Classification of DoS Attacks in Smart Underwater Wireless Sensor Network

As per the most recent literature, Orthogonal Frequency Division Multiplexing (OFDM), a multi access technique, is considered most suitable for the 3G, 4G and 5G techniques in high speed wireless communication. What made OFDM most popular is its ability to deliver high bandwidth efficiency and superior data rate. Besides it, high value of peak to average power ratio (PAPR) and Inter Carrier Interference (ICI) are the challenges to tackle down via appropriate mitigation scheme. As a research contribution in the present work, an improved self-cancellation (SC) technique is designed and simulated through Simulink to mitigate the effect of ICI. This novel proposed technique (Improved SC) is designed over discrete wavelet transform (DWT) based OFDM and compared with conventional SC scheme over different channel conditions i.e. AWGN and Rayleigh fading environments. It is found that proposed DWT-OFDM with Improved SC scheme outperforms conventional SC technique significantly, under both AWGN and Rayleigh channel conditions. Further, in order to justify the novelty in the research contribution, a Split-DWT based Simulink model for Improved SC scheme is investigated to analyse the BER performance. This Split-DWT based Simulink model presented here foretells the future research potential in wavelet hybridization of OFDM to side-line ICI effects more efficiently.

     

A branch-and-bound algorithm to minimize total weighted completion time on identical parallel machines with job release dates

In this paper, we consider an identical parallel machine scheduling problem with release dates. The objective is to minimize the total weighted completion time. This problem is known to be strongly NP-hard. We propose some dominance properties and two lower bounds. We also present an efficient heuristic. A branch-and-bound algorithm, in which the heuristic, the lower bounds and the dominance properties are incorporated, is proposed and tested on a large set of randomly generated instances.     

An industrial portrait background removal solution based on knowledge infusion

Background removal of an identity (ID) picture consists in separating the foreground (face, body, hair and clothes) from the background of the image. It is a necessary groundwork for all modern identity documents that also has many benefits for improving ID security. State of the art image processing techniques encountered several segmentation issues and offer only partial solutions. It is due to the presence of erratic components like hairs, poor contrast, luminosity variation, shadow, color overlap between clothes and background. In this paper, a knowledge infused approach is proposed that hybridizes smart image processing tasks and prior knowledge. The research is based on a divide and conquer strategy aiming at simulating the sequential attention of human when performing a manual segmentation. Knowledge is infused by considering the spatial relation between anatomic elements of the ID image (face feature, forehead, body and hair) as well as their “signal properties”. The process consists in first determining a convex hull around the person’s body including all the foreground while keeping very close to the contour between the background and the foreground. Then, a body map generated from biometric analysis associated to an automatic grab cut process is applied to reach a finer segmentation. Finally, a heuristic-based post-processing step consisting in correcting potential hair and fine boundary issues leads to the final segmentation. Experimental results show that the newly proposed architecture achieves better performances than tested current state-of-the-art methodologies including active contours, generalist popular deep learning techniques, and also two other ones considered as the smartest for portrait segmentation. This new technology has been adopted by an international company as its industrial ID foreground solution.

     

Abiotic stress of ZnO‐PEG,ZnO‐PVP,CuO‐PEG and CuO‐PVP nanoparticles enhance growth,sweetener compounds and antioxidant activities in shoots of Stevia rebaudiana Bertoni

Nanoparticles are known to play remarkable role as abiotic stress elicitors in plants. This study reports the comparative analysis of effects produced by capped [zinc oxide (ZnO)‐polyethylene glycol (PEG), ZnO‐polyvinyl pyrrolidone (PVP), copper oxide (CuO)‐PEG, CuO‐PVP] and uncapped (ZnO and CuO) nanoparticles on the medicinal plant, Stevia rebaudiana raised in vitro for the production of commercially important sweetener compounds. In context of shoot organogenesis, ZnO‐PEG, ZnO‐PVP, CuO‐PEG, CuO‐PVP were employed to the growth medium that resulted in increased growth parameters, and larger content of steviol glycosides as compared to the shoots raised in medium containing ZnO and CuO, revealed by high‐performance liquid chromatography. In the meanwhile, non‐enzymatic antioxidant activities including total phenolic content, total flavonoid content, total antioxidant capacity, total reducing power and 2,2‐diphenyl‐1‐picryl hydrazyl‐free radical scavenging activity were calculated and showed comparatively greater amounts in shoots grown in medium containing capped ZnO or CuO nanoparticles. Furthermore, the ZnO and its derivatives revealed to be more reactive at 1 mg/l of concentration. Whereas, the CuO and its derivatives produced greater response on Stevia at 10 mg/l concentration of nanoparticles. This study paves the way for more such studies encompassing capped and uncapped nanoparticles and their ultimate effect on in‐vitro grown plant tissues for the production of active metabolites on industrial scale.Inspec keywords: nanoparticles, chromatography, resins, polymers, zinc compounds, copper compounds, food technology, agricultureOther keywords: nanoparticles, sweetener compounds, antioxidant activities, Stevia rebaudiana Bertoni, abiotic stress elicitors, zinc oxide‐polyethylene glycol, zinc oxide‐polyvinyl pyrrolidone, copper oxide‐polyethylene glycol, medicinal plant, shoot organogenesis, steviol glycosides, high‐performance liquid chromatography, nonenzymatic antioxidant activities, total phenolic content, total flavonoid content, total antioxidant capacity, total reducing power, 2,2‐diphenyl‐1‐picryl hydrazylfree radical scavenging activity, derivatives, active metabolite production, copper oxide‐polyvinyl pyrrolidone     

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.     

Influence of different viscosity grade cellulose-based polymers on the development of valsartan controlled release tablets

This work is based on formulating and optimizing controlled release (CR) valsartan (160 mg) tablets using different viscosity grades of the cellulosic polymer. The objective was to develop an effective once-daily drug delivery system of this cardiovascular agent. Central composite design was used for designing the formulations. Polymers used were Methocel® K4M, K15M and K100M. Compatibility of excipients with active was studied through FT-IR. Micromeritic properties were determined and formulations exhibiting appropriate flow characteristics were compressed. Swelling behavior and in vitro buoyancy effect were studied and response surface curves were constructed to optimize the formulation. Multi-point dissolution profiles of valsartan CR tablets at pH 1.2, 4.5 and 6.8 were obtained. Model-dependent and model-independent methods were performed including f2, stability test as per ICH guidelines and ANOVA. FT-IR studies revealed the compatibility of valsartan with all excipients. Formulation K4T9 (containing 25% K4M polymer) was selected to be the best optimized trial, based on physical properties and controlled release profile (23% at 4 h, 82% at 16 h and 100% at 24 h). Results of buoyancy and swelling behavior indicated that HPMC-K4M polymer exhibited excellent floating lag time and swelling indexes. In vitro drug release kinetics showed that formulation K4T9 displayed Korsmeyer–Peppas drug release pattern with r value > 0.99. The manufacturing process of K4T9 was also found to be reproducible with a shelf life period of 41 and 36 months at room temperature and accelerated conditions, respectively. Valsartan CR matrix-based formulation was successfully prepared with Methocel K4M retardant.     

Neural network model for resilient modulus of emulsified asphalt mixtures

This paper explores the potential use of neural networks (NNs) in the field of emulsified asphalt mixtures. A neural network model is developed for predicting, with sufficient approximation, relationship between the factors affecting resilient modulus (inputs: curing time, cement addition level, and residual asphalt content) and the resilient modulus (output) of emulsified asphalt mixture. A backpropagation neural network of three layers is employed. First resilient modulus data are obtained by conducting laboratory resilient modulus tests on emulsified asphalt samples, and then the results are used to train the neural network. The effectiveness of different neural network configurations is investigated. Effect of parameters such as curing time, cement addition level and residual asphalt content that influence the resilient modulus is also explored. Results indicate that NN predicts the resilient modulus with high accuracy. It is also demonstrated that NN is an excellent method that can reduce the time consumed and can be used as an important tool in evaluating the factors affecting resilient modulus of emulsified asphalt mixture at the design stage.