Efficacy of MAA01-influenced hydroxyapatite/graphene oxide nanocomposites for bone ossification.

This study investigates hydroxyapatite (HAp) nanocomposites infused with MAA01-pretreated graphene oxide (GO) for bone tissue engineering. Hydrothermally synthesized HAp/GO (HH-G) nanocomposites showed rod with sheetlike structures with nano level crystallite size, and appropriate bioactivity and degradability. Raman spectroscopy confirmed the successful reduction of GO to reduced graphene oxide (rGO), enhancing antibacterial properties and cellular interactions.

Exploring lichen-derived compounds as potential anti-cervical cancer agents: an DFT and MD simulation analysis.

Lichen-derived compounds have demonstrated promising anti-cancer potential, attributed to their bioavailability and unique structural properties. This study evaluates the therapeutic efficacy of five lichen compounds-fumarprotocetraric acid, salazinic acid, evernic acid, sekikaic acid, and lobaric acid-against cervical cancer, using molecular docking, density functional theory (DFT), and molecular dynamics (MD) simulations. Drug-likeness validation confirmed that evernic acid, topotecan, and ifosfamide adhere to Lipinski's rule of five.

Microalgae-based bio-fabrication using zinc oxide-chitosan nanocomposite for industrial effluent degradation and pollutant reduction.

The current study investigates the photocatalytic degradation of harmful pollutants from the textile and pharmaceutical industries. Specifically, a chitosan zinc nanocomposite's high photocatalytic degradation efficiency was demonstrated. Zinc nanoparticles were synthesized using an extract derived from Chlorella sp.

Corrigendum to "Investigations on the microstructure and properties of yttria and silicon carbide reinforced aluminium composites" [Heliyon Volume 9, Issue 4, April 2023, Article e15462].

[This corrects the article DOI: 10.1016/j.heliyon.

A comprehensive review of the characterization of raw and surface-modified cellulosic plant fibers and their polyester- and epoxy-based composites.

This review provides detailed information about the extraction and characterization of different cellulosic plant fibers, which are primarily composed of biological macromolecules such as cellulose, hemicellulose, and lignin. The properties of plant fibers vary based on parameters such as extraction methods, plant maturity, the part of the fiber-yielding plant, and soil conditions, which drive researchers to study the various properties of cellulosic fibers before using them as reinforcement in polymer composites. Properties such as density, diameter, functional groups, thermal stability, tensile properties, crystallography, and surface morphology were investigated using various characterization methods discussed in this article.

Facile Synthesis of Tin-Doped Copper Oxide Loaded on Almond Shell Activated Carbon Composite for the Photodegradation of Organic Pollutants Under Sunlight.

With a wide range of development opportunities, photocatalytic technology provides a potent solution to today's energy and environmental issues. In this study, a tin-doped copper oxide nanoparticle loaded on almond shell activated carbon (Sn: CuO/ASAC) were prepared and thoroughly examined and described using UV-DRS, XRD, BET, FT-IR, SEM with EDS, and XPS. Band gap energy was decreased and visible absorbance was increased as a result of Sn-doping and ASAC loading.

Exploring the characteristics of Pithecellobium dulce cellulosic fruit peel fibers: a sustainable reinforcement for high-performance biocomposites.

This research focuses on the fiber extraction and detailed characterization of fibers from the peel of Pithecellobium dulce fruit (PDF) to evaluate their potential for composite applications. Previous research has shown that PDF peel has strong antimicrobial and antioxidant properties, and this study suggests it has potential as a natural filler for pharmaceutical and food packaging applications. This study explores compostable material as a sustainable alternative to non-biodegradable materials in various industries, highlighting its potential as a largely unexplored resource.

Fabrication of phytochemical enriched ZnO grafted on few-layered graphene sheets and evaluation of biological and catalytic activity for degradation of medical effluents.

The consumption of contaminated water by humans and animals has resulted in the proliferation of various diseases and environmental pollution. In particular, pharmaceutical effluents are notable as one of the main contaminants in water bodies. Here, zinc oxide nanoparticles (ZnO NPs) were synthesized using Strychnos potatorumseed extract and acted as a reducing and capping agent.

Comprehensive analysis of Syzygium cumini L. pomace extract as an α-amylase inhibitor: In vitro inhibition, kinetics, and computational studies.

Type 2 diabetes mellitus (T2DM) is a widespread metabolic disorder characterized by impaired regulation of blood glucose levels. Jamun (Syzygium cumini L.) fruits and seeds have been traditionally used in Ayurveda to manage diabetes.

Hybrid multi objective marine predators algorithm based clustering for lightweight resource scheduling and application placement in fog.

The Internet of Things (IoT) has boosted fog computing, which complements the cloud. This is critical for applications that need close user proximity. Efficient allocation of IoT applications to the fog, as well as fog device scheduling, enabling the realistic execution of IoT application deployment in the fog environment.

Enhanced heart disease risk prediction using adaptive botox optimization based deep long-term recurrent convolutional network.

BackgroundHeart disease is the leading cause of death worldwide and predicting it is a complex task requiring extensive expertise. Recent advancements in IoT-based illness prediction have enabled accurate classification using sensor data.ObjectiveThis research introduces a methodology for heart disease classification, integrating advanced data preprocessing, feature selection, and deep learning (DL) techniques tailored for IoT sensor data.

Quadratic discriminant feature selected broken stick regressive deep convolution neural learning classification for turmeric crop yield prediction.

In this study, a novel technique termed Quadratic Discriminant Feature Selected Broken Stick Regressive Deep Convolution Neural Learning Classification (QDFSBSRDCNLC) Technique is proposed for disease classification and hence yields prediction of turmeric crop. Initially, we gathered the images of turmeric crops with and without diseases. The images are collected from the turmeric research field at Bhavanisagar.

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment.

Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous monitoring and early detection are necessary for effective management. The Metaverse Environment (ME) has gained attention in the digital healthcare environment; yet, it lacks adequate support for disabled individuals, including deaf and dumb people, and also faces challenges in security, generalizability, and feature selection. To overcome these limitations, a novel probabilistic-centric optimized recurrent sechelliott neural network (PO-RSNN)-based diabetes prediction (DP) and Fuzzy Z-log-clipping inference system (FZCIS)-based severity level estimation in ME is carried out.

Fabrication of spinel NiCoO nanoflowers by simple hydrothermal method for effective electrochemical detection of NO in processed food sample.

This study introduces a novel, cost-effective, highly sensitive electrochemical sensor for detecting nitrite (NO) in processed food samples. The sensor was developed by fabricating spinel NiCoO nanoflowers (NCO) using a hydrothermal method. Various characterization techniques, including XRD, FT-IR, XPS, HR-SEM, EDX, and HR-TEM, were used to analyze the structure and morphology of NCO.

Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction.

Accurate forecasting of photovoltaic (PV) generated electricity is essential for efficiently managing and integrating Renewable Energy (RE) into electricity distribution systems. This research investigation optimizes Feature Selection (FS) and prediction results for PV energy prediction by applying Bayesian Density Estimation (BDE) with Elastic Net (ELNET) regression analysis. This phenomenon and unacceptable outcomes are prevalent when applying conventional regression algorithms on datasets with significant results and addressing predictor multicollinearity.

Early prediction of CKD from time series data using adaptive PSO optimized echo state networks.

Chronic Kidney Disease (CKD) is a significant problem in today's healthcare since it is challenging to detect until it has improved significantly, which increases medical expenses. If CKD was detected early, the patient might qualify for more effective treatment and prevent the disease from spreading further. Presently, existing methods that effectively detect CKD cannot detect symptoms early on.

Enhanced glioma tumor detection and segmentation using modified deep learning with edge fusion and frequency features.

Computer-aided automatic brain tumor detection is crucial for timely diagnosis and treatment, especially in regions with limited access to medical expertise. However, existing methods often overlook edge pixel information during tumor segmentation, leading to reduced boundary accuracy, and achieve high performance primarily on highly enhanced images, making them less effective for enhancement-lagging clinical data. To address these gaps, this study proposes the Edge Incorporative Fusion (EIF) algorithm, which enhances edge-pixel contrast in MRI images, combined with the Gabor Transform (GaT) for spatial-frequency domain conversion to improve detection accuracy.

Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model.

As the global energy demand continues to produce, photovoltaic (PV) solar energy has emerged as a key Renewable Energy Source (RES) due to its sustainability and potential to reduce dependence on fossil fuels. However, accurate forecasting of Solar Energy (SE) output remains a significant challenge due to the inherent variability and intermittency of solar irradiance (SI), which is affected by factors such as weather conditions, geographic location, and seasonal patterns. Reliable prediction models are crucial for optimizing energy management, ensuring grid stability, and minimizing operational costs.

SBERO: Skill Al-Biruni Earth Radius Optimization for Alzheimer's Disease Classification Using Magnetic Resonance Image.

Alzheimer's disease (AD) is the most prevalent form of dementia, characterized by progressive memory loss and cognitive decline, often affecting behavior and speech. Early detection of AD remains a challenge due to its symptomatic overlap with normal aging and other cognitive disorders, necessitating precise classification methods. This paper proposes a novel Skill Al-Biruni Earth Radius Optimization-enabled Deep Spiking Neural Network (SBERO_Deep SNN) for AD classification using magnetic resonance imaging (MRI).

Hybrid Prairie Dog and Dwarf Mongoose optimization algorithm-based application placement and resource scheduling technique for fog computing environment.

The fog computing paradigm is better for creating delay-sensitive applications in Internet of Things (IoT). As the fog devices are resource constrained, the deployment of diversified IoT applications requires effective ways for determining available resources. Therefore, implementing an efficient resource management strategy is the optimal choice for satisfying application Quality of Service (QoS) requirements to preserve the system performance.

Modulation of Proinflammatory Cytokines by Parmotrema reticulatum Derived Dioxepine Derivative and Benzoic Methyl Ester in Adjuvant-Induced Arthritis.

The present investigation aims to study the therapeutic effect and to identify the lead molecules from lichen Parmotrema reticulatum (Pr) that can solve the complications associated with arthritis. Purification of Pr extract led to isolation of two lead molecules i.e.

Characterization of anti bacterial biocomposite films based on poly lactic acid and prosopis juliflora bark powder.

Biocomposites can be a solution for environmental pollution and sustainability by acting as an alternative to petroleum-based products used in the field of packaging and biomedical applications. Herein, an attempt to valorize the potential of Prosopis juliflora plant to fabricate Polylactic acid (PLA) based biocomposites. The biocomposites comprised of Prosopis juliflora bark powder (PJBP) and PLA were fabricated by solution casting method with various weight percentages (Wt%) of PJBP.

Investigating the influence of various metakaolin combinations with different proportions of pond ash and Alccofine 1203 on ternary blended geopolymer concrete at ambient curing.

This paper explores the use of a ternary blended geopolymer concrete (TBGPC) incorporating metakaolin (MK), pond ash (PA), and Alccofine 1203 (AF). Three combinations of MK (25%, 50%, and 75%) with varying proportions of PA and AF were prepared, validating against M grade cement concrete (CC). TBGPC was prepared with an 8 molarity sodium hydroxide, sodium silicate to sodium hydroxide ratio of 2.

Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network.

Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, time-consuming and difficult to achieve better accuracy. A DNN is suggested to recognize human hand movements in order to get over these problems.