Significantly improving the solubility and anti-inflammatory activity of fenofibric acid with native and methyl-substituted beta-cyclodextrins via complexation.

The solubility of commonly used anti-inflammatory drugs has become a significant concern in contemporary medicine. Furthermore, inflammatory arthritis stands out as the most prevalent chronic inflammatory disease globally. The disease's pathology is characterized by heightened inflammation and oxidative stress, culminating in chronic pain and the loss of joint functionality.

Parametric optimization of flow in a solar chimney power plant under variable semi elliptical constraints.

In response to the ongoing quest for more efficient renewable energy sources, this research addresses a significant gap in understanding the performance variations of Solar Chimney Power Plant (SCPP) models, particularly focusing on the influence of flow parameters in full and half-inclined collector sections featuring semi-elliptical curvature. The motivation stems from the need to optimize SCPP designs for enhanced energy generation while minimizing resource utilization and environmental impact. This research focuses on investigating flow parameter variations in Solar Chimney Power Plant (SCPP) models with full and half-inclined collector sections featuring semi-elliptical curvature and variable semi-minor heights (b: 0.

Experimental and theoretical investigation of allylated chitosan and acrylic acid-based smart polymer hydrogel for the removal of brilliant green from aqueous media.

Smart polymer hydrogels with superior dye adsorption (brilliant green) characteristics were synthesized via free-radical polymerization by grafting acrylic acid segments onto allylated chitosan and inducing crosslinking with a trimethylolpropane triacrylate crosslinker. The synthesized adsorbents were characterized for their chemical structure (FT-IR and H NMR), thermal stability (TG/DTG), and morphological features (SEM). The adsorption capacity for brilliant green (934 mg/g) and water uptake (712 g/g) were determined using spectrophotometric and gravimetric methods, respectively.

Application of natural solar photocatalytic and DSSC's studies AC loaded on Ag-InO nanoparticles by hydrothermal approach.

Contaminants are repeatedly being released into the land, water and air about the world as a consequence of the high levels of human movement and development, which causes a fast an increase in the growing of pollution. In this assessment, activated charcoals supported on Ag-InO nanomaterials were blended by hydrothermal system. The morphology constitution, surface assets and optical description of synthesized nanomaterials were characterized by XRD, UV-DRS, PL, HR-SEM and EDAX, HR-TEM, SAED pattern, FT-IR, XPS, BET, CV and VSM techniques.

Enhanced lion swarm optimization and elliptic curve cryptography scheme for secure cluster head selection and malware detection in IoT-WSN.

Wireless Sensor Networks present a significant issue for data routing because of the potential use of obtaining data from far locations with greater energy efficiency. Networks have become essential to modern concepts of the Internet of Things. The primary foundation for supporting diverse service-centric applications has continued to be the sensor node activity of both sensing phenomena in their local environs and relaying their results to centralized Base Stations.

Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis.

In recent years, the research on abnormal events detection is a significant work in surveillance video. Many researchers have been attracted by this work for the past two decades. As a result, several abnormal event detection approaches have been developed.

FPGA-based low-light image enhancement using Retinex algorithm and coarse-grained reconfigurable architecture.

Advancements in digital imaging and video processing are often challenged by low-light environments, leading to degraded visual quality. This affects critical sectors such as medical imaging, aerospace, and underwater exploration, where uneven lighting can compromise safety and clarity. To enhance image quality in low-light conditions using a computationally efficient system.

Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals.

Background/objectives: Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD.

Methods: This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases.

Tree hierarchical deep convolutional neural network optimized with sheep flock optimization algorithm for sentiment classification of Twitter data.

The increasing volume of online reviews and tweets poses significant challenges for sentiment classification because of the difficulty in obtaining annotated training data. This paper aims to enhance sentiment classification of Twitter data by developing a robust model that improves classification accuracy and computational efficiency. The proposed method named Tree Hierarchical Deep Convolutional Neural Network optimized with Sheep Flock Optimization Algorithm for Sentiment Classification of Twitter Data (SCTD-THDCNN-SFOA) utilizes the Stanford Sentiment Treebank dataset.

Facile synthesis of azines by carboxylic acid esters as catalyst and facilitation of intersystem crossing (ISC) in azines by azine chromophore.

Development of new organic synthetic methods fascinating the researchers which facilitating the increasing demands of the modern society, environmental friendly with high efficiency and low cost. The introduction of chromophores in an organic molecules facilitating intersystem crossing (ISC) to harvest both singlet and triplet excitons is also currently demanding field. We report a facile synthesis of symmetrical azines from carbonyl compounds and hydrazine hydrate with carboxylic acid esters as catalyst in methanol.

Numerical analysis on the fate and transport of TiO nanoparticles in fractured porous media: Addressing intrinsic heterogeneities in sorptive mass transfer.

The aggregation and slow migration of nanoparticles in aqueous media have caused serious concerns about their fate and impacts in the subsurface environment. Anthropogenic release and distribution of TiO nanoparticles (TNP) have immense potential for surface adsorption, occlusion, impregnation, bioaccumulation, and phase partition into various environmental compartments, and the actual risks in their interactions are still unknown. In an attempt to realize the extent of source zone migration of TNP in a fracture-skin-matrix (F-S-M) medium, a numerical model is developed and analyzed for sensitivity of certain features of the flow field.

Sentiment analysis using graph-based Quickprop method for product quality enhancement.

The degree to which customers express satisfaction with a product on Twitter and other social media platforms is increasingly used to evaluate product quality. However, the volume and variety of textual data make traditional sentiment analysis methods challenging. The nuanced and context-dependent nature of product-related opinions presents a challenge for existing tools.

Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors.

Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs.

Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification.

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required.

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms.

The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually.

Cardiovascular disease detection from cardiac arrhythmia ECG signals using artificial intelligence models with hyperparameters tuning methodologies.

Cardiovascular disease (CVD) is connected with irregular cardiac electrical activity, which can be seen in ECG alterations. Due to its convenience and non-invasive aspect, the ECG is routinely exploited to identify different arrhythmias and automatic ECG recognition is needed immediately. In this paper, enhancement for the detection of CVDs such as Ventricular Tachycardia (VT), Premature Ventricular Contraction (PVC) and ST Change (ST) arrhythmia using different dimensionality reduction techniques and multiple classifiers are presented.

Performance enhancement of classifiers through Bio inspired feature selection methods for early detection of lung cancer from microarray genes.

Gene expression in the microarray is assimilated with redundant and high-dimensional information. Moreover, the information in the microarray genes mostly correlates with background noise. This paper uses dimensionality reduction and feature selection methods to employ a classification methodology for high-dimensional lung cancer microarray data.

Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.

This article investigates the effectiveness of feature extraction and selection techniques in enhancing the performance of classifier accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data, three feature extraction (FE) methods are used, namely Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson's Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection.

Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images.

A recent global health crisis, COVID-19 is a significant global health crisis that has profoundly affected lifestyles. The detection of such diseases from similar thoracic anomalies using medical images is a challenging task. Thus, the requirement of an end-to-end automated system is vastly necessary in clinical treatments.

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions.

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability.

Mechanisms and Intervention of Prebiotic Foods in Musculoskeletal Health.

The review focuses primarily on collating and analyzing the mechanistic research data that discusses the function of prebiotics to halt the frailty of musculoskeletal system. Musculoskeletal diseases (MSDs) are frequently reported to co-occur within their own categories of conditions, such as osteoarthritis, rheumatoid arthritis, gouty arthritis, and psoriatic arthritis owing to their overlapping pathogenesis. Consequently, the same drugs are often used to manage the complications of most types.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing.