Monika Bharti

The fundamental requirement for communication and computation across distinct application areas on Internet-of-Things is the resource discovery that demands appropriate reasoning for the optimal selection. With exponential growth of resources and their produced huge amount of heterogeneous data, various activities with respect to foraging and sense-making loops face challenges due to interoperability. Hence, interoperability emerges as a major bottleneck for the requirement. Therefore, to eliminate the challenge, the paper has proposed an "Optimal Resource Selection Framework for Internet-of-Things" that deals with the interoperability and ease the resource discovery and selection. The framework facilitates formation of semantic knowledge base as Shared Virtual Composite Ontology for capturing dynamic IoT heterogeneous data. Moreover, it supports optimal resource selection through the proposed algorithms, namely, Resource discovery Algorithm and Improved Firefly Algorithm. Both algorithms target coordination and optimization with Shared Ontology, respectively. The feasibility of the framework is checked against data collected from Sutlej river, Ludhiana, Punjab, India. The proposed framework is evaluated using benchmark functions with respect to metrics such as mean, standard deviation, processing and execution time. The obtained results are compared with the existing Nature-Inspired algorithms to confirm the efficiency of the proposed framework.

Auto Encoder (AE) can be used in denoising of images. It is a type of neural network that can reconstruct the input. An auto-encoder is to represent a (sparse) input dataset in a compressed form that retains the most relevant information such that it may be reconstructed at the output with minimal loss from the compressed representation. In this paper, deep AE, denoising AE, and variational AE are used. Any AE where an extra constraint is put on the bottleneck to have a low KL divergence from a Normal Distribution is a Variational AE. There are multiple ways in which Variational AE is used, but the most common one is generative. The decoders on the top of the bottleneck can be used to generate new data points. Maximum accuracy of 88.85% is observed using the Denoising autoencoder while 76.25% and 81.44% are observed for deep and Variational autoencoder, respectively. 8.3% and 14.18% accuracy improvement is observed in Variational and deep AE, respectively.

Abstract:: Noise represents a lack of data in the image, which can be removed using image denoising. Image denoising can be achieved by Gaussian filtering, anisotropic filtering, wavelet thresholding, etc. In this work, wavelet-based denoising has been used because it can effectively remove both additive and multiplicative noise from images, and preserve fine details and edges in the image. In this work, different thresholding techniques, like Visu shrink and Bayes shrink for Hard Thresholding (HT) and Soft Thresholding (ST), employing different standard deviations ranging from 0.05-0.3 with a difference of 0.05, have been used. The Peak Signal-to-Noise Ratio (PSNR) has been evaluated as a performance parameter. For grayscale images, the maximum value of PSNR has been obtained as 29.483 dB, while for RGB images, a value of 34.324dB using Bayes shrink considering ST at 0.05 variance has been achieved. 2.2% improvement has been observed in grayscale images, while an 8.6% improvement has been observed in RGB images with Bayes shrink ST compared to Bayes shrink HT. Also, while comparing PSNR values of other thresholding techniques, ST has been found to provide better results than HT. The PSNR values for images produced by Bayes shrink have been found to be high, which implies the quality of reconstructed images to be better with Bayes shrink than with Visu shrink.

With the exponential growth of resources on Internet-of-Things (IoT), discovery has emerged as one of the major challenges due to requirement of self-manageable resources. The traditional discovery approaches fail to meet the challenge with changing IoT requisition for various metrics like mobility patterns, syntax, scale of experiment, access and search type. The proposed “Intelligent Resource Inquisition Framework on Internet-of-Things (IRIF-IoT)” framework addresses the challenges through its three layers, namely, perception, discovery, and application. Its main features are linking resources through usage of semantic description and ontology, their discovery with “Semantic Matchmaking Engine using Bipartite Graph (SMEBG)” and to access information via web terminal for users. The search efficiency is evaluated using toll datasets collected from Ladowal Toll Plaza, Punjab, India. The results obtained shows that SMEBG outperforms Fuzzy Control Logic (FCL) and Genetic Algorithm (GA) by 47% and 57%, respectively.

Summary Resource discovery on Internet‐of‐Things paradigm is an eminent challenge due to data‐specific activities with respect to foraging and sense‐making loops. The prerequisite to deal with the challenge is to process and analyze the data that require resources to be indexed, ranked, and stored in an efficient manner. A novel clustering technique is proposed to resolve the specified challenge. The technique, namely, iterative k‐means clustering algorithm, targets concrete cluster formation using similarity coefficients of vector space model and performs efficient search against matching criteria with respect to complexity. It is simulated on MATLAB, and the obtained results are compared with fuzzy k‐means and fuzzy c‐means clustering algorithm with similarity coefficients of vector space model against exponential increase in the number of resources.

Social networking site Twitter, in particular, has become a popular spot for gossip. Rumors or false news spread very easily through the Twitter network by re-tweeting users without understanding the real truth. These reports trigger popular confusion, threaten the authority of the government and pose a major threat to social order. It is also a very necessary job to dispel theories as quickly as possible. In this research, multiple descriptive and consumer-based features via tweets are retrieved and integrated these features with the TF-IDF system to develop a composite set of features. This composite set of features is then used by several machine learning techniques like Support Vector Machine (SVM), Linear regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Random Forest, and Gradient Boosting. Along with these machine learning classification models, a Convolutional Neural Network (CNN) algorithm is proposed to distinguish rumour and non-rumor tweets. The proposed model is evaluated with freely accessible twitter datasets. The existing machine-based learning models have acquired an Fl-score of 0.46 to 0.76 for rumour detection, while the CNN model attained an Fl-score of 0.77 for rumour class. Overall, the CNN model yields greater results with a weighted average Fl-score of 0.84 for both rumour and non-rumor categories. The potential mechanism will help to detect misinformation as quickly as possible to counteract the dissemination of rumours and build users' deep confidence in social media sites.

Summary Decision making plays a vital role in the selection of resources so that they actively participate for communication and computation on the Internet‐of‐Things platform. For the same, they require the elimination of the challenges related to knowledge representation, discovery, trust, and security due to continuously changing mobility patterns, heterogeneity, interoperability, and scalability on the network. To address the challenges, a novel three‐layered approach, namely, middleware approach for reliable resource selection on Internet‐of‐Things (MARRS‐IoT), is proposed. It performs a search through neighbor discovery algorithm and evaluates trust score of the discovered resources, both locally and globally using fuzzy‐decision algorithm and performs efficient communication among resources via hybrid M‐gear protocol. The approach is simulated and compared against algorithms, namely, particle swarm optimization, ants colony optimization, and binary genetic to evaluate its performance. The obtained results support the efficacy of the MARRS‐IoT with respect to throughput and execution time.

The fundamental requirement for communication and computation across distinct application areas on Social Internet of Things (SIoT) is the resource discovery that demands appropriate reasoning for the optimal selection. With exponential growth of resources and their produced huge amount of heterogeneous data, various activities face challenges due to interoperability. In order to eliminate the challenge, the paper focuses on to propose an optimal resource selection technique namely, Modified Cuckoo Search (MCSA). The technique helps in reducing traffic congestion on network by selecting optimal resources in less time. The technique is tested on random dataset. The obtained results show that MCSA outperforms 22% approximately in comparison to nature-inspired, meta heuristic based machine learning algorithms i.e., Particle Swarm Optimization and Binary Genetic Algorithm.

Cloud computing gets its standard all over the world due to the necessity for delivering IT services, in excess with internet. It consist of pooled computing resources like network, applications, servers consist of parallel and distributed system based on the SLA. It has been perceived from the past decade that in a cloud environment task scheduling became concerned issue. In this paper our aim is to get better optimized results for which we do mapping of tasks to resources maintaining fairness using the PSO scheduling algorithm. The platform used for the experiment is CloudSim via which we attain more efficiency.

With the epidemic progression in resources on IoT, discovery emerges as an eminent challenge due to requirement of their self-automation. The traditional resource discovery approaches do not provide efficient methodologies due to continuously changing IoT search metrics such as syntax, access, architecture, etc. To address the gap, the paper proposes an optimized technique, namely, Modified Genetic Algorithm for Resource Selection (MGA-RS) that intends to discover optimum data (resources) is short period of time by considering the bit strings of chromosomes. It is evaluated on datasets of Ionosphere from machine learning repository of university college, London. The best and mean fitness are selected in a way that they should be close to each other at the time when MGA-RS reaches termination condition and to minimize classification error from kNN. It is found that MGA-RS outperforms well with kNN based fitness function and is approximately 14% and 15% better than simple and rastrigin fitnesses, respectively, for selecting the optimal resources in IoT.

Cloud computing is a paradigm that provides demand service resources like software, hardware, platform, and infrastructure.Under cloud environment, workflow is an emerging technique for future scalable applications.This paper discusses the various tools for generating workflow and these tools have been compared on the basis of operating system, databases, architecture and so on.The application on workflow is generated with Pegasus tool which can be further deployed on its compatible cloud platforms like Eucalyptus, Amazon EC2, Open Stack etc.

Background: In the salivary gland lesions most common presentation is of a swelling or palpable mass of varying duration. In infective lesions, pain is another presenting feature. The most accurate method of diagnosing salivary gland lesions is histopathology, yet the role of ne needle aspiration cytology (FNAC) for the diagnosis of salivary gland masses is well documented and is signicantly accurate. The present study comprise analysis of FNAC of all salivary gland lesions using Milan system of classication. The present retrospective Material and Methods: study involves 234 cases of salivary gland swellings, referred for FNAC to the Department of Pathology, Government Medical College, Jammu for a period of ve years w.e.f November, 2016 to 31 October, 2021. This study includes cytomorphological features of various salivary gland lesions on FNAC and their classication by the Milan system of reporting. It was obs Results: erved that peak incidence of salivary gland tumours was seen in the age group of 31-45 yrs. The majority of benign lesions were seen in the age group 16-30 yrs and the malignant lesions were seen in the age group 46-61yrs. There was female predominance over the male with F:M ratio is 1.46:1. The most frequently involved site was parotid gland (48.7%) followed by submandibular gland (40.6%). 109 cases were diagnosed as non-neoplastic, 98 as benign and 16 cases as malignant. Conclusion: FNAC is an important diagnostic tool to clinch the correct diagnosis of salivary gland tumours Fine needle aspiration cytology was able to distinguish between non-neoplastic and neoplastic lesions and benign and malignant tumours

This article investigates a compact slotted rectangular patch antenna with circular polarization for triple band circularly polarized (CP) operation. In this, a co-axial feed circularly polarized square patch antenna is designed using a slot with a truncated corner. The proposed antenna is designed on an Arlon AD225A dielectric substrate with a thickness of 1.6mm . The intended circular polarized antenna gives a miniaturized size of 0.58λ0×0.58λ0×0.1546λ0 at a frequency 2.9GHz. The proposed CP antenna provides impedance bandwidths of 4%, 3.8%, 2.52%, 1.57% and 3.4% at the center frequencies of 3GHz, 6GHz, 6.7GHz, 7GHz, and 9GHz respectively. The proposed antenna has a maximum gain of 10.34dB at a frequency 8.7GHz.The proposed antenna is suited for the RFID readers in the SHF band.

This research paper delves into the realm of secondary education and investigates the influence of gamification on students' motivation and learning outcomes. The increasing integration of technology and digital platforms into educational settings has sparked a surge in interest regarding gamification as a novel approach to engage and motivate students. The paper seeks to address this evolving landscape and the potential implications for educators, students, and the broader field of education. Gamification, the practice of incorporating game elements and mechanics into non-game contexts, has become a prominent strategy to enhance students' educational experiences. In the context of secondary education, where adolescents face unique challenges and motivational factors, understanding the impact of gamification is of paramount importance. This study aims to provide insight into how gamification may influence students' motivation and, subsequently, their learning outcomes. Motivation is a multifaceted construct within education, and various theories such as Self- Determination Theory and Expectancy-Value Theory underscore its significance. The paper begins by reviewing these motivational theories, emphasizing the role of autonomy, competence, and relatedness in driving students' motivation to learn. It also highlights the relevance of intrinsic and extrinsic motivation, which is vital for a comprehensive understanding of how gamification can impact students. The core of this research lies in empirical findings gathered through a mixed-methods approach. A survey instrument was administered to secondary education students who experienced gamified learning environments. Qualitative data was also collected through interviews and classroom observations. This data allowed for a multifaceted analysis of students' perspectives on gamification, their motivation levels, and their resulting learning outcomes. The empirical findings reveal several key insights. First, gamification positively influences students' motivation, as it fosters a sense of autonomy and competence. Students reported greater engagement with course materials, enhanced focus on learning tasks, and increased willingness to persist in the face of challenges. Gamification elements such as points, leaderboards, and rewards effectively cater to intrinsic and extrinsic motivation, thereby increasing students' interest in the subject matter. Second, gamification, when appropriately designed and implemented, significantly impacts learning outcomes. Students exposed to gamified learning environments exhibited improved academic performance, greater retention of course content, and a deeper understanding of complex concepts. The competitive and collaborative nature of gamification promotes active participation and knowledge transfer. The discussion section interprets the findings in the context of existing literature, highlighting the practical implications of this research for educators and policymakers. Educators can harness gamification strategies to foster a motivating classroom atmosphere and, consequently, enhance students' learning experiences. However, it is crucial to recognize that gamification should be tailored to the specific needs and preferences of students, as a one-size-fits-all approach may not yield optimal results. In conclusion, this research underscores the transformative potential of gamification in secondary education. By stimulating motivation and bolstering learning outcomes, gamification emerges as a promising tool for educators seeking to engage students effectively. However, ongoing research and tailored approaches are essential to maximize its benefits and create meaningful, sustainable educational experiences in secondary education. This study contributes to the ongoing dialogue surrounding gamification and its capacity to shape the future of education.

Nowadays where most of the works are being carried online the demand for face recognition technique is elevated. Computerized software is assisting in identifying human feelings such as happiness, sadness, anger, fear, disgust, etc. Over the decades, various research has been carried on the facial expression and emotion recognition. Emotion detection has extended applications. It is not merely related to any specific field nut instead the approach ranges from communication, advertising to hospital requisition and many more. To exist collective mechanisms through which we can accomplish the process of facial emotion recognition. In this paper we are using Convolutional Neural Network for the implementation. Upon exploring numerous datasets for the procedure of experiment we have chosen to go with Kaggle dataset.

Abstract The resource discovery on IoT paradigm requires to be efficient with respect to modeling, storage, processing, and validation of the gathered data. These requirements face challenges like interoperability, heterogeneity, etc , with respect to exponentially growing interconnected resources across distinct application domains and drastically changing search metrics. It leads resource discovery to emerge as a non‐linear constrained‐specific problem that need to be linearized for its optimization with reduced complexity. Keeping the perspective, a context‐aware search optimization framework on the internet of things is introduced, which targets knowledge presentation through schema, discovery via a multi‐modal search algorithm, and its optimization through an Iterative Gradient Descent algorithm. The multi‐modal search algorithm through keywords, value or spatial‐temporal indices performs resource discovery by finding the suited matches as a search set from a search‐space. The search set is further evaluated via the iterative gradient descent algorithm for optimization through the usage of iterative and convergence properties of the gradient descent. The search efficiency is tested using various objective functions and resources on MATLAB and is compared with Newton and Quasi‐Newton methods. The obtained results depict the efficiency of the algorithm graphically with reference to the searching time, such as validate the system performance.

Privacy concerning resource discovery and selection on the Internet of Things (IoT) platform has emerged as a crucial challenge due to constrained network environment, poor throughput, and access control system in the Resource Directory (RD), which could potentially lead to information breaches among intelligent devices. Blockchain is indeed the latest technological model for global information management, point-to-point exchange, consensus process, asymmetric authentication, cognitive agreement, and perhaps other computing innovations to address the challenge. Considering these advantages, a resource-blockchain framework for safeguarding IoT is proposed in this article. The framework facilitates efficient access of information among devices, gathers information about the constrained environment, accepts data from heterogeneous resources and stores them on CoAP-based RD. Moreover, it provides secure transactions and records authorised user’s information via Blockchain-based centralised server which acts as the frontend of CoAP based REST, dispatcher or repository and proxy server. The study demonstrates that Blockchain technologies help in strengthening privacy capabilities and safeguarding intelligent devices.

Transcription factors (TFs) play an important role in plant development; however, their role during viral infection largely remains unknown. The present study was designed to uncover the role transcription factors play in Cucumber mosaic virus (CMV) infection. During the screening of an Arabidopsis thaliana (Col-0) transcription factor library, using the CMV 2b protein as bait in the yeast two-hybrid system, the 2b protein interacted with Homeobox protein 27 (HB27). HB27 belongs to the zinc finger homeodomain family and is known to have a regulatory role in flower development, and responses to biotic and abiotic stress. The interaction between CMV 2b and HB27 proteins was further validated using in planta (bimolecular fluorescence complementation assay) and in vitro far-Western blotting (FWB) methods. In the bimolecular fluorescence complementation assay, these proteins reconstituted YFP fluorescence in the nucleus and the cytoplasmic region as small fluorescent dots. In FWB, positive interaction was detected using bait anti-MYC antibody on the target HB27-HA protein. During CMV infection, upregulation (~3-fold) of the HB27 transcript was observed at 14 days post-infection (dpi) in A. thaliana plants, and expression declined to the same as healthy plants at 21 dpi. To understand the role of the HB27 protein during CMV infection, virus accumulation was determined in HB27-overexpressing (HB27 OE) and knockout mutants. In HB27-overexpressing lines, infected plants developed mild symptoms, accumulating a lower virus titer at 21 dpi compared to wild-type plants. Additionally, knockout HB27 mutants had more severe symptoms and a higher viral accumulation than wild-type plants. These results indicate that HB27 plays an important role in the regulation of plant defense against plant virus infection.

Literature studies over the past few decades have estimated that more than 40% of drug delivery systems have various critical issues such as poor solubility, toxicity, a lack of sensitivity and poor specificity leading to poor and variable bioavailability. This nonspecific distribution of drugs in the body results in potentially strong side effects, which further limits their clinical use. To overcome the limitations of conventional drug administration, there is a great need for controlled drug delivery systems. The designing of site-specific drug delivery systems is challenging. The solution is advanced nanotechnology such as nanocarriers. Nanocarrier-based platforms (i.e., nanoparticles, nanocapsules, nanocrystals, lipid complexes, polymeric micelles, and dendrimers) are systems dedicated to the transport of chemotherapeutically active drugs composed of submicron-sized colloidal nanoparticles with (typically <500 nm). Nanocarrier's means sizes of submicron having high surface to volume ratio leading to increased dissolution rate. They have been widely used to deliver poorly soluble drugs to target tissues, organs, or cells. The major aim for their application of drug delivery is to treat an ailment efficiently with the fewest side effects. This results in an enhancement of curative outcomes by exploiting the physiology of the diseased tissue microenvironment. Nanocarriers offer a number of benefits by delivering drugs to tiny areas within the body, reducing drug toxicity. They offers more efficient drug distribution, which makes them an ideal drug delivery module. Nanocarriers are enormously efficient for to transporting drugs over the blood–brain barrier, branching pathways of the pulmonary system, and the tight epithelial junctions of the skin. Because of the leaky constitution of nanocarriers, they have better penetration power in tumors. Drug nanocrystals and nanosuspensions have been recognized as efficient and successful approaches to drug delivery. These nanocrystals are composed of unadulterated drug crystals (sizes within the nanometer range). Only a thin coating of surfactants needs to be added for steric and electrostatic surface stabilization. In this chapter, we will discuss various properties, synthetic routes for production, and the applications of nanocrystals in drug delivery systems.

The basic premise of Internet of Things (IoT) is to have intelligent devices that can communicate and cooperate directly without human involvement and to deliver a new class of applications such as Bio stamps, Wearable things, Smart City, Smart Toll Plaza etc. For the purpose, the key focus of IoT is on four key components, namely, enabling technologies, standard protocols, technological barriers and application areas. Some IoT architectures like SOA, ROA, NEBULA FIA, NDN, CASAGRAS and AKARI have been proposed by different researchers and are discussed in this article. The paper not only narrow down the promising research domains like identification technology, energy optimization, hardware and software solutions with their major research issues, but also discusses the technological tradeoffs of applications.

Social Internet of Things is a critical digital tool for companies of different sizes. Good governance of the Social Internet of Things allows successful strategy to emphasise brands, making products no longer discretionary. Buzz marketing is a powerful advertising strategy that focuses on exploiting the value of a promotion or item, through conversations between family members and friends, or through wider conversations on the Social Internet of Things platforms. Buzz's forecast allows for an overview of the ranking of retailers by posts shared by future consumers or members on social media platforms. Buzz estimation in the Social Internet of Things Platforms, like Twitter, is a daunting challenge over real-time data by defining multiple aspects for buzz exploration. The bulk of buzz predictor analysis studies are modelled on machine learning methods like Radial Base Feature, Neural Network Models, Ant Colony Optimization, and Support Vector Devices. In this study, Random Forest is being used to make buzz predictions as it creates multiple decision trees and mixes them together again to make predictions more precise. The findings of comparative studies with Support Vector Machine (SVM) in three separate kernels and Radial Basis Function (RBF), it suggest that Random Forest is better at forecasting buzz and has also established a rating list of important features in order to achieve reasonable prediction performance. The features suggest that the main important characteristics to define the subject being generated are either “Buzz” or “Not Buzz”. Random Forest showed an average improvement in accuracy with a rate of 99% and the highest training time. However, although the Radial Based Function method has proved to be quite efficient, the Support Vector Machine solution is of mediocre accuracy.

Two manganese(II) carbodithioates, i.e. (Mn(4-MPipzcdtH)2)(X)2 and two manganese(III) carbodithioates of the type (Mn2(4-MPipzcdtH)6(O2)2)(X)4 (4-MPipzcdtH = 4-methylpipearzine-1-carbodithioic acid and X = ClO4, Cl) have been prepared. These have been characterized by elemental analyses, conductance, infrared and electronic absorption spectral and room temperature as well as variable temperature magnetic susceptibility measurements. Manganese(II) complexes exhibit antiferromagnetic behavior. Antiferromagnetic interactions through superoxo bridge have been proposed for temperature dependent magnetic behaviour of manganese(III) complexes.

The root-locus method is a well-known tool for control system analysis and design. It is an important topic in introductory undergraduate engineering control disciplines. In this paper we present the analysis of a transfer function using root locus method. Through analysis of transfer function we check the effect of gain on poles and zeros.

This paper presents the effect of side diffusion and doping concentration profile produced by two different ion implantation model for UHV LDMOS device with Linear P-top and its effect on device performance. The result shows that the device using Monte Carlo Model have different side diffusion with different N-Epi layer background whereas Taurus Table is unable to explain the side diffusion and Monte Carlo also gives 22% of improvement in breakdown Voltage, 10% lower on-resistance as compared to Taurus Table model with uniform electric field distribution. The P-top implantation in different background, the advantages of linear P-top structure will have different degrees of presentation in the experimental result.

Salivary glands are very common targets for fine needle aspiration.The hallmark of pleomorphic adenoma is to exhibit wide spectrum of morphology.Morphological spectrum can vary from predominantly epithelial types to predominantly stromal types.This morphological diversity poses a diagnostic challenge to cytopathologists.The aim of the present study was to asses the cytomorpological features of pleomorphic adenoma and to highlight the difficulties faced in its cytolological diagnosis.The study was based on 42 salivary gland FNA cases with cytological diagnosis of pleomorphic adenoma.26 were females and age ranges from 9-84 years (mean-36.92).We conclude that adequate and representative samples are required for proper diagnosis.It is essential for the cytopathologists to be aware of the cytomorphological variations of pleomorphic adenoma on FNAC to avoid the possibility of diagnostic errors.

Cryptocurrencies are a sort of digital currency in which all transactions are carried out through the internet. It is a soft currency that does not exist in hard cash form. We emphasize the difference between a decentralized currency and a centralized currency in that all virtual currency users can acquire services without the intervention of a third party. Using these cryptocurrencies, however, has an influence on international relations and trade because to their severe price volatility. Furthermore, the rapid variations in cryptocurrency prices indicate that a reliable method for estimating this price is urgently required. To anticipate a company’s stock price/ cryptocurrency based on past prices, the methods namely, Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) deep learning architectures are employed. The proposed method is written in Python and tested on benchmark datasets. The results show that the proposed method can be used to make reliable predictions.

The simulation method to represent the effect of the charge existence in the passivation layer between Silicon Dioxide and Silicon Nitride under HTRB test has been investigated. The dynamic concentration of trapped charge as a function of temperature, high bias voltage, trap concentration and depassivation coefficient will be the factors under investigation that causing overtime degradation of the UHV LDMOS device. Localized and accumulated trapped charge is proven to be responsible to distort the surface electric field of device.

The word has many different definitions from conventional to strategic. A conscious definition of quality generally describes the quality points as follows: One fits well, is best served or the decision is free for a longer period of time. Meeting Customer needs, which strategically determine quality, is often associated with identifying and meeting customer needs in a mode that is better than that of competitors. According to Oxford Dictionary, quality is a measure of a similar type of problem, something that is measured at an excellent level. Quality is also determined by the unique characteristics or characteristics of someone or something.

Angular correlations serve as a very important tool to study the complex dynamics of particle collisions at colliders, which is not very well understood. Angular correlation is generally quantified in terms of the ∆η and ∆Ø. In the current paper, Monte Carlo events using simulator Pythia are generated and studied in terms of the variables sensitive towards the angular correlations.

Consumer goods sector has long been considered as defensive and not prone to business cycles.However, the recent economic meltdowns, volatility spillovers and investment patterns suggest that the sector is subject to market movements and no more insulated.The present study examines the fast moving consumer groups industry stocks trading in the Indian equity market for the behavioral bias of herding.We use the modified methodology of cross sectional absolute deviation on the daily prices of the index and its constituents for the time period January 01, 2008 to December 31, 2018.No evidence of herd behavior is found at the aggregate market level or during market asymmetries of bear and bull phase.However, significant negative herding is seen during the sample period.The paper suggests the scope of future research and the implications of the study for the market participants.