Scientists and engineers have experimentally noticed that the heat transfer is essential for preeminence of multi scale production. Thermal properties are naturally accomplished via continuous phase ...liquids. Its importance is nevertheless constrained by the low capacity for heat transfer. Consequently, the increased heat transference phenomenon must be employed to accomplish the expected results. In this regard, the nature of the Arrhenius activation energy in the flow of hybrid nanoliquid over a curved stretchable surface (CSS) in the presence of exponential heat generation is scrutinized. The fluid suspended with Manganese zinc ferrite (MnZnFe2O4) and Nickel zinc ferrite (NiZnFe2O4) as nanoparticles along with Kerosene oil as a base liquid is accounted in this study. The described flow equations are transformed by using appropriate similarity variables and then they are tackled with Runge Kutta Fehlberg‐45 (RKF‐45) scheme by adopting shooting process. It can be concluded that, the increasing values of Biot number and heat source/sink parameter improves the thermal gradient. Further, hybrid nanofluid shows high heat transfer rate when compared to nanoliquid for improved values of heat source/sink parameter.
Scientists and engineers have experimentally noticed that the heat transfer is essential for preeminence of multi scale production. Thermal properties are naturally accomplished via continuous phase liquids. Its importance is nevertheless constrained by the low capacity for heat transfer. Consequently, the increased heat transference phenomenon must be employed to accomplish the expected results. In this regard, the nature of the Arrhenius activation energy in the flow of hybrid nanoliquid over a curved stretchable surface (CSS) in the presence of exponential heat generation is scrutinized.…
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often ...viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.
The primary goal of this research is to examine the thermal variance in a dovetail fin under fully wet conditions with ternary hybrid nanofluid ZnFe2O4 +MnZnFe2O4 +NiZnFe2O4- H2O taking temperature ...and humidity ratio differences into account as driving forces for heat and mass transfer systems, respectively. The consequences of surface convection, radiation, and internally generated heat on the heat exchange of the fin have been taken into account. The mathematical modeling involves dimensionless transformation to convert the balanced energy equation to ordinary differential equation, and the problem is then solved numerically as well as analytically using the fourth-fifth order Runge-Kutta-Fehlberg's (RKF) methodology and DTM-Pade approximant. The significance of major thermal parameters such as radiation-conduction, wet factor, heat generation, and the ambient temperature variable on the temperature profile is explored graphically, contributing to an analysis of thermal performance. As the main outcome, ternary hybrid nanoliquid exhibits higher thermal response compared to mono and binary hybrid nanoliquid. Also, the thermal dispersion is higher for the lower values of wet parameter and radiative variable.
Code-driven systems have extent to more than half of the world’s populations in ambient data and connectivity, offering formerly unimagined opportunities and unexpected threats. Evolutions in ...Artificial Intelligence (AI) are seen increasing day by day especially in industrial builds. The unconventional technique of AI in cyber-attacks seems to be quite daunting. The idea of a machine growing its own knowledge through self-learning becomes sophisticated to attack things is a fretful problem to the cyber world. Most of the time, these AI enabled cyber-attacks are performed using advanced malwares which incorporates advanced evasion techniques to evade security perimeters. Traditional cyber security methods fail to cope with these attacks. In order to address these issues, robust traffic classification system using Principal Component Analysis (PCA) and Artificial Neural Network (ANN) is proposed for providing extreme surveillance. Further, these proposed method aims to expose various AI based cyber-attacks with their present-day impact, and their fortune in the future. Simulation is carried out using a self-developed autonomous agent which learns by itself. Experimental results confirm that the proposed schemes are efficient to classify the attack traffic with 99% of accuracy when compared to the state of the art methods.
Abstract
The thermal distribution in a convective-radiative concave porous fin appended to an inclined surface has been examined in this research. The equation governing the temperature and heat ...variation in fin with internal heat generation is transformed using non-dimensional variables, and the resulting partial differential equation (PDE) is tackled using an analytical scheme, generalized residual power series method (GRPSM). Moreover, a graphical discussion is provided to examine the consequence of diverse non-dimensional variables including the parameters of convection-conduction, ambient temperature, radiation, heat generation, and porosity effect on the thermal field of the fin. Also, a graph is plotted to analyze the variations in unsteady temperature gradient using the finite difference method (FDM) and generalized residual power series method (GRPSM). The major result of this investigation unveils that as the convection-conduction parameter scale upsurges, the distribution of temperature in the fin diminishes. For the heat-generating parameter, the thermal distribution inside the fin increases.
Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study ...investigates the influence of a wavy fin structure subjected to convective effects with internal heat generation. The thermal distribution, considered a steady condition in one dimension, is described by a unique implementation of a physics-informed neural network (PINN) as part of machine-learning intelligent strategies for analyzing heat transfer in a convective wavy fin. This novel research explores the use of PINNs to examine the effect of the nonlinearity of temperature equation and boundary conditions by altering the hyperparameters of the architecture. The non-linear ordinary differential equation (ODE) involved with heat transfer is reduced into a dimensionless form utilizing the non-dimensional variables to simplify the problem. Furthermore, Runge-Kutta Fehlberg's fourth-fifth order (RKF-45) approach is implemented to evaluate the simplified equations numerically. To predict the wavy fin's heat transfer properties, an advanced neural network model is created without using a traditional data-driven approach, the ability to solve ODEs explicitly by incorporating a mean squared error-based loss function. The obtained results divulge that an increase in the thermal conductivity variable upsurges the thermal distribution. In contrast, a decrease in temperature profile is caused due to the augmentation in the convective-conductive variable values.
Most of the enterprises are transforming their conventional networks into Software-Defined Network (SDN) to avail the cost efficiency and network flexibility. But recent attacks and security breaches ...against SDNs expose the security weakness of the technology. Distributed Denial of Service (DDoS) is the most common attack launched against various SDN architecture layers. Hence, DDoS has been claimed to be the most dangerous attack and threat to SDN. The existing mitigation techniques are traffic volumetric methods, entropical methods and traffic flow analysis methods. They depend on traffic sampling to achieve truly inline against DDoS detection accuracy in real time. However, traffic sampling-based methods are expensive with chances for incomplete approximation of underlying traffic patterns being very high. Early detection of DDoS attack in the controller is critical and requires highly adaptive and accurate methods. In this paper, an effective and accurate DDoS detection method using Lion optimization algorithm is proposed. The proposed detection technique is robust enough to detect DDoS attack within the least magnitude of attack traffic. Further, to evaluate the performance, the proposed method is compared with the state-of-the-art techniques. The outcome of this paper is current method limitation and scope for improvement depicted from overall study and analysis. The experimental results have proved that the proposed method outperforms the existing state-of-the-art methods with 96% accuracy.
A numerical investigation was carried out in Solar Air Heater (SAH) by implementing an artificial rough absorber plate for higher thermal performances. A polygonal transfer rib, the forward and ...backward trapezoidal rough ribs were nominated for simulation analysis using ANSYS, Fluent version 13.0. The Renormalization k-ε model was selected to predict the augmentation of Nusselt number (Nu), friction factor (ƒ) characterization and Thermo Hydraulic Performances (THP) for a proposed rib by varying relative roughness pitch P/e = 3.33–20 and relative roughness height e/D = 0.03–0.09. Dittus Boelter and Blasius correlation were used for validating the smooth surface of Nu and ƒ besides compared with the rough surface to ascertain augmentation of heat transfer. The investigation reported on the performance of Nu and ƒ of the proposed rib at a Reynolds number ranges from 3800 to 18,000. The result reveals that the polygonal rib shape with relative roughness pitch P/e = 3.33 has produced higher Nu and gradual reduction of ƒ at Reynolds number 18000. It was found that THP has achieved a maximum of 1.89 in P/e = 10 & e/D = 0.06 at Reynolds number 15000 in the backward trapezoidal rib.
•CFD analysis in SAH with polygonal and trapezoidal shapes of ribs is proposed.•Significant Nusselt number enhancement in polygonal rib at p/e = 3.33 is observed.•Maximum Thermo hydraulic performances achieved 1.89 in P/e = 10 & e/D = 0.06•An empirical correlation for Nusselt number and friction factor is developed.
Background
Coronaviruses mainly affect the respiratory system; however, there are reports of SARS-CoV and MERS-CoV causing neurological manifestations. We aimed at discussing the various neurological ...manifestations of SARS-CoV-2 infection and to estimate the prevalence of each of them.
Methods
We searched the following electronic databases; PubMed, MEDLINE, Scopus, EMBASE, Google Scholar, EBSCO, Web of Science, Cochrane Library, WHO database, and
ClinicalTrials.gov
. Relevant MeSH terms for COVID-19 and neurological manifestations were used. Randomized controlled trials, non-randomized controlled trials, case-control studies, cohort studies, cross-sectional studies, case series, and case reports were included in the study. To estimate the overall proportion of each neurological manifestations, the study employed meta-analysis of proportions using a random-effects model.
Results
Pooled prevalence of each neurological manifestations are, smell disturbances (35.8%; 95% CI 21.4–50.2), taste disturbances (38.5%; 95%CI 24.0–53.0), myalgia (19.3%; 95% CI 15.1–23.6), headache (14.7%; 95% CI 10.4–18.9), dizziness (6.1%; 95% CI 3.1–9.2), and syncope (1.8%; 95% CI 0.9–4.6). Pooled prevalence of acute cerebrovascular disease was (2.3%; 95%CI 1.0–3.6), of which majority were ischaemic stroke (2.1%; 95% CI 0.9–3.3), followed by haemorrhagic stroke (0.4%; 95% CI 0.2–0.6), and cerebral venous thrombosis (0.3%; 95% CI 0.1–0.6).
Conclusions
Neurological symptoms are common in SARS-CoV-2 infection, and from the large number of cases reported from all over the world daily, the prevalence of neurological features might increase again. Identifying some neurological manifestations like smell and taste disturbances can be used to screen patients with COVID-19 so that early identification and isolation is possible.
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