Comparative study of different approaches to solve batch process scheduling and optimisation problems

Effective approaches are important to batch process scheduling problems, especially those with complex constraints. However, most research focus on improving optimisation techniques, and those concentrate on comparing their difference are inadequate. This study develops an optimisation model of batch process scheduling problems with complex constraints and investigates the performance of different optimisation techniques, such as Genetic Algorithm (GA) and Constraint Programming (CP). It finds that CP has a better capacity to handle batch process problems with complex constraints but it costs longer time

Brain Tumor Detection Based on a Novel and High-Quality Prediction of the Tumor Pixel Distributions

In this paper, we propose a system to detect brain tumor in 3D MRI brain scans of Flair modality. It performs 2 functions: (a) predicting gray-level and locational distributions of the pixels in the tumor regions and (b) generating tumor mask in pixel-wise precision. To facilitate 3D data analysis and processing, we introduced a 2D histogram presentation that comprehends the gray-level distribution and pixel-location distribution of a 3D object. In the proposed system, particular 2D histograms, in which tumor-related feature data get concentrated, are established by exploiting the left-right asymmetry of a brain structure. A modulation function is generated from the input data of each patient case and applied to the 2D histograms to attenuate the element irrelevant to the tumor regions. The prediction of the tumor pixel distribution is done in 3 steps, on the axial, coronal and sagittal slice series, respectively. In each step, the prediction result helps to identify/remove tumor-free slices, increasing the tumor information density in the remaining data to be applied to the next step. After the 3-step removal, the 3D input is reduced to a minimum bounding box of the tumor region. It is used to finalize the prediction and then transformed into a 3D tumor mask, by means of gray level thresholding and low-pass-based morphological operations. The final prediction result is used to determine the critical threshold. The proposed system has been tested extensively with the data of more than one thousand patient cases in the datasets of BraTS 2018~21. The test results demonstrate that the predicted 2D histograms have a high degree of similarity with the true ones. The system delivers also very good tumor detection results, comparable to those of state-of-the-art CNN systems with mono-modality inputs, which is achieved at an extremely low computation cost and no need for training

A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation

In this paper, a Convolutional Neural Network (CNN) system is proposed for brain tumor segmentation. The system consists of three parts, a pre-processing block to reduce the data volume, an application-specific CNN(ASCNN) to segment tumor areas precisely, and a refinement block to detect/remove false positive pixels. The CNN, designed specifically for the task, has 7 convolution layers, 16 channels per layer, requiring only 11716 parameters. The convolutions combined with max-pooling in the first half of the CNN are performed to localize tumor areas. Two convolution modes, namely depthwise convolution and standard convolution, are performed in parallel in the first 2 layers to extract elementary features efficiently. For a fine classification of pixel-wise precision in the second half of the CNN, the feature maps are modulated by adding the individually weighted local feature maps generated in the first half of the CNN. The performance of the proposed system has been evaluated by an online platform with dataset of Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2018. Requiring a very low computation volume, the proposed system delivers a high segmentation quality indicated by its average Dice scores of 0.75, 0.88 and 0.76 for enhancing tumor, whole tumor and tumor core, respectively, and also by the median Dice scores of 0.85, 0.92, and 0.86. The consistency in system performance has also been measured, demonstrating that the system is able to reproduce almost the same output to the same input after retraining. The simple structure of the proposed system facilitates its implementation in computation restricted environment, and a wide range of applications can thus be expected

A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation

Brain tumor diagnosis is an important issue in health care. Automated brain tumor segmentation can help timely diagnosis. It is, however, very challenging to achieve high-quality segmentation results, because the shapes, sizes, textures and locations of brain tumors vary from patient to patient. To develop a Convolutional Neural Network (CNN) system for a high-quality brain tumor segmentation at the lowest computation cost, the CNN should be custom-designed to extract efficiently sufficient critical features particularly related to the tumors from brain images for the multi-class segmentation of tumor areas. In this thesis, a CNN system is proposed for brain tumor segmentation. The system consists of three parts, a pre-processing block to reduce the data volume, an application-specific CNN (ASCNN) to segment tumor areas precisely, and a refinement block to detect false positive voxels. The CNN, designed specifically for the task, has 7 convolution layers, and the number of output channels per layer is no more than 16. The convolutions combined with max-pooling in the first half of the CNN are performed to localize brain tumor areas. Two convolution modes, namely depthwise convolution and standard convolution, are performed in parallel in the first 2 layers to extract elementary features efficiently. In the second half of the CNN, the convolutions combined with upsampling are to segment different tumor areas. For a fine classification of pixel-wise precision, the feature maps are modulated by adding the weighted local feature maps generated in the first half of the CNN. The system has only 11716 parameters to be trained and, for a patient case of (240x240x155 x3) voxels, it requires only 21.14G Flops to complete the test. Hence, it is likely the simplest CNN system, so far reported, for brain tumor segmentation. The performance of the proposed system has been evaluated by means of CBICA Image Processing Portal with samples from dataset BRATS2018. Requiring a very low computation volume, the proposed system delivers a high segmentation quality indicated by its average Dice scores of 0.75, 0.88 and 0.76 for enhancing tumor, whole tumor and tumor core, respectively, and the median Dice scores of 0.85, 0.92, and 0.86. Its processing quality is comparable to the best ones so far reported. The consistency in system performance has also been measured, and the results have demonstrated that the system is able to reproduce almost the same output to the same input after retraining. In conclusion, the proposed CNN system has been designed to meet the specific needs to segment brain tumors or other kinds of tumors in medical images. In this way, the redundancy in computation can be minimized, the information density in data flow increased, and the computation efficiency/quality improved. This design demonstrates that a CNN system can be made to perform a high-quality processing, at a very low computation cost, for a specific application. Hence, ASCNN is an effective approach to lower the barrier of computation resource requirement of CNN systems in order to make them more implementable and applicable for general public

Multiple Attribute Decision Making with Interval Uncertainty for Artillery Recoil Resistance

To reduce loads acting on artillery carriages and obtain better values of recoil resistance, a study on multiple attribute decision making with interval uncertainty of the liquid orifice of a recoil mechanism was conducted. Taking the dimensions of the liquid orifice as the uncertainty variables, the uncertainty optimization model and algorithm based on three parameter interval were used to achieve the optimization schemes of the throttling bar outer dimensions with different tolerance grades. The multiple optimization schemes were sorted by employing the multiple attribute decision making method, in which the attribute weights were determined based on the maximum deviation method. The results show that the optimal design scheme is the one which considers simultaneously the parameter design and tolerance design of the throttling bar outer diameters. The optimal interval of the recoil resistance peaks and the optimal recoil resistance curve with sufficient fullness and flatness were obtained. The study results are beneficial for artillery design and evaluation concerning both the manufacturability of artillery and particular requirements of recoil resistance

Cellular Immune Response and Abomasum worm burden in Goats Vaccinated with HC58cDNA Vaccine against H. contortus Infection

Vaccination with DNA vaccines derived from adult H. contortus induces significant level of protection against homologous infection in goat. To date however, mechanism of protection is not well understood, especially in goat. In this study, HC58 DNA vaccinated goats were artificially infected with 5, 000 dose of infective H. contortus L3 (third larval stage), and cellular immune responses and abomasum worm burden examined. The results showed that peripheral CD4+, CD8+ T and B lymphocytes for nematode challenged Groups 1, 2 and 4 increased subsequent to L3 infection compared to negative control Group 3. Likewise, the mean eosinophil and lymphocyte counts increased substantially after vaccination and L3 challenge. On the contrary, circulating neutrophil and white blood cells reduced under similar experimental conditions in goats carrying an equal L3 nematode burden. These findings suggested that regulation of H. contortus expulsion in goat is a complex mechanism orchestrated by CD4+ and CD8+T cells, recruitment of eosinophil and lymphocytes and inclined towards development of Th2 responses. Keywords: Haemonchus contortus; goat; HC58DNA vaccine; cellular immune responses

Determination of sunset yellow and tartrazine using silver and poly (L-cysteine) composite film modified glassy carbon electrode

Silver and poly(L-cysteine) composite film modified glassy carbon electrode (PLC/Ag/GCE) has been fabricated via cyclic voltammetry and used for investigation of the electrochemical behavior of sunset yellow (SY) and tartrazine (TT). A pair of anodic peak at 0.760 V (vs. Ag/AgCl) and cathodic peak at 0.701 V (vs. Ag/AgCl) for SY and an anodic peak at 1.013 V (vs. Ag/AgCl) of TT are observed in pH 4.5 phosphate buffer solution. Based on the two well-resolved anodic peaks of SY and TT, a novel electrochemical method has been successfully developed for simultaneous determination of SY and TT using differential pulse voltammetry. Under the optimized experimental conditions, the linear range for the determination of SY and TT are 5.00×10-7 –3.00×10-4 mol L-1 and 7.50×10-7–7.50×10-4 mol L-1, respectively with detection limits of 7.50×10-8 mol L-1 and 2.50×10-7 mol L-1, respectively. The proposed method has been applied for simultaneous determination SY and TT in beverage with satisfactory results

The Optimal Timing of Antiretroviral Therapy Initiation in HIV-Infected Patients with Cryptococcal Meningitis: A Multicenter Prospective Randomized Controlled Trial

The optimal timing of antiretroviral therapy (ART) initiation in human immunodeficiency virus (HIV)-infected patients with cryptococcal meningitis (HIV/CM) is controversial. We designed a clinical trial to inves-tigate the optimal timing for ART initiation in HIV/CM patients. This will be a multicenter, prospective, and randomized clinical trial. Each enrolled patient will be randomized into either the early ART arm or the deferred ART arm. We will compare the mortality and incident rates of immune reconstitution inflammatory syndrome between the two arms. We hope to elucidate the optimal timing for ART initiation in HIV/CM patients

A novel method for inference of acyclic chemical compounds with bounded branch-height based on artificial neural networks and integer programming

Analysis of chemical graphs is becoming a major research topic in computational molecular biology due to its potential applications to drug design. One of the major approaches in such a study is inverse quantitative structure activity/property relationship (inverse QSAR/QSPR) analysis, which is to infer chemical structures from given chemical activities/properties. Recently, a novel two-phase framework has been proposed for inverse QSAR/QSPR, where in the first phase an artificial neural network (ANN) is used to construct a prediction function. In the second phase, a mixed integer linear program (MILP) formulated on the trained ANN and a graph search algorithm are used to infer desired chemical structures. The framework has been applied to the case of chemical compounds with cycle index up to 2 so far. The computational results conducted on instances with n non-hydrogen atoms show that a feature vector can be inferred by solving an MILP for up to n=40, whereas graphs can be enumerated for up to n=15. When applied to the case of chemical acyclic graphs, the maximum computable diameter of a chemical structure was up to 8. In this paper, we introduce a new characterization of graph structure, called “branch-height” based on which a new MILP formulation and a new graph search algorithm are designed for chemical acyclic graphs. The results of computational experiments using such chemical properties as octanol/water partition coefficient, boiling point and heat of combustion suggest that the proposed method can infer chemical acyclic graphs with around n=50 and diameter 30

Cloning and Sequence Analysis of Wild Argali ISG15 cDNA

The complete coding sequence of Wild Argali ISG15 cDNA was generated by rapid amplification of cDNA ends. The ISG15 cDNA was 642 bp with an open reading frame of 474 bp, which encoded a 17.47 kDa protein composed of 157 amino acids. Its amino acid sequence shared 97.9%, 80.8%, 91.4%, 94.3%, 78.3% identity with those of ISG15cDNA from Ovis aries (accession no. NM001009735.1), Capra hircus (accession no. HQ329186.1), Bos taurus (accession no. BC102318.1), Bubalus bubalis (accession no. HM543269.1), and Sus scrofa (accession no. EU647216.1), respectively. The entire coding sequence was inserted into the pET-28a vector and expressed in E. coli. The recombinant protein corresponded to the expected molecular mass of 25 kDa as judged by SDS-PAGE, and it was detected in the bacterial inclusion bodies. The expressed protein could be purified by Ni2+ chelate affinity chromatography and the results from the lymphocyte proliferation test showed that the product could stimulate lymphocyte proliferation very well (p<0.05), which further confirmed its biological activity