Secure and Optimal LOADng Routing for IoT with Composite Routing Metric

Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things (IoT) network. To overcome this challenge, a secure Lightweight On-demand Ad hoc Distance-vector—Next Generation (LOADng) Routing Protocol is proposed in this paper. As the LOADng protocol is the second version of Ad Hoc On-Demand Distance Vector (AODV) protocol, it retains most of the basic functionality and characteristics of AODV. During the route discovery process, the cyclic shift transposition algorithm (CSTA) is used to encrypt the control packets of the LOADng protocol to improve its security. CSTA approach only derives transposition and substitution without product cipher with respect to input data. Besides this, for choosing the best probable path between the source and destination, routing metrics such as link quality Indicator (LQI), hop count (HC) and queue length (QL) are included in the control packets. The data is then securely sent using CSTA using the optimal secure path selected. Experimental Results depict that the proposed secure and optimal LOADng (SO-LOADng) using CSTA encryption obtains better throughput, delivery ratio encryption time and decryption time than the existing state-of-art approaches.     

Reconfiguration of Amorphous Complex Oxides: A Route to a Broad Range of Assembly Phenomena,Hybrid Materials,and Novel Functionalities

Reconfiguration of amorphous complex oxides provides a readily controllable source of stress that can be leveraged in nanoscale assembly to access a broad range of 3D geometries and hybrid materials. An amorphous SrTiO₃ layer on a Si:B/Si_1?xGe_x:B heterostructure is reconfigured at the atomic scale upon heating, exhibiting a change in volume of ≈2% and accompanying biaxial stress. The Si:B/Si_1?xGe_x:B bilayer is fabricated by molecular beam epitaxy, followed by sputter deposition of SrTiO₃ at room temperature. The processes yield a hybrid oxide/semiconductor nanomembrane. Upon release from the substrate, the nanomembrane rolls up and has a curvature determined by the stress in the epitaxially grown Si:B/Si_1?xGe_x:B heterostructure. Heating to 600 °C leads to a decrease of the radius of curvature consistent with the development of a large compressive biaxial stress during the reconfiguration of SrTiO₃. The control of stresses via post-deposition processing provides a new route to the assembly of complex-oxide-based heterostructures in 3D geometry. The reconfiguration of metastable mechanical stressors enables i) synthesis of various types of strained superlattice structures that cannot be fabricated by direct growth and ii) technologies based on strain engineering of complex oxides via highly scalable lithographic processes and on large-area semiconductor substrates.     

Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection

This proposal aims to enhance the accuracy of a dermoscopic skin cancer diagnosis with the aid of novel deep learning architecture. The proposed skin cancer detection model involves four main steps: (a) preprocessing, (b) segmentation, (c) feature extraction, and (d) classification. The dermoscopic images initially subjected to a preprocessing step that includes image enhancement and hair removal. After preprocessing, the segmentation of lesion is deployed by an optimized region growing algorithm. In the feature extraction phase, local features, color morphology features, and morphological transformation-based features are extracted. Moreover, the classification phase uses a modified deep learning algorithm by merging the optimization concept into recurrent neural network (RNN). As the main contribution, the region growing and RNN improved by the modified deer hunting optimization algorithm (DHOA) termed as Fitness Adaptive DHOA (FA-DHOA). Finally, the analysis has been performed to verify the effectiveness of the proposed method.     

Using a 3-D multicellular simulation of spinal cord injury with live cell imaging to study the neural immune barrier to nanoparticle uptake

Development of nanoparticle (NP) based therapies to promote regeneration in sites of central nervous system (CNS;i.e.brain and spinal cord) pathology relies critically on the availability of experimental models that offer biologically valid predictions of NP fate in vivo.However,there is a major lack of biological models that mimic the pathological complexity of target neural sites in vivo,particularly the responses of resident neural immune cells to NPs.Here,we have utilised a previously developed in vitro model of traumatic spinal cord injury (based on 3-D organotypic slice arrays) with dynamic time lapse imaging to reveal in real-time the acute cellular fate of NPs within injury foci.We demonstrate the utility of our model in revealing the well documented phenomenon of avid NP sequestration by the intrinsic immune cells of the CNS (the microglia).Such immune sequestration is a known translational barrier to the use of NP-based therapeutics for neurological injury.Accordingly,we suggest that the utility of our model in mimicking microglial sequestration behaviours offers a valuable investigative tool to evaluate strategies to overcome this cellular response within a simple and biologically relevant experimental system,whilst reducing the use of live animal neurological injury models for such studies.     

Further insight into interfacial interactions in nickel/liquid Sn–Ag solder system at 230–350 °C

Interfacial reactions are investigated between electrochemical deposited Sn-2 wt%Ag alloy and Ni for isothermal heating at various temperature (230–350 °C) and for various time to study initial stages (1–4 min) and latter stages of reaction (15 min–4 h). During the isothermal heating a continuous compound layer of Ni₃Sn₄ is formed at the interface between liquid Sn–Ag and solid Ni. In this study scallop like morphology with round and smooth surfaces of Ni₃Sn₄ intermetallic (IMC) layer is observed for shorter time of isothermal holding, which is in fact contradictory to the observations reported by recent studies which describe the morphology of IMC as elongated and faceted needles. For longer reaction times (>1 h) the scallop-like morphology is transformed gradually to a facetted abnormal growth morphology but not elongated structure. The average thickness of the reaction layer is proportional to a power function of the annealing time with an exponent n varying from 0.35 to 0.40 and the apparent activation energy for liquid–solid Ni₃Sn₄ formation was evaluated to be of about 21 kJ mol^?1. The role of deposition method of Ni and Sn layers on the morphology and the growth kinetics of the reaction layer is discussed. A theoretical analysis of the initial formation and growth of Ni₃Sn₄ phase at the Ni/Sn interface is also presented.     

A unified framework for the numerical solution of optimal control problems using pseudospectral methods

A unified framework is presented for the numerical solution of optimal control problems using collocation at Legendre-Gauss (LG), Legendre-Gauss-Radau (LGR), and Legendre-Gauss-Lobatto (LGL) points. It is shown that the LG and LGR differentiation matrices are rectangular and full rank whereas the LGL differentiation matrix is square and singular. Consequently, the LG and LGR schemes can be expressed equivalently in either differential or integral form, while the LGL differential and integral forms are not equivalent. Transformations are developed that relate the Lagrange multipliers of the discrete nonlinear programming problem to the costates of the continuous optimal control problem. The LG and LGR discrete costate systems are full rank while the LGL discrete costate system is rank-deficient. The LGL costate approximation is found to have an error that oscillates about the true solution and this error is shown by example to be due to the null space in the LGL discrete costate system. An example is considered to assess the accuracy and features of each collocation scheme.     

An Analytical Model for Identifying Suspected Users on Twitter

Abstract

With the use of identity resolution, both information leakage and identity hacking can be reduced to some extent. In this paper, a prototype has been developed to classify Twitter users as suspicious and nonsuspicious on the basis of features which identify user demographics and their tweeting activity using Twitter APIs. A model has been devised based upon user and tweet meta-data which is used to calculate user score and tweet score, and further aggregate the values generated by these scores to label suspicious and nonsuspicious users in the collected dataset of around 21,492 Twitter users. Further, support vector machine classifier has been used to classify the labeled data. Through this paper, our analysis about the role of features and the characteristics of dataset used for the categorization of users in Twitter has been reported. The experimental results illustrate that the proposed system can identify suspicious users with an accuracy of 94.1%.     

Optimized Tuning of LOADng Routing Protocol Parameters for IoT

Interconnected devices and intelligent applications have slashed human intervention in the Internet of Things (IoT), making it possible to accomplish tasks with less human interaction. However, it faces many problems, including lower capacity links, energy utilization, enhancement of resources and limited resources due to its openness, heterogeneity, limited resources and extensiveness. It is challenging to route packets in such a constrained environment. In an IoT network constrained by limited resources, minimal routing control overhead is required without packet loss. Such constrained environments can be improved through the optimal routing protocol. It is challenging to route packets in such a constrained environment. Thus, this work is motivated to present an efficient routing protocol for enhancing the lifetime of the IoT network. Lightweight On-demand Ad hoc Distance-vector Routing Protocol—Next Generation (LOADng) protocol is an extended version of the Ad Hoc On-Demand Distance Vector (AODV) protocol. Unlike AODV, LOADng is a lighter version that forbids the intermediate nodes on the route to send a route reply (RREP) for the route request (RREQ), which originated from the source. A resource-constrained IoT network demands minimal routing control overhead and faster packet delivery. So, in this paper, the parameters of the LOADng routing protocol are optimized using the black widow optimization (BWO) algorithm to reduce the control overhead and delay. Furthermore, the performance of the proposed model is analyzed with the default LOADng in terms of delay, delivery ratio and overhead. Obtained results show that the LOADng-BWO protocol outperforms the conventional LOADng protocol.     

Characterization of the Survival Influential Genes in Carcinogenesis

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.     

Green-Inspired Fabrication of Silver Nanoparticles and Examine its Potential In-Vitro Cytotoxic and Antibacterial Activities

Journal of Inorganic and Organometallic Polymers and Materials - The present work demonstrates the non-hazardous and environmentally benevolent green fabrication of silver nanoparticles using...