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The aim of the present investigation was to study numerically the transient of thermal convection in a square cavity filled with low‐Prandtl‐number fluids. The flow is driven by the horizontal temperature gradient between the vertical walls maintained at different temperatures. Two‐dimensional equations of conservation and energy are solved using a finite element method and a fractional step time. The discrete equations systems are solved in the lap of each element‐mesh with the aim of verifying the Boussinesq hypothesis locally. To compare our results with the earlier predictions, we have chosen the fluids for Prandtl‐numbers 0.001, 0.005 and 0.01 and with Grashof numbers up to 1 × 10⁷. To predict the steady state solutions with an oscillary transient period, the results are reduced in terms of the time series average Nusselt‐number at the vertical walls, the velocity at the center of the cavity and near right boundary. In addition, the isotherms and the velocity field are produced with the aim of showing the main circulation and particularly the weak circulations at the corners of the cavity.

Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites.

A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones.

Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026.

The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection.

The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.

The purpose of this paper is to review previous research studies on mathematical models for entropy generation in the magnetohydrodynamics (MHD) flow of nanofluids. In addition, the influence of various parameters on the velocity profiles, temperature profiles and entropy generation was studied. Furthermore, the numerical methods used to solve the model equations were summarized. The underlying purpose was to understand the research gap and develop a research agenda.

This paper reviews 141 journal articles published between 2010 and 2022 on topics related to mathematical models used to assess the impacts of various parameters on the entropy generation, heat transfer and velocity of the MHD flow of nanofluids.

This review clarifies the application of entropy generation mathematical models, identifies areas for future research and provides necessary information for future research in the development of efficient thermodynamic systems. It is hoped that this review paper can provide a basis for further research on the irreversibility of nanofluids flowing through different channels in the development of efficient thermodynamic systems.

Entropy generation analysis and minimization constitute effective approaches for improving the performance of thermodynamic systems. A comprehensive review of the effects of various parameters on entropy generation was performed in this study.

This study aims to delve into the coupled mixed convective heat transport process within a grooved channel cavity using CuO-water nanofluid and an inclined magnetic field. The cavity undergoes isothermal heating from the bottom, with variations in the positions of heated walls across the grooved channel. The aim is to assess the impact of heater positions on thermal performance and identify the most effective configuration.

Numerical solutions to the evolved transport equations are obtained using a finite volume method-based indigenous solver. The dimensionless parameters of Reynolds number (1 ≤ Re ≤ 500), Richardson number (0.1 ≤ Ri ≤ 100), Hartmann number (0 ≤ Ha ≤ 70) and magnetic field inclination angle (0° ≤ γ ≤ 180°) are considered. The solved variables generate both local and global variables after discretization using the semi-implicit method for pressure linked equations algorithm on nonuniform grids.

The study reveals that optimal heat transfer occurs when the heater is positioned at the right corner of the grooved cavity. Heat transfer augmentation ranges from 0.5% to 168.53% for Re = 50 to 300 compared to the bottom-heated case. The magnetic field’s orientation significantly influences the average heat transfer, initially rising and then declining with increasing inclination angle. Overall, this analysis underscores the effectiveness of heater positions in achieving superior thermal performance in a grooved channel cavity.

This concept can be extended to explore enhanced thermal performance under various thermal boundary conditions, considering wall curvature effects, different geometry orientations and the presence of porous structures, either numerically or experimentally.

The findings are applicable across diverse fields, including biomedical systems, heat exchanging devices, electronic cooling systems, food processing, drying processes, crystallization, mixing processes and beyond.

This work provides a novel exploration of CuO-water nanofluid flow in mixed convection within a grooved channel cavity under the influence of an inclined magnetic field. The influence of different heater positions on thermomagnetic convection in such a cavity has not been extensively investigated before, contributing to the originality and value of this research.

The purpose of this study is to analyze heat transfer and entropy generation of a Cu-water nanofluid in a vertical channel. The channel walls are maintained at a hot temperature Tw. An up flow penetrates the channel at a uniform velocity v₀ and a cold temperature T₀ (T₀ < T_w). The effects of Reynolds number Re, Grashof number Gr and solid volume fraction ϕ on streamlines, isotherms, entropy generation, friction factor, local and mean Nusselt numbers are evaluated.

The Cu-water nanofluid is used in this study. The software Ansys-fluent 14.5, based on the finite-volume method and SIMPLE algorithm, is used to simulate the mixed convection problem with entropy generation in a vertical channel.

The results show that the increase of Reynolds and Grashof numbers and solid volume fraction improves heat transfer and reduces entropy generation. Correlations for the mean Nusselt number and friction factor in terms of Reynolds number and solid volume fraction are obtained. The present results are compared with those found in the literature, which reveal a very good agreement.

The originality of this work is to understand the heat transfer and entropy generation for mixed convection of a Cu-water nanofluid in a vertical channel.

This paper aims to study the anti-diabetes activity of the Kombucha prepared from different snake fruit cultivars.

The juices of snake fruits of Suwaru, Madura, Pondoh and Bali cultivars were fermented for 14 days. Anti-diabetes activity of the products was analyzed. Twenty-four male albino Wistar rats were used and randomly divided into six experimental groups, i.e. four groups of the diabetic rats treated with the Kombucha, plus the normal group and diabetic control group. The Kombucha were orally administered to the streptozotocin induced-diabetic rats at 5 mL/kg body weight per day during the 28-day experiment. The fasting plasma glucose (FPG), oxidative stress indices (superoxide dismutase [SOD] activity and Malondialdehyde [MDA] level) and lipid profile of the blood plasma were measured. The pancreas was used for immunohistochemical study and β-cells quantification. Data were analysed by ANOVA followed by Fisher test using Minitab version 16.0.

FPG of the diabetic rats treated with the Kombucha (110.3-189.3 mg/dL) was significantly lower (p = 0.000) than the diabetic control group (413.3 mg/dL). Those were in line with the number of pancreatic β-cells of 42.1 in diabetic rats that lower (p = 006) than those in treated the diabetic rats (61.2-73.5). The treated diabetic rats had lower oxidative stress (SOD activity: 20.9-44.6 unit/100 µL with p = 0.000; MDA level: 0.37-0.48 ng/100 µL with p = 0.000) than those in the diabetic rats (SOD activity: 18.7 unit/100µL; MDA level: 0.84 ng/100 µL). The treated diabetic rats also showed better lipid profile than those in the diabetic control rats. There were cultivar differences, and the Suwaru and Madura snake fruit Kombucha demonstrated the most potential for diabetes management.

This is the first study on in vivo anti-diabetes activity of snake fruit Kombucha prepared from different snake fruit cultivars.

Wireless Body Area Network (WBAN) technology envisions a network in which sensors continuously operate on and obtained critical physical and physiological readings. Sensors deployed in WBANs have restricted resources such as battery energy, computing power and bandwidth. We can utilize these resources efficiently. By devising a mechanism that is energy efficient with following characteristics, i.e. computational complexity is less, routing overhead is minimized, and throughput will be maximum. A lot of work has been done in this area but still WBAN faces some challenges like mobility, network lifetime, transmission range, heterogeneous environment, and limited resources. In the present years well, contemplative studies have been made through a large body to reach some holistic points pertaining to the energy consumption in WBAN. Thus we/put forward appropriate algorithm for energy efficiency which can vividly corroborate the advances in this specific domain. We have also focused on various aspects and phases of the studies like study computational complexity, routing overhead and throughput type of characteristics. There is still a room for improvement to get the desired energy optimization in WBAN. The network performance mainly relies upon the algorithm used for optimization process. In this work, we intended to develop an energy optimization algorithm for energy consumption in WBAN which is based on evolutionary algorithms for inter-BAN communications using cluster-based routing protocol.

In this paper we propose a meta heuristics algorithm Goa to solve the optimization problem in WBAN. Grasshopper is an insect. Generally, this insect is viewed individually and creating large swarm in nature. Figure 5 shows the individual grasshoppers' primitive patterns in swarm. Figure 7 depicts the pseudo code of Goa. In Goa, experiments are done to view the behavior of grasshoppers in swarm. How they gradually move towards the stationary and mobile target. Through experimentation it is conceived that swarm gradually converge towards their target. Another interesting pattern related to convergence of grasshopper is that it slowly towards its target. This shows that grasshopper does not trapped in local optima. In starting iterations of exploration process Goa, search globally and in last iterations it searches local optima. Goa makes the exploration and exploitation process balanced while solving challenging optimization problems.

Energy efficiency is achieved in the optimization process of cluster formation process. As the use of proposed algorithm Goa creates the optimal number of clusters. Shorter cluster lifetime means more times clustering procedure is called. It increases the network computational cost and the communication overhead. Experimentation results show that proposed Goa algorithm performs well. We compare the results of Goa with existing optimization Algorithms ACO and MFO. Results are generated using MATLAB.

A lot of work has done for the sake of energy optimization in WBAN. Many algorithms are proposed in past for energy optimization of WBAN. All of them have some strengths and weaknesses. In this paper we propose a nature inspired algorithm Goa. We use the Goa algorithm for the sake of energy optimization in WBAN.

The purpose of this study is to carry out a comprehensive analysis of magneto-hydrodynamics (MHD), nanofluidic flow dynamics and heat transfer as well as thermodynamic irreversibility, within a novel butterfly-shaped cavity. Gaining a thorough understanding of these phenomena will help to facilitate the design and optimization of thermal systems with complex geometries under magnetic fields in diverse applications.

To achieve the objective, the finite element method is used to solve the governing equations of the problem. The effects of various controlling parameters such as butterfly-shaped triangle vertex angle (T), Rayleigh number (Ra), Hartmann number (Ha) and magnetic field inclination angle (γ ) on the hydrothermal performance are analyzed meticulously. By investigating the effects of these parameters, the authors contribute to the existing knowledge by shedding light on their influence on heat and fluid transport within butterfly-shaped cavities.

The major findings of this study reveal that the geometrical shape significantly alters fluid motion, heat transfer and irreversibility production. Maximum heat transfer, as well as entropy generation, occurs when the Rayleigh number reaches its maximum, the Hartmann number is minimized and the angle of the magnetic field is set to 30° or 150°, while the butterfly wings angle or vertex angle is kept at a maximum of 120°. The intensity of the magnetic field significantly controls the heat flow dynamics, with higher magnetic field strength causing a reduction in the flow strength as well as heat transfer. This configuration optimizes the heat transfer characteristics in the system.

Further research can be expanded on this study by examining thermal performance under different curvature effects, orientations, boundary conditions and additional factors. This can be accomplished through numerical simulations or experimental investigations under various multiphysical scenarios.

The geometric configurations explored in this research have practical applications in various engineering fields, including heat exchangers, crystallization processes, microelectronic devices, energy storage systems, mixing processes, food processing, air-conditioning, filtration and more.

This study brings value by exploring a novel geometric configuration comprising the nanofluidic flow, and MHD effect, providing insights and potential innovations in the field of thermal fluid dynamics. The findings contribute a lot toward maximizing thermal performance in diverse fields of applications. The comparison of different hydrothermal behavior and thermodynamic entropy production under the varying geometric configuration adds novelty to this study.

Wireless sensor network (WSN) and mobile crowd sensing (MCS) technologies face some challenges, especially when deployed in a large environment such as a smart city environment. WSN faces network latency, packets delivery and limited lifetime due to the nature of the used constrained internet of things small devices and low power network. On the other hand, most of the current applications that adapt MCS technology use 3G or long term evalution network to collect data and send them directly to the server. This leads to higher battery and bandwidth consumption and higher data cost.

This paper proposes a hybrid routing protocol based on the routing protocol (RPL) protocol that combines the two wireless sensing technologies (WSN and MCS) and allows the integration between them. The aim is to use MCS nodes in an opportunistic way to support static WSN nodes to enhance the performance.

The evaluation of the proposed protocol was conducted in a static WSN to study the impact of the integration on the WSN performance. The results reveal a good enhancement on packet delivery ratio (17% more), end-to-end delay (50% less) and power consumption (25% less) compared with native RPL (without MCS integration).

The authors believe that the hybrid-RPL protocol can be useful for sensing and data collection purposes, especially in urban areas and smart city contexts.

The direct nexus between board characteristics, earnings management (EM) practices and dividend payout is examined in this study, followed by an examination of the indirect mediation impact of EM practices in the nexus between board characteristics and dividend payout. It aims to provide new empirical evidence from the Jordanian market, which is an emerging market.

The study population consists of all service firms that were listed on the Amman Stock Exchange (ASE) between 2012 and 2019. Due to the lack of availability of their complete data during the period, four service firms were omitted from the population; hence, a sample of 43 service firms was acquired over the time frame (2012–2019), yielding a total of 344 firm-year observations. Moreover, panel data analysis was employed in this study, and data for the study were acquired from yearly reports as well as the ASE's database.

Based on the GMM estimator findings, board size and independence have a negative and significant influence on the EM, but CEO/chairman duality has a positive and significant impact. Simultaneously, the impacts of female representation on the board of directors and the number of board meetings were both positive but insignificant. The findings also found that four board characteristics, including board size, female representation on the board of directors, CEO/chairman duality and the number of board meetings, had a significant negative or positive effect on dividend payout, while board independence did not. Additional findings show that EM practices have a direct negative insignificant effect on dividend payout, whereas EM practices partially mediate the relationship between board characteristics and dividend payout.

The current study's limitation is that it only searched in Jordanian service firms listed on ASE from 2012 to 2019 to fulfill the study's objectives; thus, we urge that future work explores the study models for other sectors, whether in Jordan or other growing markets such as the Middle East and North Africa.

The findings of this study may be utilized by analysts, investors and other strategic decision-makers to enhance Jordan's financial market's efficiency and efficacy. These findings will improve policymakers' willingness to impose appropriate constraints, perhaps boosting Jordan's financial market performance and efficacy. These findings may also help investors make more enlightened judgments by utilizing board characteristics and EM factors that predict firm dividend policy.

Contradictions in the results of earlier investigations inspired the current study, with the findings filling a gap in the existing literature. This study differs from previous studies by constructing a novel research model and analyzing the mediating influence of EM in the nexus between board characteristics and dividend payout.