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Experimental investigation and influence of filling ratio on heat transfer performance of a pulsating heat pipe
Experimental investigation of the two-phase system of a pulsating heat pipe taken into account useful heat transfer In the field of thermal management, many new prospective concepts and techniques have been developed, one of which is the pulsating heat pipe, a classic heat transfer technique. The PHP is constructed from 8 turns of copper tubes with inner diameters of 2 mm, wall widths of 1 mm, and a total length of 5324 mm. The CLPHP uses ethylene glycol as the functioning liquid at different fill proportions of 45 %, 55 %, 65 %, 75 %, and 85 % of its amount. The evaporator section is heated electrically by a plate heater ranging from 120 W to 600 W, and the condenser section is cooled by a continuous flow of cooling water. The results thermal resistance decreases gradually with an increase in heat transfer rate. It is apparent that a lower rate of thermal resistance is by a fill ratio of 55 %. The evaporator temperature is 181.57 C and the condenser temperature is 41.06 C for ethylene glycol measured for calculating heat transfer performance at 600 W, thermal resistance is 0.136 C/W, heat transfer coefficient is 526.45 W/m2-C, and enhanced heat transfer is thus good, exhibiting good improvement at a full percentage of 55 % and when compared with CFD results. 2023 Elsevier Ltd -
Analgesic and Anti-Inflammatory Potential of Indole Derivatives
Some indole analogues show a good analgesic activity but on the other hand, it has some serious side effects like gastric ulcer. Therefore, there is still a need to develop derivatives of non-steroidal anti-inflammatory drugs (NSAIDs) with fewer side effects. For this purpose, some indole derivatives were prepared with objectives to develop new derivatives with maximum efficacy and minimum side effects. 1-(1H-indol-1-yl)-2-(sstituephenoxy)-ethan-1-one derivatives (M1M4) were analyzed further by thin-layer chromatorgarphy (TLC), melting point, IR, and 1H-NMR. The synthesized compounds then underwent oral toxicity studies that include hematological, biochemical, and histopathological findings. The compound was then evaluated for invivo anti-inflammatory and analgesic activities on carrageenan-induced rat paw edema and acetic acid-induced writhing methods. As a result of the biological activities, promising results were obtained in the compound M2 (2-(2-aminophenoxy)-1-(1H-indol-1-yl)ethanone) and it was subjected to further studies. It was found that compound M2 was practically nontoxic, and no clinical abnormalities were found in hematology and biochemistry, correlated with histopathological observation. It also showed significant anti-inflammatory and analgesic activities at its oral high dose (400 mg/kg). The study suggested that compound M2 was found to have significant anti-inflammatory and analgesic activities. The possible mechanism of M2 might suggest being act as a central anti-nociceptive agent and peripheral inhibitor of painful inflammation. The possible mechanism of action of the compounds whose biological activity was evaluated was explained by molecular docking study against COX-1 and COX-2, and the most active compound M2 formed ?9.3 and ?8.3 binding energies against COX-1 and COX-2. In addition, molecular dynamics (MD) simulation of both M2s complexes with COX-1 and COX-2 was performed to examine the stability and behavior of the molecular docking pose, and the MM-PBSA binding free energies were measured as ?153.820 11.782 and ?172.604 9.591, respectively. Based on computational ADME studies, compounds comply with the limiting guidelines. 2022 Taylor & Francis Group, LLC. -
Attitude of public towards higher education: Conceptual analysis /
Scholedge International Journal Of Multidisciplinary And Allied Studies, Vol.2, Issue 12, pp.19-28, ISSN No: 2394-336X. -
Optical and Infrared studies of herbig Ae Be stars
The work makes use of the unprecedented capability of the Gaia mission to study various properties of Herbig Ae/Be stars. We placed the Herbig Ae/Be stars in the Gaia color-magnitude diagram and accurately estimated their age and mass. The mass accretion rate is calculated from Hand#945; line newlineflux measurements of 106 HAeBe stars. The mass accretion rate is found to decay exponentially with the age of Herbig Ae/Be stars. Further, the immediate neighborhood of two Herbig Ae/Be stars, V1787 Ori and IL newlineCep, are studied using the astrometric and photometric data from the Gaia mission. We discovered a low mass binary companion to V1787 Ori using the analysis of distance and proper motion values from Gaia DR2. The newlinemass ratio of the coeval binary system is found to be 0.23. Such a skewed mass ratio system is rarely identified in Herbig Ae/Be binary systems. The method of identification and characterization of the V1787 Ori wide binary system opens up the possibility of identifying more such systems. The HBe newlinestar IL Cep tells a much more complex story. The star is identified with a cluster of low mass stars associated with it. We identified 79 co-moving stars that are coeval to IL Cep, within 2 pc radius, from the analysis of newlineGaia EDR3 astrometry. The triggered star formation process called the quotRocket effectquot caused by a massive star HD 216658 is identified to be the cause of the clustered star formation near IL Cep. The effect of this process is demonstrated for the first time using the proper motion data from Gaia. newlineThe immediate neighborhood of Herbig Ae/Be stars is identified as the formation region of long-chain carbon molecules such as Fullerenes and Polycyclic Aromatic Hydrocarbons. -
Optical and infrared studies of herbig Ae/Be stars
The work makes use of the unprecedented capability of the Gaia mission to study various properties of Herbig Ae/Be stars. We placed the Herbig Ae/Be stars in the Gaia color-magnitude diagram and accurately estimated their age and mass. The mass accretion rate is calculated from Hα line flux measurements of 106 HAeBe stars. The mass accretion rate is found to decay exponentially with the age of Herbig Ae/Be stars. Further, the immediate neighborhood of two Herbig Ae/Be stars, V1787 Ori and IL Cep, are studied using the astrometric and photometric data from the Gaia mission. We discovered a low mass binary companion to V1787 Ori using the analysis of distance and proper motion values from Gaia DR2. The mass ratio of the coeval binary system is found to be 0.23. Such a skewed mass ratio system is rarely identified in Herbig Ae/Be binary systems. The method of identification and characterization of the V1787 Ori wide binary system opens up the possibility of identifying more such systems. The HBe star IL Cep tells a much more complex story. The star is identified with a cluster of low mass stars associated with it. We identified 79 co-moving stars that are coeval to IL Cep, within 2 pc radius, from the analysis of Gaia EDR3 astrometry. The triggered star formation process called the "Rocket effect" caused by a massive star HD 216658 is identified to be the cause of the clustered star formation near IL Cep. The effect of this process is demonstrated for the first time using the proper motion data from Gaia. The immediate neighborhood of Herbig Ae/Be stars is identified as the formation region of long-chain carbon molecules such as Fullerenes and Polycyclic Aromatic Hydrocarbons. -
Analysis of some important fulid flow problems using differential geometry based methods
In this thesis we have studied MHD and EMFD flow of viscous and inviscid fluid for different cases when magnetic field and velocity are variably or constantly inclined. In particular magnetic and velocity vector are orthogonal. The pattern of streamlines and magnetic lines are derived in every problem and the effect of density and magnetic permeability on the variation of pressure is studied. The problems studied in this thesis give further investigation on the analytical solution of magnetohydrodynamic and electromagnetic fluid dynamic flow. The problems that studied analytically in this thesis have possible application in theoretical analysis of fluid dynamics and the analytical findings in this thesis can be applied in engineering fields such as aeronautics, plasmas, liquid metals and salt water or electrolytes. We have studied five problems here in this thesis. These problems are to find analytical solution of different types of fluid flows in the presence of magnetic field. Here we give a brief summary about the problems discussed in detail in this research work. (i) GEOMETRY OF CONSTANTLY INCLINED VISCOUS MHD FLOWS newlineProblems on incompressible MHD flow of viscous and inviscid fluids having newlinefinite or infinite electrical conductivity have been investigated by many researchers newlineusing different transformation methods. Transformation method is applied from newlineone plane to another plane for studying the flows by reducing the order of the equation. In this problem we have studied a viscous MHD flow having infinite electrical conductivity when the magnetic field is inclined to the velocity vector in a constant angle. Hodograph transformation is applied to shift variables from the physical plane to the hodograph plane. Streamlines and magnetic lines are analyzed along with determining the solutions to the flow problems. Finally the newlinepressure variation is analyzed graphically. Flow pattern along with pressure variation, also studied in this problem for an orthogonal MHD flow. -
Analysis of some important fluid flow problems using differential geometry based methods
In this thesis we have studies MHD and EMFD flow of viscous and inviscid fluid for different cases when magnetic field and velocity are variably or constantly inclined. In particular magnetic and velocity vector are orthogonal. The pattern of streamlines and magnetic lines are derived in every problem and the effect of density and magnetic permeability on the variation of pressure is studied. The problems studied in this thesis give further investigation on the analytical solution of magnetohydrodynamic and electromagnetic fluid dynamic flow. -
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Lightweight Spectral-Spatial Squeeze-and- Excitation Residual Bag-of-Features Learning for Hyperspectral Classification
Of late, convolutional neural networks (CNNs) find great attention in hyperspectral image (HSI) classification since deep CNNs exhibit commendable performance for computer vision-related areas. CNNs have already proved to be very effective feature extractors, especially for the classification of large data sets composed of 2-D images. However, due to the existence of noisy or correlated spectral bands in the spectral domain and nonuniform pixels in the spatial neighborhood, HSI classification results are often degraded and unacceptable. However, the elementary CNN models often find intrinsic representation of pattern directly when employed to explore the HSI in the spectral-spatial domain. In this article, we design an end-to-end spectral-spatial squeeze-and-excitation (SE) residual bag-of-feature (S3EResBoF) learning framework for HSI classification that takes as input raw 3-D image cubes without engineering and builds a codebook representation of transform feature by motivating the feature maps facilitating classification by suppressing useless feature maps based on patterns present in the feature maps. To boost the classification performance and learn the joint spatial-spectral features, every residual block is connected to every other 3-D convolutional layer through an identity mapping followed by an SE block, thereby facilitating the rich gradients through backpropagation. Additionally, we introduce batch normalization on every convolutional layer (ConvBN) to regularize the convergence of the network and scale invariant BoF quantization for the measure of classification. The experiments conducted using three well-known HSI data sets and compared with the state-of-the-art classification methods reveal that S3EResBoF provides competitive performance in terms of both classification and computation time. 1980-2012 IEEE. -
SVD-CLAHE boosting and balanced loss function for Covid-19 detection from an imbalanced Chest X-Ray dataset
Covid-19 disease has had a disastrous effect on the health of the global population, for the last two years. Automatic early detection of Covid-19 disease from Chest X-Ray (CXR) images is a very crucial step for human survival against Covid-19. In this paper, we propose a novel data-augmentation technique, called SVD-CLAHE Boosting and a novel loss function Balanced Weighted Categorical Cross Entropy (BWCCE), in order to detect Covid 19 disease efficiently from a highly class-imbalanced Chest X-Ray image dataset. Our proposed SVD-CLAHE Boosting method is comprised of both oversampling and under-sampling methods. First, a novel Singular Value Decomposition (SVD) based contrast enhancement and Contrast Limited Adaptive Histogram Equalization (CLAHE) methods are employed for oversampling the data in minor classes. Simultaneously, a Random Under Sampling (RUS) method is incorporated in major classes, so that the number of images per class will be more balanced. Thereafter, Balanced Weighted Categorical Cross Entropy (BWCCE) loss function is proposed in order to further reduce small class imbalance after SVD-CLAHE Boosting. Experimental results reveal that ResNet-50 model on the augmented dataset (by SVD-CLAHE Boosting), along with BWCCE loss function, achieved 95% F1 score, 94% accuracy, 95% recall, 96% precision and 96% AUC, which is far better than the results by other conventional Convolutional Neural Network (CNN) models like InceptionV3, DenseNet-121, Xception etc. as well as other existing models like Covid-Lite and Covid-Net. Hence, our proposed framework outperforms other existing methods for Covid-19 detection. Furthermore, the same experiment is conducted on VGG-19 model in order to check the validity of our proposed framework. Both ResNet-50 and VGG-19 model are pre-trained on the ImageNet dataset. We publicly shared our proposed augmented dataset on Kaggle website (https://www.kaggle.com/tr1gg3rtrash/balanced-augmented-covid-cxr-dataset), so that any research community can widely utilize this dataset. Our code is available on GitHub website online (https://github.com/MrinalTyagi/SVD-CLAHE-and-BWCCE). 2022 Elsevier Ltd -
The development and primary validation of employee green behavior scale
Purpose: The increasing adverse impact of human behavior toward the environment has brought in changes in research focus on environmental behavior toward the workplace. Because the employee spends one-third of his day in his workplace, the initiatives taken by the employee also have an impact on the companys environmental stance. Therefore, the researchers gradually focus on employee green behavior (EGB) and its measurement. The study aims to devise a tool for measuring EGB. Design/methodology/approach: Two studies were carried out using the survey method using the purposive sampling technique. The data were collected (Studies 1 and 2) from managers and supervisors working in manufacturing companies located in Kolkata, India. Findings: The first study was done to extract the principal factors using an initial 30 items (N = 220). The result of the principal component analysis shows the emergence of three factors spread over 20 items with loadings above 0.40. The 20-item scale was again administered on managers and supervisors (N = 243). The second study was carried out to examine the convergent and discriminant validity as well as stability of the tool through confirmatory factor analysis (CFA) (N = 243). The result of CFA showed the presence of 16 items spread through three factors: practice and policy, digital use and recycle and reuse. Multiple fit indices support a three-factor model of the 16-item EGB scale. Research limitations/implications: The scale would be a good measure of EGB and can be used for further research. The EGB scale is a composite scale containing three major dimensions that can be used as a complete measure of EGB. Originality/value: The present research aims to fill the current gap by building a comprehensive tool for measuring EGB. The present scale has also addressed the shortcoming of the previous scale and tried to include varied proenvironmental behaviors exhibited in the workplace. 2024, Emerald Publishing Limited. -
Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach
Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. 2024 IEEE. -
Performance Improvement in E-Gun Deposited SiOx- Based RRAM Device by Switching Material Thickness Reduction
A performance improvement by reduction in switching material thickness in a e-gun deposited SiOx based resistive switching memory device was investigated. Reduction in thickness cause thinner filamentary path formation during ON-state by controlling the vacancydefects. Thinner filament cause lowering of operation current from 500 ?A to 100 ?A and also improves the reset current (from >400 ?A to <100 ?A). Switching material thickness reductionalso cause the forming free ability in the device. All these electrical parametric improvements enhance the device reliability performances. The device show >200 dc endurance, >3-hour dataretention and >1000 P/E endurance with 100 ns pulses. 2022 Institute of Physics Publishing. All rights reserved. -
The presence of others increases prosociality: examining the role of dating Partners accompany on donation
Research in the field of prosocial behavior has shown that the presence of others has a significant effect on individuals prosociality. However, no research has explored such an effect of romantic partners presence. Studies in evolutionary psychology have shown benevolence/prosociality as an important factor when choosing a romantic partner. Therefore, in the present study, we hypothesized that people will donate more in the presence of dating partners to maintain a positive impression on them. The research followed a mixed-method approach. The first study, a vignette-based experiment showed that people believed the presence of a dating partner significantly enhances the chances of donation. The second study was a between-subject experiment that confirmed the findings of study 1 from both donors and receivers perspectives. The third study was a qualitative investigation, where a semi-structured interview method was used to find out how and why the presence of a dating partner may influence donation. The interviews showed that the presence of dating partners increases prosociality mainly because donors want to make a good impression and project the right image of them in their partners eyes. The research overall suggests that the human need for self-presentation that projects them more socially likable shapes their willingness to extend a helping hand to others in the presence of their romantic partners. 2024 Taylor & Francis Group, LLC. -
Log-Base2 of Gaussian Kernel for Nuclei Segmentation from Colorectal Cancer H and E-Stained Histopathology Images
Nuclei Segmentation is a very essential and intermediate step for automatic cancer detection from H and E stained histopathology images. In the recent advent, the rise of Convolutional Neural Network (CNN), has enabled researchers to detect nuclei automatically from histopathology images with higher accuracy. However, the performance of automatic nuclei segmentation by CNN is fraught with overfitting, due to very less number of annotated segmented images available. Indeed, we find that the problem of nuclei segmentation is an unsupervised problem, because still now there is no automatic tool available which can make annotated images (nuclei segmented images) accurately, to the best of our knowledge. In this research article, we present a Logarithmic-Base2 of Gaussian (Log-Base2-G) Kernel which has the ability to track only the nuclei portions automatically from Colorectal Cancer H and E stained histopathology images. First, Log-Base2-G Kernel is applied to the input images. Thereafter, we apply an adaptive Canny Edge detector, in order to segment only the nuclei edges from H and E stained histopathology images. Experimental results revealed that our proposed method achieved higher accuracy and F1 score, without the help of any annotated data which is a significant improvement. We have used two different datasets (Con-SeP dataset, and Glass-contest dataset, both contains Colorectal Cancer histopathology images) to check the effectiveness and validity of our proposed method. These results have shown that our proposed method outperformed other image processing or unsupervised methods both qualitatively and quantitatively. 2023 SPIE. -
Mobile Freeze-Net with Attention-based Loss Function for Covid-19 Detection from an Imbalanced CXR Dataset
In this paper, we present a novel framework, that is, Mobile Freeze-Net along with Attention-based Loss Function, for Covid-19 detection from a Chest X-Ray (CXR) dataset. First, we have observed that by freezing 50% of a Mobile Net-V2 model (means fine-tuning 50% layers from ImageNet dataset) has automatically removed the class imbalance problem from the CXR dataset considerably. We call this 50% frozen Mobile Net-V2 model as Mobile Freeze-Net. Secondly, we have proposed an Attention-based Loss function, which provides more attention to the class, having higher inter-class similarity. We have computed attention weights for each class from the statistical inference of the dataset itself, by employing a Monte-Carlo method and thereafter, we have incorporated those weights into WCCE loss function of Mobile Freeze-Net model. By utilizing Mobile freeze-Net, we have achieved testing accuracy, F1 score, precision and recall of 93%, 94%, 93% and 94% respectively. This is approximately 3-4% improvement compared to 100% fine tuning of Mobile-Net V2. Furthermore, we have achieved approximate 1-2% improvement of Mobile Freeze-Net, after incorporating Attention-based Loss function. For the validity of the proposed framework, we have conducted experiments with 10-fold cross validation. All these experimental results suggest that our proposed framework has outperformed other existing models considerably. 2023 Owner/Author(s). -
Mathematical analysis of histogram equalization techniques for medical image enhancement: a tutorial from the perspective of data loss
This tutorial demonstrates a novel mathematical analysis of histogram equalization techniques and its application in medical image enhancement. In this paper, conventional Global Histogram Equalization (GHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Histogram Specification (HS) and Brightness Preserving Dynamic Histogram Equalization (BPDHE) are re-investigated by a novel mathematical analysis. All these HE methods are widely employed by researchers in image processing and medical image diagnosis domain, however, this has been observed that these HE methods have significant limitation of data loss. In this paper, a mathematical proof is given that any kind of Histogram Equalization method is inevitable of data loss, because any HE method is a non-linear method. All these Histogram Equalization methods are implemented on two different datasets, they are, brain tumor MRI image dataset and colorectal cancer H and E-stained histopathology image dataset. Pearson Correlation Coefficient (PCC) and Structural Similarity Index Matrix (SSIM) both are found in the range of 0.6-0.95 for overall all HE methods. Moreover, those results are compared with Reinhard method which is a linear contrast enhancement method. The experimental results suggest that Reinhard method outperformed any HE methods for medical image enhancement. Furthermore, a popular CNN model VGG-16 is implemented, on the MRI dataset in order to prove that there is a direct correlation between less accuracy and data loss. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Effect of psychological pricing on consumer buying behaviour: A study on indian consumers
Consumer behaviour is a topic most sought after when it comes to creating successful marketing practices that affect consumers' psychology, acting as a stimulus and inducing them to make purchases. Evidence explains that the psychological pricing strategy communicates with the subconscious mind of consumers, creating a perceptual illusion. This makes the deal seem more appealing to them. This chapter entails a practical study examining the impact of psychological pricing strategies on consumers' buying behaviour. This study has used authentic primary data that has been collected directly from consumers in India based on their buying experiences when encountering psychological pricing. The findings of this research show how socio-demographic factors like age, income, education, gender and family size influence consumers' buying behaviour when encountered with psychological pricing and if psychological patterns such as the anchoring heuristics, recency bias, scarcity effect and halo effect can overpower the influence of psychological pricing strategies in consumer buying behaviour. 2024, IGI Global. -
Enhanced Stock Market Prediction Using Hybrid LSTM Ensemble
Stock market value prediction is the activity of predicting future market values so as to increase gain and profit. It aids in forming important financial decisions which help make smart and informed investments. The challenges in stock market predictions come due to the high volatility of the market due to current and past performances. The slightest variation in current news, trend or performance will impact the market drastically. Existing models fall short in computation cost and time, thereby making them less reliable for large datasets on a real-time basis. Studies have shown that a hybrid model performs better than a stand-alone model. Ensemble models tend to give improved results in terms of accuracy and computational efficiency. This study is focused on creating a better yielding model in terms of stock market value prediction using technical analysis, and it is done by creating an ensemble of long short-term memory (LSTM) model. It analyzes the results of individual LSTM models in predicting stock prices and creates an ensemble model in an effort to improve the overall performance of the prediction. The proposed model is evaluated on real-world data of 4 companies from Yahoo Finance. The study has shown that the ensemble has performed better than the stacked LSTM model by the following percentages: 21.86% for the Tesla dataset, 22.87% for the Amazon dataset, 4.09% for Nifty Bank and 20.94% for the Tata dataset. The models implementation has been justified by the above results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The mediating role of parental playfulness on parentchild relationship and competence among parents of children with ASD
Purpose: The difficulties of a child diagnosed with autism spectrum disorder (ASD) can lead to behaviours that are quite challenging for parents to understand and address. Most of the parental studies of ASD focus on the challenges faced by the parents. This study aims to adopt a strength-based model that investigates the mediating role of parental playfulness in the association between parentchild relationship and parental competence. Design/methodology/approach: This study is a quantitative study that adopts a correlational research design. The mediation analysis explores the role of parental playfulness as a mediator in the association between parentchild relationship and parental competence. The sample consisted of 120 parents of children diagnosed with ASD from India, selected using a purposive sampling technique. Findings: The mediation analysis results indicate that playfulness among parents of children with ASD was found to function as a partial mediator in the relationship between parentchild relationship and parental competence. This could suggest that more playful parents have better parentchild relationships and are competent in parenting. Research limitations/implications: These findings have importance in understanding the role of playful interaction on parentchild relationships and parenting competence, having implications for further research. Enabling playfulness in parenting will enhance children and parents to promote their relationship and thus feel competent to bring positive light in their lives. Practical implications: Most often, the clinicians are concerned with addressing only the autistic symptoms; it is also essential to look into parental well-being. Practical playful interaction training should help parents establish a rapport, understand, adjust and adapt with their child. Social implications: Practical intervention and training plans can be suggested to all family members to improve the condition of the child and the familys general well-being. As the study focused on the clinical population, the findings could provide useful inputs for mental health professionals and counsellors. Originality/value: There are some theoretical and empirical evidence that support positive outcomes of playfulness on personal well-being (Atzaba Poria, in press; Yue et al., 2016; Proyer, 2014). Although there has been some interest in the impact of childrens playfulness on their development (Bundy, 1997), little is known about the influence of parental playfulness on parents and children. Therefore, addressing these gaps, this empirical study focusses on investigating the role of parental playfulness in parentchild relationship and parental competence, rather than considering external challenges of parents based on the ASD childs behavioural challenges and autistic features. 2021, Emerald Publishing Limited.