Innovative ultrasound assisted synthesis of sponge like cerium dioxide nanostructure using Rosa Damascena extract and its efficient performance for cancer therapy.

In this work, cerium dioxide nanostructures were synthesized in an easy sonochemical way. CeO nanoparticles have received much attention in nanotechnology. CeONPs, exhibit biomimetic properties depending on their size, ratio of valency on their surface, and the ambient physico-chemical properties.

Controlling the drug delivery efficiency of chitosan-based systems through silver nanoparticles and oxygen plasma.

Today, curcumin's therapeutic properties are used in drug delivery systems. In this work, chitosan (CS) /Montmorillonite (MMT) hydrogels were synthesized to improve the performance of curcumin molecules. According to the results, drug release characteristics of CS/MMT/curcumin highly depend on the pH of the environment and properties of Ag nanoparticles.

An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP.

In recent years, significant advancements have been made in the field of brain-computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial-temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset.

Pioneering non-thermal plasma as a defect passivator: a new Frontier in ambient metal halide perovskite synthesis.

Growing energy demands make cost-effective, high-performance perovskite solar cells (PSCs) desirable. However, their commercial applications are limited due to defect formation and instability. Passivation technologies help enhance their favorable traits.

Empowering EFL writing through digital storytelling: A quasi-experimental assessment of CALF measures and multidimensional engagement.

This mixed-methods study aims to investigate the impact of traditional and digital storytelling (DST) on English as a foreign language (EFL) learners' writing performance in terms of complexity, accuracy, lexical diversity, and fluency (CALF) measures. Moreover, the study sheds light on how learners were engaged along behavioral, cognitive, affective, and agentive dimensions when completing the DST task. The participants were 45 intermediate-level college students who were assigned to the DST and traditional storytelling (TST) groups.

Complementary Metal-Oxide-Semiconductor-Based Magnetic and Optical Sensors for Life Science Applications.

Optical and magnetic sensing methods are integral to both research and clinical applications in biological laboratories. The ongoing miniaturization of these sensors has paved the way for the development of point-of-care (PoC) diagnostics and handheld sensing devices, which are crucial for timely and efficient healthcare delivery. Among the various competing sensing and circuit technologies, CMOS (complementary metal-oxide-semiconductor) stands out due to its distinct cost-effectiveness, scalability, and high precision.

The effect of language learning engagement on L2 motivation over Instagram.

This study investigated the relationship between English language learners` Instagram engagement and second language (L2) motivation. It also aimed to determine engagement types (behavioral, cognitive, and affective engagement) as predictors of L2 motivation. Through stratified sampling, we recruited 102 Iranian users engaged in learning English via the Instagram platform as an informal learning context.

Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.

Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions-the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition.

Mapping climate suitability index for rainfed cultivation of medicinal plants by developing an AI-based probabilistic framework.

The Climate Suitability Index (CSI) can increase agricultural efficiency by identifying the high-potential areas for cultivation from the climate perspective. The present study develops a probabilistic framework to calculate CSI for rainfed cultivation of 12 medicinal plants from the climate perspective of precipitation and temperature. Unlike the ongoing frameworks based on expert judgments, this formulation decreases the inherent subjectivity by using two components: frequency analysis and Particle Swarm Optimization (PSO).

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks.

Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML).

Evaluation of suppressor behavior of guanidine-derived metformin and galegine as novel potential drugs for cancer treatment: an in silico study.

There are some natural products from plants that can prevent and treat disease. Metformin, a derivative of galegine, is the basic drug to treat diabetes. Moreover, this molecule has anticancer properties that inhibit cancer cell growth and proliferation.

Harnessing the Missing Spectral Correlation for Metasurface Inverse Design.

A long-held tenet in computer science asserts that the training of deep learning is analogous to an alchemical furnace, and its "black box" signature brings forth inexplicability. For electromagnetic metasurfaces, the related intelligent applications also get stuck into such a dilemma. Although the past 5 years have witnessed a proliferation of deep learning-based works across complex photonic scenarios, they neglect the already existing but untapped physical laws.

An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks.

In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. However, the polygraph instrument has limitations and needs to be more reliable.

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties.

This paper presents an optimal framework for demand response aggregators in wholesale electricity markets. Demand response aggregators provide customers with flexible contracts in the proposed model. These contracts allow for hourly load reductions through load curtailment, load shifting, onsite generation utilization, and energy storage systems.

Co electrospinning -poly (vinyl alcohol)-chitosan/gelatin-poly (-caprolacton) nanofibers for diabetic wound-healing application.

With the increasing prevalence of diabetes, the healing of diabetic wounds has become a significant challenge for both healthcare professionals and patients. Recognizing the urgent need for effective solutions, it is crucial to develop suitable scaffolds specifically tailored for diabetic wound healing. In line with this objective, we have developed novel hybrid nanofibrous scaffolds by combining polyvinyl alcohol/chitosan (PVA/CS) and gelatin/poly(ε-caprolactone) (Gel/PCL) polymers through a double-nozzle electrospinning technique.

Facile fabrication of next-generation sustainable brick and mortar through geopolymerization of construction debris.

Waste from construction and demolition (also known as CDW) is one of the most harmful environmental issues. This study's primary goal is to produce new mortar and brick materials from recycled concrete powder (RCP) and recycled brick powder (RBP), two of the most popular CDW. Geopolymeric mortar and brick samples were produced by passing RCP and RBP through sieve No.

Enhancing the efficacy of nano-curcumin on cancer cells through mixture design optimization of three emulsifiers.

Curcumin, a vital bioactive compound found naturally, has diverse biological applications. However, a major limitation of curcumin is its low bioavailability caused by its limited solubility in water. Hence, it is possible to overcome this problem through preparing oil in water nanodispersion of curcumin that emulsifier can play key role to produce nanodispersion.

Development of carboxymethyl cellulose-based nanocomposite incorporated with ZnO nanoparticles synthesized by cress seed mucilage as green surfactant.

This study aimed to enhance carboxymethyl cellulose (CMC)-based films by incorporating zinc oxide nanoparticles (ZnO NPs) and cress seed mucilage (CSM), with a view to augmenting the physical, mechanical, and permeability properties of the resulting nanocomposite films. For the first time, CSM was exploited as a green surfactant to synthetize ZnO NPs using hydrothermal method. Seven distinct film samples were meticulously produced and subjected to a comprehensive array of analyses.

Dielectric anisotropy changes in MBBA liquid crystal doped with barium titanate by a new method.

In response to the burgeoning interest in enhancing the properties of liquid crystal composites, this research systematically explores the intricate interplay between MBBA nematic liquid crystals and ferroelectric barium titanate nanoparticles. The focus is modulating dielectric properties under temperature, frequency, and an applied electric field. Nuanced insights into temperature-dependent behavior, parallel and perpendicular component alterations, and a nonlinear correlation between nanoparticle concentration and dielectric constant are revealed.

Extraction and characterization of cellulose microfibers from cornhusk for application as reinforcing agent in biocomposite.

This study aims to extract and characterize cellulose microfibers from cornhusk, an agricultural by-product. The extracted fibers will then be used as a reinforcing agent in a biocomposite made of thermoplastic corn starch. The process of extracting cellulose microfibers involved two treatments: sequential alkali treatment (using sodium hydroxide at 120 °C for 120 min) and peroxide bleach treatment (using hydrogen peroxide at 90 °C for 60 min).

A Multidisciplinary Approach toward CMOS Capacitive Sensor Array for Droplet Analysis.

This paper introduces an innovative method for the analysis of alcohol-water droplets on a CMOS capacitive sensor, leveraging the controlled thermal behavior of the droplets. Using this sensing method, the capacitive sensor measures the total time of evaporation (ToE), which can be influenced by the droplet volume, temperature, and chemical composition. We explored this sensing method by introducing binary mixtures of water and ethanol or methanol across a range of concentrations (0-100%, with 10% increments).

Enhancement of the catalytic performance of Co-ZIF/WO heterostructures for selective catalytic reduction of NO.

Nitrogen oxides (NOx) are one of the growing air pollutants in industrial countries, and their emissions are regulated by stringent legislation. Therefore, the design of the catalyst comprised of metal oxides and ZIFs a potential solution for improving selective catalytic reduction (SCR) of NOx. Here, an efficient strategy was described to fabricate Co-ZIF/WO heterostructures for SCR of NOx.

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks.

Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability.