Influence of antibiotic adsorption on biocidal activities of silver nanoparticles

Excessive use of antibiotics has posed two major challenges in public healthcare. One of them is associated with the development of multi‐drug resistance while the other one is linked to side effects. In the present investigation, the authors report an innovative approach to tackle the challenges of multi‐drug resistance and acute toxicity of antibiotics by using antibiotics adsorbed metal nanoparticles. Monodisperse silver nanoparticles (SNPs) have been synthesised by two‐step process. In the first step, SNPs were prepared by chemical reduction of AgNO₃ with oleylamine and in the second step, oleylamine capped SNPs were phase‐transferred into an aqueous medium by ligand exchange. Antibiotics – tetracycline and kanamycin were further adsorbed on the surface of SNPs. Antibacterial activities of SNPs and antibiotic adsorbed SNPs have been investigated on gram‐positive (Staphylococcus aureus, Bacillus megaterium, Bacillus subtilis), and gram‐negative (Proteus vulgaris, Shigella sonnei, Pseudomonas fluorescens) bacterial strains. Synergistic effect of SNPs on antibacterial activities of tetracycline and kanamycin has been observed. Biocidal activity of tetracycline is improved by 0–346% when adsorbed on SNPs; while for kanamycin, the improvement is 110–289%. This synergistic effect of SNPs on biocidal activities of antibiotics may be helpful in reducing their effective dosages.Inspec keywords: silver, silver compounds, nanomedicine, nanoparticles, drug delivery systems, drugs, antibacterial activity, microorganisms, nanofabrication, materials preparation, reduction (chemical), adsorption, surface chemistry, surface treatmentOther keywords: public healthcare, multi‐drug resistance, side effect, acute antibiotic toxicity, antibiotic adsorbed metal nanoparticle, monodisperse silver nanoparticle, two‐step SNP synthesis, SNP preparation, AgNO₃ chemical reduction, oleylamine capped SNP phase‐transfer, aqueous medium, ligand exchange, tetracycline, kanamycin, antibacterial activity, antibiotic adsorbed SNP, gram‐positive bacterial strain, Staphylococcus aureus, Bacillus megaterium, Bacillus subtilis, gram‐negative bacterial strain, Proteus vulgaris, Shigella sonnei, Pseudomonas fluorescens, SNP synergistic effect, effective dosage reduction, Ag, AgNO₃      

A modified AODV protocol for preventing blackhole attack in MANETs

Wireless Mobile Ad-Hoc Networks (MANETs) are infrastructure-less networks commonly used for dynamic connection between nodes. These networks work without any external router for maintaining the connection and the nodes themselves behave like a router. Ad-Hoc On Demand Distance Vector (AODV) routing is a widely used protocol for managing communications between the mobile nodes. A blackhole attack is one in which the nodes create a false route through them and then perform malicious activities on the transmitted packets. This paper presents a modification of the existing AODV routing protocol to prevent blackhole attacks in MANETs in an erratic terrain with a high probability of packet loss. A mathematical proof is given to confirm the effectiveness of the proposed algorithm with respect to the previous solution in the literature.     

High‐gain compact rectangular dielectric resonator antenna using metamaterial as superstrate

A compact high‐gain rectangular dielectric resonator antenna (RDRA) using metamaterial (MTM) as superstrate for C‐band applications is proposed in this article. The proposed antenna consists of coaxial‐fed RDRA with 50 unit cells of MTM arranged in 5 × 10 layout as superstrate. Each unit cell is constructed of two parallel eight‐shaped copper strips printed over both faces of a dielectric substrate to provide negative refractive index from 7.3 to 8.1 GHz covering the maximum bandwidth of RDRA. The extracted lumped equivalent circuit model of unit cell of MTM shows concurrence with electromagnetic simulations. The use of MTM superstrate increases the peak gain of the antenna by 89% through simulation and 86% experimentally. The measured results show that the proposed antenna achieves an impedance bandwidth of 16.1% over a band of 7.18‐8.44 GHz, with a peak gain of 14 dBi at 7.8 GHz.     

Estimation of Place of Articulation During Stop Closures of Vowel–Consonant–Vowel Utterances

Production of vowel-oral stop consonant-vowel utterances involves movement of articulators from the articulatory position of the initial vowel towards that of the oral stop closure, and then to that of the final vowel. As the closure segments have zero or low signal energy, linear predictive coding (LPC)-based estimation of vocal tract shape fails during stop closure. This paper reports a technique for estimation of place of articulation during stop closures by performing bivariate polynomial modeling on vocal tract area values during transition segments preceding and following the closure. The technique with second-degree polynomial modeling was found to be suitable for estimating the place of maximum constriction during stop closure segments of vowel-consonant-vowel utterances with bilabial, alveolar, and velar stops. The estimated places compared well with the actual constriction locations observed from the articulatory data. The technique may be useful for improving effectiveness of speech-training aids for production of stop consonants by providing visual feedback of the estimated place of articulation.     

Designing an integrated multi-echelon agile supply chain network: a hybrid taguchi-particle swarm optimization approach

The present paper attempts to explore the integration of production, distribution and logistics activities at the strategic decision making level where, the objective is to design a multi-echelon supply chain network considering agility as a key design criterion. The design network conceived here addresses a class of five echelons of supply chains including suppliers, plants, distribution centers, cross-docks and customer zones. The problem has been mathematically formulated as a multi-objective optimization model that aims to minimize the cost (fixed and variable) and maximizes the plant flexibility and volume flexibility. The notion of cross-dock has been introduced as an intermediate level between distribution centers and customer zones to increase the profitability of manufacturing and service industries. In order to solve the underlying problem, a novel algorithm entitled hybrid taguchi-particle swarm optimization (HTPSO) has been proposed that incorporates the characteristics of statistical design of experiments and random search techniques. The main idea is to integrate the fundamentals of taguchi method i.e. orthogonal array and signal to noise ratio (SNR) in the PSO meta-heuristic to minimize the effect of the causes of variations. The proposed model has been authenticated by undertaking problem instances of varying size. Extensive computational experiments are conducted to validate the same and also the efficacy of the proposed HTPSO algorithm. The results obtained reveal that proposed solution methodology is an effective approach to solve the underlying problem.     

Real world disassembly modeling and sequencing problem: Optimization by Algorithm of Self-Guided Ants (ASGA)

Decrease in product life along with the advent of stringent regulations and environmental consciousness have led to increased concern for methodological product recovery through disassembly operations. This research proposes a fuzzy disassembly optimization model (FDOM) and is aimed at determining the optimal disassembly sequence as well as the optimal depth of disassembly to maximize the net revenue at the end-of-life (EOL) disposal of the product in the real world situations. In order to account for the uncertainty inherent in quality of the returned products, fuzzy control theory is incorporated in the problem environment for modeling the expected value of the recovered modules. Considering the computational complexity of the problem at hand, an innovative approach of Algorithm of Self-Guided Ants (ASGAs) is proposed for the same. The performance of the proposed methodology is benchmarked against a set of test instances that were generated using design of experiment techniques and analysis of variance is performed to determine the impact of various factors on the objective. The robustness of proposed algorithm is authenticated against Ant Colony Optimization and Genetic Algorithm over which it always demonstrated better results thereby proving its superiority on the concerned problem.     

TRUST-TECH-based expectation maximization for learning finite mixture models

In spite of the initialization problem, the Expectation-Maximization (EM) algorithm is widely used for estimating the parameters of finite mixture models. Most popular model-based clustering techniques might yield poor clusters if the parameters are not initialized properly. To reduce the sensitivity of initial points, a novel algorithm for learning mixture models from multivariate data is introduced in this paper. The proposed algorithm takes advantage of TRUST-TECH (TRansformation Under STability-reTaining Equilibra CHaracterization) to compute neighborhood local maxima on likelihood surface using stability regions. Basically, our method coalesces the advantages of the traditional EM with that of the dynamic and geometric characteristics of the stability regions of the corresponding nonlinear dynamical system of the log-likelihood function. Two phases namely, the EM phase and the stability region phase, are repeated alternatively in the parameter space to achieve improvements in the maximum likelihood. The EM phase obtains the local maximum of the likelihood function and the stability region phase helps to escape out of the local maximum by moving towards the neighboring stability regions. The algorithm has been tested on both synthetic and real datasets and the improvements in the performance compared to other approaches are demonstrated. The robustness with respect to initialization is also illustrated experimentally.     

Class-AB Flipped Voltage Follower Cell with High Current Driving Capability and Low Output Resistance for High Frequency Applications

In this paper, a class-AB flipped voltage follower cell with high current driving capability is proposed. The proposed flipped voltage follower (FVF) cell offers increased current sourcing capability and large input/output voltage swing due to the use of bulk-driven and level shifter techniques, respectively. Further, it uses an additional NMOS transistor connected between output and ground terminals to increase the current sinking capability and to reduce the output resistance. The stability analysis has been performed by using Routh–Hurwitz stability criteria which confirms that the proposed FVF cell is stable. The proposed FVF cell also offers a high symmetrical slew rate. The proposed FVF cell has been simulated in Cadence virtuoso analog design environment using BSIM3v3 180 nm CMOS technology and simulation results are presented to validate the effectiveness of the proposed circuit.

     

State of the Art on Neural Rendering

Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic images from hand-crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo-realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state-of-the-art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. Specifically, our emphasis is on the type of control, i.e., how the control is provided, which parts of the pipeline are learned, explicit vs. implicit control, generalization, and stochastic vs. deterministic synthesis. The second half of this state-of-the-art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems.