National Institute Of Technology Silchar

, the mechanochemical process, controlled precipitation, sol-gel method, vapour transport method, solvothermal and hydrothermal methods, and methods using emulsion and micro-emulsion environments. The paper further describes the green methods employing the use of plant extracts, in particular, for the synthesis of ZnO NPs. The modifications of ZnO with organic (carboxylic acid, silanes) and inorganic (metal oxides) compounds and polymer matrices have then been described. The multitudinous applications of ZnO NPs across a variety of fields such as the rubber industry, pharmaceutical industry, cosmetics, textile industry, opto-electronics and agriculture have been presented. Elaborative narratives on the photocatalytic and a variety of biomedical applications of ZnO have also been included. The ecotoxic impacts of ZnO NPs have additionally been briefly highlighted. Finally, efforts have been made to examine the current challenges and future scope of the synthetic modes and applications of ZnO NPs.

Rice husk, which is a relatively abundant and inexpensive material, is currently being investigated as an adsorbent for the removal of various pollutants from water and wastewaters. Various pollutants, such as dyes, phenols, organic compounds, pesticides, inorganic anions, and heavy metals can be removed very effectively with rice husk as an adsorbent. This article presents a brief review on the role of rice husk and rice husk ash in the removal of various pollutants from wastewater. Studies on the adsorption of various pollutants by rice husk materials are reviewed and the adsorption mechanism, influencing factors, favorable conditions, etc., discussed in this article. It is evident from the review that rice husk and its ash can be potentially utilized for the removal of various pollutants from water and wastewaters.

This review summarizes and elaborates the new findings in this research domain of the green synthesis of silver nanoparticles (AgNPs) using different plant extracts and their potential applications as antimicrobial agents covering the literature since 2015. While highlighting the recently used different plants for the synthesis of highly efficient antimicrobial green AgNPs, we aim to provide a systematic in-depth discussion on the possible influence of the phytochemicals and their concentrations in the plants extracts, extraction solvent, and extraction temperature, as well as reaction temperature, pH, reaction time, and concentration of precursor on the size, shape and stability of the produced AgNPs. Exhaustive details of the plausible mechanism of the interaction of AgNPs with the cell wall of microbes, leading to cell death, and high antimicrobial activities have also been elaborated. The shape and size-dependent antimicrobial activities of the biogenic AgNPs and the enhanced antimicrobial activities by synergetic interaction of AgNPs with known commercial antibiotic drugs have also been comprehensively detailed.

Mobile Edge Computing is an emerging technology that provides cloud and IT services within the close proximity of mobile subscribers. Traditional telecom network operators perform traffic control flow (forwarding and filtering of packets), but in Mobile Edge Computing, cloud servers are also deployed in each base station. Therefore, network operator has a great responsibility in serving mobile subscribers. Mobile Edge Computing platform reduces network latency by enabling computation and storage capacity at the edge network. It also enables application developers and content providers to serve context-aware services (such as collaborative computing) by using real time radio access network information. Mobile and Internet of Things devices perform computation offloading for compute intensive applications, such as image processing, mobile gaming, to leverage the Mobile Edge Computing services. In this paper, some of the promising real time Mobile Edge Computing application scenarios are discussed. Later on, a state-of-the-art research efforts on Mobile Edge Computing domain is presented. The paper also presents taxonomy of Mobile Edge Computing, describing key attributes. Finally, open research challenges in successful deployment of Mobile Edge Computing are identified and discussed.

It is an exciting time for power systems as there are many ground-breaking changes happening simultaneously. There is a global consensus in increasing the share of renewable energy-based generation in the overall mix, transitioning to a more environmental-friendly transportation with electric vehicles as well as liberalizing the electricity markets, much to the distaste of traditional utility companies. All of these changes are against the status quo and introduce new paradigms in the way the power systems operate. The generation penetrates distribution networks, renewables introduce intermittency, and liberalized markets need more competitive operation with the existing assets. All of these challenges require using some sort of storage device to develop viable power system operation solutions. There are different types of storage systems with different costs, operation characteristics, and potential applications. Understanding these is vital for the future design of power systems whether it be for short-term transient operation or long-term generation planning. In this paper, the state-of-the-art storage systems and their characteristics are thoroughly reviewed along with the cutting edge research prototypes. Based on their architectures, capacities, and operation characteristics, the potential application fields are identified. Finally, the research fields that are related to energy storage systems are studied with their impacts on the future of power systems.

Abstract The northeast region (NER) of India covers an area of 0.26 million km 2 . This region is one of the highest rainfall‐receiving regions on the planet. Consequently, it has huge water and hydropower potential and analysis of rainfall and temperature trends would be of interest to water and energy planners. Trends in monthly, seasonal, and annual rainfall and temperature on the subdivision and regional scale for the NER were examined in this study. Trend analysis of rainfall data series for 1871–2008 did not show any clear trend for the region as a whole, although there are seasonal trends for some seasons and for some hydro‐meteorological subdivisions. Similar analysis for temperature data showed that all the four temperature variables (maximum, minimum, and mean temperatures and temperature range) had rising trend. Notably for the post‐monsoon season, the Sen's estimator of slope ( °C/year) was 0.019, 0.011, and 0.015 for the maximum, minimum, and mean temperature, respectively. Copyright © 2012 Royal Meteorological Society

A maiden attempt is made to examine and highlight the effective application of bacterial foraging (BF) to optimize several important parameters in automatic generation control (AGC) of interconnected three unequal area thermal systems, such as integral controller gains (K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ii</sub> ) for the secondary control, governor speed regulation parameters (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ) for the primary control and frequency bias parameters (B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ), and compare its performance to establish its superiority over genetic algorithm (GA) and classical methods. Comparison of convergence characteristics of BF, GA, and classical approach reveals that the BF algorithm is quite faster in optimization, leading to reduction in computational burden and giving rise to minimal computer resource utilization. Simultaneous optimization of K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ii</sub> , R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , and B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> parameters which surprisingly has never been attempted in the past, provides not only best dynamic response for the system but also allows use of much higher values of R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> (than used in practice), that will appeal to the power industries for easier and cheaper realization of governor. Sensitivity analysis is carried out which demonstrates the robustness of the optimized K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ii</sub> , R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , and B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> to wide changes in inertia constant (H), reheat time constant (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> ), reheat coefficient (K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> ), system loading condition, and size and position of step load perturbation.

An ever-increasing energy demand and environmental problems associated with exhaustible fossil fuels have led to the search for an alternative renewable source of energy. In this context, biodiesel has attracted attention worldwide as an eco-friendly alternative to fossil fuel for being renewable, non-toxic, biodegradable, and carbon-neutral. Although the homogeneous catalyst has its own merits, much attention is currently paid toward the chemical synthesis of heterogeneous catalysts for biodiesel production as it can be tuned as per specific requirement and easily recovered, thus enhancing reusability. Recently, biomass-derived heterogeneous catalysts have risen to the forefront of biodiesel productions because of their sustainable, economical and eco-friendly nature. Furthermore, nano and bifunctional catalysts have emerged as a powerful catalyst largely due to their high surface area, and potential to convert free fatty acids and triglycerides to biodiesel, respectively. This review highlights the latest synthesis routes of various types of catalysts (including acidic, basic, bifunctional and nanocatalysts) derived from different chemicals, as well as biomass. In addition, the impacts of different methods of preparation of catalysts on the yield of biodiesel are also discussed in details.

Sentiment analysis has come long way since it was introduced as a natural language processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying attitudes and opinions toward an entity. It has become a powerful tool used by governments, businesses, medicine, marketing, and others. The traditional sentiment analysis model focuses mainly on text content. However, technological advances have allowed people to express their opinions and feelings through audio, image and video channels. As a result, sentiment analysis is shifting from unimodality to multimodality. Multimodal sentiment analysis brings new opportunities with the rapid increase of sentiment analysis as complementary data streams enable improved and deeper sentiment detection which goes beyond text-based analysis. Audio and video channels are included in multimodal sentiment analysis in terms of broadness. People have been working on different approaches to improve sentiment analysis system performance by employing complex deep neural architectures. Recently, sentiment analysis has achieved significant success using the transformer-based model. This paper presents a comprehensive study of different sentiment analysis approaches, applications, challenges, and resources then concludes that it holds tremendous potential. The primary motivation of this survey is to highlight changing trends in the unimodality to multimodality for solving sentiment analysis tasks.

H NMR. A biodiesel yield of 98% was achieved under optimized reaction conditions such as a calcination temperature of 900 °C, a catalyst loading of 3 wt%, a reaction time of 7 h and a methanol to oil ratio of 6 : 1, and biodiesel conversion was confirmed by FT-NMR and IR spectroscopies. A total of 9 fatty acid methyl esters (FAMEs) were identified in the synthesized biodiesel by the retention time and fragmentation pattern data of GC-MS analysis. The catalyst was recycled 8 times without appreciable loss in its catalytic activity. A high biodiesel yield of 98% was obtained under these optimised conditions. The catalyst has the advantage of being a waste material, therefore it is easily prepared, cost free, highly efficient, biogenic, labor effective and environmentally friendly, making it a potential candidate as a green catalyst for low cost production of biodiesel at an industrial scale.

Background: The COVID virus epidemic has produced another era on the planet while we sort out the outcomes in various parts of our everyday life. The food industry and food supply chain do not incorporate an exception. Considering a food supply chain, the business activities and supply of various food products have been suspended due to restriction of demand, closing the food production facilities, financial restrictions. Scope and approach: According to the spreadability of the pandemic, every nation must understand the seriousness of the circumstance. This work aims to discuss the effect of COVID-19 on socioeconomic implications and the impact of lockdown on the food supply chain and agri-business. The paper also summarizes the suggestions needed to control and deduce the impact of COVID-19. For now, the chance of transmission through the food area is viewed as immaterial, and following Covid in workspaces isn't considered as a need by public authorities. However, the unfriendly impacts on the climate, food frameworks and individuals along the food store network are obvious. Key findings and conclusion: A food supply chain facilities should concentrate on facilities like maintenance of employees' safety and health, change of conditions in working. To prevent the increment of food prices, the protectionist policy should avoid. We have also suggested a PDS system to overcome food supply chain disruption. In conclusion, the supply chain should respond and overcome the challenging situation in the food supply chain.

India has witnessed 30% of the cases of breast cancer during the last few years and it is likely to increase. Breast cancer in India accounts that one woman is diagnosed every two minutes and every nine minutes, one woman dies. Early detection and diagnosis can save the lives of cancer patients. This paper presents a novel method to detect breast cancer by employing techniques of Machine Learning. The authors carried out an experimental analysis on a dataset to evaluate the performance. The proposed method has produced highly accurate and efficient results when compared to the existing methods.

Since the phenomenon of cloud computing was proposed, there is an unceasing interest for research across the globe. Cloud computing has been seen as unitary of the technology that poses the next-generation computing revolution and rapidly becomes the hottest topic in the field of IT. This fast move towards Cloud computing has fuelled concerns on a fundamental point for the success of information systems, communication, virtualization, data availability and integrity, public auditing, scientific application, and information security. Therefore, cloud computing research has attracted tremendous interest in recent years. In this paper, we aim to precise the current open challenges and issues of Cloud computing. We have discussed the paper in three-fold: first we discuss the cloud computing architecture and the numerous services it offered. Secondly we highlight several security issues in cloud computing based on its service layer. Then we identify several open challenges from the Cloud computing adoption perspective and its future implications. Finally, we highlight the available platforms in the current era for cloud research and development.

Today, biodiesel, a renewable, non-toxic and environmentally friendly fuel, is attracting increasing attention worldwide as an alternative to fossil fuel.

This paper compares circular gate (CG) tunnel field effect transistor (TFET) and uniform gate Heterojunction (HJ) TFET as label-free biosensors based on dielectric modulation. Neutral and charged biomolecules with different values of dielectric constant are considered. Sensitivities of partially filled nanogaps arising out of steric hindrance in both the biosensors for concave, convex, increasing and decreasing step profiles of biomolecules are compared. The effect of probe position on sensitivities of the two biosensors is reported for various cases. A status map is presented, plotting the sensitivities of some of the most significant works in applications of FET as label-free biosensors along with sensitivities of the proposed devices. CG TFET exhibits higher sensitivity than HJ TFET due to its non-uniform gate architecture. The sensitivities of the TFETs are highly dependent on the position of biomolecules (steric hindrance and probe position) with respect to the tunnel junction. A maximum sensitivity of 1.31 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> (3.382 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> ) is achieved for fully filled nanogap in CG TFET (HJ TFET) for dielectric constant 12.

The occurrence of nonlinear phenomena like subharmonics and chaos in power electronic circuits has been reported recently. In this paper, the authors investigate these phenomena in the current-mode-controlled boost power converter. A nonlinear model in the form of a mapping from one point of observation to the next has been derived. The map has a closed form even when the parasitic elements are included. The bifurcation behavior of the boost power converter has been investigated with the help of this discrete model.

The virtually unlimited available resources and wide range of services provided by the cloud have resulted in the emergence of new cloud-based applications, such as smart grids, smart building control, and virtual reality. These developments, however, have also been accompanied by a problem for delay-sensitive applications that have stringent delay requirements. The current cloud computing paradigm cannot realize the requirements of mobility support, location awareness, and low latency. Hence, to address the problem, an edge computing paradigm that aims to extend the cloud resources and services and enable them to be nearer the edge of an enterprise's network has been introduced. In this article, we highlight the significance of edge computing by providing real-life scenarios that have strict constraint requirements on application response time. From the previous literature, we devise a taxonomy to classify the current research efforts in the domain of edge computing. We also discuss the key requirements that enable edge computing. Finally, current challenges in realizing the vision of edge computing are discussed.

Abstract CdS thin films of varying thicknesses were deposited on cleaned glass substrates at room temperature by thermal evaporation technique in a vacuum of about 2 x 10 ‐5 torr. UV‐VIS spectra of the films were studied using the optical transmittance measurements which were taken in the spectral region from 300 nm to 1100 nm. The absorbance and reflectance spectra of the films in the UV‐VIS region were also studied. Optical constants such as optical band gap, extinction coefficient, refractive index, optical conductivity and complex dielectric constant were evaluated from these spectra. All the films were found to exhibit high transmittance (∼ 60 ‐ 93 %), low absorbance and low reflectance in the visible/near infrared region from ∼ 500 nm to 1100 nm. The optical band gap energy was found to be in the range 2.28 – 2.53 eV. All the films annealed at 300°C for 4 hours in vacuum (∼ 10 ‐2 torr) showed a decrease in the optical transmittance with its absorption edge shifted towards the longer wavelength, leading to the result that the optical band gap decreases on annealing the films. Also, on annealing crystallinity of the films improves, resulting in decrease in the optical transmittance. (© 2007 WILEY‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)

A comprehensive evaluation of sensitivity between double gate tunnel FET and n+ pocket doped vertical tunnel FET based label-free biosensors is reported in this work. Both the biosensors possess nanogaps on the left and right of the fixed dielectric (HfO2) which enhances the capture area of the biosensors. Comparison has been made on the TCAD simulation studies of their sensitivities considering neutral/charged biomolecules having different dielectric constants. The sensitivity of VB is found to be approximately 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> times the sensitivity of DB due to its current conduction in both vertical and lateral directions. Also, the effects of steric hindrance and irregular position of probes/receptors are analyzed to understand the non-ideal behavior of the sensors. Sensitivity is calculated from the simulated results for four different cases of partially filled nanogaps - decreasing, increasing, concave and convex profiles. It rises by about 5-7% when filled factor is increased from 40 to 66%. Finally, benchmarking of proposed VB is done against other published literature as it gives better result in terms of sensitivity.

Class imbalance can be defined as a span among data mining, machine learning and pattern recognition domains that provides to learn from a data-space having unequal class distribution. Common classifiers when trained by imbalanced data tend to bias towards the class possessing bulk instances causing misclassification of upcoming patterns/instances. The study reveals that presence of redundant borderline instances and outliers in the data-space severely catalyzes the effect of class imbalance. The Condensed Nearest Neighbor and Tomek-link undersampling techniques are used as the baseline systems for the present study, and an improved undersampling algorithm is proposed to be employed in the pre-processing stage by amalgamating aspects of outlier and redundancy detection to the baseline system. The proposed scheme imparts to detect outlier, redundant and noisy instances having least contribution in estimating accurate class labels. Thus, a data-level solution has been offered to the concerned problem with novelty in effective elimination of majority instances without losing valuable information. The proposed scheme is implemented and validated with Back Propagation Neural Network (BPNN), K-Nearest-Neighbor (K-NN), Support Vector Machine (SVM) and Naive Bayes classifiers for 10 real-life datasets. The experimental results obtained clearly manifest the superiority of the proposed scheme over the baseline schemes.