Showing papers by "Heritage Institute of Technology published in 2019"

A review on the application of different treatment processes for emulsified oily wastewater

Abstract: Oily wastewater, one of the major threats to environment nowadays, mainly originates from petrochemical, heavy metal, food processing, paint, automobile industrial premises. This oil contaminated wastewater is mutagenic and carcinogenic to human health as well as inhibitory to plant growth. Without any proper treatment if such oily water stream disposed into water bodies, apart from increasing the BOD and COD, it also imparts a sunlight impervious layer at the top of the stream restricting the entrance of sunlight followed by disruption of aquatic ecosystem. Hence proper treatment of oily wastewater before its discharge to environment is one of the primary concerns. Researchers have used several technologies such as, gravity sedimentation, coagulation, flotation, coagulation composite flotation, demulsification, membrane separation, flocculation, chemical precipitation and bioremediation have been explored to purify this oil contaminated wastewater to a desired level. Especially, the uniqueness of the treatment will require a through merit analysis of the process, when the wastewater comprises of oil–water emulsion. Hence, in this review an analytical insight on the merits of the process for the treatment of such emulsified system has been provided. The review article also discusses different microorganisms that are required for bioremediation of either oil spill over a large aquatic zone or oil–water emulsion at source point. Finally, the manuscript highlighted some of the effluent treatment plants’ operational process from different industries, which might provide a typical understanding of a comparative view between different treatment processes.

A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms

Abstract: In this paper, a new bi-objective fuzzy portfolio selection model is proposed, for which Sharp ratio (SR) and Value at Risk ratio (VR) of a portfolio are chosen as objectives. SR is an important nonsystematic risk measurement that examines the investment risk by aspiring the diversification of the capital allocation. On the other hand, VR measures the systematic risk, which reduces the largest loss of an investment at a given confidence level. The proposed fuzzy portfolio model assumes both SR and VR as maximization objectives for which the associated fuzzy parameters are considered as triangular fuzzy numbers. The proposed model is solved using multi-objective genetic algorithms, namely multi-objective cellular genetic algorithm (MOCell), archive-based hybrid scatter search (AbYSS), and nondominated sorting genetic algorithm II (NSGA-II). We have used a data set from the Shenzhen Stock Exchange to illustrate the performance of the proposed model and algorithms. Finally, a comparative study in terms of five standard performance metrics is presented, among the MOCell, AbYSS, and NSGA-II algorithms that are mentioned extensively in various research articles to exhibit the best suitable algorithm.

Data Augmentation using Generative Adversarial Networks for Pneumonia classification in chest Xrays

Abstract: In medical images, data augmentation is essentially important for accurate classification of images especially when available data is limited. This paper proposes a noble data augmentation method of generating synthetic chest Xray images of patients with Pneumonia using Generative Adversarial Networks (GANs). The proposed model first uses conventional data augmentation techniques along with GANs to generate more training samples. A specific implementation of GANs allows us to produce unprecedented Chest X-Ray images of patients suffering from Pneumonia. The generated samples are then used to train a DCNN model to classify chest X-Ray images. The classifier significantly improves its accuracy after the introduction of synthetic data produced by the GAN model.

A Post-Disaster Demand Forecasting System Using Principal Component Regression Analysis and Case-Based Reasoning Over Smartphone-Based DTN

Abstract: The most dominant challenges in post-disaster emergency resource planning are forecasting the exact demand for emergency resources, communicating such demands to the control station, and validating these demands before using them for resource planning. Forecasting the exact demand for resources in a relief shelter becomes tricky, because situational parameters influencing these demands keep on changing. Moreover, the collection and transmission of demands of far-flung shelters are challenging, owing to the post-disaster disruption of communication infrastructure. All these lead to ad hoc allocation of emergency resources to the shelters. In this paper, we first derive a principal component regression model to forecast demand for emergency resources based on situational parameters at the shelters. Subsequently, we propose an opportunistic demand sharing scheme for gathering and disseminating resource demands to the control station using a smartphone-based delay-tolerant network (DTN). Finally, we suggest a case-based reasoning driven demand validation technique to ratify these demands and also to project the demands that do not get transmitted. Experimental results show that our system forecasts dynamically changing resource demands at the shelters with high precision, transmits near accurate demands to the control station, and perfectly validates the received resource demands.

Wood-Based Cellulose Nanofibrils: Haemocompatibility and Impact on the Development and Behaviour of Drosophila melanogaster

Abstract: Wood-based cellulose nanofibrils (CNF) offer an excellent scaffold for drug-delivery formulation development. However, toxicity and haemocompatibility of the drug carrier is always an important issue. In this study, toxicity-related issues of CNF were addressed. Different doses of CNF were orally administered to Drosophila and different tests like the developmental cycle, trypan blue exclusion assay, larva crawling assay, thermal sensitivity assay, cold sensitivity assay, larval light preference test, climbing behaviour, nitroblue tetrazolium (NBT) reduction assay, adult phenotype, and adult weight were conducted to observe the impact on its development and behaviour. A haemocompatibility assay was done on the blood taken from healthy Wistar rats. In Drosophila, the abnormalities in larval development and behaviour were observed in the behavioural assays. However, the cytotoxic effect could not be confirmed by the gut staining and level of reactive oxygen species. The larvae developed into an adult without any abnormality in the phenotype. The CNF did cause loss of weight in the adult flies and did not cause much toxicity within the body since there was no phenotypic defect. Hemolysis data also suggested that CNF was safe at lower doses, as the data was well within acceptable limits. All these results suggest that cellulose nanofibres have no significant cytotoxic effects on Drosophila. However, the developmental and behavioural abnormalities suggest that CNF may act as a behavioural teratogen.

Metal–ligand ring aromaticity in a 2D coordination polymer used as a photosensitive electronic device

Abstract: In this study, aromaticity of a 14-membered metal–ligand ring present in a two-dimensional coordination polymer (2D CP), [Zn2(fum)2(4-phpy)4(H2O)2] (1) (H2fum = fumaric acid and 4-phpy = 4-phenyl pyridine), was theoretically investigated by calculating the nucleus-independent chemical shifts (NICS). Furthermore, the calculation was supported by free of in-plane component NICS (FiPC-NICS) used specifically for inorganic heterocycle and electron localization function (ELF) analysis. The calculations reveal that the metal–ligand ring has an aromatic character and a moderate C–H⋯π interaction energy (−8.56 kcal mol−1 at the HF/6-311++G(d,p) level of theory) of 4-phpy with the 14-membered ring, which plays an active role in the molecular self-assembly for crystal packing. Furthermore, the compound 1 has a substantive photosensitivity with an appreciable on–off ratio; therefore, this material can be used as a photosensitive electronic device.

Development and performance analysis of a low-cost MEMS microphone-based hearing aid with three different audio amplifiers

Abstract: In this paper, MEMS-based capacitive microphone and low-cost amplifier are designed for low-cost power-efficient hearing aid application. The developed microphone along with the associated circuitry is mounted on a common board in the form of pocket-type (body-worn) device. The designed microphone consists of a flexible circular silicon nitrite (Si3N4) diaphragm and a polysilicon-perforated back plate with air as dielectric between them. The incident acoustic waves on the sensor cause deflection of the diaphragm to alter the air gap between the perforated back plate (fixed electrode) and the diaphragm (moving plate) which causes a change in capacitance. The acoustic pressure applied to the microphone is from 0 to 100 Pa for an operating range of 100 Hz–10 kHz which corresponds to the audible frequency range in case of human beings. The main purpose of this work is to increase the longevity of battery used in conventional hearing aids. The designed MEMS microphone with Si3N4 diaphragm is capable of identifying acoustic frequencies (100 Hz to 10 kHz) which correspond to a specific change in absolute pressure from 0 to 100 Pa for 2-micron-thick diaphragm with a sensitivity of about 0.08676 mV/Pa. The design of the sensor and the characteristics analysis are performed in FEM-based simulation software, which are later validated in real time. The prototype is designed using MEMS microphone and low-cost amplifier ICs with biasing components in the form of pocket-type (body-worn) hearing aid. In order to study the performance of proposed device, three different market-available amplifiers with controllable gain are used. Finally, the performance of the hearing aid is studied through audio spectrogram analysis to choose the best-suited amplifier among the three.

Collaborative Recommendations using Hierarchical Clustering based on K-d Trees and Quadtrees

Abstract: Majority of the e-commerce sites implement Recommender Systems (RS) to help users navigate through the large search space and assist their decision making process by suggesting products that the us...

Intelligent catalytic support by Ni / NiO / Ni(OH)2 in low level of Pd/Pt boosting the performance of alkaline DEFC

Abstract: The present investigations explore the important contribution of transitional metal/metal oxide/hydroxide in ternary alloys of Ni, Pd, and Pt NPs for boosting the EOR in alkaline DEFCs. A facile one-pot synthesis method is employed to prepare ultra small ternary nano-alloy catalysts. The impact of Ni/NiO /Ni(OH)2 species in EOR on Pd/Pd catalyst is strongly recognized by the diminution of overvoltage ∼ 546 mV in case of the multi metallic surface. The oxy/hydroxy species of the multi-metallic formulation of Ni29Pd34Pt37 not only trigger the electrode kinetics at the lower electro-chemical bias-potential but also facilitate towards sustained product formation and electro-extraction as manifested by the output power. A substantial rise of peak power density is obtained by more than 241% higher as compared to Pt, 78%, and 51%, compared to the bimetallic Ni41Pt59 and Pd55Pt45 catalysts, respectively. The increase of CH3CO2− and CO32− yields on the ternary catalyst compared to its unary/binary counterparts, further confirms the catalytic preeminence of the combinatorial formulation of Ni29Pd34Pt37 in the present investigation.

Fast detection of community structures using graph traversal in social networks

Abstract: Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in $$O(|V| + |E|)$$ time to create an initial cover before using modularity maximization to get the final cover.

A blockchain based incentive scheme for post disaster opportunistic communication over DTN

Abstract: Delay tolerant network (DTN) is successfully proposed for setting up emergency post disaster communication networks when normal communication infrastructure is incapacitated. Performance of such networks get affected by selfish nodes that do not participate in message forwarding. Thus, nodes must receive satisfactory rewards for cooperation. The available incentive schemes either rely on central trusted authorities or do not use an explicit and secure digital currency. Blockchain, a decentralized digital ledger of immutable transactions, is an attractive approach for addressing the incentive challenges in peer-to-peer networks that lack central trusted authorities. Bitcoin, the Blockchain based cryptocurrency, make it possible to devise practical credit based incentive schemes for such networks. In this paper, we propose a Blockchain based incentive scheme for DTN based post disaster communication network that uses Bitcoin to incentivize nodes for cooperation. The scheme uses a novel reward strategy to bring rationality in the incentivizing process.

Corrosion protection of reinforcement steel rebars by the application of electroless nickel coatings

Abstract: Pitting corrosion and susceptibility to chloride attacks are the shortfalls of reinforcement steel used in concrete structures. The present work proposes the deposition of electroless nickel coatings to provide barrier protection due to their proven corrosion resistance. The effect of deposition of Ni-P, Ni-P-W and Ni-P-Cu coatings on the corrosion behavior of Fe ? 600 grade rebars in 3.5% NaCl has been investigated with the aid of potentiodynamic polarization and electrochemical impedance spectroscopy. The corrosion potential of bare rebar was observed to be -653 mV while that of electroless Ni-P, Ni-P-W and Ni-P-Cu coatings was -436, -391 and -356 mV respectively. The corrosion current density of the rebars also decreased significantly by the application of electroless nickel coatings. Nyquist plots also revealed a bigger semicircular loop for the coated rebars. The chloride attack was severe on the bare uncoated rebar and scanning electron micrograph revealed severe cracking. On the other hand, the vulnerability of the electroless coated rebars to pitting corrosion decreased greatly.

Multi-objective Inventory Model with Both Stock-Dependent Demand Rate and Holding Cost Rate Under Fuzzy Random Environment

Abstract: In this paper, we investigated a multi-objective inventory model under both stock-dependent demand rate and holding cost rate with fuzzy random coefficients. Chance constrained fuzzy random multi-objective model and a traditional solution procedure based on an interactive fuzzy satisfying method are discussed. In addition, the technique of fuzzy random simulation is applied to deal with general fuzzy random objective functions and fuzzy random constraints which are usually difficult to converted into their crisp equivalents. The purposed of this study is to determine optimal order quantity and inventory level such that the total profit and wastage cost are maximized and minimize for the retailer respectively. Finally, illustrate example is given in order to show the application of the proposed model.

Emulsions stabilized by soy protein nanoparticles as potential functional non‐dairy yogurts

Abstract: Background Rice bran oil and soy protein nanoparticles (SPNs) may be considered as novel functional food ingredients for soy yogurt production. Formulation of soy yogurt with SPNs and rice bran oil, which has significant physiological functions, will convert them into functional food products. This study was conducted to develop rice bran oil-based soy protein nanoparticles emulsion (SPNE) and to evaluate physical properties, antioxidant activities, oxidative stability and microbiological load as well as textural attributes of SPNs incorporated yogurt (SPNY) during storage at 4 °C for 45 days. Results SPNs were prepared from soy protein isolate of defatted soy flour. Solubilization, crystallization and ultrasonication was carried out six times. After the sixth cycle of repeated solubilizing, crystallization and ultrasonication, the size of nano protein was reduced to 72.42 ± 0.22 nm from 586.72 ± 0.75 nm (after first cycle). Viscosity, penetration values and water-holding capacity of SPNs added to yogurt were decreased with increase in reduction size of SPNs. SPNs added to yogurt exhibited greater antiradical scavenging ability and ferric reducing antioxidant property than control yogurt. Fortified soy yogurt had significant higher oxidative stability and proteolytic activity. Conclusion Fortification of non-dairy food products with SPNs, which has significant physiological functions, convert conventional soy yogurt into functional food products. © 2019 Society of Chemical Industry.

Image feature based high capacity steganographic algorithm

Abstract: Steganography is the growing field of research, where hiding techniques are used to secure the communicative elements (e.g., images). In this paper, the message is hidden in the color image in spatial domain, exploring multi-bit Least Significant Bit (mLSB) steganography. Path trace, based on eccentricity of pixels gives the potential pixels to capacitate more hiding scope. Perspective based technique and meticulous statistical analysis are applied to immune the algorithm from sterilization along with other attacks. The algorithm overcome different tests done by benchmark like StirMark Benchmark 4.0, Receiver Operating Characteristic (ROC) curve. Visual Steganalysis and statistical tools like Dual Statistical Method and Histogram Difference are used to test the security and imperceptibility.The algorithm also ensures security with insignificant visual disturbance/distortion. Capacity per pixel after embedding, ranges from 9-bits to 12-bits and the minimum capacity per color per pixel is 2.37 and maximum is 2.69. The time complexity of the proposed algorithm is O(n2). The robustness is increased by unpredictable selection of bit for embedding.

Intention aware misbehavior detection for post-disaster opportunistic communication over peer-to-peer DTN

Abstract: Delay tolerant network (DTN) has been successfully proposed for setting up emergency post disaster communication networks when normal communication infrastructure is typically incapacitated. DTN being a network where participating nodes transmit messages to the final destination in multiple hops, its success depends on the cooperation of these participating nodes. Performance of such cooperation based networks get severely affected by misbehaving nodes that do not participate in message forwarding either due to reasons that are beyond its control (non-availability of appropriate forwarders, decreasing battery life, etc.) or out of certain malicious intentions. The misbehavior detection schemes, proposed so far, rarely investigate the actual intention behind misbehavior of participating nodes and do not attempt to restore the reputation of falsely alleged nodes. In this paper, we propose iDetect, a reputation based intention aware misbehavior detection scheme that uses contextual evidences to analyze the intention behind a node’s misbehavior. The scheme boosts the low reputation of incorrectly ostracized nodes and includes them in future communication. Re-inclusion of these nodes increases the number of genuine forwarders in the network which, in turn, assists in efficient delivery of crucial post disaster situational messages. Results of extensive simulation, using ONE simulator, substantiate the effectiveness of the proposed iDetect scheme over state-of-the-art competing schemes, in terms of detection ratio, availability ratio, etc. while not compromising on standard network performance in a post disaster communication scenario.

Unconventional localization phenomena in a spatially non-uniform disordered material

Abstract: A completely opposite behavior of electronic localization is revealed in a spatially non-uniform disordered material compared to the traditional spatially uniform disordered one. This fact is substantiated by considering an order-disorder separated (ODS) nanotube and studying the response of non-interacting electrons in presence of magnetic flux . We critically examine the behavior of flux induced energy spectra and circular current for different band fillings, and it is observed that maximum current amplitude (MCA) gradually decreases with disorder strength for weak disorder regime, while surprisingly it (MCA) increases in the limit of strong disorder suppressing the effect of disorder, resulting higher conductivity . This is further confirmed by investigating Drude weight and exactly same anomalous feature is noticed. It clearly gives a hint that localization-to-delocalization transition (LTD) is expected upon the variation of disorder strength which is justified by analyzing the nature of different eigenstates. Our analysis may give some significant inputs in analyzing conducting properties of different doped materials.

Nanoindentation and Scratch Resistance Characteristics of AZ31–WC Nanocomposites

Abstract: This work examines the effects of WC nanoparticles on nanohardness, elastic modulus and scratch-induced wear behavior of Mg-based metal matrix nanocomposites. Ultrasonic vibrator-equipped stir cast...

Coverage and rate analysis in two‐tier heterogeneous networks under suburban and urban scenarios

Abstract: Massive boost in data traffic demand and inconsistent user's behavior have necessitated modern cellular networks to evolve toward heterogeneous architectural framework consisting of macro ...

A Classification-based Approach to Prediction of Dengue Virus and Human Protein-Protein Interactions using Amino Acid Composition and Conjoint Triad Features

Abstract: Dengue Virus (DENV) is one of the most significant mosquito-borne viral infections of humans. Accoding to WHO, every year more than 50 million people are affected by DENV, resulting in 20,000 deaths. Protein-protein interactions play an important role in the cellular process of dengue virus infection in the human body. Although recently some studies have predicted protein-protein interactions (PPI) between human and DENV, many still remain to be identified. In this paper, we have predicted PPI between dengue and its human host combining amino acid composition and the conjoint triad of human protein sequences as a feature vector. We have concentrated on three well-known supervised machine learning methods, viz., Support Vector Machine (SVM), Naive Bayes (NB) and K-nearest neighbor (KNN) for prediction. SVM-based method achieved better accuracy, specificity and F1 score over other algorithms. Subsequently, unknown target human proteins of DENV infection are predicted using SVM to generate 411 new dengue-human PPI. Furthermore, the Gene Ontology (GO) and KEGG pathway of these predicted human proteins are analyzed. Both known and predicted human proteins share similar GO annotations. The pathways of the predicted human proteins are also found to be supported by recent literature. Identification of such interactions may accelerate a way for predicting new drugs to prevent dengue-related diseases.

Post-disaster Need Assessment

Abstract: Post-disaster need assessment deals with the accurate assessment of the need (i.e. demand and utility) for emergency resource at the shelters. While demand signifies the amount of resource required, utility represents the exigency of that requirement. Due to lack of, or imprecise need assessments immediately after a disaster, relief requirements are generally set up based on coarse estimates by logisticians regarding what people would normally need. The effectiveness of this estimation depends on the competencies and experience of the logistician in control, often leading to impromptu allocation of typically scarce emergency resources. Thus, forecasting the exact demand and enumerating the correct utility of emergency resources are inevitable.