Current Scenario in Associating Clinical COVID-19 Biomarkers for Developing Surveillance Platforms.

The novel coronavirus that caused the epidemic and pandemic resulting in the acute respiratory illness known as coronavirus disease 2019 (COVID-19) has plagued the world. This is unlike other coronavirus outbreaks that have occurred in the past, such as Middle East respiratory syndrome (MERS) or severe acute respiratory syndrome (SARS). COVID-19 has spread more quickly and posed special challenges due to the lack of appropriate treatments and vaccines.

Evaluation of mesoporous polyaniline for glucose sensor under different pH electrolyte conditions.

Objectives: Nonenzymatic biosensor-based-conductive polymers like polyaniline are highly electrochemically stable, cheap, and easy to synthesize biosensors, which is the main objective of research as well as testing applied in different pH conditions to get optimum sensitivity.

Methods: A nonenzymatic glucose biosensor based on polyaniline was electrochemically deposited on a glassy carbon electrode; the cyclic voltammetry under range applied voltage -0.2 to 1.

COVID-19 vaccinations and their side effects: a scoping systematic review.

The COVID-19 virus has impacted people worldwide, causing significant changes in their lifestyles. Since the emergence of the epidemic, attempts have begun to prepare a vaccine that can eliminate the virus and restore balance to life in the entire world. Over the past two years, countries and specialized companies have competed to obtain a license from the World Health Organization for the vaccines that were discovered.

Modification of MWCNTs with Bi2WO6 nanoparticles targeting IL-1β and NLRP3 inflammasome via augmented autophagy.

This study reports the facile hydrothermal synthesis of pure Bi2WO6 and Bi2WO6\MWCNTs nanocomposite at specific molar ratio 1:2.5 of Bi2WO6:MWCNTs and elucidates their role in modulating the NLRP3 inflammasome pathway via autophagy induction. Comprehensive characterization techniques, including XRD, Raman, UV.

Ag/Mo Doping for Enhanced Photocatalytic Activity of Titanium (IV) Dioxide during Fuel Desulphurization.

Regarding photocatalytic oxidative desulphurization (PODS), titanium oxide (TiO) is a promising contender as a catalyst due to its photocatalytic prowess and long-term performance in desulphurization applications. This work demonstrates the effectiveness of double-doping TiO in silver (Ag) and molybdenum (Mo) for use as a novel catalyst in the desulphurization of light-cut hydrocarbons. FESEM, EDS, and AFM were used to characterize the morphology, doping concentration, surface features, grain size, and grain surface area of the Ag/Mo powder.

Towards dental diagnostic systems: Synergizing wavelet transform with generative adversarial networks for enhanced image data fusion.

The advent of precision diagnostics in pediatric dentistry is shifting towards ensuring early detection of dental diseases, a critical factor in safeguarding the oral health of the younger population. In this study, an innovative approach is introduced, wherein Discrete Wavelet Transform (DWT) and Generative Adversarial Networks (GANs) are synergized within an Image Data Fusion (IDF) framework to enhance the accuracy of dental disease diagnosis through dental diagnostic systems. Dental panoramic radiographs from pediatric patients were utilized to demonstrate how the integration of DWT and GANs can significantly improve the informativeness of dental images.

Protocol for UAV fault diagnosis using signal processing and machine learning.

Unmanned aerial vehicles (UAVs) require fault diagnosis for safe operation. Here, we present a protocol for UAV fault diagnosis using signal processing and artificial intelligence. We describe steps for collecting vibration-based signal data, preprocessing, and feature extraction using a 3-axis accelerometer or similar sensors.

Are hybrid approaches combining EKF-UKF and artificial neural networks key to unlocking the full potential of sustainable energy technologies and reducing environmental footprint?

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed.

Selective detection of alpha synuclein amyloid fibrils by faradaic and non-faradaic electrochemical impedance spectroscopic approaches.

This study utilized faradaic and non-faradaic electrochemical impedance spectroscopy to detect alpha synuclein amyloid fibrils on gold interdigitated tetraelectrodes (AuIDTE), providing valuable insights into electrochemical reactions for clinical use. AuIDE was purchased, modified with zinc oxide for increased hydrophobicity. Functionalization was conducted with hexacyanidoferrate and carbonyldiimidazole.

Microstructure and Microhardness Evolution of Mg-8Al-1Zn Magnesium Alloy Processed by Differential Speed Rolling at Elevated Temperatures.

Mg-8Al-1Zn magnesium alloy was successfully processed using deferential speed rolling (DSR) at temperatures of 400 and 450 °C for thickness reduction of 30, 50, and 70% with no significant grain growth and dynamic recrystallization. Using optical microscopy (OM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), the rolled microstructures were examined. Although the results indicate a slight reduction in grain size from the initial condition, the DSR processing of alloy at an elevated temperature was associated with a significant number of twins and a distribution of the fine particles of the second phase.

The effect of using two stabilization temperatures and fixation process on preparation of carbon nanofibers.

Carbon nanofibers (CNFs) are prepared from electrospun polyacrylonitrile (PAN) because of their high carbon content. Heat treatment (oxidative stabilization and carbonization) is necessary to convert PAN nanofibers into CNFs. The fixation of fibrous structure of polymer precursor during heat treatment is always considered as a problem.

UAV propeller fault diagnosis using deep learning of non-traditional χ-selected Taguchi method-tested Lempel-Ziv complexity and Teager-Kaiser energy features.

Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety and efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel-Ziv Complexity (LZC), and Teager-Kaiser Energy Operator (TKEO), on the PADRE dataset, which encapsulates various rotor fault configurations.

The effect of constant electric field on the crack growth process of aluminum nanosheet using molecular dynamics simulation.

Aluminum nanosheets are a form of Al nanoparticle that have been recently manufactured on an industrial scale and have a variety of uses. Al nanoparticles are extensively used in a variety of sectors, including aerospace, construction, medical, chemistry, and marine industries. Crack propagation in various constructions must be investigated thoroughly for structural design purposes.

Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features.

Predictive maintenance to avoid fatigue and failure enhances the reliability of mechanics, herewith, this paper explores vibrational time-domain data in advancing fault diagnosis of predictive maintenance. This study leveraged a belt-drive system with the properties: operating rotational speeds of 500-2000 RPM, belt pretensions at 70 and 150 N, and three operational cases of healthy, faulty and unbalanced, which leads to 12 studied cases. In this analysis, two one-axis piezoelectric accelerometers were utilized to capture vibration signals near the driver and pulley.

A review on development and modification strategies of MOFs Z-scheme heterojunction for photocatalytic wastewater treatment, water splitting, and DFT calculations.

Increasing water pollution and decreasing energy reserves have emerged as growing concerns for the environment. These pollution are due to the dangerous effects of numerous pollutants on humans and aquatic organisms, such as hydrocarbons, biphenyls, pesticides, dyes, pharmaceuticals, and metal ions. On the other hand, the need for a clean environment, finding alternatives to fossil and renewable fuels is very important.

Hybrid controller with neural network PID/FOPID operations for two-link rigid robot manipulator based on the zebra optimization algorithm.

The performance of the robotic manipulator is negatively impacted by outside disturbances and uncertain parameters. The system's variables are also highly coupled, complex, and nonlinear, indicating that it is a multi-input, multi-output system. Therefore, it is necessary to develop a controller that can control the variables in the system in order to handle these complications.

Enoxaparin Effect on Interleukin-10 Levels in Iraqi Patients with COVID-19: A Case-Control Study.

Background: Coronavirus disease 19 (COVID-19), an infectious disease resulting from a virus known as severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), was discovered in China in 2019 and causes several mild to moderate respiratory conditions. This study aimed to reveal the changes in serum interleukin-10 (IL-10) and other parameters in Iraqi COVID-19 patients compared with healthy controls by studying the effects of enoxaparin and evaluating the potential of IL-10 as a disease activity marker.

Methods: This was a case-control study that included 180 samples: 90 patients hospitalized with COVID-19 from November 2022 to 20 April 2023 (40 patients had never used enoxaparin, whereas 50 patients had taken enoxaparin) and 90 healthy, age- and sex-matched control.

Dysregulation of pancreatic β-cell autophagy and the risk of type 2 diabetes.

Macroautophagy/autophagy is an essential degradation process that removes abnormal cellular components, maintains homeostasis within cells, and provides nutrition during starvation. Activated autophagy enhances cell survival during stressful conditions, although overactivation of autophagy triggers induction of autophagic cell death. Therefore, early-onset autophagy promotes cell survival whereas late-onset autophagy provokes programmed cell death, which can prevent disease progression.

Optimizing charge transport in hybrid GaN-PEDOT:PSS/PMMADevice for advanced application.

Organic-inorganic hybrid light-emitting devices have garnered significant attention in the last few years due to their potential. These devices integrate the superior electron mobility of inorganic semiconductors with the remarkable optoelectronic characteristics of organic semiconductors. The inquiry focused on analyzing the optical and electrical properties of a light-emitting heterojunction that combines p-type GaN with organic materials (PEDOT, PSS, and PMMA).

COVID-19 Vaccine: Predicting Vaccine Types and Assessing Mortality Risk Through Ensemble Learning Algorithms.

Background: There is no doubt that vaccination is crucial for preventing the spread of diseases; however, not every vaccine is perfect or will work for everyone. The main objective of this work is to predict which vaccine will be most effective for a candidate without causing severe adverse reactions and to categorize a patient as potentially at high risk of death from the COVID-19 vaccine.

Methods: A comprehensive analysis was conducted using a dataset on COVID-19 vaccine adverse reactions, exploring binary and multiclass classification scenarios.

Hammerstein-Wiener Motion Artifact Correction for Functional Near-Infrared Spectroscopy: A Novel Inertial Measurement Unit-Based Technique.

Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein-Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU).