Mechanical characterization and chemical resistances of cured unsaturated polyester resins modified with vinyl ester resins based on recycled poly(ethylene terephthalate)

Unsaturated polyester resin (UP) was prepared from glycolyzed oligomer of poly(ethylene terephthalate) (PET) waste based on diethylene glycol (DEG). New diacrylate and dimethacrylate vinyl ester resins prepared from glycolysis of PET with tetraethylene glycol were blended with UP to study the mechanical characteristics of the cured UP. The vinyl ester resins were used as crosslinking agents for unsaturated polyester resin diluted with styrene, using free‐radical initiator and accelerator. The mechanical properties of the cured UP resins were evaluated. The compressive properties of the cured UP/styrene resins in the presence of different vinyl ester concentrations were evaluated. Increasing the vinyl ester content led to a pronounced improvement in the compression strength. The chemical resistances of the cured resins were evaluated through hot water, solvents, acid, and alkali resistance measurements. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 3175–3182, 2007     

Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection (FS) technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting DDoS flooding attacks (FA) based on ICMPv6 messages using a Binary Flower Pollination Algorithm (BFPA-FA). The proposed method (BFPA-FA) employs FS technology with a support vector machine (SVM) to identify the most relevant, influential features. Moreover, The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack scenarios. The results show that the proposed method BFPA-FA achieved the best accuracy rate (97.96%) for the ICMPv6 DDoS detection with a reduced number of features (9) to half the total (19) features. The proven proposed method BFPA-FA is effective in the ICMPv6 DDoS attacks via IDS.     

Hybrid image steganography method using Lempel Ziv Welch and genetic algorithms for hiding confidential data

Multidimensional Systems and Signal Processing - Digital images are commonly used in steganography due to the popularity of digital image transfer and exchange through the Internet. However, the...     

Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Adaptive predistortion using threshold decomposition‐based piecewise linear modeling

A new adaptive predistortion approach for nonlinear power amplifiers based on the piecewise linear (PWL) approximation of nonlinear functions is presented. The PWL model is obtained using threshold decomposition (TD) of the input‐output characteristics. The TD based PWL model is used to develop a baseband predistorter using both the direct and the indirect learning architectures. The proposed predistorter is proved to provide a better performance in suppressing nonlinear distortion than conventional polynomial predistorters and a reduced hardware complexity as compared to lookup table (LUT) approach. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Anticoagulants as Potential SARS-CoV-2 Mpro Inhibitors for COVID-19 Patients: In Vitro,Molecular Docking,Molecular Dynamics,DFT, and SAR Studies

In this article, 34 anticoagulant drugs were screened in silico against the main protease (M^pro) of SARS-CoV-2 using molecular docking tools. Idraparinux, fondaparinux, eptifibatide, heparin, and ticagrelor demonstrated the highest binding affinities towards SARS-CoV-2 M^pro. A molecular dynamics study at 200 ns was also carried out for the most promising anticoagulants to provide insights into the dynamic and thermodynamic properties of promising compounds. Moreover, a quantum mechanical study was also conducted which helped us to attest to some of the molecular docking and dynamics findings. A biological evaluation (in vitro) of the most promising compounds was also performed by carrying out the MTT cytotoxicity assay and the crystal violet assay in order to assess inhibitory concentration 50 (IC₅₀). It is worth noting that ticagrelor displayed the highest intrinsic potential for the inhibition of SARS-CoV-2 with an IC₅₀ value of 5.60 µM and a safety index of 25.33. In addition, fondaparinux sodium and dabigatran showed promising inhibitory activities with IC₅₀ values of 8.60 and 9.40 µM, respectively, and demonstrated safety indexes of 17.60 and 15.10, respectively. Moreover, the inhibitory potential of the SARS-CoV-2 M^pro enzyme was investigated by utilizing the SARS-CoV-2 M^pro assay and using tipranavir as a reference standard. Interestingly, promising SARS-CoV-2 M^pro inhibitory potential was attained for fondaparinux sodium with an IC₅₀ value of 2.36 µM, surpassing the reference tipranavir (IC₅₀ = 7.38 µM) by more than three-fold. Furthermore, highly eligible SARS-CoV-2 M^pro inhibitory potential was attained for dabigatran with an IC₅₀ value of 10.59 µM. Finally, an SAR was discussed, counting on the findings of both in vitro and in silico approaches.     

Selective Recovery of Cadmium,Cobalt, and Nickel from Spent Ni–Cd Batteries Using Adogen® 464 and Mesoporous Silica Derivatives

Spent Ni–Cd batteries are now considered an important source for many valuable metals. The recovery of cadmium, cobalt, and nickel from spent Ni–Cd Batteries has been performed in this study. The optimum leaching process was achieved using 20% H₂SO₄, solid/liquid (S/L) 1/5 at 80 °C for 6 h. The leaching efficiency of Fe, Cd, and Co was nearly 100%, whereas the leaching efficiency of Ni was 95%. The recovery of the concerned elements was attained using successive different separation techniques. Cd(II) ions were extracted by a solvent, namely, Adogen^® 464, and precipitated as CdS with 0.5% Na₂S solution at pH of 1.25 and room temperature. The extraction process corresponded to pseudo-2nd-order. The prepared PTU-MS silica was applied for adsorption of Co(II) ions from aqueous solution, while the desorption process was performed using 0.3 M H₂SO₄. Cobalt was precipitated at pH 9.0 as Co(OH)₂ using NH₄OH. The kinetic and thermodynamic parameters were also investigated. Nickel was directly precipitated at pH 8.25 using a 10% NaOH solution at ambient temperature. FTIR, SEM, and EDX confirm the structure of the products.     

An E-Business Event Stream Mechanism for Improving User Tracing Processes

With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets; namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets.