Isolation and characterization of cellulose nanowhiskers from Acacia caesia plant

Acacia caesia (L.) Willd (soap bark) fiber is an abundant natural resource, that is rich in cellulose. The study reports the effective utilization of underutilized Acacia caesia fiber for the isolation of nanocellulose whiskers. The nanocellulose whiskers were isolated successfully from Acacia caesia fibers by following alkali, bleaching, and sulfuric acid treatment. The obtained nanocellulose whiskers were carefully investigated for its chemical composition, structure, morphology, crystallinity, and thermal stability. The chemical composition and Fourier transform infrared spectra of nanocellulose whiskers showed the elimination of the non-cellulosic parts present in the raw Acacia caesia fibers. The X-ray diffraction analysis showed an upsurge in the crystallinity of the cellulose fibers after the chemical treatments. The isolation of nanocellulose whiskers from Acacia caesia raw fiber was confirmed by electron microscopy analysis. The thermogravimetric analysis showed remarkably high char residue for the nanocellulose whiskers compared to raw fibers. Based on the properties of nanocellulose whiskers, it can be concluded that the nanocellulose whiskers produced from Acacia caesia raw fibers are potential reinforcing material for developing high-performance green composites.     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.     

Design of a password-based authenticated key exchange protocol for SIP

The Session Initiation Protocol (SIP) is a signaling communications protocol, which has been chosen for controlling multimedia communication in 3G mobile networks. In recent years, password-based authenticated key exchange protocols are designed to provide strong authentication for SIP. In this paper, we address this problem in two-party setting where the user and server try to authenticate each other, and establish a session key using a shared password. We aim to propose a secure and anonymous authenticated key exchange protocol, which can achieve security and privacy goal without increasing computation and communication overhead. Through the analysis, we show that the proposed protocol is secure, and has computational and computational overheads comparable to related authentication protocols for SIP using elliptic curve cryptography. The proposed protocol is also provably secure in the random oracle model.     

Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.     

Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.     

Design and performance of a punch mechanism based pellet injector for alternative injection in the large helical device

A low speed single barrel pellet injector, using a mechanical punch device has been developed for alternative injection in the large helical device. A pellet is injected by the combined operation of a mechanical punch and a pneumatic propellant system. The pellet shape is cylindrical, 3 mm in diameter and 3 mm in length. Using this technique the speed of the pellet can be controlled flexibly in the range of 100-450 m/s, and a higher speed can be feasible for a higher gas pressure. The injector is equipped with a guide tube selector to direct the pellet to different injection locations. Pellets are exposed to several curved parts with the curvature radii R(c) = 0.8 and 0.3 m when they are transferred in guided tubes to the respective injection locations. Pellet speed variation with pressure at different pellet formation temperatures has been observed. Pellet intactness tests through these guide tubes show a variation in the intact speed limit over a range of pellet formation temperatures from 6.5 to 9.8 K. Pellet speed reduction of less than 6% has been observed after the pellet moves through the curved guide tubes.     

Bipartite coherent-state quantum key distribution with strong reference pulse

We propose an optical scheme for quantum key distribution in which bits are encoded in relative phases of four bipartite weak coherent states ${|\alpha, \alpha\rangle, |-\alpha, -\alpha\rangle, |-\alpha, \alpha\rangle}$ and ${|\alpha, -\alpha \rangle}$ , with respect to a strong reference pulse. We discuss security of the scheme against eavesdropping strategies like, photon number splitting, photon beam splitting and intercept-resend attacks. It is found that present scheme is more sensitive against these eavesdropping strategies than the two-dimensional non-orthogonal state based protocol and BB84 protocol. Our scheme is very simple, requires only passive optical elements like beam splitters, phase shifters and photon detectors, hence is at the reach of presently available technology.     

Highly Efficient Quasi 2D Blue Perovskite Electroluminescence Leveraging a Dual Ligand Composition

Perovskite light-emitting diodes (PeLEDs) are advancing because of their superior external quantum efficiencies (EQEs) and color purity. Still, additional work is needed for blue PeLEDs to achieve the same benchmarks as the other visible colors. This study demonstrates an extremely efficient blue PeLED with a 488 nm peak emission, a maximum luminance of 8600 cd m⁻², and a maximum EQE of 12.2% by incorporating the double-sided ethane-1,2-diammonium bromide (EDBr₂) ligand salt along with the long-chain ligand methylphenylammonium chloride (MeCl). The EDBr₂ successfully improves the interaction between 2D perovskite layers by reducing the weak van der Waals interaction and creating a Dion–Jacobson (DJ) structure. Whereas the pristine sample (without EDBr₂) is inhibited by small stacking number (n) 2D phases with nonradiative recombination regions that diminish the PeLED performance, adding EDBr₂ successfully enables better energy transfer from small n phases to larger n phases. As evidenced by photoluminescence (PL), scanning electron microscopy (SEM), and atomic force microscopy (AFM) characterization, EDBr₂ improves the morphology by reduction of pinholes and passivation of defects, subsequently improving the efficiencies and operational lifetimes of quasi-2D blue PeLEDs.     

Event-triggered control for discrete-time multi-agent average consensus

We consider the average consensus problem for the multi-agent system in the discrete-time domain. Three triggering based control protocols are developed, which dictate the broadcast and control update instants of individual agents to alleviate communication and computational burden. Lyapunov-based design methods prescribe when agents should communicate and update their control so that the network converges to the average of agents' initial states. We start with a static version of the distributed event-triggering law and then generalize it so that it involves an internal auxiliary variable to regulate the threshold dynamically for each agent. The third protocol uses a self-triggering algorithm to avoid continuous listening wherein each agent estimates its next triggering time and broadcasts it to its neighbors at the current triggering time. Numerical simulations are shown to validate the efficacy of the proposed algorithms.     

CNFET Based Ultra-Low-Power Schmitt Trigger SRAM for Internet of Things (IoT) Applications

Wireless Personal Communications - This paper presents Carbon Nanotube FET (CNFET) based ultra-low-power Schmitt trigger SRAM designs which can operate at voltage levels as low as 200 mV, with high...