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Computational Study of MHD Nanofluid Flow with Effects of Variable Viscosity and Non-uniform Heat Generation
The thermodynamic study of an unsteady two-dimensional nanofluid flow through a permeable stretched sheet is looked at. Water is used as the primary fluid, along with four different nanoparticles, including copper (Cu), titanium dioxide (TiO2), copper oxide (CuO), and aluminium oxide (Al2O3). The heat transfer phenomenon is explained by an outside source. Additionally considered are the impacts of heat generation and absorption. A similarity transformation is used to convert the considered set of mathematical equations into a system of ODEs. The BVP4C method is then mathematically applied, coupled with shooting fashion. The results are given for cases involving copper nanoparticles. The effects of various physical parameters on the profiles of the dimensionless flow field, temperature, average entropy generation function, skin friction, and the Nusselt number are examined with illustrations and thorough explanations. As exceptional circumstances of the current inquiry, there is a strong agreement between the current conclusion and the findings of the other researchers. The average entropy generation number, temperature, and velocity profiles are shown to be strongly influenced by regulating factors. The authors conclude that the average entropy production number decreased in the existence of a temperature- and space-dependent heat source/sink, but it increased with increasing the viscosity parameter. 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Phytochemical Analysis and Antibacterial Potential of Stevia rebaudiana (Bertoni, 1899) Leaf Extracts against Aeromonas Species: Influence of Extraction Methods and Solvents in Aquaculture Applications
Recent studies have explored Stevia rebaudiana Bertoni leaf extracts for their antibacterial potential and phytochemical content. However, the impact of extraction methods and solvents on aquaculture bacteria remains understudied. This research aimed to evaluate the antibacterial, radical scavenging, and phytochemical properties of S. rebaudiana extracts against Aeromonas species. Dried S. rebaudiana leaves were extracted using methanol (Mt) and ethanol (Et) through Soxhlet and maceration methods (SMt, SEt, MMt and MEt respectively). Soxhlet extraction yielded higher amounts (36.29% for Mt, 23.87% for Et) compared to maceration. Phytochemical analysis identified phenolics, flavonoids, alkaloids, saponin, tannin, and steroids in all extracts. Notably, MEt had elevated phenolic and flavonoid content, while SEt contained more tannins. MEt exhibited the strongest antioxidant activity (IC50 = 67.95g/mL), aligning with its high phenolic and flavonoid levels. In antibacterial assays against Aeromonas strains, ethanol extract showed the largest zone of inhibition (ZOI) of 16.67mm for A. salmonicida, followed by methanol extract (15mm) at 250 mg/mL, using maceration and Soxhlet methods, respectively. However, none of the extracts displayed activity against A. hydrophila. This suggests that cold maceration is a cost-effective method that preserves heat-sensitive secondary metabolites within a shorter extraction time. In conclusion, this study highlights the significance of extraction techniques and solvents in obtaining potent antibacterial and antioxidant extracts from S. rebaudiana leaves. The findings emphasize the potential of these extracts in aquaculture practices and open avenues for further research in utilizing natural compounds for sustainable aquaculture strategies. The Author(s) 2023. -
Goodness of fit test for Rayleigh distribution with censored observations
We develop new goodness of fit tests for Rayleigh distribution based on fixed point characterization. We use U-Statistic theory to derive the test statistics. First we develop a test for complete data and then discuss, how the right censored observations can be incorporated in the testing procedure. The asymptotic properties of the test statistic in both uncensored and censored cases are studied in detail. Extensive Monte Carlo simulation studies are carried out to validate the performance of the proposed tests. We illustrate the procedures using real data sets. We also provide, a goodness of fit test for the standard Rayleigh distribution based on jackknife empirical likelihood. 2023, Korean Statistical Society. -
Unlocking the Antimicrobial, Antifungal, and Anticancer Power of Chitosan-Stabilized Silver Nanoparticles
Silver nanoparticles (AgNPs) have become a research focus due to its antimicrobial, anticancer applications and cost-effective properties. In this study the effectiveness of green synthesis of NPs using biological macromolecule chitosan which isacting as a reducingcum stabilizing agent is carried out. An in-depth analysis of the synthesized AgNPs was conducted using a variety of sophisticatedcharacterization techniques such as UV-visible spectroscopy, particle size analysis, zeta potentialmeasurements, transmission electron microscopy (TEM), photoluminescence, and Fourier transform infrared (FTIR) spectroscopy. The antimicrobialactivity of the formulated AgNPs were inspected against two human pathogenic strains. In the antimicrobial activity, the synthesized AgNPs exhibited a reduction in the growth of both themicrobes. The minimum inhibitory concentration (MIC) of 1.22 M was observed for both Candida albicans and Mycobacterium smegmatis. Consequently, AgNPs may be used as an opportunity for modern-day antibiotics to treat infections due to human pathogens. Antiproliferative analysis revealed that AgNPs showed antiproliferative characteristics against MDA-MB-231 cells compared to the control. Such AgNPs have an anticancer effect and are likely to be used as smart drug delivery mediators to treat late-stage cancer. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things
Recently, healthcare monitoring systems have emerged as significant tolls for constant monitoring of patient's physiological characteristics. These systems use implanted sensors. IoT (Internet of Things) have revolutionized healthcare systems where health care equipment's are equipped with many sensors that actively collect data from patients and pass it on to cloud based storages using gateway sensors. Securing data have been significant barriers in many applications as false information get injected, or important information are modified or stolen at different phases of health care systems dependent on IoT. The attacks can also result in fatalities making it imperative to secure IoT based health care systems. A Hybrid technique combining MOAES (Modified Optimal Advanced Encryption Standard) with CM (Chaotic Map) Encryptions called HMOAES-CM technique is proposed. This technique can be helpful in securely accessing the patient data over online mode, and in addition, the data sharing can be performed in an encrypted form for the necessary targets of stakeholders. The proposed authentication approach is aimed at IoT, which is resilient to all kinds of network attacks and its implementation is also simpler. Comparing the suggested work to similar works, the level of evaluation is much improved. 2023 The Authors -
The Cartesian product of wheel graph and path graph is antimagic
Suppose each edge of a simple connected undirected graph is given a unique number from the numbers 1, 2, . . ., q, where q is the number of edges of that graph. Then each vertex is labelled with sum of the labels of the edges incident to it. If no two vertices have the same label, then the graph is called an antimagic graph. We prove that the Cartesian product of wheel graph and path graph is antimagic. 2023 Azarbaijan Shahid Madani University. -
Hi line analysis of Herbig Ae/Be stars using X-Shooter spectra
Herbig Ae/Be stars are intermediate-mass pre-main sequence stars undergoing accretion through their circumstellar disk. The optical and infrared (IR) spectra of HAeBe stars show Hi emission lines belonging to Balmer, Paschen and Brackett series. We used the archival X-Shooter spectra available for 109 HAeBe stars from Vioque et al. (2018) and analysed the various Hi lines present in them. We segregated the stars into different classes based on the presence of higher-order lines in different Hi series. We discussed the dependence of the appearance of higher-order lines on the stellar parameters. We found that most massive and younger stars show all the higher-order lines in emission. The stars showing only lower-order lines have Teff< 12 , 000 K and an age range of 510 Myr. We performed a case B line ratio analysis for a sub-sample of stars showing most of the Hi lines in emission. We noted that all but four stars belonging to the sub-sample show lower Hi line ratios than theoretical values, owing to the emitting medium being optically thick. The Hiline flux ratios do not depend on the stars spectral type. Further, from the line ratios of lower-order lines and Paschen higher-order lines, we note that line ratios of most HAeBe stars match with electron density value in the range of 10 9 10 11 cm - 3 . The electron temperature, however, could not be ascertained with confidence using the line ratios studied in this work. 2023, Indian Academy of Sciences. -
Purple Blue emission in MoO3:Ce3+ nanorods: Investigation on the structural and photoluminescence properties
MoO3:Ce3+ nanophosphors were synthesized with different doping concentrations of cerium by hydrothermal method. Structural, optical, and photoluminescence properties were explored. Raman spectroscopy and X-ray diffraction studies were carried out to examine the crystal structure, vibrational modes and symmetry. XRD and Raman spectroscopy assures the orthorhombic phase of MoO3 for the pure and doped samples. Nanorod like morphology was obtained for pure and doped samples. XPS analysis confirms the presence of Ce3+. While analyzing UVVis absorption spectra, the change in optical bandgap was observed with doping concentration in agreement with size effect. The refractive index of the prepared nanorods was evaluated. For MoO3:Ce3+nanorods excited at 250 nm, purple-blue emission comprising of transitions between 2D ? 2F7/2 along with 2D ? 2F5/2 levels of Ce3+ in MoO3 lattice were obtained. The concentration quenching mechanism was observed in MoO3:Ce3+ nanophosphors. Purple-blue emission from different samples were quantified using CIE chromaticity diagram. This work marks MoO3:Ce3+ nanorods as a suitable candidate for display and fluorescence imaging application. 2023 Elsevier Masson SAS -
Review on impacts of micro- and nano-plastic on aquatic ecosystems and mitigation strategies
The rapid proliferation of microplastics (MPs) and nanoplastics (NPs) in our environment presents a formidable hazard to both biotic and abiotic components. These pollutants originate from various sources, including commercial production and the breakdown of larger plastic particles. Widespread contamination of the human body, agroecosystems, and animals occurs through ingestion, entry into the food chain, and inhalation. Consequently, the imperative to devise innovative methods for MPs and NPs remediation has become increasingly apparent. This review explores the current landscape of strategies proposed to mitigate the escalating threats associated with plastic waste. Among the array of methods in use, microbial remediation emerges as a promising avenue for the decomposition and reclamation of MPs and NPs. In response to the growing concern, numerous nations have already implemented or are in the process of adopting regulations to curtail MPs and NPs in aquatic habitats. This paper aims to address this gap by delving into the environmental fate, behaviour, transport, ecotoxicity, and management of MPs and NPs particles within the context of nanoscience, microbial ecology, and remediation technologies. Key findings of this review encompass the intricate interdependencies between MPs and NPs and their ecosystems. The ecological impact, from fate to ecotoxicity, is scrutinized in light of the burgeoning environmental imperative. As a result, this review not only provides an encompassing understanding of the ecological ramifications of MPs and NPs but also highlights the pressing need for further research, innovation, and informed interventions. 2023 Elsevier B.V. -
Estimation of stellar parameters and mass accretion rate of classical TTauri stars from LAMOST DR6
Classical T Tauri stars (TTS) are low-mass pre-main sequence stars with an active circumstellar environment. In this work, we present the identification and study of 260 classical TTS using LAMOST Data Release 6, among which 104 stars are newly identified. We distinguish classical TTS from giants and main-sequence dwarfs based on the log g values, and the presence of H ? emission line and infrared excess that arises from the circumstellar accretion disk. We estimated the mass and age of 210 stars using the Gaia colormagnitude diagram. The age is from 0.1 to 20 Myr, where 90% of the stars have age <10 Myr and the mass ranges between 0.11 and 1.9 M? . From the measured H ? equivalent widths, we homogeneously estimated the mass accretion rates for 172 stars, with most values ranging from 10 - 7 to 10 - 10M? yr - 1 . The mass accretion rates are found to follow a power law distribution with the mass of the star, having a relation of the form M?acc?M?1.430.26 , in agreement with previous studies. 2023, Indian Academy of Sciences. -
An effective analytical method for fractional Brusselator reactiondiffusion system
In recent years, reactiondiffusion models have attracted researchers for their wide applications. In this article, we consider Brusselator reactiondiffusion system (BRDS), which is known for its cross diffusion and pattern formations in biology and chemistry. We derive an analytical solution of the fractional Brusselator reactiondiffusion system (FBRDS) with the help of the initial condition by a novel method, residual power series method (RPSM). The system solution has been analyzed by graph. 2023 John Wiley & Son Ltd. -
OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure
Network Intrusion Detection Systems (NIDS) and firewalls are the de facto solutions in the modern cloud to detect cyberattacks and minimize potential hazards for tenant networks. Most of the existing firewalls, perimeter security, and middlebox solutions are built on static rules/signatures or simple rule matching, making them inflexible, susceptible to bugs, and difficult to introduce new services. This paper aims to improve network management in OpenStack Clouds by taking advantage of the combination of software-defined networking (SDN), Network Function Virtualization (NFV), and machine learning/artificial intelligence (ML/AI) and for making networks more predictable, reliable, and secure. Artificial intelligence is being used to monitor the behavior of the virtual machines and applications running in the OpenStack SDN cloud so that when any issues or degradations are noticed, the decision can be quickly made on how to handle that issue, being able to analyze data in motion, starting at the edge. The OpenStackDP framework comprises lightweight monitoring, anomaly-detecting intelligent sensors embedded in the data plane, a threat analytics engine based on ML/AI algorithms running inside switch hardware/network co-processor, and defensive actions deployed as virtual network functions (VNFs). This network data plane-based architecture makes high-speed threat detection and rapid response possible and enables a much higher degree of security. We have built the framework with advanced streaming analytics technologies, algorithms, and machine learning to draw knowledge from this data that is in motion before the malicious traffic goes to the tenant compute nodes or long-term data store. Cloud providers and users will benefit from improved Quality-of-Services (QoS) and faster recovery from cyber-attacks and compromised switches. The multi-phase collaborative anomaly detection scheme demonstrates an accuracy of 99.81%, average latencies of 0.27 ms, and response speed within 9 s. The simulations and analysis show that the OpenStackDP network analytics framework substantially secures and outperforms prior SDN-based OpenStack solutions for Cloud architectures. 2023, The Author(s). -
Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN
Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection. 2023, Modern Education and Computer Science Press. All rights reserved. -
Sensitive crop leaf disease prediction based on computer vision techniques with handcrafted features
Agricultural production is considered the primary source of the economy of many countries. Tomato and Potatoes are the most sensitive and consumable vegetables worldwide. However, during the growth of these crops, they suffer from many leaf diseases, which lead to loss of productivity and economy of the farmers. Many farmers detect and find plant diseases that are more time-consuming, expensive, and require expert decisions following the naked eye method. Therefore, early and accurate diagnosis of Tomato and Potato crops leaf diseases plays a vital role in sustainable agriculture. So, this research paper proposes an efficient leaf disease classification model based on computer vision techniques. The proposed Adaptive Deep Neural Network (ADNN) leaf disease classification method is a hybrid model which combines an optimized long short-term memory (OLSTM) and convolution neural network (CNN). The weight values supplied in the LSTM classifier are optimally selected using the Adaptive Raindrop Optimization algorithm. The handcrafted features are extracted from the segmented image and fused with the hybrid deep neural network to improve the classifier performance. The ADNN method consists of five steps: preprocessing, feature extraction, segmentation, handcrafted feature extraction, and classification. At first, the images are given to the preprocessing stage to remove the noise from leaf images. Then, the image-affected portion is segmented using an enhanced radial basis function neural network. After the segmentation process, the segmented image is given as an input to the adaptive deep neural network (ADNN) that classifies various types of diseases in the Potato and Tomato leaves. The efficiency of the ADNN model based on the OLSTM-CNN approach is determined concerning multiple metrics, namely Accuracy, Precision, Recall, F-measure, Specificity, and Sensitivity. The ADNN model achieved the best Accuracy of 98.02% for Tomatoes and 98% for Potatoes. The ADNN is compared with existing state-of-the-art CNN, LSTM, ResNet50, and MobileNet techniques. The performance analysis proved that the ADNN model improved efficiency in terms of all metrics and methods. 2023, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
Analysis of nonlinear compartmental model using a reliable method
The goal of this work is to investigate nonlinear models and their complexity using techniques that are universal and have connections to historical and material aspects. Using the premise of a constant population that is uniformly mixed, a nonlinear compartmental model that depicts the movement between voter classes is taken into consideration. In the current work, we investigate the dynamical framework that supports the interactions between the three parties. It is discussed how rate change affects various metrics. The conditions for boundedness, stability, existence, and other dynamics are obtained. We derive the effects of generalizing the model in any order. The current study supports investigations into complex real-world issues and forecasts of necessary plans. 2023 The Author(s) -
Study of nanofluid flow and heat transfer in a stationary cone-disk system
Rheometric, viscosimetric, bio-medical, and several other pharmaceutical machineries utilize the structural advantages provided by the geometry of a stationary conical diffuser. The problem of the Buongiorno nanofluid flow in the conical gap of a stationary cone-disk system for isothermal boundaries is studied. The governing system, comprising the incompressibility condition, NavierStokes equation, energy conservation equation, and conservation of Nanoparticle Volume Fraction (NVF) equation, is analyzed. The Lie-group theory has been used to derive a self-similar model. Solutions of the self-similar equations were computed numerically, and the expressions for the Nusselt number and Sherwood number are obtained. The parametric investigation reveals that the heat and mass transfer rate subside significantly when pre-swirl is introduced to the flow. Furthermore, the nanofluid slip mechanisms enhance the effective temperature of the system. 2023 Elsevier Ltd -
Sandwich structured pedot-TiO2/GO/PEDOT-TiO2 electrodes for supercapacitor
In this study, we fabricated a divergent strategy to enhance the electrochemical capacitive properties of electrodes via the cost-effective multistep green and facile electrodeposition and brush coating technique of PEDOT-TiO2/GO/PEDOT-TiO2 composite. The synthesised composite showed both EDLCs and pseudocapacitive behaviour with a good specific capacitance of 501 Fg?1 for sandwiched structure at 1 Ag?1. From the results, synthesized composite has a better ion transportation mechanism which leads to a fast chargedischarge cycle as well as a very high value of power density (500 kW/ kg) suitable for supercapacitor applications. The substance demonstrated excellent electrochemical stability, retaining 94 % of capacitance after 2000 cycles. The obtained nanocomposites were examined by FTIR, XRD, Raman, SEM-EDX and electrochemical analyses such as CV, GCD and EIS analyses. We consider that the highly stable PEDOT-TiO2/GO/PEDOT-TiO2 nanocomposite with super-capacitive behaviours is a very promising material for high-performance electrochemical storage. 2023 The Authors -
FinTech and Financial Capability, What Do We Know and What We Do Not Know: A Scoping Review
Purpose: The scoping review of this study was to investigate the existing literature on FinTech's impact on financial competence and draw conclusions from it. We applied Danielle Levac's recommendations and used the scoping review framework developed by Arksey and O'Malley. Design/Methodology/Approach: The study involved identifying and analyzing 246 papers from major databases, followed by a rigorous screening process to select 54 relevant studies. Data coding, inclusion, and exclusion screening were conducted by us independently. Findings: According to the findings, studies on FinTech and financial capacity started in 2012, and since 2020, the number of studies has sharply increased. The analysis showed that financial inclusion was the primary focus of the major FinTech studies, suggesting possible research gaps in other aspects of financial competence. We suggested recommendations and prospective directions for further research in this developing subject based on these findings. Managerial Implications: This knowledge will help managers find opportunities for collaboration, offer fresh perspectives, and make wise choices, which will improve the sector. Theoretical Implications: Important concepts and relationships were found, new trends were highlighted, and theoretical advancements were suggested as a result of the inquiry. Through the filling of knowledge gaps, the study would guide future theoretical development, facilitate diverse perspectives, and support the construction of robust frameworks in the FinTech and financial capabilities sector. Originality/Value: This study offered a comprehensive review of the body of literature, pointing out areas in need of more research and knowledge gaps. The review's unique perspective for future study and innovation in understanding the relationship between FinTech and financial capacity is derived from its thorough synthesis of many studies. 2023, Associated Management Consultants Pvt. Ltd. All rights reserved. -
Numerical simulation and mathematical modeling for heat and mass transfer in MHD stagnation point flow of nanofluid consisting of entropy generation
The primary goal of this article is to explore the radiative stagnation point flow of nanofluid with cross-diffusion and entropy generation across a permeable curved surface. Moreover, the activation energy, Joule heating, slip condition, and viscous dissipation effects have been considered in order to achieve realistic results. The governing equations associated with the modeling of this research have been transformed into ordinary differential equations by utilizing appropriate transformation variable. The resulting system of equations was solved numerically by using Bvp4c built-in package in MATLAB. The impact of involved parameters have been graphically examined for the diverse features of velocity, temperature, and concentration profiles. Throughout the analysis, the volume fraction is assumed to be less than 5 % while the Prandtl number is set to be 6. In addition, the entropy generation, friction drag, Nusselt, and Sherwood numbers have been plotted for describing the diverse physical aspects of the underlying phenomena. The major outcomes reveal that the curvature parameter reduces the velocity profile and skin friction coefficient whereas the magnetic parameter, temperature difference parameter, and radiation parameter intensify the entropy generation. 2023, The Author(s). -
Fuel coke derived nitrogen and phosphorus co-doped porous graphene structures for high-performance supercapacitors: The trail towards a brown-to-green transition
As the looming crisis of global energy market exponentially intensifies, the scientific community prompts the use of supercapacitors as a sustainable energy production/storage model and emission reduction strategy. Therefore, in this work, we present a cutting-edge approach for the high-value utilization of fossil fuel-derived materials in supercapacitor applications, promoting an integrated brown-to-green transition for energy, ecology, and the environment. Herein, nitrogen and phosphorus co-doped porous graphene sheets (a-NPGO) have been prepared from fuel coke, which exhibits outstanding electrochemical performance as a supercapacitor electrode material. The a-NPGO shows a high specific capacitance (322 F/g at 1 A/g) almost 11 times greater than the undoped coke-based graphene derivative. Furthermore, the symmetric supercapacitor assembled with a-NPGO-modified electrodes delivered an exceptional power density of 812 W/kg at energy density of 14 Wh/kg and an excellent capacitance retention of 90 % after 5000 charge-discharge cycles. This impression of coke-derived material in high-performance supercapacitors may broaden the horizon of the current electrochemical energy storage paradigm and afford the eco-conscious implementation of fossil fuel resources. 2023 Elsevier Ltd