On the applicability of diploid genetic algorithms in dynamic environments

Diploid genetic algorithms (DGAs) promise robustness as against simple genetic algorithms which only work towards optimization. Moreover, these algorithms outperform others in dynamic environments. The work examines the theoretical aspect of the concept by examining the existing literature. The present work takes the example of dynamic TSP to compare greedy approach, genetic algorithms and DGAs. The work also implements a greedy genetic approach for the problem. In the experiments carried out, the three variants of dominance were implemented and 115 runs proved the point that none of them outperforms the other.     

Designing parity check matrix to achieve linear encoding time in LDPC codes

Low-density parity-check (LDPC) codes have become the part of various communication standards due to their excellent error correcting performance. Existing methods require matrix inverse computation for obtaining a systematic generator matrix from parity check matrix. With the change in code rate or code length the process is repeated and hence, a large number of pre-processing computations time and resources are required. In the existing methods, the complexity of encoding is essentially quadratic with respect to the block length. In this paper, it is shown that the parity check matrix can be constructed using patterned sub-matrix structure such that the matrix inverse operation is replaced by matrix multiplication of sparse matrices. The sparseness of matrices is then utilized to obtain efficient encoders which can achieve encoding in real time with reduced pre-computation complexity. Hardware implementation of encoder and simulation results show that the proposed encoder achieves throughput in excess of 1 Gbps with the same error correcting performance as the conventional designs.     

SVM based robust watermarking for enhanced medical image security

Medical images are more typical than any other ordinary images, since it stores patient’s information for diagnosis purpose. Such images need more security and confidentiality as total diagnosis depends on it. In telemedicine applications, transmission of medical image via open channel, demands strong security and copyright protection. In our proposed robust watermarking model, a double layer security is introduced to ensure the robustness of embedded data. The embedded data is scrambled using a unique key and then a transform domain based hybrid watermarking technique is used to embed the scrambled data into the transform coefficients of the host image. The data embedding in medical images involves more attention, so that the diagnosis part must not be affected by any modification. Therefore, Support Vector Machine (SVM) is used as a classifier, which classify a medical image into two regions i.e. Non Region of Interest (NROI) and Region of Interest (ROI) to embed watermark data into the NROI part of the medical image, using the proposed embedding algorithm. The objective of the proposed model is to avoid any quality degradation to the medical image. The simulation is performed to measure the Peak Signal to Noise Ratio (PSNR) for imperceptibility and Structural Similarity Index (SSIM) to test the robustness. The experimented result shows, robustness and imperceptibility with SSIM of more than 0.50 and PSNR of more than 35 dB for proposed watermarking model.

     

Simultaneous automated design of structured QFT controller and prefilter using nonlinear programming

This paper describes a nonlinear programming‐based robust design methodology for controllers and prefilters of a predefined structure for the linear time‐invariant systems involved in the quantitative feedback theory. This controller and prefilter synthesis problem is formulated as a single optimization problem with a given performance optimization objective and constraints enforcing stability and various specifications usually enforced in the quantitative feedback theory. The focus is set on providing constraints expression that can be used in standard nonlinear programming solvers. The nonlinear solver then computes in a single‐step controller and prefilter design parameters that satisfy the prescribed constraints and maximizes the performance optimization objective. The effectiveness of the proposed approach is demonstrated through a variety of difficult design cases like resonant plants, open‐loop unstable plants, and plants with variation in the time delay. Copyright © 2016 John Wiley & Sons, Ltd.     

Identification of Candidate mRNA Isoforms for Prostate Cancer-Risk SNPs Utilizing Iso-eQTL and sQTL Methods

Single nucleotide polymorphisms (SNPs) impacting the alternative splicing (AS) process (sQTLs) or isoform expression (iso-eQTL) are implicated as important cancer regulatory elements. To find the sQTL and iso-eQTL, we retrieved prostate cancer (PrCa) tissue RNA-seq and genotype data originating from 385 PrCa European patients from The Cancer Genome Atlas. We conducted RNA-seq analysis with isoform-based and splice event-based approaches. The MatrixEQTL was used to identify PrCa-associated sQTLs and iso-eQTLs. The overlap between sQTL and iso-eQTL with GWAS loci and those that are differentially expressed between cancer and normal tissue were identified. The cis-acting associations (FDR < 0.05) for PrCa-risk SNPs identified 42, 123, and 90 PrCa-associated cassette exons, intron retention, and mRNA isoforms belonging to 25, 95, and 83 genes, respectively; while assessment of trans-acting association (FDR < 0.05) yielded 59, 65, and 196 PrCa-associated cassette exons, intron retention and mRNA isoforms belonging to 35, 55, and 181 genes, respectively. The results suggest that functional PrCa-associated SNPs can play a role in PrCa genesis by making an important contribution to the dysregulation of AS and, consequently, impacting the expression of the mRNA isoforms.     

Artificial neural network-driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

In recent years, smart healthcare, artificial intelligence (AI)-aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL-based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre-established datasets that fail to capture real-life applicability. Therefore, this study proposes an AI-enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare-based wearable devices (WD) for real-time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL-based architecture aggregates the optimizer weights of many clients using a fifth-generation (5G) communication channel. Then, the performance of the proposed FL-based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.     

Root-secreted Allelochemical in the Noxious Weed <Emphasis Type="Italic">Phragmites Australis</Emphasis> Deploys a Reactive Oxygen Species Response and Microtubule Assembly Disruption to Execute Rhizotoxicity

Phragmites australis is considered the most invasive plant in marsh and wetland communities in the eastern United States. Although allelopathy has been considered as a possible displacing mechanism in P. australis, there has been minimal success in characterizing the responsible allelochemical. We tested the occurrence of root-derived allelopathy in the invasiveness of P. australis. To this end, root exudates of two P. australis genotypes, BB (native) and P38 (an exotic) were tested for phytotoxicity on different plant species. The treatment of the susceptible plants with P. australis root exudates resulted in acute rhizotoxicity. It is interesting to note that the root exudates of P38 were more effective in causing root death in susceptible plants compared to the native BB exudates. The active ingredient in the P. australis exudates was identified as 3,4,5-trihydroxybenzoic acid (gallic acid). We tested the phytotoxic efficacy of gallic acid on various plant systems, including the model plant Arabidopsis thaliana. Most tested plants succumbed to the gallic acid treatment with the exception of P. australis itself. Mechanistically, gallic acid treatment generated elevated levels of reactive oxygen species (ROS) in the treated plant roots. Furthermore, the triggered ROS mediated the disruption of the root architecture of the susceptible plants by damaging the microtubule assembly. The study also highlights the persistence of the exuded gallic acid in P. australis’s rhizosphere and its inhibitory effects against A. thaliana in the soil. In addition, gallic acid demonstrated an inhibitory effect on Spartina alterniflora, one of the salt marsh species it successfully invades. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Thermal and aqueous stability improvement of graphene oxide enhanced diphenylalanine nanocomposites

Abstract

Nanocomposites of diphenylalanine (FF) and carbon based materials provide an opportunity to overcome drawbacks associated with using FF micro- and nanostructures in nanobiotechnology applications, in particular their poor structural stability in liquid solutions. In this study, FF/graphene oxide (GO) composites were found to self-assemble into layered micro- and nanostructures, which exhibited improved thermal and aqueous stability. Dependent on the FF/GO ratio, the solubility of these structures was reduced to 35.65% after 30 min as compared to 92.4% for pure FF samples. Such functional nanocomposites may extend the use of FF structures to e.g. biosensing, electrochemical, electromechanical or electronic applications.     

Assessment of waste plastic oil blends on performance,combustion and emission parameters in direct injection compression ignition engine

The present experimental investigation deals with the transformation of waste plastic into oil in a pyrolysis reactor. A single feed at a rate of 8?kg yielded 675?ml of pyrolytic oil. The physiochemical properties of plastic oil (PO) were found to be within American Society for Testing and Material standards with higher kinematic viscosity and carbon residue. The GC-MS and Fourier Transform Infrared Analysis studies revealed the presence of 14 different compounds in PO. Straight diesel–PO blending was carried out at 15% and 30% in volume ratio. At full-load condition, in-cylinder pressure, rate of pressure rise, rate of heat release and peak pressure were higher for PO30% than straight diesel. The brake thermal efficiency for PO blends was found to be slightly higher than straight diesel with a significant increase in brake-specific fuel consumption. Unburned hydrocarbon (UBHC), CO NO _x and smoke emission showed significant variation with PO blends. The physiochemical properties of PO blends significantly affect the engine performance.