National Institute of Technology Calicut

Fenton processes have gained much attention in the field of wastewater treatment during recent years.

This paper reports the results of experimental investigations on the influence of the addition of cerium oxide in the nanoparticle form on the major physicochemical properties and the performance of biodiesel. The physicochemical properties of the base fuel and the modified fuel formed by dispersing the catalyst nanoparticles by ultrasonic agitation are measured using ASTM standard test methods. The effects of the additive nanoparticles on the individual fuel properties, the engine performance, and emissions are studied, and the dosing level of the additive is optimized. Comparisons of the performance of the fuel with and without the additive are also presented. The flash point and the viscosity of biodiesel were found to increase with the inclusion of the cerium oxide nanoparticles. The emission levels of hydrocarbon and NOx are appreciably reduced with the addition of cerium oxide nanoparticles.

SUMMARY This paper describes the development of a non-heuristic algorithm for solving group techology problems. The problem is first formulated as a bipartite graph, and then an expression for the upper limit to the number of groups is derived. Using this limit, a non-hierarchical clustering method is adopted for grouping components into families and machines into cells. After diagonally correlating the groups, an ideal-seed method is used to improve the initial grouping. A quantitative criterion called grouping efficiency is then developed for comparing alternative solutions. The algorithm and the criterion are demonstrated through an example.

Abstract This paper deals with the development of an algorithm for concurrent formation of part-families and machine-cells in group technology. The acronym ZODTAC stands for zero-one data: ideal seed algorithm for clustering. The present algorithm is an expanded and improved version of the earlier ideal seed method. The formation of part-families and machine-cells has been treated as a problem of block diagonalization of the zero-one matrix. Different methods of choosing seeds have been developed and tested. A new concept called ‘relative efficiency’ has been developed and used as a stopping rule for the iterations. The ZODIAC procedure and its theory are given in detail. Test results with a 40 × 100 matrix are shown.

Abstract This paper is an extension of the well known rank order clustering algorithm for group technology problems. The ROC method is analysed and its main drawbacks are identified. The present method uses the ROC algorithm in conjunction with a block and slice method for obtaining a set of intersecting machine cells and non-intersecting part families. Then a hierarchical clustering method is applied based on a measure of association among pairs of machine cells. Clustering is terminated when all the surviving cells are non-intersecting or when a single group is formed. In the latter case, the number of cells is determined on the basis of a suitable decision criterion and the bottleneck machines are identified at the appropriate hierarchical level in the clustering process.

SUMMARY Block diagonalization of binary matrices is a primary step in the design of cellular production systems. ‘Grouping efficiency’ which is a weighted average of two measures that consider the voids in the diagonal blocks and exceptional elements in the off-diagonal blocks was the only criterion available to measure the goodness of block diagonal forms. The present work critically analyses this function and brings out its shortcomings, the most severe of them being its low discriminating power. A simple and elegant function has been derived in its place. The new function called grouping efficacy obviates all the defects of the earlier function while retaining the requisite properties. The mathematical properties of the function have been analysed and the function values compared with those of grouping efficiency in the case of well-structured and ill-structured data sets.

BACKGROUND: The marine environment hosts a wide variety of species that have evolved to live in harsh and challenging conditions. Marine organisms are the focus of interest due to their capacity to produce biotechnologically useful compounds. They are promising biocatalysts for new and sustainable industrial processes because of their resistance to temperature, pH, salt, and contaminants, representing an opportunity for several biotechnological applications. Encouraged by the extensive and richness of the marine environment, marine organisms' role in developing new therapeutic benefits is heading as an arable field. There is currently much interest in biologically active compounds derived from natural resources, especially compounds that can efficiently act on molecular targets, which are involved in various diseases. Studies are focused on bacteria and fungi, isolated from sediments, seawater, fish, algae, and most marine invertebrates such as sponges, mollusks, tunicates, coelenterates, and crustaceans. In addition to marine macro-organisms, such as sponges, algae, or corals, marine bacteria and fungi have been shown to produce novel secondary metabolites (SMs) with specific and intricate chemical structures that may hold the key to the production of novel drugs or leads. The marine environment is known as a rich source of chemical structures with numerous beneficial health effects. Presently, several lines of studies have provided insight into biological activities and neuroprotective effects of marine algae, including antioxidant, anti-neuroinflammatory, cholinesterase inhibitory activity, and neuronal death inhibition. CONCLUSION: The application of marine-derived bioactive compounds has gained importance because of their therapeutic uses in several diseases. Marine natural products (MNPs) display various pharmaceutically significant bioactivities, including antibiotic, antiviral, neurodegenerative, anticancer, or anti-inflammatory properties. The present review focuses on the importance of critical marine bioactive compounds and their role in different diseases and highlights their possible contribution to humanity.

We create a model which analyses the various risks involved in a food supply chain with the help of interpretive structural modelling (ISM). The various types of risks were identified based on a review of the literature and in consultation with experts in the food industry. The types of risks are clustered into five categories and risk mitigation is discussed. The model developed is validated with the help of a case study involving a food products manufacturing firm.

Recent theoretical studies are reviewed which show that the naked group 14 atoms E = C-Pb in the singlet (1)D state behave as bidentate Lewis acids that strongly bind two σ donor ligands L in the donor-acceptor complexes L→E←L. Tetrylones EL2 are divalent E(0) compounds which possess two lone pairs at E. The unique electronic structure of tetrylones (carbones, silylones, germylones, stannylones, plumbylones) clearly distinguishes them from tetrylenes ER2 (carbenes, silylenes, germylenes, stannylenes, plumbylenes) which have electron-sharing bonds R-E-R and only one lone pair at atom E. The different electronic structures of tetrylones and tetrylenes are revealed by charge- and energy decomposition analyses and they become obvious experimentally by a distinctively different chemical reactivity. The unusual structures and chemical behaviour of tetrylones EL2 can be understood in terms of the donor-acceptor interactions L→E←L. Tetrylones are potential donor ligands in main group compounds and transition metal complexes which are experimentally not yet known. The review also introduces theoretical studies of transition metal complexes [TM]-E which carry naked tetrele atoms E = C-Sn as ligands. The bonding analyses suggest that the group-14 atoms bind in the (3)P reference state to the transition metal in a combination of σ and π∥ electron-sharing bonds TM-E and π⊥ backdonation TM→E. The unique bonding situation of the tetrele complexes [TM]-E makes them suitable ligands in adducts with Lewis acids. Theoretical studies of [TM]-E→W(CO)5 predict that such species may becomes synthesized.

Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. The electroencephalogram (EEG) signals are highly complex, nonlinear, and nonstationary in nature. The characteristics of the signal vary with the age and mental state of the subject. The signs of abnormality may be invisible to the naked eyes. Even when they are visible, deciphering the minute changes indicating abnormality is tedious and time consuming for the clinicians. This paper presents a novel method for automated EEG-based diagnosis of depression using nonlinear methods: fractal dimension, largest Lyapunov exponent, sample entropy, detrended fluctuation analysis, Hurst's exponent, higher order spectra, and recurrence quantification analysis. A novel Depression Diagnosis Index (DDI) is presented through judicious combination of the nonlinear features. The DDI calculated automatically based on the EEG recordings can be used to diagnose depression objectively using just one numeric value. Also, these features extracted from nonlinear methods are ranked using the t value and fed to the support vector machine (SVM) classifier. The SVM classifier yielded the highest classification performance with an average accuracy of about 98%, sensitivity of about 97%, and specificity of about 98.5%.

SUMMARY Block-diagonalization of the machine-component incidence matrix is the first step in the implementation of group technology. Even powerful algorithms will fail to achieve this if the matrix itself is not amenable to block-diagonalization. The present work analyses the properties of the matrix and identifies the standard deviation of the pairwise similarities (Jaccard7rpar; of the vectors as the major factor that decides the groupability of the data set. Many data sets ranging from the perfectly groupable to the most ill structured ones are analysed and presented. The groupability curves show the variation of the property against the relevant factors.

The tremendous enhancement in heat transport obtained by employing microchannels has provided an effective alternative to conventional methods of heat dissipation, especially in applications related to cooling of microelectronics. A number of theoretical and experimental studies have been reported on the fluid flow and heat transfer mechanisms in mini and microchannels as well as microtubes. Anomalies and deviations from the behavior expected for conventional channels, both in terms of the frictional and heat transfer characteristics have been noticed in microchannels under specific flow conditions and flow regimes. The present work compiles and analyzes the results of the important investigations on fluid flow and heat transfer in microchannels and microtubes.

Self-compacting concrete (SCC) is extensively applied in many construction projects due to its excellent fresh and hardened concrete properties. In recent years, manufactured sand (Msand) produced by crushing rock deposits is being identified as a suitable alternative source for river sand in concrete. The main objective of this study is to explore the possibility of using Msand in SCC. In this process, an attempt was made to understand the influence of paste volume and w/p ratio (water to powder ratio) on the properties of self-compacting concrete (SCC) using Msand. The powder and aggregate combinations were optimised by using the particle packing approach, which involves the selection of combinations having maximum packing density. The chemical admixtures (superplasticisers, viscosity modifying agent) were optimised based on simple empirical tests. Fresh concrete tests such as slump flow, T500 and J-ring were performed on SCC; hardened concrete tests were limited to compressive strength. From the results, it was observed that relatively higher paste volume is essential to achieve the required flow for SCC using Msand, as compared to river sand. Low and medium strength (25–60 MPa) SCCs were achieved by using Msand based on the approach adopted in the study. Results showed that it is possible to successfully utilise manufactured sand in producing SCC.

Several industrial by-products are extensively used again as a supplementary cementitious material or aggregates in the interest to reduce environmental footprints in terms of energy depletion, pollution, waste disposition, resource depletion, and global warming related with conventional cement. A remarkable quantity of industrial scrap materials, primarily designated as construction and demolition waste from the construction industry, has transformed into crucial apprehension of governments. In the recent past, substantial explorations have been accomplished to appreciate the distinct characteristics of concrete, employing recycled aggregates from construction and demolition waste. Geopolymer composite is a new cementitious material, and it appears to be a potential replacement for conventional cement concrete. This paper summarises the previous research concerning the utilisation of recycled aggregate as a partial or complete supplants for conventional aggregates in geopolymer concrete. The influence of recycled aggregate addition on the fresh and hardened properties of geopolymer concrete is comprehensively reviewed in this paper. The studies suggest significant improvement in the workability on addition of recycled aggregates to geopolymer concrete. However, the addition results in increased water absorption and sorptivity.

In recent times, the Convolutional Neural Networks have become the most powerful method for image classification. Various researchers have shown the importance of network architecture in achieving better performances by making changes in different layers of the network. Some have shown the importance of the neuron's activation by using various types of activation functions. But here we have shown the importance of preprocessing techniques for image classification using the CIFAR10 dataset and three variations of the Convolutional Neural Network. The results that we have achieved, clearly shows that the Zero Component Analysis(ZCA) outperforms both the Mean Normalization and Standardization techniques for all the three networks and thus it is the most important preprocessing technique for image classification with Convolutional Neural Networks.

This book covers the fundamental aspects and the application of electrochemical impedance spectroscopy (EIS), with emphasis on a step-by-step procedure for mechanistic analysis of data. It enables the reader to learn the EIS technique, correctly acquire data from a system of interest, and effectively interpret the same. Detailed illustrations of how to validate the impedance spectra, use equivalent circuit analysis, and identify the reaction mechanism from the impedance spectra are given, supported by derivations and examples. MATLAB® programs for generating EIS data under various conditions are provided along with free online video lectures to enable easier learning. Features: Covers experimental details and nuances, data validation method, and two types of analysis – using circuit analogy and mechanistic analysis Details observations such as inductive loops and negative resistances Includes a dedicated chapter on an emerging technique (Nonlinear EIS), including code in the supplementary material illustrating simulations Discusses diffusion, constant phase element, porous electrodes, and films Contains exercise problems, MATLAB codes, PPT slide, and illustrative examples This book is aimed at senior undergraduates and advanced graduates in chemical engineering, analytical chemistry, electrochemistry, and spectroscopy.

Fault detection and localization in electrical power lines has long been a crucial challenge for electrical engineers as it allows the detected fault to be isolated and recovered promptly. These faults, if neglected, can rupture the normal operation of the network and drastically damage the power lines and the equipment attached to it. The wastage of power and money due to these faults can be harmful to the economy of an industry or even a country. Therefore, efficient fault detection mechanisms have become crucial for the well-being of this power-hungry world. This research presents an end-to-end deep learning strategy to detect and localize symmetrical and unsymmetrical faults as well as high-impedance faults (HIFs) in a distribution system. This research proposes a novel deep convolutional neural network (CNN) transformer model to automatically detect the type and phase of the fault as well as the location of the fault. The proposed model utilizes 1-D deep CNNs for feature extraction and transformer encoder for sequence learning. The transformer encoder utilizes an attention mechanism to integrate the sequence embeddings and focus on significant time steps to learn long-term dependence to extract the context of the temporal current data. The different faults were simulated in MATLAB Simulink using IEEE 14-bus distribution system. The proposed models were found to produce better performance on the test database when evaluated using F1-Score, Matthews correlation coefficient (MCC), and accuracy. The models also produced better predictions on HIFs compared to conventional fault-detection techniques.

The past few decades have witnessed transition metal oxides (TMOs) as promising candidates for a plethora of applications in numerous fields. The exceptional properties retained by these materials have rendered them of paramount emphasis as functional materials. Thus, the controlled and scalable synthesis of transition metal oxides with desired properties has received enormous attention. Out of different top-down and bottom-up approaches, template-assisted synthesis predominates as an adept approach for the facile synthesis of transition metal oxides, owing to its phenomenal ability for morphological and physicochemical tuning. This review presents a comprehensive examination of the recent advances in the soft-template-assisted synthesis of TMOs, focusing on the morphological and physicochemical tuning aided by different soft-templates. The promising applications of TMOs are explained in detail, emphasizing those with excellent performances.

Adsorption is the most extensively used technique for dye sequestration. Magnetic separation of toxic pollutant is becoming a potential method in waste water purification and found to have predominant significance in the removal of dyes more effectively compared to conventional method of treatments. Numerous natural and synthetic adsorbents were used, out of which magnetic composites (MCs) and magnetic nanocomposites (MNCs) have gained much attention presently in the removal of dyes from aqueous solution. Abundant references are existing pertaining to synthesis of various magnetic composites and its application in adsorption of dyes. This report displays the exploitation of MCs and MNCs for adsorption of dyes, hazards posed by dyes, sorption mechanism, preparation methods, magnetic behavior and characteristics of magnetized particles with the relevant literature on the basic principle of adsorption using MCs and MNCs for separation of dyes under optimum physicochemical condition. Adsorption reaction model, diffusion model and isotherms which facilitate in understanding the reaction mechanism between adsorbent and adsorbate are concisely discussed.

The reaction of [LAlH2 ] (L=HC(CMeNAr)2 , Ar=2,6-iPr2 C6 H3 ) with MeOTf (Tf=SO2 CF3 ) resulted in the formation of [LAlH(OTf)] (1) in high yield. The triflate substituent in 1 increases the positive charge at the aluminum center, which implies that 1 has a strong Lewis acidic character. The excellent catalytic activity of 1 for the hydroboration of organic compounds with carbonyl groups was investigated. Furthermore, it was shown that 1 effectively initiates the addition reaction of trimethylsilyl cyanide (TMSCN) to both aldehydes and ketones. Quantum mechanical calculations were carried out to explore the reaction mechanism.