Surface Atomic Arrangement of Aluminum Ultra-Thin Layers Grown on Si(111).

Surface atomic arrangement and physical properties of aluminum ultrathin layers on c-Si(111)-7 × 7 and hydrogen-terminated c-Si(111)-1 × 1 surfaces deposited using molecular beam epitaxy were investigated. X-ray photoelectron spectroscopy spectra were collected in two configurations (take-off angle of 0° and 45°) to precisely determine the surface species. Moreover, 3D atomic force microscopy (AFM) images of the air-exposed samples were acquired to investigate the clustering formations in film structure.

COVID-19 chest X-ray image analysis by threshold-based segmentation.

COVID-19 is a severe acute respiratory syndrome that has caused a major ongoing pandemic worldwide. Imaging systems such as conventional chest X-ray (CXR) and computed tomography (CT) were proven essential for patients due to the lack of information about the complications that could result from this disease. In this study, the aim was to develop and evaluate a method for automatic diagnosis of COVID-19 using binary segmentation of chest X-ray images.

A critical review of social media research in sensory-consumer science.

The collection and analysis of digital data from social media is a rapidly growing methodology in sensory-consumer science, with a wide range of applications for research studying consumer attitudes, preferences, and sensory responses to food. The aim of this review article was to critically evaluate the potential of social media research in sensory-consumer science with a focus on advantages and disadvantages. This review began with an exploration into different sources of social media data and the process by which data from social media is collected, cleaned, and analyzed through natural language processing for sensory-consumer research.

Security Analysis of Cyber-Physical Systems Using Reinforcement Learning.

Future engineering systems with new capabilities that far exceed today's levels of autonomy, functionality, usability, dependability, and cyber security are predicted to be designed and developed using cyber-physical systems (CPSs). In this paper, the security of CPSs is investigated through a case study of a smart grid by using a reinforcement learning (RL) augmented attack graph to effectively highlight the subsystems' weaknesses. In particular, the state action reward state action (SARSA) RL technique is used, in which the agent is taken to be the attacker, and an attack graph created for the system is built to resemble the environment.

Genetic Algorithm Optimization of Rainfall Impact Force Piezoelectric Sensing Device, Analytical and Finite Element Investigation.

In this paper, rainfall droplet impact force is transformed into a measurable voltage signal output via the piezoelectric material direct effect utilized for sensing purposes. The motivating sensor is utilized to measure the peak impact forces of rainfall droplets for further analysis and processing. Constructing a sense for the impact force of rainfall droplets has great implications in many real-life applications that can provide vital information regarding the amplifications of the impact force of rainfall on soil erosion, and the impact on small creatures and plants, etc.

RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks.

In the recent past, Distributed Denial of Service (DDoS) attacks have become more abundant and present one of the most serious security threats. In a DDoS attack, the attacker controls a of residing in vulnerable hosts that send a significant amount of traffic to flood the victim or the network infrastructure. In this paper, a common type of DDoS attacks known as "TCP SYN-Flood" is studied.

The extreme vertices of the power graph of a group.

For a fixed finite group , the power graph of was defined to be the simple graph whose vertex set , and edge set . In this paper the extreme vertices of the power graph of abelian groups, dihedral groups and dicyclic groups have been characterized.

MetaAnalyst: a user-friendly tool for metagenomic biomarker detection and phenotype classification.

Background: Many metagenomic studies have linked the imbalance in microbial abundance profiles to a wide range of diseases. These studies suggest utilizing the microbial abundance profiles as potential markers for metagenomic-associated conditions. Due to the inevitable importance of biomarkers in understanding the disease progression and the development of possible therapies, various computational tools have been proposed for metagenomic biomarker detection.

Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review.

In this paper, a detailed review of microcontroller unit (MCU)-based wireless sensor node platforms from recently published research articles is presented. Despite numerous research efforts in the fast-growing field of wireless sensor devices, energy consumption remains a challenge that limits the lifetime of wireless sensor networks (WSNs). The Internet-of-Things (IoT) technology utilizes WSNs for providing an efficient sensing and communication infrastructure.

A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals.

The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep stage scoring rely on hand-engineered features that require prior knowledge of sleep analysis.

An Electrochemical Sensor for the Detection of Albendazole Using Glassy Carbon Electrode Modified with Platinum-Palladium Nanocomposites.

An electroanalytical electrode for the detection of albendazole (ABZ) active ingredient in pharmaceutical dosage form and in contaminated animal-derived products was developed using a glassy carbon electrode modified with platinum-palladium nanoparticles. The electro-catalytic performance of the bimetallic-modified glassy carbon electrode was compared with its bare counterpart. Under optimized conditions, the modified electrode revealed two well-resolved anodic peak currents at 1.

An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images.

Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image.

Nitrogen plasma modification boosts up the hemocompatibility of new PVDF-carbon nanohorns composite materials with potential cardiological and circulatory system implants application.

To design new material for blood-related applications one needs to consider various factors such as cytotoxicity, platelet adhesion, or anti-thrombogenic properties. The aim of this work is the design of new, highly effective materials possessing high blood compatibility. To do this, the new composites based on the poly(vinylidene fluoride) (PVDF) support covered with a single-walled carbon nanohorns (CNHs) layer were prepared.

Entropy Generation and Statistical Analysis of MHD Hybrid Nanofluid Unsteady Squeezing Flow between Two Parallel Rotating Plates with Activation Energy.

Squeezing flow is a flow where the material is squeezed out or disfigured within two parallel plates. Such flow is beneficial in various fields, for instance, in welding engineering and rheometry. The current study investigates the squeezing flow of a hybrid nanofluid (propylene glycol-water mixture combined with paraffin wax-sand) between two parallel plates with activation energy and entropy generation.

Optimization of energy production from biogas fuel in a closed landfill using artificial neural networks: A case study of Al Ghabawi Landfill, Jordan.

Landfills have high potency as renewable energy sources by producing biogas from organic waste degradation. Landfills biogas (LFG) can be used for power plant purposes instead of allowing it to flare to the atmosphere which contributes to the global warming. The aim of this work was to introduce and examine an optimization model for maximizing the power generation of Al Ghabawi landfill in Amman city, Jordan.

Validation of earlobe site as an alternative blood glucose testing approach.

Background: Drawing blood from the fingertips for glucose testing is painful and likely to cause tissue damage over time. Earlobes are an alternative site for glucose measurement.

Objective: This work aims to validate the earlobe as an alternate test site for blood glucose testing by demonstrating valid and reliable statistically significant differences between the earlobes and standard reference sites.

Integrated Clinical Environment Security Analysis Using Reinforcement Learning.

Many communication standards have been proposed recently and more are being developed as a vision for dynamically composable and interoperable medical equipment. However, few have security systems that are sufficiently extensive or flexible to meet current and future safety requirements. This paper aims to analyze the cybersecurity of the Integrated Clinical Environment (ICE) through the investigation of its attack graph and the application of artificial intelligence techniques that can efficiently demonstrate the subsystems' vulnerabilities.

Potential linear B-cells epitope change to a helix structure in the spike of Omicron 21L or BA.2 predicts increased SARS-CoV-2 antibodies evasion.

The world health organization has announced that SARS-CoV-2 Omicron variant (B.1.1.

Three-dimensional numerical analysis of flow and heat transfer of bi-directional stretched nanofluid film exposed to an exponential heat generation using modified Buongiorno model.

The heat transfer characteristics of copper/water nanofluid flow over a bi-directional stretched film are theoretically studied. The used mathematical model accounts for nanofluid effective dynamic viscosity and thermal conductivity. The model of the current study utilizes the modified Buongiorno model to scrutinize the effect of haphazard motion, nanoparticles' thermo-migration, and effective nanofluid properties.

The role of industry 4.0 technologies in overcoming pandemic challenges for the manufacturing sector.

Industry 4.0 aims to revolutionize the manufacturing sector to achieve sustainable and efficient production. The novel coronavirus pandemic has brought many challenges in different industries globally.

Smart Home IoT Network Risk Assessment Using Bayesian Networks.

A risk assessment model for a smart home Internet of Things (IoT) network is implemented using a Bayesian network. The directed acyclic graph of the Bayesian network is constructed from an attack graph that details the paths through which different attacks can occur in the IoT network. The parameters of the Bayesian network are estimated with the maximum likelihood method applied to a data set obtained from the simulation of attacks, in five simulation scenarios.

Needle detection using ultrasound B-mode and power Doppler analyses.

Background: Ultrasound is employed in needle interventions to visualize the anatomical structures and track the needle. Nevertheless, needle detection in ultrasound images is a difficult task, specifically at steep insertion angles.

Purpose: A new method is presented to enable effective needle detection using ultrasound B-mode and power Doppler analyses.

Predicting COVID-19 future trends for different European countries using Pearson correlation.

The ability to accurately forecast the number of COVID-19 cases and future case trends would certainly assist governments and various organisations in strategising and preparing for the newly infected cases well in advance. Many predictions have failed to foresee future COVID-19 cases due to the lack of reliable data; however, such data are now widely available for predicting future trends in COVID-19 after more than one and a half years of the pandemic. Also, various countries are closely monitoring other countries that are experiencing a surge in COVID-19 cases in the expectation of similar scenarios, but this does not always produce correct results, as no research has identified specific correlations between different countries in terms of COVID-19 cases.

Hydrothermal and Entropy Investigation of Nanofluid Natural Convection in a Lid-Driven Cavity Concentric with an Elliptical Cavity with a Wavy Boundary Heated from Below.

This work investigates mixed convection in a lid-driven cavity. This cavity is filled with nanofluid and subjected to a magnetic field. The concentric ovoid cavity orientation (γ), 0−90°, and undulation number (N), 1−4, are considered.

activation of green sorbents for CO capture upon end group backbiting.

Thermolysis of a urethane end group was observed as a first time phenomenon during activation. This unzipping mechanism revealed a new amine tethering point producing a diamine-terminated oligourea ([10]-OU), acting as a green sorbent for CO capturing. The oligomer backbites its end group to form propylene carbonate (PC), as proved by TGA-MS, which can reflect the polymer performance by maximizing its capturing capacity.