Influence of Carbon Equivalent Content on Phase Transformation During Inter-critical Heating of Dual Phase Steels Using Discrete Micro-scale Cellular Automata Model

In this paper, a cellular automata method based model is proposed for simulating phase transformation kinetics of inter-critical heating of dual phase (DP) steel. This developed model deals with the kinetics of pearlite dissolution, ferrite transformation and austenite grain growth based on carbon diffusion process. Diffusion equation is discretized and solved by finite difference method whereas austenite grain growth is controlled by transition rules applied in cellular automata algorithm. The model is operated in the temperature range of 730–890 °C for four different specimens of DP steel. This model predicts appropriately the microstructure and volume fraction of formed austenite during inter-critical heating of DP steel. In addition, this study shows that the presence of carbon and alloying elements enhances carbon equivalent of DP steel, helps in austenite formation.     

RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data

S-palmitoylation is a reversible covalent post-translational modification of cysteine thiol side chain by palmitic acid. S-palmitoylation plays a critical role in a variety of biological processes and is engaged in several human diseases. Therefore, identifying specific sites of this modification is crucial for understanding their functional consequences in physiology and pathology. We present a random forest (RF) classifier-based consensus strategy (RFCM-PALM) for predicting the palmitoylated cysteine sites on synaptic proteins from male/female mouse data. To design the prediction model, we have introduced a heuristic strategy for selection of the optimum set of physicochemical features from the AAIndex dataset using (a) K-Best (KB) features, (b) genetic algorithm (GA), and (c) a union (UN) of KB and GA based features. Furthermore, decisions from best-trained models of the KB, GA, and UN-based classifiers are combined by designing a three-star quality consensus strategy to further refine and enhance the scores of the individual models. The experiment is carried out on three categorized synaptic protein datasets of a male mouse, female mouse, and combined (male + female), whereas in each group, weighted data is used as training, and knock-out is used as the hold-out set for performance evaluation and comparison. RFCM-PALM shows ~80% area under curve (AUC) score in all three categories of datasets and achieve 10% average accuracy (male—15%, female—15%, and combined—7%) improvements on the hold-out set compared to the state-of-the-art approaches. To summarize, our method with efficient feature selection and novel consensus strategy shows significant performance gains in the prediction of S-palmitoylation sites in mouse datasets.     

Assessing Possibilities and Limits for Solar Cells

What are the solar cell efficiencies that we can strive towards? We show here that several simple criteria, based on cell and module performance data, serve to evaluate and compare all types of today's solar cells. Analyzing these data allows to gauge in how far significant progress can be expected for the various cell types and, most importantly from both the science and technology points of view, if basic bounds, beyond those known today, may exist, that can limit such progress. This is important, because half a century after Shockley and Queisser (SQ) presented limits, based on detailed balance calculations for single absorber solar cells, those are still held to be the only ones, we need to consider; most efforts to go beyond SQ are directed towards attempts to circumvent them, primarily via smart optics, or optoelectronics. After formulating the criteria and analyzing known loss mechanisms, use of such criteria suggests–additional limits for newer types of cells, Organic and Dye‐Sensitized ones, and their siblings,–prospects for progress and–further characterization needs, all of which should help focusing research and predictions for the future.     

First principles study to identify the reversible reaction step of a multinary hydrogen storage “Li–Mg–B–N–H” system

A density functional theory study with the generalized gradient approximation (GGA) and projected augmented wave (PAW) method is performed for the hydrogen storage properties of the complex multinary storage Li–Mg–B–N–H system. Using ab initio methods, stability of the structures at finite temperatures is confirmed via. phonon spectrum calculations. Thermodynamic properties such as heat of reaction, and Gibbs energy for each reactant and product in the reaction steps in different temperature zones are calculated. It is found that reversibility occurs in the temperature range of 160–225 °C with approximately 4.38 wt % hydrogen storage capacity. The enthalpy of reversible re-/de-hydrogenation is found to be 55.17 kJ/mol H₂, which is supported by experimental data. The total hydrogen storage capacity of this material is calculated to be 8.76 wt% from the desorption behavior observed at different temperatures up to 350 °C. These theoretically established reactions are validated with the suggested mechanism from experimental observations for the dehydrogenation reaction of this Li–Mg–B–N–H multinary system. These efforts are expected to contribute toward identification of suitable hydrogen storage materials.     

Evolutionary Modular MLP with Rough Sets and ID3 Algorithm for Staging of Cervical Cancer

This paper describes a way of designing a hybrid system for detecting the different stages of cervical cancer. Hybridisation includes the evolution of knowledge-based subnetwork modules with GAs using rough set theory and the ID3 algorithm. Crude subnetworks for each module are initially obtained via rough set theory and the ID3 algorithm. These subnetworks are then combined, and the final network is evolved using genetic algorithms. The evol-ution uses a restricted mutation operator which utilises the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conven-tional MLP. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for rule extraction.       

Moving object detection and tracking in videos through turbulent medium

This paper addresses the problem of identifying and tracking moving objects in a video sequence having a time-varying background. This is a fundamental task in many computer vision applications, though a very challenging one because of turbulence that causes blurring and spatiotemporal movements of the background images. Our proposed approach involves two major steps. First, a moving object detection algorithm that deals with the detection of real motions by separating the turbulence-induced motions using a two-level thresholding technique is used. In the second step, a feature-based generalized regression neural network is applied to track the detected objects throughout the frames in the video sequence. The proposed approach uses the centroid and area features of the moving objects and creates the reference regions instantly by selecting the objects within a circle. Simulation experiments are carried out on several turbulence-degraded video sequences and comparisons with an earlier method confirms that the proposed approach provides a more effective tracking of the targets.     

A dynamic TDMA based scheme for securing query processing in WSN

Nodes in a wireless sensor network (WSN) are generally deployed in unattended environments making the nodes susceptible to attacks. Therefore, the need of defending such attacks becomes a big challenge. We propose a scheme to build a security mechanism in a query-processing paradigm within WSN. The scheme is capable of protecting replay attack while preserving essential properties of security such as authentication, data integrity and data freshness. The solution is made lightweight using symmetric key cryptography with very short-length key. Further, the key used in our scheme is neither pre-deployed nor is transmitted directly. The key information is established among nodes through an efficient use of one variant of dynamic TDMA mechanism which ensures security of key. Another variant of dynamic TDMA is used to make the scheme bandwidth saving, an essential quality of WSN. Performance of the scheme is analyzed in terms of storage, computation and communication overhead. Finally the analytical results are compared with two of the existing schemes including the previous version of the present scheme that show significant reduction of all such overheads thereby proving the suitability of the proposed scheme for a resource-constrained network like WSN.     

Synthesis of boron nitride nanotubes employing mechanothermal process and its characterization

Multi-walled boron nitride (BN) nanotubes having cylindrical structure were synthesized employing the mechanothermal process. In this process hexagonal boron nitride powder (hBN) was first ball milled for 50–100 h using a high-energy ball mill and the ball-milled samples were annealed in N₂ atmosphere for about 10 h in the temperature range of 950–1300 °C. The BN nanotubes exhibited a well-crystallized hexagonal structure with about 25–40 nm in diameter and up to 1 μm length. These BN nanotubes were well characterized using various techniques, such as, XRD, SEM, TEM and Raman Spectroscopy.