EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism.

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) represents a widely prevalent and heterogeneous neurodevelopmental condition in pediatric populations, often exhibiting a substantial propensity to persist into adulthood. ADHD is a multifaceted disorder that resists straightforward diagnostic tests. Clinicians must invest substantial time and effort to secure an accurate diagnosis and implement effective treatment.

CERVIXNET: An Efficient Approach for the Detection and Classifications of the Cervigram Images Using Modified Deep Learning Architecture.

Introduction: The earlier detection of cervical cancer in women patients can save human life. This article proposes a novel methodology for detecting abnormal cervigram images from healthy cervigram images and segments the cancer regions in the abnormal cervigram images using the deep learning method. The conventional deep learning architecture has been modified into the proposed CervixNet architecture to improve the cervical cancer detection rate.

Performance, isotherm, kinetics and mechanism of simultaneous removal of Cr(VI), Cu(II) and F ions by CeO-MgO binary oxide nanomaterials.

This study focuses on the synthesis of a novel Cerium-Magnesium (CeO-MgO) binary oxide nanomaterials by a simple co-precipitation process and used to remove harmful pollutants such as Cr(VI), Cu(II), and F. The morphology, phase, crystallite size, thermal stability, functional groups, surface area, and porosity of the synthesized nanomaterial were determined by using XRD, SEM, FTIR, TGA/DTA, and BET studies. The prepared nanomaterials showed adsorption selectivity of Cu(II) ≈ F> Cr(VI) with a high adsorption capacity of 84.

Lignosulfonate-based deflocculant and its derivatives for water-based drilling mud: A review.

Chromium-based lignosulfonate (CrLS) deflocculants that are commonly used in water-based drilling muds (WBDMs) to deflocculate bentonites under high temperature (HT), high-pressure (HP), and high-salinity (HS) oil well drilling conditions have been found to contain heavy metals such as chromium, which is toxic and degrades rapidly. However, different ways of addressing this issue have been proffered, including the use of natural polymers such as starch, cellulose, or anionic inorganic agents such as sodium polyphosphates with little or no impact. Other lignosulfonate (LS)-based deflocculants, like sodium-based LS and bio-based LS, have shown a number of benefits, such as being better for the environment, more soluble and evenly distributed in WBDMs, more resistant to salt contamination, easily biodegradable, safe, and able to go through different chemical changes.

Solution combustion synthesis of ZnO doped CuO nanocomposite for photocatalytic and sensor applications.

ZnO-doped CuO nanocomposites (CuO-ZnO NPs) of 1, 3, and 5 mol% were prepared by the solution combustion method using ODH as a fuel (Oxlyl-hydrazide) at 500 °C and calcining at 1000 °C for two hours and the Structural, photocatalytic, and electrochemical properties were investigated by experimental and theoretical methods. X-ray diffraction (XRD) patterns revealed a crystallite size (D) range of 25 to 31 nm for pure CuO and 1, 3, and 5 mol% CuO-ZnO NPs. According to calculations, the optical energy band gap (Eg) of the NPs is between 2.

An African vulture optimization algorithm based energy efficient clustering scheme in wireless sensor networks.

Energy efficiency plays a major role in sustaining lifespan and stability of the network, being one of most critical factors in wireless sensor networks (WSNs). To overcome the problem of energy depletion in WSN, this paper proposes a new Energy Efficient Clustering Scheme named African Vulture Optimization Algorithm based EECS (AVOACS) using AVOA. The proposed AVOACS method improves clustering by including four critical terms: communication mode decider, distance of sink and nodes, residual energy and intra-cluster distance.

Novel biocomposite of ionic cross-linked chitosan and acid-treated potato (Solanum tuberosum L.) peel agro-waste for highly efficient removal of methylene blue dye from water.

In this study, a biocomposite material (CS-OXA/PP-SA) composed of ionic crosslinked chitosan-oxalate (CS-OXA) and chemically modified lignocellulosic biomass (potato (Solanum tuberosum L.) peel-HSO acid, PP-SA) was synthesized to serve as a bioadsorbent for removing methylene blue (MB) dye from aquatic systems. The research utilized response surface methodology (RSM) to evaluate the effects of three variables: CS-OXA/PP-SA dosage (0.

VME-EFD : A novel framework to eliminate the Electrooculogram artifact from single-channel EEGs.

The diagnosis of neurological disorders often involves analyzing EEG data, which can be contaminated by artifacts from eye movements or blinking (EOG). To improve the accuracy of EEG-based analysis, we propose a novel framework, VME-EFD, which combines Variational Mode Extraction (VME) and Empirical Fourier Decomposition (EFD) for effective EOG artifact removal. In this approach, the EEG signal is first decomposed by VME into two segments: the desired EEG signal and the EOG artifact.

Enhanced IoMT security framework using group teaching optimized auto-encoder for intrusion detection.

Providing security to Internet of Medical Things (IoMT) is significant worldwide problem for future generations its implementation to be successful. The traditional security methodologies developed for IoMT struggles with the specific issues of high false positives and lower detection rate. Therefore, the proposed work aims to develop a ground-breaking intrusion detection model, named as, Group Teaching Optimized Probabilistic Deep Auto-Encoder (GTPDA) for increasing the security of IoMT networks.

Novel bionanocomposite of grafted chitosan-phthalic anhydride/CoO nanoparticles for efficient removal of brilliant green dye: Adsorption optimization using Box-Behnken design.

A novel bionanocomposite of grafted chitosan-phthalic anhydride/CoO nanoparticles (CHT-PHT/CoO) was synthesized and used for the elimination of brilliant green (BG) dye from aquatic systems. The CHT-PHT/CoO material underwent several instrumental characterizations including, XRD, BET, FTIR, FESEM-EDX, and pH examinations. The impact of the key uptake factors, namely A: CHT-PHT/CoO dose, B: starting solution pH, and C: contact duration, on the effectiveness of BG removal, was mathematically optimized using the response surface methodology (RSM).

Design and Synthesis of Various Aryl Amide Derivatives of Imidazo[1,5-a] Pyridine-1,2,4-Thiadiazoles: In Vitro Cytotoxicity Evaluation and In Silico Molecular Docking Studies.

A new series of various aryl amide derivatives of imidazo[1,5-a]pyridine-1,2,4-thiadiazoles (15a-j) were designed, synthesized, and evaluated for their cytotoxic profiles against four human cancer cell lines such as breast cancer (MCF-7), lung cancer (A549), colon cancer (Colo-205), and ovarian cancer (A2780), via the MTT assay, with etoposide as the standard known chemotherapeutic agent. Five compounds, 15a, 15b, 15c, 15f, and 15j, exhibited more potent cytotoxic effects than did etoposide. Among them, compound 15a exhibited potent cytotoxic effects against the MCF-7, A549, Colo-205, and A2780 cell lines, with IC values of 0.

Comprehensive analysis of faults and diagnosis techniques in cascaded multi-level inverters.

Reliability is a crucial factor to consider for multi-level inverters (MLIs) used in industrial applications. With the increasing number of power semiconductor devices, the potential for defects to significantly degrade the overall system is heightened. A highly effective fault-detection technique is required to minimize the impact of faults.

Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence.

Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts early-stage ROP from neonatal fundus images using Machine Learning (ML) classifiers and Convolutional Neural Networks (CNN) based pre-trained networks.

XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach.

Ensemble Learning (EL) has been used for almost ten years to classify heart diseases, but it is still difficult to grasp how the "black boxes", or non-interpretable models, behave inside. Predicting heart disease is crucial to healthcare, since it allows for prompt diagnosis and treatment of the patient's true state. Nonetheless, it is still difficult to forecast illness with any degree of accuracy.

Exploring 7β-amino-6-nitrocholestens as COVID-19 antivirals: , synthesis, evaluation, and integration of artificial intelligence (AI) in drug design: assessing the cytotoxicity and antioxidant activity of 3β-acetoxynitrocholestane.

In light of the ongoing pandemic caused by SARS-CoV-2, effective and clinically translatable treatments are desperately needed for COVID-19 and its emerging variants. In this study, some derivatives, including 7β-aminocholestene compounds, and 3β-acetoxy-6-nitrocholesta-4,6-diene were synthesized, in quantitative yields from 7β-bromo-6-nitrocholest-5-enes (1-3) with a small library of amines. The synthesized steroidal products were then thoroughly characterized using a range of physicochemical techniques, including IR, NMR, UV, MS, and elemental analysis.

Effect of amikacin-humic acid combination on biofilm: an and study.

(AB) is a clinically important bacterial pathogen responsible for nosocomial infections. The biofilm-forming capability of these pathogens reduces the antibiotic penetration and its efficacy, thereby complicating the treatment. The current work aims to isolate the most potent biofilm-forming species from clinical isolates of the patient samples and to evaluate the efficacy of the amikacin-humic acid combination against it.

Multi-Modal Graph Aggregation Transformer for image captioning.

The current image captioning directly encodes the detected target area and recognizes the objects in the image to correctly describe the image. However, it is unreliable to make full use of regional features because they cannot convey contextual information, such as the relationship between objects and the lack of object predicate level semantics. An effective model should contain multiple modes and explore their interactions to help understand the image.

Coordination of protection and operation in micro-grids with inverter based distributed generation using SAOA.

In the proposed protection coordination scheme, the depreciation of the operation time of the entire relay in the primary and backup protection modes for all possible fault locations is considered as the objective function. The limitations of this problem include the equations for calculating the operation time of the relays in both forward and reverse directions, the limitation of the coordination time interval, the limitation of the setting parameters of the proposed relays, the restriction of the size of the reactance that limits the fault current, and the limitation of the standing time of distributed generation per small signal fault. The operation time of the relays depends on the short circuit current passing through them, so it is necessary to calculate the network variables before the fault occurs.

Effect of Graphite Nanoadditives on the Behavior of a Diesel Engine Fueled with Pyrolysis Fuel Recovered from Used Plastics.

Plastic waste accumulation is a significant threat to the environment and humans. Pyrolysis is a promising method for recycling plastic waste since all of the yields are useful, reducing the associated environmental risks of plastic waste. Energy recovery from used plastic waste can help restore ecosystems by utilizing waste as fuel while addressing the environmental problem of plastic disposal.

Synthesis and functional mechanism evaluation of novel polymer-graphene oxide-biochar nanocomposite for efficient removal of diethyl phthalate.

Phthalates, categorized as a main constituent of endocrine-disrupting chemicals (EDCs), are present in polymeric products. These substances can enter the environment through several pathways, including improper handling, which leads to their presence in toilet water, floor washings, surface runoff, and landfill leachate. This study focuses on the performance analysis of nanocomposite materials made of polymer (polypyrrole), quasi-metal (graphene oxide), and biochar (from palmyra seed) for the elimination of diethyl phthalates (DEP) from aqueous environments.

Resiliency planning of distribution network using of active distribution network partitioning.

Today, micro-grids (MGs) include all kinds of energy storage systems (ESSs), wind turbines (WTs), photovoltaic (PV), combined heat and power (CHP), etc., also demand response are active on the demand side. In this paper, single-level robust methods for partitioning and planning the active distribution network (ADN) into several MGs are presented.

Rigdelet neural network and improved partial reinforcement effect optimizer for music genre classification from sound spectrum images.

In this paper, a new approach has been introduced for classifying the music genres. The proposed approach involves transforming an audio signal into a unified representation known as a sound spectrum, from which texture features have been extracted using an enhanced Rigdelet Neural Network (RNN). Additionally, the RNN has been optimized using an improved version of the partial reinforcement effect optimizer (IPREO) that effectively avoids local optima and enhances the RNN's generalization capability.