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Peristaltic flow of hybrid nanofluid inside a duct having sinusoidally advancing boundaries and elliptic cross-section is mathematically investigated. The notable irreversibility effects are also examined in this mathematical research by considering a descriptive entropy analysis. In addition, this work provides a comparison analysis for two distinct nanofluid models: a hybrid model (Cu-Ag/water) and a phase flow model (Cu/water). A comprehensive graphical description is also provided to interpret the physical aspects of this mathematical analysis.

The present analysis deals with the entropy analysis of the blood flow through an anisotropically tapered arteries under the suspension of magnetic Zinc-oxide (ZnO) nanoparticles (NPs). The Jeffrey fluid model is contemplated as blood that is electrically conducting and incompressible. The lubrication approach is used for the mathematical modeling. The second law of thermodynamics is used to examine the entropy generation. The exact solutions are obtained against velocity and temperature profile with the use of computational software. The results for Entropy, Velocity, Bejan number, temperature profile, and impedance profile are discussed by plotting the graphs. ZnO-NPs have promising applications in biomedical engineering due to its low toxicity, economically reliable, and excellent biocompatibility. ZnO-NPs also emerged in medicine i.e., antibacterial and anticancer activity, and also beneficial in antidiabetic treatment. The monitoring of the blood temperature in the case of the tapered artery has supreme importance in controlling the temperature of blood in the living environment. The presence of a magnetic field is advantageous to manage and control the blood motion at different temperatures. The present outcomes are enriched to give valuable information for the research scientists in the field biomedical science, who are looking to examine the blood flow with stenosis conditions and also beneficial in treating multiple diseases.

Undoubtedly, Location-based Social Networks (LBSNs) provide an interesting source of geo-located data that we have previously used to obtain patterns of the dynamics of crowds throughout urban areas. According to our previous results, activity in LBSNs reflects the real activity in the city. Therefore, unexpected behaviors in the social media activity are a trustful evidence of unexpected changes of the activity in the city. In this paper we introduce a hybrid solution to early detect these changes based on applying a combination of two approaches, the use of entropy analysis and clustering techniques, on the data gathered from LBSNs. In particular, we have performed our experiments over a data set collected from Instagram for seven months in New York City, obtaining promising results. This work is funded by: the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC), Spain, the Spanish Ministry of Economy and Competitiveness under the National Science Program (TEC2014-54335-C4-3-R, TEC2014-54335-C4-2-R, TEC2017-84197-C4-3-R and TEC2017-84197-C4-2-R), and by the Madrid Regional Government eMadrid Excellence Network, Spain (S2013/ICE-2715).

The magnitude of shear stress at the ciliated wall is considered as the measure of efficiency of cilia beatings as it describes the momentum transfer between the medium and the cilia. Under high shear rate, some non-Newtonian fluids behave as visco-inelastic fluids. We consider here a ciliated channel coated with Prandtl fluid, a visco-inelastic fluid, with Hartmann layer under momentum and thermal slip effects. The flow in the channel is produced due to beatings of cilia that obey an elliptic path of motion in the flow direction. An entropy analysis of the flow is also conducted in wave frame. After introducing lubrication approximations in the governing equation, the perturbation solutions are calculated. The data for pressure rise per metachronal wavelength and frictional force at the ciliated wall are obtained by numerical integration. The analysis reveals that the higher values of cilia length and velocity slip parameters support fluid flow near the channel wall surface. Fluid temperature is an increasing function of thermal slip but a decreasing function of cilia length and slip parameters. Entropy in the channel can be minimized with an increase in cilia length and slip effect at the boundary. The magnitude of the heat transfer coefficient decreases by taking the substantial slippage and tiny cilia in length at the microchannel wall.

To investigate the influence of the change of tip clearance size on the control effect of the endwall suction, the effects of endwall suction on the aerodynamic performance of the axial compressor cascade were studied numerically. Three tip clearance sizes of 0.5% h, 1.0% h, and 2.0% h (h is the blade height) were mainly considered. The results show that the endwall suction scheme whose coverage range was 8&ndash, 33% axial chord can reduce the leakage flow and improve the aerodynamic performance by directly influencing the structure of tip leakage vortex. The overall total pressure loss coefficients of the three clearance size schemes at 0&deg, angle of incidence with 0.4 inlet Mach number are reduced by about 10.3%, 10.8%, and 6.0%, respectively, at the suction flow rate of 0.7%. Under the same suction flow rate, the onset position of the tip leakage vortex of the cascade with small clearance is shifted from the 15% of the axial chord length of original to the 48% of the axial chord length, which with large clearance is nearly no changed. The leakage flow rate and the distance from the leakage vortex to the suction slot are the main reasons for the different control effect of the endwall suction under different tip clearance sizes. The difference of the spanwise distribution of flow field parameters may also cause the difference of flow control effect.

The majority of existing objective Image Quality Assessment (IQA) methods are designed for evaluation of images corrupted by single distortion types. However, images may be degraded with multiple distortions during processing stages. In this paper, we propose a reduced-reference IQA algorithm to predict the quality of multiply-distorted images. An image is first decomposed into predicted and disorderly portions based on the internal generative mechanism theory. The structural information is captured from the predicted image by using a shearlet representation and Renyi directional entropy is deployed to measure the disorderly information changes. Finally, we introduce the application of a framework namely Learning Using Privileged Information (LUPI) to build a quality model and obtain quality scores. During training, the LUPI framework utilizes a set of additional privileged data to learn an improved quality model. Experimental results on multiply-distorted image datasets (MLIVE and MDID2015) confirm the effectiveness of the proposed IQA model.

Decomposition of professional thinking of the human operator in process control, requiring intense attention for a long time, was performed. The problem of assessing the ability of the operator to adapt to mental stress was formulated. The authors proposed a model for calculation of the indicators of the functional state of the body during the transition from normal to hard work. It is based on a dynamic model of the system, which is not in a stationary state under external forces, but will be changing until the rate of entropy production within the system reaches the minimum in these conditions. It is shown that this model can be used to solve the problem.

This article presents the application of a new educational thermodynamic software called MOLECULARDISORDER, based on graphical user interfaces created in Matlab&reg, to promote critical thinking in youth engineering students, by means of the energy and entropy balance application in different systems. Statistics of the results obtained by the youth students are shown to determine the influence of the software in a regular course in thermodynamics to promote critical thinking. Two case studies were done by the students, where parameters such as temperature of the fluid and metal surfaces, pressure of the system, mass of the fluid and solid, volume, and velocity of the fluid are used to obtain output variables such as enthalpy, entropy, changes in entropy, entropy production, and energy transfer in the chosen system. Four cognitive skills were considered to evaluate the cognitive competencies of interpreting, arguing and proposing, and interacting with the different graphical user interfaces, these cognitive skills (CS) were argumentative claim (CS1), modeling (CS2), interpreting data/information (CS3), and organization (CS4). Student&acute, s T-test was used to compare the degree of difficulty of each criterion. The case studies were evaluated first without using the software and then with the use of the software to determine the significant effect of the software quantitatively. A population of 130 youth students was taken to perform the statistical analysis with a level of significance of 5%. With the help of the software, the students obtained an improvement when performing case study 1 since the p-value obtained was 0.03, indicating that there are significant differences between the results before and after taking the software. The overall averages of the grades for case study 1 had an increase after using the software from 3.74 to 4.04. The overall averages for case study 2 were also higher after taking the software from 3.44 to 3.75.

Second law analysis (entropy generation) for the steady two-dimensional laminar forced convection flow, heat and mass transfer of an incompressible viscous fluid past a nonlinearly stretching porous (permeable) wedge is numerically studied. The effects of viscous dissipation, temperature jump, and first-order chemical reaction on the flow over the wedge are also considered. The governing boundary layer equations for mass, momentum, energy and concentration are transformed using suitable similarity transformations to three nonlinear ordinary differential equations (ODEs). Then, the ODEs are solved by using a Keller’s box algorithm. The effects of various controlling parameters such as wedge angle parameter, velocity ratio parameter, suction/injection parameter, Prandtl number, Eckert number, temperature jump parameter, Schmidt number, and reaction rate parameter on dimensionless velocity, temperature, concentration, entropy generation number, and Bejan number are shown in graphs and analyzed. The results reveal that the entropy generation number increases with the increase of wedge angle parameter, while it decreases with the increase of velocity ratio parameter. Also, in order to validate the obtained numerical results of the present work, comparisons are made with the available results in the literature as special cases, and the results are found to be in a very good agreement.

Flow separation, which often occurs at the junction of blades and endwalls and seriously limits the aerodynamic performance of turbomachinery, is caused mainly by the boundary layer mixing on the blades and endwall surfaces and the transverse secondary flow. Focusing on a linear diffusion cascade with 42&deg, turning angle, the transverse secondary flow is found to be the dominant factor for flow separation, based on detailed analysis. Therefore, controlling the secondary flow to reduce the flow separation is very important. Based on the investigations, the flow separation can be controlled by cutting off the secondary flow. Therefore, nine kinds of streamwise grooves were designed and analyzed herein. Grooves at the endwall substantially inhibited the transverse secondary flow, but the flow structure varied over different spans. An optimum combination of groove width and height was identified, with the height being more important. A detailed flow analysis of the best scheme (with a smaller width and moderate height) was conducted. The loss reduction mechanism was obtained by 3D flow field and entropy analysis. This configuration can reduce the corner zone separation effect, energy loss coefficient, and flow loss.

Nowadays, the images are transferred through open channels that are subject to potential attacks, so the exchange of image data requires additional security in many fields, such as medical, military, banking, etc. The security factors are essential in preventing the system from brute force and differential attacks. We propose an Enhanced Logistic Map (ELM) while using chaotic maps and simple encryption techniques, such as block scrambling, modified zigzag transformation for encryption phases, including permutation, diffusion, and key stream generation to withstand the attacks. The results of encryption are evaluated while using the histogram, correlation analysis, Number of Pixel Change Rate (NPCR), Unified Average Change Intensity (UACI), Peak-Signal-to-Noise Ratio (PSNR), and entropy. Our results demonstrate the security, reliability, efficiency, and flexibility of the proposed method.

Smart cameras and image sensors are widely used in industrial processes, from the designing to the quality checking of the final product. Images generated by these sensors are at continuous risk of disclosure and privacy breach in the industrial Internet of Things (IIoT). Traditional solutions to secure sensitive data fade in IIoT environments because of the involvement of third parties. Blockchain technology is the modern-day solution for trust issues and eliminating or minimizing the role of the third party. In the context of the IIoT, we propose a permissioned private blockchain-based solution to secure the image while encrypting it. In this scheme, the cryptographic pixel values of an image are stored on the blockchain, ensuring the privacy and security of the image data. Based on the number of pixels change rate (NPCR), the unified averaged changed intensity (UACI), and information entropy analysis, we evaluate the strength of proposed image encryption algorithm ciphers with respect to differential attacks. We obtained entropy values near to an ideal value of 8, which is considered to be safe from brute force attack. Encrypted results show that the proposed scheme is highly effective for data leakage prevention and security.

With the rapid development of marine IoT (Internet of Things), ocean MDTN (Mobile Delay Tolerant Network) has become a research hot spot. Long-term trajectory prediction is a key issue in MDTN. There are no long-term fine-grained trajectory prediction methods proposed for ocean vessels because a vessel&rsquo, s mobility pattern lacks map topology support and can be easily influenced by the fish moratorium, sunshine duration, etc. A traditional on-land trajectory prediction algorithm cannot be directly utilized in this field because trajectory characteristics of ocean vessels are far different from that on land. To address the problem above, we propose a novel long-term trajectory prediction algorithm for ocean vessels, called L-VTP, by utilizing multiple sailing related parameters and K-order multivariate Markov Chain. L-VTP utilizes multiple sailing related parameters to build multiple state-transition matrices for trajectory prediction based on quantitative uncertainty analysis of trajectories. Trajectories&rsquo, sparsity of ocean vessels results in a critical state missing problem of a high-order state-transition matrix. L-VTP automatically traverses other matrices in a specific sequence in terms of quantitative uncertainty results to overcome this problem. Furthermore, the different mobility models of the same vessel during the day and the night are also exploited to improve the prediction accuracy. Privacy issues have been taken into consideration in this paper. A quantitative model considering Markov order, training metadata and privacy leak degree is proposed to help the participant make the trade-off based on their customized requirements. We have performed extensive experiments on two years of real-world trajectory data that include more than two thousand vessels. The experiment results demonstrate that L-VTP can realize fine-grained long-term trajectory prediction with the consideration of privacy issues. The average error of 4.5-hour fine-grained prediction is less than 500 m. In addition, the proposed method can be extended to 10-hour prediction with an average error of 2.16 km, which is also far less than the communication range of ocean vessel communication devices.

A chaotic system and two Nonlinear Feadback Shift Registers (NFSRs) are used to generate a new stream cipher in this paper. This design can be used for efficient encryption in resource-constrained devices or environments. The chaotic system is quantified and integrated with two NFSRs based on the technology of Field Programmable Gate Array (FPGA). Many analyses are made from the angle of entropy in order to verify the cryptographic characteristics of the stream cipher, and National Institute of Standards and Technology (NIST) statistical test is completed to analyze the cipher. The test results show that the stream cipher here has good cryptographic characteristics.

PurposeThis paper aims to present and then resolve the thermodynamic inconsistencies inherent in the usual Boussinesq model, especially with respect to the second law, and to highlight the effects of the correction.Design/methodology/approachThe Boussinesq model (i.e. still assuming ▽v=0) is made thermodynamically consistent by maintaining in the heat equation, primarily the work of pressure forces, secondarily the heat generated by viscous friction. Numerically speaking, the modifications are very easy and hardly affect the computing time. However, new non‐dimensional parameters arise, especially the non‐dimensional adiabatic temperature gradient, ϕ.FindingsThere are presented and interpreted results of systematic numerical simulations done for a two‐dimensional square differentially‐heated cavity filled with air at 300K, with Rayleigh number ranging from 3,000 to 108 and ϕ ranging from 10−3 to 2. All configurations are stationary and the fluid is far from its critical state. Nevertheless, the pressure‐work effect (similar to the piston effect) enhances the heat transfer while diminishing the convection intensity. The magnitude of this effect is non‐negligible as soon as ϕ reaches 0.02.Practical implicationsThe domain where the thermodynamic Boussinesq model must be used encompasses configurations relevant to building engineering.Originality/valueExact second‐law analyses can be developed with the so‐corrected model.

The magnitude of the information content associated with a particular implementation of a Physical Unclonable Function (PUF) is critically important for security and trust in emerging Internet of Things (IoT) applications. Authentication, in particular, requires the PUF to produce a very large number of challenge-response-pairs (CRPs) and, of even greater importance, requires the PUF to be resistant to adversarial attacks that attempt to model and clone the PUF (model-building attacks). Entropy is critically important to the model-building resistance of the PUF. A variety of metrics have been proposed for reporting Entropy, each measuring the randomness of information embedded within PUF-generated bitstrings. In this paper, we report the Entropy, MinEntropy, conditional MinEntropy, Interchip hamming distance and National Institute of Standards and Technology (NIST) statistical test results using bitstrings generated by a Hardware-Embedded Delay PUF called HELP. The bitstrings are generated from data collected in hardware experiments on 500 copies of HELP implemented on a set of Xilinx Zynq 7020 SoC Field Programmable Gate Arrays (FPGAs) subjected to industrial-level temperature and voltage conditions. Special test cases are constructed which purposely create worst case correlations for bitstring generation. Our results show that the processes proposed within HELP to generate bitstrings add significantly to their Entropy, and show that classical re-use of PUF components, e.g., path delays, does not result in large Entropy losses commonly reported for other PUF architectures.

The purpose of this article is to study and analyze the convective flow of a third grade non-Newtonian fluid due to a linearly stretching sheet subject to a magnetic field. The dimensionless entropy generation equation is obtained by solving the reduced momentum and energy equations. The momentum and energy equations are reduced to a system of ordinary differential equations by a similarity method. The optimal homotopy analysis method (OHAM) is used to solve the resulting system of ordinary differential equations. The effects of the magnetic field, Biot number and Prandtl number on the velocity component and temperature are studied. The results show that the thermal boundary-layer thickness gets decreased with increasing the Prandtl number. In addition, Brownian motion plays an important role to improve thermal conductivity of the fluid. The main purpose of the paper is to study the effects of Reynolds number, dimensionless temperature difference, Brinkman number, Hartmann number and other physical parameters on the entropy generation. These results are analyzed and discussed.