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Simulation, optimisation and analysis of energy saving in crude oil distillation unit
Physical distillation is the major process in crude oil refineries as of now. To ensure quality control in the final products, it is essential to ascertain the true boiling point of the crude oil and the products. The work is mainly concentrated to an industrial crude oil distillation unit. The objective of the paper is to present the simulation and optimisation of crude distillation unit (CDU) along with the analysis of energy saving, using Aspen HYSYS V8.8.2. Different crudes are taken into account, their properties and amount of distillate are analysed. The process optimisation is done in an easier manner using Aspen HYSYS rather than the conventional mathematical method, together with the advanced process control tools; make it profitable in the operation in real-time. The simulation results are validated with the actual plant results. Copyright 2018 Inderscience Enterprises Ltd. -
Progressive crude oil distillation: An energy-efficient alternative to conventional distillation process
Distillation, the major process in crude oil refineries as of now. In this work we focused the attention to energy saving with respect to an industrial crude oil distillation unit. An alternative to the conventional crude oil distillation model present in the Bharat Petroleum Corporation, Kochi Refinery is proposed and simulated. The theoretical predictions as well as the simulated results indicate that the Progressive crude oil distillation reduces the utility burden as well as increase the extraction of more valuable light components. The simulation was carried out using Aspen HYSYS V8.8.2. Different crudes are taken into account and their properties and amount of distillate are analyzed. The optimization is done in an easy manner rather than the conventional mathematical method, together with the advanced process control tools; make it profitable in the operation in real time. 2018 Elsevier Ltd -
Synthesis, properties, and state-of-the-art advances in surface tuning of borophene for emerging applications
Being composed of boron atoms that can be maneuvered to orchestrate low planar hexagonal structures, this two-dimensional material carefully exhibits versatility and has conventional covalent bonds between each atom. Borophene has recently proliferated the scientific research community by storm, trailblazing industries from fine chemicals, electrical equipment manufacturing, and biomedical innovation up to sustainable energy. Here, we provide streamlined information and particulars about the recent advances in the evolution of borophene since its inception and the essence of its electrocatalytic applications. We first introduce the sophisticatedly cultivated progress in borophene's structural, mechanical, optical, and electrical properties and further discuss its variegated polymorphism. Subsequently, we also delve into several capable synthesis techniques and recently concocted surface tuning and doping methods. Finally, we analyze the advancing state-of-the-art applications of this two-dimensional nanomaterial under investigation, ranging from bioimaging, energy storage, electrode reduction, and electrochemical sensing. Further, we have broadly discussed the future insights and challenges that borophene brings. 2024 -
Impact ofFeature Selection Techniques forEEG-Based Seizure Classification
A neurological condition called epilepsy can result in a variety of seizures. Seizures differ from person to person. It is frequently diagnosed with fMRI, magnetic resonance imaging and electroencephalography (EEG). Visually evaluating the EEG activity requires a lot of time and effort, which is the usual way of analysis. As a result, an automated diagnosis approach based on machine learning was created. To effectively categorize epileptic seizure episodes using binary classification from brain-based EEG recordings, this study develops feature selection techniques using a machine learning (ML)-based random forest classification model. Ten (10) feature selection algorithms were utilized in this proposed work. The suggested method reduces the number of features by selecting only the relevant features needed to classify seizures. So to evaluate the effectiveness of the proposed model, random forest classifier is utilized. The Bonn Epilepsy dataset derived from UCI repository of Bonn University, Germany, the CHB-MIT dataset collected from the Childrens Hospital Boston and a real-time EEG dataset collected from EEG clinic Bangalore is accustomed to the proposed approach in order to determine the best feature selection method. In this case, the relief feature selection approach outperforms others, achieving the most remarkable accuracy of 90% for UCI data and 100% for both the CHB-MIT and real-time EEG datasets with a fast computing rate. According to the results, the reduction in the number of feature characteristics significantly impacts the classifiers performance metrics, which helps to effectively categorize epileptic seizures from the brain-based EEG signals into binary classification. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation
Seizure detection is the most crucial area of investigation when it comes to understanding brain disorders. This proposed research study embarked on an automated model for epileptic seizure diagnosis by means of different kinds of Spectral transformation using EEG inputs from seizure sufferers and healthy subjects. This automated model accommodates non-invasive brain electrical activity monitoring. This method aims to facilitate the analysis and identification of epileptic seizure states since, monitoring and diagnosing such brain electrical activity is a complex task due to its numerous divisions and underlying features. The primary objective of this research study is to distinguish between EEG-based seizures and healthy individuals. To achieve this goal, a combination of spectral transformation and EEG analysis techniques is utilized. These techniques include examining the frequency spectrum, magnitude spectrum, correlation, and T-Distributed Stochastic Neighboring Embedding (T-SNE) analysis. This analysis yields valuable insights from EEG data, refining the input data and making it more suitable for prediction and identification. The models performance is evaluated using two distinct datasets: real-time EEG data from individuals experiencing epileptic seizures and EEG data from healthy subjects. These datasets are sourced from the Bangalore EEG Epilepsy Dataset (BEED), India and the BONN epilepsy dataset from the UCI repository. In a comparative study of spectral transformation methods, including Complex Fast Fourier Transform (CFFT) and Real-Valued Fast Fourier Transform (RFFT), it is discovered that reducing the data dimension by using feature extraction is not the optimal approach. This simplification leads to the loss of valuable information. Therefore, preserving the full spectrum of EEG characteristics is crucial for gaining valuable insights into brain neuronal functions, ultimately enabling more accurate seizure prediction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Automated epileptic seizure classification using adaptive fast Fourier transform with non-uniform sampling and improved deep belief network
In automated brain-computer interaction (BCI), EEG signals are essential. This research uses AI to detect epileptic seizures, employing data from the BONN dataset (UCI), CHB-MIT dataset (physionet server), and Bangalore EEG Epilepsy Dataset (BEED). The goal is to develop an automated system for accurate seizure detection using adaptive fast Fourier transform with non-uniform sampling (AIFFT-NS) and an improved deep belief network (IDBN) model to enhance classification accuracy. The AIFFT-NS model serves as a channel for transforming spectro-temporal data. Using various EEG datasets, a number of extensive experiments are carried out, resulting in the validation of the efficacy of the proposed approach. High accuracy metrics, with 96.16% for the BEED dataset, 99.41% for the BONN dataset, and 96.31% for the CHB-MIT dataset, represent the evidentiary outcomes. This study emphasises the critical function of AI-facilitated spectro-temporal EEG analysis within the domain of medical diagnostics, going beyond the realm of automated seizure onset classification. Copyright 2024 Inderscience Enterprises Ltd. -
Impact of Multi-domain Features for EEG Based Epileptic Seizures Classification
Accurate detection and classification of epileptic seizures play a pivotal role in clinical diagnosis and treatment. This study introduces an innovative approach that leverages multi-domain features extracted from Electroencephalogram (EEG) data in conjunction with Supervised learning classification techniques. Initially, EEG data undergoes preprocessing through data standardization, followed by the extraction of essential features per instance, encompassing combination of Time domain, Frequency domain, and Time-Frequency domain features. These extracted feature combinations are subsequently fed into the machine learning-based boosting classifier Adaptive Boosting (ADABOOST) for an accurate and precise classification of epileptic signals. Validation of the proposed method is conducted using EEG data from the BEED (Bangalore EEG Epilepsy Dataset) and BONN (University of BONN, Germany) database to detect epileptic seizures. The experimental results show remarkably high levels of classification accuracy for various conditions: 99% accuracy for BEED data, 98% accuracy for BONN data for classifying seizures from healthy states, and 91% accuracy for classifying seizure onset from seizure events. Furthermore, the study applies the Gaussian Nae Bayes (GNB) classifier to differentiate various types of epileptic seizures, employing evaluation metrics such as the confusion matrix, ROC curve, and diverse performance measures. This method demonstrates significant potential in supporting experienced neurophysiologists decision in the clinical classification of epileptic seizure types. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Media's portrayal of women activists - A comparative case study on Malala Yousafzai & Irom Sharmila /
Media has always played a major role in depicting various sections of the society, various aspects of life and people and thus enabled the common man have various perspectives. Sometimes, media becomes selective and refuses to properly execute certain news. Media can play a useful role in dissemination of information, but it when it comes to the process of portraying the women who have stood up for something, is the efficiency same? -
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Impact of gamification on learning outcomes in organizations
Background Operational Excellence is a philosophy of leadership, teamwork and problem solving, to focus on the needs of the consumer, to empower employees, for ptimizing existing activities, continuous improvement and excellence. It is a competitive advantage which translates increased flexibility to improved consumer responsiveness, and lean management. Quality of care is about patient safety, institutional culture, attitude, clinical performance, clinical freedom with management as facilitators, efficient delivery of quality, high standard services, effective patient outcome, integration of legislation with regards to communities, health service providers, local health authorities and the government (WHO, 2013). The outcome of quality of care is health consumer (patient) satisfaction. High newlineperformance Engagement reflects how employees are engaged in their work, with commitment and passion, rather than mere compliance to impact performance. Health care is a balancing act between business excellence newlineand quality outcomes in practice. It is from the premise of high performance engagement and quality of care provided to health consumers with patient centered focus, the pedestal of success in operational excellence is achieved. Purpose This study focuses on establishing Operational Excellence in relation to High Performance Engagement and Quality of Care among executives in the health care sector. Method A descriptive study was carried out using quantitative method with a sample of 410 health care executives from NABH accredited and nonaccredited hospitals and qualitative analysis among patients in Kerala. Results newlineThe results indicate a positive correlation of operational excellence with high performance engagement and quality of care. The independent variables, high performance engagement and quality of care are significant predictors of operational excellence. -
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Operational excellence in relation to high performance engagement and quality of care among executives in the healthcare sector in kerala
Background Operational Excellence is a philosophy of leadership, teamwork and problem solving, to focus on the needs of the consumer, to empower employees, for ptimizing existing activities, continuous improvement and excellence. It is a competitive advantage which translates increased flexibility to improved consumer responsiveness, and lean management. Quality of care is about patient safety, institutional culture, attitude, clinical performance, clinical freedom with management as facilitators, efficient delivery of quality, high standard services, effective patient outcome, integration of legislation with regards to communities, health service providers, local health authorities and the government (WHO, 2013). The outcome of quality of care is health consumer (patient) satisfaction. High newlineperformance Engagement reflects how employees are engaged in their work, with commitment and passion, rather than mere compliance to impact performance. Health care is a balancing act between business excellence newlineand quality outcomes in practice. It is from the premise of high performance engagement and quality of care provided to health consumers with patient centered focus, the pedestal of success in operational excellence is achieved. Purpose This study focuses on establishing Operational Excellence in relation to High Performance Engagement and Quality of Care among executives in the health care sector. Method A descriptive study was carried out using quantitative method with a sample of 410 health care executives from NABH accredited and nonaccredited hospitals and qualitative analysis among patients in Kerala. Results newlineThe results indicate a positive correlation of operational excellence with high performance engagement and quality of care. The independent variables, high performance engagement and quality of care are significant predictors of operational excellence. -
Changes in portrayal of antagonists in post 9/11 American cinema- Case studies on 'The Joker' and 'Khan' /
Alfred Hitchcock said, the better the villain, the better the film. If cinema has been a mirror to our times, the characters that populate one are reflections of us- the manifestations of our times. The antagonists in cinema are all that we dread and fear presented in different degrees of hyperbole, and to unswervingly desire and cheer their downfall at the hands has been an enduring appeal of cinema. -
India's outward foreign direct investment a study of host country determinants and implications on trade
India s outward foreign direct investment newline(OFDI) has registered substantial increase since the 1990s. It is significant to analyse the motivation, location choices and implications of this trend since empirical newlineresearch has largely addressed OFDI originating from developed countries. newlineThis study analyses the pattern of India s OFDI, its changing contours in terms of host country location, composition across sectors and its implication on exports. The study aims to link motivation of investment newlineto location factors across developed and developing countries. Using OFDI newlinedata from 2001to 2013, the study analyses host country economic factors that are significant in the location of India s OFDI in the ambit of eclectic paradigm. The study empirically tests market seeking, resource seeking and strategic asset seeking nature of India s OFDI adopting a panel data newlinemethodology. newlineThe results of the study show that India s OFDI is motivated by market seeking and resource seeking in both developed and developing countries. Strategic assets are a significant motivation for investment in the newlinedeveloped countries and not in the developing countries.India s OFDI is also positively associated with host country policies like openness. To study the implication of OFDI, the study empirically tests the relation between India s OFDI and exports using time series methodology. The empirical test newlineof the relation between OFDI and exports shows a significant long term positive relation between the two. newlineThe current research is presented in six chapters. Chapter 1 consists of the introduction, objectives, hypotheses and significance of the study. Chapter 2 consists of the literature review. The changing direction and composition by sectors is analysed in chapter 3. Chapter 4 contains the methodology. Chapter 5 presents the results of the study and chapter 6 contains the conclusion and policy implications. newline -
A perspective reading of photographs of the Ahmedabad city /
The present study talks about the usage of photographs as a major tool for storytelling. It shows how various photographs of various places, if shown to people and analyzed according to their perspectives and experiences can give us a rich definition and an idea about the events that had shaped the place and led to their current being. Also, it describes the place according to the way or the angle the respondents saw it to be. -
Marketing strategies of surrogate advertisements of liquor products in India /
Advertisements have a strong influence in our life we like them because they provide information and create awareness about the market. But, the Government of India has restricted promotion of certain products on television like liquor, tobacco, pan etc. To overcome this companies use surrogate advertising. -
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An efficient framework for scientific article recommendation system
Excess data makes it challenging to extract information that is relevant to a domain of study or research. Existing state-of-the-art systems focus majorly on the selection of highly connected, prestigious and cited articles, regardless of the relevance of papers. To improve quality of findings, recommender systems which are a subclass of information filtration systems are used. They filter out relevant information over prestigious data from an existing repository of information. There are various sub-domains under recommender systems. This study focuses on citation recommendation. Citations are an integral part of any scientific paper, academic dissertation or projects. Finding appropriate citations for any work is a scholar's most time-consuming task. Thus, a well-defined citation recommendation system provides fulfillment and completeness for citing the giants works. The thesis aims to study existing frameworks for citation recommendation systems and identify the best dataset to work on graph- based recommender systems. A framework that recommends the most similar and relevant article to the user rather than prestigious authors or papers is here by proposed. The study explores various machine learning and deep learning techniques and methods which can be used effectively in recommending loosely connected yet highly relevant articles. -
Synthesis, spectral and DNA/Protein binding evaluation of novel Cu(II) chelates of an NNO donor tridentate aroylhydrazone: Halogen bonding directed close packing
An NNO donor aroylhydrazone monohydrate, HFPBH2O (3-fluoropyridine-2-carbaldehyde benzoylhydrazone monohydrate) was synthesized from 3-fluoropyridine-2-carbaldehyde and benzhydrazide and physicochemically characterised. The coordination behaviour of the aroylhydrazone with the metal ion is investigated through various physicochemical techniques and it is concluded that it binds to the metal ion predominantly in the enolate resonance form, while few complexes exhibit keto form of the ligand. The structure of [Cu(FPB)(OAc)(H2O)]H2O (4a) established by single-crystal X-ray diffraction method unveiled that the metal ion has a distorted square-pyramidal geometry in this complex. The coordination sites of Cu(II) ion are occupied by azomethine N, pyridyl N and iminolate O from a monodeprotonated hydrazone moiety and the remaining two positions are occupied by two oxygen atoms, one each from acetate ion and the water molecule. Potential applications of the complexes were studied by subjecting them to DNA/protein (BSA) binding studies using electronic and fluorescence spectroscopy. The complexes were found to bind with DNA/protein (BSA) with binding constants in the order of 104 M?1 to 105 M?1. The intercalative mode of binding of the complexes with DNA was proved using spectral studies and molecular docking. Furthermore, the complex [Cu(FPB)(N3)(H2O)2] (5) was found to cleave the DNA from form I to form II during gel electrophoresis studies. 2020 Elsevier B.V.