A fully deterministic micro–macro simulation of complex flows involving reversible network fluid models

Micro–macro models associate the coarse-grained molecular scale of the kinetic theory to the macroscopic scale of continuum mechanics. The conservation equations are solved along with the microscopic equation or the so-called Fokker–Planck equation. In this paper, a micro–macro approach based on the separated representation introduced in 2 and 3 with the Stream-Tube method 10, 11, 12, 21 and 22 is implemented to study the main features of fiber and polymer networks solutions in complex flows. The Fokker–Planck equation, that defines the fluid microstructure, is solved using a separated representation strategy and is coupled to the macroscopic equations through the macroscopic extra-stress tensor evaluated at the microscopic level. Then, the flow kinematics is solved by applying the Stream-Tube method.     

Application of shadow price in capacity expansion of state dependent routing

We develop a capacity expansion method for state dependent routing which utilizes the concept of shadow price. To cope with the computational complexity which is inherent with the capacity expansion process, special forms of fixed point iteration are developed for network blocking evaluation. The proposed capacity expansion method is illustrated through the example of a well known state dependent routing, namely Least Busy Alternate Routing. We verify the method in simulated networks and the resulting network cost is seen to be quite close to the ones derived from the methods with much higher computational complexity. The proposed method also results in a network design that satisfies the prescribed network blocking probability very accurately.     

A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

The prime purpose for the image reconstruction of a multi-frame super-resolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images, which is useful in numerous fields. Nevertheless, super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts, which include blurring distortion, noises, and stair-casing effects. Consequently, it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image. In this research work, we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception, which improves human analysis and interpretation processes. Accordingly, we propose a new approach to the image reconstruction of multi-frame super-resolution, so that it is created through the use of the regularization framework. In the proposed approach, the bilateral edge preserving and bilateral total variation regularizations are employed to approximate a high-resolution image generated from a sequence of corresponding images with low-resolution to protect significant features of an image, including sharp image edges and texture details while preventing artifacts. The experimental results of the synthesized image demonstrate that the new proposed approach has improved efficacy both visually and numerically more than other approaches.     

Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

Social media networks are becoming essential to our daily activities, and many issues are due to this great involvement in our lives. Cyberbullying is a social media network issue, a global crisis affecting the victims and society as a whole. It results from a misunderstanding regarding freedom of speech. In this work, we proposed a methodology for detecting such behaviors (bullying, harassment, and hate-related texts) using supervised machine learning algorithms (SVM, Naïve Bayes, Logistic regression, and random forest) and for predicting a topic associated with these text data using unsupervised natural language processing, such as latent Dirichlet allocation. In addition, we used accuracy, precision, recall, and F1 score to assess prior classifiers. Results show that the use of logistic regression, support vector machine, random forest model, and Naïve Bayes has 95%, 94.97%, 94.66%, and 93.1% accuracy, respectively.     

A Secure Hardware Implementation for Elliptic Curve Digital Signature Algorithm

Since the end of the 1990s, cryptosystems implemented on smart cards have had to deal with two main categories of attacks: side-channel attacks and fault injection attacks. Countermeasures have been developed and validated against these two types of attacks, taking into account a well-defined attacker model. This work focuses on small vulnerabilities and countermeasures related to the Elliptic Curve Digital Signature Algorithm (ECDSA) algorithm. The work done in this paper focuses on protecting the ECDSA algorithm against fault-injection attacks. More precisely, we are interested in the countermeasures of scalar multiplication in the body of the elliptic curves to protect against attacks concerning only a few bits of secret may be sufficient to recover the private key. ECDSA can be implemented in different ways, in software or via dedicated hardware or a mix of both. Many different architectures are therefore possible to implement an ECDSA-based system. For this reason, this work focuses mainly on the hardware implementation of the digital signature ECDSA. In addition, the proposed ECDSA architecture with and without fault detection for the scalar multiplication have been implemented on Xilinx field programmable gate arrays (FPGA) platform (Virtex-5). Our implementation results have been compared and discussed. Our area, frequency, area overhead and frequency degradation have been compared and it is shown that the proposed architecture of ECDSA with fault detection for the scalar multiplication allows a trade-off between the hardware overhead and the security of the ECDSA.     

The effect of water and sodium hydrogen carbonate content on flexible polyurethane's microstructure,water, and mechanical properties

It is well known that blowing agents (BAs) and polyol are essential components in polyurethane (PU) composition. Utilizing renewable sources in the material's formulation might reduce its environmental hazards while extending its possible engineering applications. In this study, the samples have been synthesized by using palm kernel oil-based polyol (PKOP). Water and sodium hydrogen carbonate (SHB) have been used as BAs. Scanning electron microscopy (SEM) has shown that adding the mix of BAs is causing the cells' average size to increase up to 227% and have reduced the lamellae width by up to 2% in comparison with the reference sample. The water tests have illustrated that combining two parts per hundred polyol by weight (php) water and 25 php SHB to the sample has increased its water capacity up to 617%. However, the samples are only able to retain 6% of the absorbed water at the 7th day. It has been also found that porosity has affected the water uptake and all the samples are following Fick's diffusion law, and diffusion is correlated to the square root of time. Multivariable power least squares method (MPLSM) and moving least squares method (MLSM) have been applied to find the relation between tear resistance value and BAs ratio. It is found that both methods have a dominant variable compared to the other variables, but MLSM provided optimizable equation with better R².     

A new multidimensional discriminant representation for robust person re-identification

Pattern Analysis and Applications - Person Re-Identification (PRe-ID) or person retrieval is a challenging task of computer vision, aiming to identify a specific person across disjoint cameras...     

Age and Gender Classification Using Backpropagation andBaggingAlgorithms

Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories. It has high precision compared to other algorithms used in this problem, as the adaptive backpropagation algorithm had an accuracy of 98% and the Bagging algorithm had an accuracy of 98.10% in the gender identification data. Bagging has the best accuracy among all algorithms, with 55.39% accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.     

Assessment of the bioactive lipid profiles of oil extracted from Tunisian table olive cultivars (Barouni,Besbassi, and Marsaline cvs.)

Bioactive compounds are components extracted from biological matrices that may offer physiological health benefits and have nutritive value. The present study characterized bioactive lipid components such as fatty acids, phytosterols, policosanol, and triterpenes from table olives. The table olives were processed according to the green Spanish-style method. The obtained results indicate that phytosterol fraction constitutes the major portion of the total unsaponifiable matter. The analysis of phytosterols showed the presence of 11 compounds, β-sitosterol the predominant one. The policosanol composition indicated that hexacosanol, tetracosanol, octacosanol, and docosanol were the main compounds, accounting for over 85% of total policosanols. Pentacyclic triterpenes (cycloartenol and 24-methylene cycloartenol) were found at a higher level (over 85%) compared to tetracyclic triterpenes (β-amyrin, δ-amyrin). These findings reveal that processed table olives contain an interesting amount of various bioactive compounds compared to marine and other floral biological matrices. Thus, processed table olives represent an interesting natural functional food that presents high stability and bioavailability of their natural bioactive ingredients. Practical Applications: Nowadays, with health food gaining popularity among consumers, the demand for natural bioactive compounds and functional food is expanding considerably all over the world. This study focuses on analyzing bioactive lipid components from processed table olives. Fruits were processed according to the green Spanish-style method. The salt content of the brine was adjusted to the minimum sodium chloride value required by the Codex Alimentarius Comission, which is 5%, taking into consideration people suffering from hypertension. This concentration is considered the minimum amount of salt necessary to exhibit antibacterial activities. Considering the results obtained, processed table olives present one of the most valuable potential resources for bioactive lipid compounds and have a considerable ability to preserve the stability of their lipid components. Thus, the food industry in Mediterranean countries can promote processed table olives as conventional functional food with no need for any enrichment or fortification.