by Monte Carlo simulation of intermolecular binary interactions

We have evaluated conformational and orientational averages of binary interaction integrals for pairs of chains constituting atomistic representations of short polymer molecules. By considering A-A, B-B and A-B pairs, we relate these results with the Flory-Huggins parameter for the A-B mixtures. This parameter is commonly accepted as a good indicator of compatibility. Since the method ignores the simultaneous interactions with other molecules in the mixture, the local environment is approximately described by introducing an effective medium dielectric constant whose value is conveniently parameterized. The results for four different real systems are compared with data obtained from experimental neutron scattering data. The method qualitatively predicts the sign and variation with temperature in the four different cases, also showing a reasonable quantitative agreement in some of the cases. Its performance is discussed in comparison with a standard method that evaluates the Flory-Huggins parameter by calculating an average of the intermolecular energy of two molecules in contact, taking also into account their off-lattice Flory-Huggins coordination numbers.     

A Proxy MIPv6 Handover Scheme for Vehicular Ad-hoc Networks

With the rapid growth in the number of mobile devices, such as cellular phones, PDAs and laptops, the need for seamless and ubiquitous Internet connectivity is tangible. Vehicular ad-hoc network (VANET) is a rapidly developing technology, which makes vehicle-to-vehicle and vehicle-to-infrastructure communication feasible. However, when a vehicle travels from one point of attachment to another, handoff delays and provision of seamless connectivity are considered as important issues. Ubiquitous and integrated Internet connectivity can be achieved if on road moving vehicles are connected. However, when vehicle density is small and/or vehicle velocities are different, end users may suffer from a high level of connection failure. IP mobility protocols are designed by Internet Engineering Task Force to provide acceptable levels of continuous Internet connectivity, maintaining mobile node communications as they travel amongst points of attachments. However, the current IP mobility approaches applied on VANET did not resolve the connection failure issues efficiently. Therefore, in this paper a new effective solution is proposed in order to eliminate the large amount of handover latency and eventually high packet loss ratio.     

Using orthogonal pairs of rollers on concave beds (OPRCB) as a base isolation system—part II: application to multi‐story and tall buildings

Application of orthogonal pairs of rollers on concave beds (OPRCB) isolating system to short‐ and mid‐rise buildings is presented in this paper. At first, the analytical formulation of the set of equations, governing the motion of Multi Degree of Freedom (MDOF) systems, isolated by OPRCB isolators, has been developed. Then, some multi‐story regular buildings of shear type have been considered, once on fixed bases and once installed on the OPRCB isolators. Next, some horizontal and vertical accelerograms of both far‐ and near‐fault earthquakes with low‐ to high‐frequency content, particularly those with remarkable peak ground displacement values, have been selected and normalized to three peak ground acceleration levels of 0.15 g, 0.35 g and 0.7 g, and their stronger horizontal component simultaneous with their vertical component have been used for response analysis of the considered buildings. Story drifts and absolute acceleration response histories of isolated buildings have been calculated by using a program, developed in MATLAB environment by using the fourth‐order Runge–Kutta method, considering the geometrically nonlinear behavior of isolators. Maximum relative displacement and story drifts as well as absolute acceleration responses of considered isolated buildings for various earthquakes have been compared with those of corresponding fixed‐base buildings to show the high efficiency of using OPRCB isolators in multi‐story and tall regular buildings. Copyright © 2010 John Wiley & Sons, Ltd.     

Simple linear profiles monitoring in the presence of within profile autocorrelation

Quality of some processes or products can be characterized effectively by a function referred to as profile. Many studies have been done by researchers on the monitoring of simple linear profiles when the observations within each profile are uncorrelated. However, due to spatial autocorrelation or time collapse, this assumption is violated and leads to poor performance of the proposed control charts. In this paper, we consider a simple linear profile and assume that there is a first order autoregressive model between observations in each profile. Here, we specifically focus on phase II monitoring of simple linear regression. The effect of autocorrelation within the profiles is investigated on the estimate of regression parameters as well as the performance of control charts when the autocorrelation is overlooked. In addition, as a remedial measure, transformation of Y-values is used to eliminate the effect of autocorrelation. Four methods are discussed to monitor simple linear profiles and their performances are evaluated using average run length criterion. Finally, a case study in agriculture field is investigated.     

Molecular dynamics simulation of miscibility in several polymer blends

The miscibility in several polymer blend mixtures (polymethylmethacrylate/polystyrene, (1,4-cis) polyisoprene/polystyrene, and polymethylmethacrylate/polyoxyethylene) has been investigated by using Molecular Dynamics simulations performed for fully atomistic representations of short chains. The trajectories obtained from simulation boxes representing the mixtures have been analyzed in terms of the collective scattering structure function. The Flory-Huggins parameter is determined from fits of the simulation results for this function to the random phase approximation expression. The numerical values of this parameter and its variation with temperature obtained with this procedure show a general qualitative and semi-quantitative agreement with existing experimental data for the different systems, though with significant error bars. These results together with those previously obtained for the polyvinylmethylether/polystyrene blends with the same method are compared with data yielded by other computational simpler approaches, which are considerably more sensitive to different parameter choices.     

Change point estimation in phase I monitoring of logistic regression profile

In statistical process control, an important issue in phase I is to identify the time of a change in process parameters. Control charts monitor the process over time, but the time an alarm is signaled by a control chart is not necessarily the real time of change in the process. Finding the real time of change, called as change point, is important because it leads to saving cost and time in detecting the assignable cause. Recently, profile monitoring in which a response variable and one or more explanatory variables are modeled by a regression function is attracted by many researchers. One type of profiles considered in the literature is a logistic profile where the distribution of the response variable is binary. In this paper, we develop two methods including likelihood ratio test and clustering to estimate the real time of a step change in phase I monitoring of the logistic profiles. The performance of the proposed methods is evaluated and compared through simulation studies. The results show the efficiency of both estimator methods. A real case is also studied to show the applicability of the proposed methods in practice.     

Monitoring Correlated Profile and Multivariate Quality Characteristics

Monitoring multivariate quality characteristics is very common in production and service environment. Therefore, many control charts have been suggested by authors for monitoring multivariate processes. In another side, profile monitoring is a new approach in the area of statistical process control. In this approach, the quality of a product or a process is characterized by a relation between one response variable and one or more independent variables. In practice, sometimes the quality of a product or a process is represented by a correlated profile and multivariate quality characteristics. To the best of our knowledge, there is no method for monitoring this type of quality characteristics. Note that monitoring correlated profile and multivariate quality characteristics separately leads to misleading results. In this article, we specifically focus on correlated simple linear profile and multivariate normal quality characteristics and propose a method using multivariate exponentially weighted moving average control chart to monitor the correlated profile and multivariate quality characteristics simultaneously. The performance of the proposed control chart is evaluated by simulation studies in terms of average run length criterion. Finally, the proposed method is applied to a real case in the electronics industry. Copyright © 2013 John Wiley & Sons, Ltd.     

A case study on monitoring polynomial profiles in the automotive industry

In some statistical process control applications, the quality of a process or product can be characterized by a relationship between a response variable and one explanatory variable, which is referred to as profile. We give an example here of a profile that can be described using a polynomial model. This example comes from the automotive industry, where one of the most important quality characteristics of an automobile engine is the relationship between the torque produced by an engine and the engine speed in revolutions per minute. We find for this data set that a second‐order polynomial works well. In addition, we show that there is autocorrelation within each profile, thus an ordinary least‐square method that ignores the autocorrelation is inappropriate. We propose a linear mixed model method as an alternative approach. After the reduction of the data to a series of parameter estimates, we then conduct a step‐by‐step Phase I analysis of the polynomial profiles monitoring using a T²‐based procedure to check the stability of the process and whether or not there are outlying profiles. The remaining profiles are used to form the estimated mean vector and variance–covariance matrix to be used in Phase II studies. Finally, a brief discussion is presented to show how one can use these parameters in Phase II. Copyright © 2009 John Wiley & Sons, Ltd.