Miscibility of poly(acrylic acid)/poly(methyl vinyl ketone) blend and in vitro application as drug carrier system

A series of poly(acrylic acid)/poly(methyl vinyl ketone) (PAA/PMVK) blends with different compositions were prepared by the solvent casting method. The miscibility of this pair of polymers was investigated by differential scanning calorimetry(DSC), Fourier transform infra-red (FTIR) and X-Ray diffraction (XRD) techniques. An in-vitro cytotoxicity test of the drug-carrier system via MTT (3-(4,5-demethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay revealed no significant cytotoxic effects at concentrations up to 100 µg· ml^?1. The STX/PAA-50 drug carrier systems were also prepared by solvent casting of solutions containing the sulfamethoxazole (STX) used as drug model and PAA/PMVK blend in N.N-dimethylformamide then crosslinked with acidified ethylene glycol. The release dynamic of STX from the prepared hydrogels was investigated in which the diffusion through the polymer matrix, the enhancement of the water solubility of STX, the influence of the initial drug concentration, the pH of the medium, and the effect of the degree of swelling of the polymer matrix on the release dynamic was evaluated. According to the total gastrointestinal transit time estimated by Belzer, the estimate distribution of STX released in the different organs indicated that the performance is obtained with the drug – carrier-system containing equal ratios of polymer and 10 wt% of STX (STX-10/PAA-50).     

An adjusted K‐medoids clustering algorithm for effective stability in vehicular ad hoc networks

Recently, the routing problem in vehicular ad hoc networks is one of the most vital research. Despite the variety of the proposed approaches and the development of communications technologies, the routing problem in VANET suffers from the high speed of vehicles and the repetitive failures in communications. In this paper, we adjusted the well‐known K‐medoids clustering algorithm to improve the network stability and to increase the lifetime of all established links. First, the number of clusters and the initial cluster heads will not be selected randomly as usual, but based on mathematical formula considering the environment size and the available transmission ranges. Then the assignment of nodes to clusters in both k‐medoids phases will be carried out according to several metrics including direction, relative speed, and proximity. To the best of our knowledge, our proposed model is the first that introduces the new metric named “node disconnection frequency.” This metric prevents nodes with volatile and suspicious behavior to be elected as a new CH. This screening ensures that the new CH retains its property as long as possible and thus increases the network stability. Empirical results confirm that in addition to the convergence speed that characterizes our adjusted K‐medoids clustering algorithm (AKCA), the proposed model achieves more stability and robustness when compared with most recent approaches designed for the same objective.     

A Unified Decision-Making Technique for Analysing Treatments inPandemicContext

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF AHP-TOPSIS Methodology, which is a well-known and highly effective Multi-Criteria Decision Making (MCDM) technique. The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries, based on the recommendations of medical practitioners and professionals. The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives. We also perform robustness analysis to validate the conclusions of our analysis. As a result, we obtained highly corroborative results that can be used as a reference. The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand, implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion. Peepli also has the lowest priority in the estimation.     

On-site low-pressure diesel HDS for fuel cell applications: Deepening the sulfur content to ?1 ppm

This work investigates the feasibility of ultra-deep hydrodesulfurization (i.e. ?1 ppm of sulfur content) of several diesel feedstocks, viz., regular (R), premium (P) and hydrotreated straight-run (HSR) at low pressures, i.e. 10 bar, to lower significantly the operation costs. The premium and regular diesel contain additive packages with several components such as cetane boosters, antioxidants that show to negatively affect the sulfur conversion at low pressures. In the hydrotreated straight-run diesel fuel, which does not contain an additive package, total desulfurization can be obtained at 10 bar, T = 340 °C and LHSV = 1 h⁻¹. As a model for the additive package, FAME (fatty acid methyl ester), an ingredient that encounters the demands of a sustainable future, was added to the hydrotreated straight-run diesel (HSR + FAME) in order to check its influence on the total sulfur conversion. Results show that this biofuel component hindered tremendously the sulfur removal process by lowering the sulfur removal from 98% to zero at 10 bar, probably by competitive adsorption. At higher pressures, e.g. 30 bar, when FAME was present, new sulfur compounds were formed during the HDS process and the effective sulfur removal was very low.     

Construction of a stable vehicular ad hoc network based on hybrid genetic algorithm

Telecommunication Systems - In vehicular ad hoc networks, the vehicle speed can exceed 120 kmph. Therefore, any node can enter or leave the network within a very short time. This mobility adversely...     

Integrating Blockchain Technology into Healthcare Through an Intelligent Computing Technique

The blockchain technology plays a significant role in the present era of information technology. In the last few years, this technology has been used effectively in several domains. It has already made significant differences in human life, as well as is intended to have noticeable impact in many other domains in the forthcoming years. The rapid growth in blockchain technology has created numerous new possibilities for use, especially for healthcare applications. The digital healthcare services require highly effective security methodologies that can integrate data security with the available management strategies. To test and understand this goal of security management in Saudi Arabian perspective, the authors performed a numerical analysis and simulation through a multi criteria decision making approach in this study. The authors adopted the fuzzy Analytical Hierarchy Process (AHP) for evaluating the effectiveness and then applied the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique to simulate the validation of results. For eliciting highly corroborative and conclusive results, the study referred to a real time project of diabetes patients’ management application of Kingdom of Saudi Arabia (KSA). The results discussed in this paper are scientifically proven and validated through various analysis approaches. Hence the present study can be a credible basis for other similar endeavours being undertaken in the domain of blockchain research.     

Effect of self etching ceramic primer and universal adhesive on bond strength of lithium disilicate ceramic

The clinical success of ceramic restorations is affected by the ceramic bonding procedure. The objective of the study was to evaluate the effect of different surface treatments, including the use of self-etching ceramic primer (SECP), on resin cement-glass ceramic bond strength. Thirty lithium disilicate ceramic (LDC) rectangles (3 mm × 3 mm × 8 mm) were fabricated Specimens were randomly assigned into three experimental groups (n = 10) according to the surface treatment: Group 1-hydrofluoric acid [HF acid]+ silane + universal adhesive; Group 2-HF + universal adhesive; Group 3-SECP + universal adhesive. All specimens were stored in distilled water for 24 h at 37 °C and shear bond strength (SBS) was tested at a crosshead speed of 0.5 mm (Universal Testing machine-Instron). Failure modes were evaluated using a digital microscope for all specimens. Analysis of variance and the Tukey post hoc tests using SPSS (Version 22.0, IBM, New York, USA) were used to analyze data. The SBS of groups 1 (19.74 ± 1.28 MPa) and 3 (21.11 ± 2.07 MPa) were significant higher than group 2 (14.80 ± 1.94 MPa). The SBS values for specimens in groups 1 (19.74 ± 1.28 MPa) and 3 (21.11 ± 2.07 MPa) were comparable. Adhesive, mixed and cohesive failure modes were observed and mixed failure was the most common in all groups. Therefore, the use of SECP and universal adhesive is recommended as an alternative to the use of HF acid in combination with ceramic primer while bonding to LDC.     

Hesitant Fuzzy-Sets Based Decision-Making Model for Security Risk Assessment

Security is an important component in the process of developing healthcare web applications. We need to ensure security maintenance; therefore the analysis of healthcare web application's security risk is of utmost importance. Properties must be considered to minimise the security risk. Additionally, security risk management activities are revised, prepared, implemented, tracked, and regularly set up efficiently to design the security of healthcare web applications. Managing the security risk of a healthcare web application must be considered as the key component. Security is, in specific, seen as an add-on during the development process of healthcare web applications, but not as the key problem. Researchers must ensure that security is taken into account right from the earlier developmental stages of the healthcare web application. In this row, the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving security-durability. This approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own, and not depend solely on the external security of healthcare web applications. Furthermore, in terms of healthcare web application's security-durability, the security risk variable is measured, and vice versa. Hence, the findings of our study will also be useful in improving the durability of several web applications in healthcare.     

Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

Industrial Control Systems (ICS) can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate effectively. Internet of Things (IoT) integrates numerous sets of sensors and devices via a data network enabling independent processes. The incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things (IIoT), which find use in water distribution system, power plants, etc. Since the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection, an effective forensic investigation process becomes essential. This study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial infrastructures. The proposed strategy involves the design of manta ray foraging optimization (MRFO) based feature selection with optimal stacked autoencoder (OSAE) model, named MFROFS-OSAE approach. The primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial infrastructures. The MFROFS-OSAE approach involves several subprocesses namely data gathering, data handling, feature selection, classification, and parameter tuning. Besides, the MRFO based feature selection approach is designed for the optimal selection of feature subsets. Moreover, the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm (COA). The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures.     

An Effective Online Collaborative Training in Developing Listening Comprehension Skills

The COVID-19 outbreak severely affected formal face-to-face classroom teaching and learning. ICT-based online education and training can be a useful measure during the pandemic. In the Pakistani educational context, the use of ICT-based online training is generally sporadic and often unavailable, especially for developing English-language instructors’ listening comprehension skills. The major factors affecting availability include insufficient IT resources and infrastructure, a lack of proper online training for speech and listening, instructors with inadequate academic backgrounds, and an unfavorable environment for ICT-based training for listening comprehension. This study evaluated the effectiveness of ICT-based training for developing secondary-level English-language instructors’ listening comprehension skills. To this end, collaborative online training was undertaken using random sampling. Specifically, 60 private-school instructors in Chakwal District, Pakistan, were randomly selected to receive online-listening training sessions using English dialogs. The experimental group achieved significant scores in the posttest analysis. Specifically, there were substantial improvements in the participants’ listening skills via online training. Given the unavailability of face-to-face learning during COVID-19, this study recommends using ICT-based online training to enhance listening comprehension skills. Education policymakers should revise curricula based on online teaching methods and modules.