Browse Items (11810 total)
Sort by:
-
Dynamical analysis fractional-order financial system using efficient numerical methods
The motivation of this work is to analyse the nonlinear models and their complex nature with generalized tools associated with material and history-based properties. With the help of well-known and widely used numerical scheme, we study the stimulating behaviours of the financial system in this work. The impact of parameters on price index, rate of interest, investment demand, influence changes and investment cost with respect to saving amount, and the elasticity of commercial markets demand are discussed. The consequences of generalizing the model within the arbitrary order are derived. The existence of the solution for the considered system is presented. This study helps beginner researchers to investigate complex real-world problems and predict the corresponding consequences. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Sign reversal of the spontaneous and induced polarisation in a mixture of achiral liquid crystal host and chiral azo dopant
Achiral liquid crystal, possessing orthogonal smectic A and tilted smectic C phases in its phase sequence, was doped with a chiral photochromic azo dopant. It was found that the spontaneous and induced polarisation in the tilted smectic C* phase and in the orthogonal smectic A phase, respectively, change their sign, as well as their magnitude, under illumination with UV light. The origin of this sign reversal effect is considered to be the different sign of the molecular net dipole moment component y of trans- and cis-isomers of the photochromic azo dopant, respectively. This light-induced sign reversal effect seems to have large potential for applications in the light-light controlled photonic liquid crystal devices, based on this effect. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Classification of Psychological Disorders by Feature Ranking and Fusion using Gradient Boosting Classification of Psychological Disorders
Negative emotional regulation is a defining element of psychological disorders. Our goal was to create a machine-learning model to classify psychological disorders based on negative emotions. EEG brainwave dataset displaying positive, negative, and neutral emotions. However, negative emotions are responsible for psychological health. In this paper, research focused solely on negative emotional state characteristics for which the divide-and-conquer approach has been applied to the feature extraction process. Features are grouped into four equal subsets and feature selection has been done for each subset by feature ranking approach based on their feature importance determined by the Random Forest-Recursive Feature Elimination with Cross-validation (RF-RFECV) method. After feature ranking, the fusion of the feature subset is employed to obtain a new potential dataset. 10-fold cross-validation is performed with a grid search created using a set of predetermined model parameters that are important to achieving the greatest possible accuracy. Experimental results demonstrated that the proposed model has achieved 97.71% accuracy in predicting psychological disorders 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved. -
Heat Convection in a Viscoelastic Nanofluid Flow: A Memory DescriptiveModel
Modeling of physical phenomena with fractional differential equations is as old as modeling with ordinary differential equations. There are two stages in modeling of a memory process. One of them is short with persistent impact and other is usually governed by fractional mathematical model. It is established that fractional models fit the experimental data for the memory phenomena in better way when compared with the ordinary models, particularly in mechanics, psychology and in biology. Fractional model of viscoelastic nanofluid flow through permeable medium is studied in this communication. Convection parameters in the flow domain are used to account for buoyancy forces. The governing flow equations are computed using a numerical algorithm that combines finite difference and finite element techniques. The governing models friction coefficient, Sherwood numbers, and Nusselt numbers are calculated. Change in noninteger numbers behave similarly in concentration, temperature, and velocity fields, according to simulations. It is also noted that heat flux, ?1 and mass flux, ?2 numbers have contradictory effects on friction coefficient. Various flow patterns, particularly in the polymer industry and electrospinning for nanofiber manufacture, can be addressed in a similar manner 2023 L&H Scientific Publishing, LLC. All rights reserved -
Novel approach for nonlinear time-fractional Sharma-Tasso-Olever equation using Elzaki transform
In this article, we demonstrated the study of the time-fractional nonlinear Sharma-Tasso-Olever (STO) equation with different initial conditions. The novel technique, which is the mixture of the q-homotopy analysis method and the new integral transform known as Elzaki transform called, q-homotopy analysis Elzaki transform method (q-HAETM) implemented to find the adequate approximated solution of the considered problems. The wave solutions of the STO equation play a vital role in the nonlinear wave model for coastal and harbor designs. The demonstration of the considered scheme is done by carrying out some examples of time-fractional STO equations with different initial approximations. q-HAETM offers us to modulate the range of convergence of the series solution using ?, called the auxiliary parameter or convergence control parameter. By performing appropriate numerical simulations, the effectiveness and reliability of the considered technique are validated. The implementation of the new integral transform called the Elzaki transform along with the reliable analytical technique called the q-homotopy analysis method to examine the time-fractional nonlinear STO equation displays the novelty of the presented work. The obtained findings show that the proposed method is very gratifying and examines the complex nonlinear challenges that arise in science and innovation. 2023 Balikesir University. All rights reserved. -
Green synthesis of MgO nanoparticles and its antibacterial properties
Magnesium oxide nanostructured particles (NP) were prepared using a simple solution combustion technique using different leaf extracts such as Mangifera indica (Mango - Ma), Azadirachta indica (NeemNe), and Carica papaya (PapayaPa) as surfactants. The highly crystalline phase of MgO nanostructures was confirmed by PXRD and FTIR studies for 2h 500C calcined samples. To analyze the characteristics of obtained materialMaNP, NeNP, and PaNP for dosimetry applications, thermoluminescence (TL) studies were carried out for Co-60 gamma rays irradiated samples in the dose range 1050KGy; PaNP and NeNP exhibited well-defined glow curve when compared with MaNP samples. In addition, it was observed that the TL intensity decreases, with increase in gamma dose and the glow peak temperature is shifted towards the higher temperature with the increase in heating rate. The glow peak was segregated using glow curve deconvolution and thermal cleaning method. Kinetic parameters estimated using Chens method, trap depth (E), and frequency factor (s) were found to be 0.699, 7.408, 0.4929, and 38.71, 11.008, and 10.71 for PaNP, NeNP, and MaNP respectively. The well-resolved glow curve, good linear behavior in the dose range of 1050, KGy, and less fading were observed in PaNP as compared with MaNP and NeNP. Further, the antibacterial activity was checked against human pathogens such as Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. A visible zone of clearance was observed at 200 and 100?g/mL by the PaNP and NeNP, indicating the death of colonies by the nanoparticles. Therefore, PaNP nanomaterial is a potential phosphor material for dosimetry and antibacterial application compared to NeNP and MaNP. Copyright 2023 Rotti, Sunitha, Manjunath, Roy, Mayegowda, Gnanaprakash, Alghamdi, Almehmadi, Abdulaziz, Allahyani, Aljuaid, Alsaiari, Ashgar, Babalghith, Abd El-Lateef and Khidir. -
Cop-edge critical generalized Petersen and Paley graphs
Cop Robber game is a two player game played on an undirected graph. In this game, the cops try to capture a robber moving on the vertices of the graph. The cop number of a graph is the least number of cops needed to guarantee that the robber will be caught. We study cop-edge critical graphs, i.e. graphs G such that for any edge e in E(G) either c(G?e) < c(G) or c(G?e) > c(G). In this article, we study the edge criticality of generalized Petersen graphs and Paley graphs. 2023 Azarbaijan Shahid Madani University. -
Listen to the heart or mind first? Examining sequential coping mechanisms among Indians during the COVID-19 pandemic
The present study examines the mediating role of emotion-focused and problem-focused coping between stress and psychological well-being during the COVID-19 pandemic. The sample comprised 501 (312 women and 184 men aged between 18 and 42) Indians who experienced the first-ever continued lockdown in India during the COVID-19 pandemic. The results of this study confirmed the presence of perceived stress due to the lockdown and pandemic among participants. Furthermore, perceived stress, coping including emotion-focused and problem-focused, and psychological well-being were found to be interrelated. The serial mediation analysis revealed that participants dealt with stress by choosing emotion-focused coping first as an immediate resort. After a reappraisal of stress-inducing situations, they used problem-focused coping, and this sequence of constant coping mechanisms helped maintain their psychological well-being. The findings of this study can be applied to develop strategies for peoples mental health by public health organizations and health professionals. Copyright 2023 Srivastava, Upadhaya and Jain. -
SWITCHING INTENTION AND SWITCHING BEHAVIOR OF ADULTS IN THE NON-LIFE INSURANCE SECTOR: MEDIATING ROLE OF BRAND LOVE
In this digital era, customers in the insurance sector always look for better insurance products and services at an affordable price. When customers are unsure about service, they switch over to a better service provider. This behavior is more relevant to non-life insurance. However, the switching behavior of customers is hampered by certain switchover barriers such as brand consciousness, brand pride, brand loyalty, etc. This study focuses on exploring switching intentions and switching behaviors of adults in India keeping brand love as a mediator. A structured questionnaire was employed to collect the primary data from adults having non-life insurance products to analyze switching intentions and switching behaviors. The collected data were analyzed employing SPSS software and Hayes Process Model and appropriate statistical tools. The study results show that the switching intentions of adults vary based on their age, annual income, and education. Mean scores reveal that the lesser the age, the higher the intention to switch over. Further, based on annual income, adults who earn up to Rs 2 lakhs annually have more switching-over intentions (Mean score: 3.9719) followed by adults who earn Rs more than 2 lakhs to 5 lakhs annually (Mean score: 3.7590). Mean scores of education levels regarding switching intentions are higher among more educated adults and less among those who are qualified up to the school level. Arun Kumar N., Girish S., Suresha B., Mahesh E., 2023. -
Review on Durability of Geopolymer Concrete Developed with Industrial and Agricultural Byproducts
High population growth has increased the requirement for infrastructure development tremendously. Building materials like ordinary portland cement which is the primary component in concrete is growing due to the increased demand for new infrastructure. Concrete is the world's second most consumed material. Special concrete called Geopolymer concrete (GPC), is grabbing the interest of researchers as substitute to ordinary portland cement concrete (OPCC). Manufacture of cement is highly energy intensive and leads to large quantity of CO2 emission to atmosphere which in turn leads to global warming. Thus, replacement of cement with geopolymer material minimises pollution in two ways: by lowering cement consumption and utilisation and by lowering CO2 emission. Various industrial and agricultural waste materials like fly ash, metakolin, ground granulated blast furnace slag, silica fume, rice husk ash, sugarcane bagasse ash etc. are abundantly available. These aluminosilicate sources have been widely employed to develop geopolymer concrete with high strength, thermal resistance, and durability. This article is a review of research on the durability aspects of geopolymer concrete, its most significant durability parameters like resistance to acid attack, sulphate attack, water absorption, porosity, sorptivity, rapid chloride penetration, wet and dry cycle have been reviewed to comprehend these vital issues. 2023 -
Resilience in Children from Different Socioeconomic Backgrounds: An Exploratory Study
Poverty, violence, substance abuse, family dissonance and illness represent a few potential vulnerabilities in the lives of children who are at risk of failing in their future prospects. It is thus essential to explore resilience in children, owing to the excess or deficit of exposure and access in a childs life. This study aims at exploring the resilience of children of the age group 710years, from different socioeconomic backgrounds. The socioeconomic status was determined using the Kuppuswamy socioeconomic scale and these children had parents with authoritarian and permissive parenting styles which were screened through the Parenting Styles and Dimensions Questionnaire which act as risk factors for the children. Data was collected through individual semi-structured interviews with the participants and was analysed using thematic analysis. For the lower socioeconomic status group, the main themes identified were social interaction and competence, overcoming distress and future focus, and for the upper socioeconomic status group, the main themes identified were social interaction and competence and emotional management. The study paves the way for further exploration of the impact of economic status on childrens wellbeing and might inform changes at a clinical, research and policy level. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence
In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brain function is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer's disease, Schizophrenia and Parkinson's diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm (DBNJZZ) approach. The suggested approach is executed and tested by using the performance metric measure such as accuracy, root mean square error, Mean absolute error and mean absolute percentage error. Proposed DBNJZZ gives better performance than previously available methods. 2023 Authors. All rights reserved. -
Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems
The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches. 2023 CRL Publishing. All rights reserved. -
Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis
Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individuals sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individuals tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment. The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection. The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing. The proposed approach outperformed considered algorithms in terms of accuracy. Additionally, we augmented the newly created dataset to make it balanced to increase performance. A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm. It shows that augmentation improves the overall accuracy of tweet analysis. After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVM with 10-fold cross-validation significantly improved compared to the primary dataset. 2023 Tech Science Press. All rights reserved. -
Dependence of Eigen frequency on the output performance of a piezoelectric nano sensor: A comparative study
Energy harvesting is an approach to generating electricity that uses the energy of the local environment directly. Instead of relying on batteries or power generated elsewhere, the world needs a new generation of energy-producing products. Generation or accumulation and energy consumption must be balanced when designing such a system. Before implementation into the real world, simulations are available for optimizing device designs, comparing them, predicting them, and formulating methodologies. In this comparative study, using a finite element simulation software COMSOL Multiphysics, an Eigen frequency analysis is performed to validate the relationship between Eigen frequency and output voltage and also shows how much the selection of a piezo electric base material depends on the natural frequency, excitation frequency, and output voltage relationship. A piezoelectric sensor is constructed with an initial base material and using material sweeps, another six materials are added and switched for the purpose. After applying allowable stress and frequency analysis, measured the output electric potential for the first five eigenmodes. Selecting seven different piezo electric base materials that possess unique properties from traditional lead zirconate titanate (PZT) to upcoming polymer material polyvinylidene fluoride (PVDF), there reveals the role of selecting suitable energy harvesting medium in generating proper output. From the experimented materials, zinc oxide (ZnO), aluminum nitride (AlN), and PVDF are found to be reliable towards the resonance concept and attaining optimum electric potential. Thus, our study strongly supports previous works carried out by the researchers regarding the effect of various piezo electric base materials on output response. 2023 -
Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications
Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. 2022 River Publishers. -
In Silico Identification of 1-DTP Inhibitors of Corynebacterium diphtheriae Using Phytochemicals from Andrographis paniculata
A number of phytochemicals have been identified as promising drug molecules against a variety of diseases using an in-silico approach. The current research uses this approach to identify the phyto-derived drugs from Andrographis paniculata (Burm. f.) Wall. ex Nees (AP) for the treatment of diphtheria. In the present study, 18 bioactive molecules from Andrographis paniculata (obtained from the PubChem database) were docked against the diphtheria toxin using the AutoDock vina tool. Visualization of the top four molecules with the best dockscore, namely bisandrographolide (?10.4), andrographiside (?9.5), isoandrographolide (?9.4), and neoandrographolide (?9.1), helps gain a better understanding of the molecular interactions. Further screening using molecular dynamics simulation studies led to the identification of bisandrographolide and andrographiside as hit compounds. Investigation of pharmacokinetic properties, mainly ADMET, along with Lipinskis rule and binding affinity considerations, narrowed down the search for a potent drug to bisandrographolide, which was the only molecule to be negative for AMES toxicity. Thus, further modification of this compound followed by in vitro and in vivo studies can be used to examine itseffectiveness against diphtheria. 2023 by the authors. -
Sesquiterpenoid-rich Java Ginger rhizome extract prompts autophagic cell death in cervical cancer cell SiHa mainly by modulating cellular redox homeostasis
Java Ginger or Curcuma zanthorrhiza Roxb. has long gained focus among tribal people of Java, for its medicinal properties mainly against gynaecological challenges. The present study aims to identify the most potent phytocompound present in the extract and determine primary mode of action accountable for cytotoxic activity of Curcuma zanthorrhiza rhizome extract against HPV16-positive SiHa cervical cancer cells. The phytochemically-rich extract of rhizome (CZM) was capable to inhibit proliferation of target cells in a dose-dependent manner with an IC50 of 150?g/ml. Dysregulation of intercellular antioxidant defence system resulted to surges in ROS and RNS level, increased calcium concentration and compromised mitochondrial membrane potential. Nucleus got affected, cell cycle dynamics got impaired while clonogenicity and migration ability diminished. Expression of viral oncogenes E7 and E6 decreased significantly. Accumulation of toxic cell metabolite and decrease in level of essential ones continued. Finally, alteration in PI3K/AKT/mTOR signalling route was followed by onset of autophagic cell death concomitant with the upregulated expression of Beclin1, Atg5-12 and LC3II. Curcumin and a novel crystal as well as few phyto-fractions were isolated by column chromatography. Of these, curcumin was found to be most potent in inducing cytotoxicity in SiHa while two other fractions also showed significant activity. Thus, CZM acted against SiHa cells by inducing autophagy that commences in compliance to the changes in PI3K/AKT/mTOR pathway mainly in response to oxidative stress. To the best of our knowledge this is the first report of Curcuma zanthorrhiza Roxb. inducing autophagy. 2022, King Abdulaziz City for Science and Technology. -
Exploring the agency of policy through ecological urbanism for climate action: water and sanitation systems of Bengaluru
The cities of the world have been the exploiters of resources and the largest generators of waste. This paper explores the concept of Ecological Urbanism as a framework to convert cities from being waste generators to resource producers. The example of the wastewater from Bengaluru going into the lakes of Kolar is studied. The treated wastewater of the city reaches Kolar to fill its lakes, which subsequently recharges the groundwater. One citys waste becomes anothers resource in this process. The case of Kolar-Bengaluru is studied while asking critical questions of urban-rural planning with ecology as a main premise. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
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.