Molecular tracking: A concept for side-draw distillation column design

A simple approach is introduced to locate a side-draw tray for ternary and multi-component mixtures with middle boiling component(s) present in the system at trace levels. The concept is based on a probability function defined by the thermodynamic properties of the system. The advantage of this method over existing methods is the ability to quickly and efficiently provide a feasible configuration of the distillation unit without relying on rigorous optimization or trial and error approaches. Moreover, it provides an intuitive understanding of the movements of the middle boiling components in the column.     

Integrated design and control of reactive distillation processes using the driving force approach

Superior controllability of reactive distillation (RD) systems, designed at the maximum driving force (design-control solution) is demonstrated in this article. Binary or multielement single or double feed RD systems are considered. Reactive phase equilibrium data, needed for driving force analysis and design of the RD system, is generated through an in-house property prediction tool. Rigorous steady-state simulation is carried out in ASPEN plus in order to verify that the predefined design targets and dynamics are met. A multiobjective performance function is employed to evaluate the performance of the RD system in terms of energy consumption, sustainability metrics (total CO₂ footprint), and control performance. Controllability of the designed system is evaluated using indices like the relative gain array (RGA) and Niederlinski index (N_I ), to evaluate the degree of loop interaction, as well as through dynamic simulations using proportional-integral (PI) controllers and model predictive controllers (MPC). The design-control of the RD systems corresponding to other alternative designs that do not take advantage of the maximum driving force is also investigated. The analysis shows that the RD designs at the maximum driving force exhibit enhanced controllability and lower carbon footprint than the alternative RD designs.     

Liquid Biomarkers for Improved Diagnosis and Classification of CNS Tumors

Liquid biopsy, as a non-invasive technique for cancer diagnosis, has emerged as a major step forward in conquering tumors. Current practice in diagnosis of central nervous system (CNS) tumors involves invasive acquisition of tumor biopsy upon detection of tumor on neuroimaging. Liquid biopsy enables non-invasive, rapid, precise and, in particular, real-time cancer detection, prognosis and treatment monitoring, especially for CNS tumors. This approach can also uncover the heterogeneity of these tumors and will likely replace tissue biopsy in the future. Key components of liquid biopsy mainly include circulating tumor cells (CTC), circulating tumor nucleic acids (ctDNA, miRNA) and exosomes and samples can be obtained from the cerebrospinal fluid, plasma and serum of patients with CNS malignancies. This review covers current progress in application of liquid biopsies for diagnosis and monitoring of CNS malignancies.     

Soy-based,tannin-modified plywood adhesives

In this research, two different types of commercial tannins, namely a hydrolysable tannin (chestnut) and a condensed flavonoid tannin (mimosa), were used to prepare two types of soy-based (soy flour (SF) and soy protein isolate) adhesives for making plywood. Thermogravimetric properties (TGA) and its derivative as function of temperature (DTG) of different soy-based adhesive were measured in the range 40°C–300°C. Thermomechanical analysis (TMA) from 25°C to 250°C was done for the different resin formulations. Duplicate three-ply laboratory plywood panels were prepared by adding 300 g/m² of the adhesives’ total resin solid content composed of SF or isolated soy protein (ISP), urea, chestnut, and mimosa tannin extracts with hexamine as hardener. Based on the results obtained, tannins can improve SF adhesion properties. The TMA showed that chestnut tannin extract appeared to react well with SF, while mimosa tannin extract appeared to react well with ISP. Matrix-assisted laser desorption ionization time of flight (MALDI-TOF) mass spectrometry also showed that among other reactions, the soy protein amino acids reacted with the tannins. Furthermore, delamination and shear strength test results showed the good water resistance of plywood bonded with soy-based tannin modified adhesive.     

Intensity of class I antigen expression on human tumour cell lines and its relevance to the efficiency of non-MHC-restricted killing

A modified tetrazolium reduction assay (MTT) was used to assess the relation between HLA class I antigen expression on tumour cells and their susceptibility as a target for non-MHC restricted LAK/NK cytotoxicity using interleukin-2 activated peripheral blood mononuclear cells (MNC) from normal individuals. At 20/1 effector/target ratio this ranged from no killing to 77%. The efficiency of killing was dependent on duration of effector cell culture with IL-2, peaking at day 10 and declining thereafter. This killing could be enhanced by addition of other cytokines including interferons alpha, beta and gamma. Study of a panel of 15 tumour cell lines using a single effector showed that there was no statistically significant inverse correlation (using Spearman rank test) between the degree of tumour class I expression and LAK/NK killing at 20/1 (r = 0.23 P = 0.39) and 10/1 (r = 0.30, P = 0.27) and at 5/1 E/T ratio r = 0.47, P = 0.08) respectively. Lack of inverse correlation between these two parameters came from study of one bladder tumour line (FEN), whose absent class I antigens had been corrected by transfection with beta 2 microglobulin gene. At high E/T ratio (20/1) there was an increase in the susceptibility of target cells to lysis (36% parent cell, 45% transfected cell), whilst at lower E/T ratios (1/1) there was significantly more killing of the non-transfected cells (10% vs 31%). The addition of anti-class I antibody W6/32 increased killing by 18% but this was non-specific as the same increase occurred with a class II antibody. These data suggest that overall there was not an inverse correlation between class I expression and LAK/NK killing at high E/T ratios, whilst at low (5/1 or lower) E/T ratios this correlation nearly reached statistical significance suggesting that the conflicting literature reports may be due to a threshold levels of effector cells above which the masking effects of MHC antigens disappears.     

Representation and estimation of spectral reflectances using projection on PCA and wavelet bases

In this article, we deal with the problem of spectral reflectance function representation and estimation in the context of multispectral imaging. Because the reconstruction of such functions is an inverse problem, slight variations in input data completely skew the expected results. Therefore, stabilizing the reconstruction process is necessary. To do this, we propose to use wavelets as basis functions, and we compare those with Fourier and PCA bases. We present the idea and compare these three methods, which belong to the class of linear models. The PCA method is training‐set dependent and confirms its robustness when applied to reflectance estimation of the training sets. Fourier and wavelet bases allow good generalization; an advantage of wavelets is that they avoid boundary artifacts. The results are evaluated with the commonly used goodness‐of‐fit coefficient (GFC), and the reliability of the use of wavelets is proved. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 485–493, 2008     

Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks

In this paper, we address the problem of genetic algorithm optimization for jointly selecting the best group of candidate sensors and optimizing the quantization for target tracking in wireless sensor networks. We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering best group of candidate sensors selection problem. The main objective of this paper is twofold. Firstly, the quantization level and the group of candidate sensors selection are to be optimized in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor network. Secondly, the target position is to be estimated using quantized variational filtering (QVF) algorithm. The optimization of quantization and sensor selection are based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II). The proposed multi-objective (MO) function defines the main parameters that may influence the relevance of the participation in cooperation for target tracking and the transmitting power between one sensor and the cluster head (CH). The proposed algorithm is designed to: i) avoid the problem lot of computing times and operation counts, and ii) reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The computation of these criteria is based on the predictive information provided by the QVF algorithm. The simulation results show that the NSGA-II -based QVF algorithm outperforms the standard quantized variational filtering algorithm and the centralized quantized particle filter.     

Recursive‐clustering‐based approach for denial of service (DoS) attacks in wireless sensors networks

This paper presents a novel approach for detecting denial of service attacks. In particular, the concern is on the sleeping deprivation attacks such as the malicious nodes that use flooding technique. Our approach is based on wireless sensor network (WSN) clustering. It consists in recursively clustering sensors until a required granularity (chosen by the expert) is achieved. We apply our approach with two different clustering algorithms. Indeed, we use the common clustering WSN algorithm Low Energy Algorithm Adaptive Clustering Hierarchy and the general clustering method Fast and Flexible Unsupervised Clustering Algorithm (FFUCA) based on ultrametric properties. We discuss the behavior of the approach with the two algorithms. Also, we present numerical results that show the efficiency of recursive clustering using the FFUCA algorithm. Copyright © 2013 John Wiley & Sons, Ltd.