Green synthesis of zinc oxide nanoparticles from Sida acuta leaf extract for antibacterial and antioxidant applications, and catalytic degradation of dye through the use of convolutional neural network.

This study synthesized zinc oxide nanoparticles (ZnO NPs) using a novel green approach, with Sida acuta leaf extract as a capping and reducing agent to initiate nucleation and structure formation. The innovation of this study lies in demonstrating the originality of utilizing zinc oxide nanoparticles for antibacterial action, antioxidant potential, and catalytic degradation of Congo red dye. This unique approach harnesses eco-friendly methods to initiate nucleation and structure formation. The synthesized nanoparticles' structure and conformation were characterized using UV-vis (λmax = 280 nm), X-ray, atomic force microscopy, SEM, HR-TEM and FTIR. The antibacterial activity of the Nps was tested against Pseudomonas sp, Klebsiella sp, Staphylococcus aureus, and E. coli, demonstrating efficacy. The nanoparticles exhibited unique properties, with a crystallite size of 20 nm (XRD), a surface roughness of 2.5 nm (AFM), and a specific surface area of 60 m2/g (SEM). A Convolutional Neural Network (CNN) was effectively employed to accurately classify and analyze microscopic images of green-synthesized zinc oxide nanoparticles. This research revealed their exceptional antioxidant potential, with an average DPPH scavenging rate of 80% at a concentration of 0.05 mg/mL. Additionally, zeta potential measurements indicated a stable net negative surface charge of approximately -12.2 mV. These quantitative findings highlight the promising applications of green-synthesized ZnO NPs in healthcare, materials science, and environmental remediation. The ZnO nanoparticles exhibited catalytic capabilities for dye degradation, and the degradation rate was determined using UV spectroscopy. Key findings of the study encompass the green synthesis of versatile zinc oxide nanoparticles, demonstrating potent antibacterial action, antioxidant capabilities, and catalytic dye degradation potential. These nanoparticles offer multifaceted solutions with minimal environmental impact, addressing challenges in various fields, from healthcare to environmental remediation.

Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements.

Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.

Predicting concrete strength early age using a combination of machine learning and electromechanical impedance with nano-enhanced sensors.

To ensure the structural integrity of concrete and prevent unanticipated fracturing, real-time monitoring of early-age concrete's strength development is essential, mainly through advanced techniques such as nano-enhanced sensors. The piezoelectric-based electro-mechanical impedance (EMI) method with nano-enhanced sensors is emerging as a practical solution for such monitoring requirements. This study presents a strength estimation method based on Non-Destructive Testing (NDT) Techniques and Long Short-Term Memory (LSTM) and artificial neural networks (ANNs) as hybrid (NDT-LSTMs-ANN), including several types of concrete strength-related agents. Input data includes water-to-cement rate, temperature, curing time, and maturity based on interior temperature, allowing experimentally monitoring the development of concrete strength from the early steps of hydration and casting to the last stages of hardening 28 days after the casting. The study investigated the impact of various factors on concrete strength development, utilizing a cutting-edge approach that combines traditional models with nano-enhanced piezoelectric sensors and NDT-LSTMs-ANN enhanced with nanotechnology. The results demonstrate that the hybrid provides highly accurate concrete strength estimation for construction safety and efficiency. Adopting the piezoelectric-based EMI technique with these advanced sensors offers a viable and effective monitoring solution, presenting a significant leap forward for the construction industry's structural health monitoring practices.

Effect of nanoparticle macroalgae in the treatment of fatty liver disease using logistic regression, and support vector machine.

One of the major health issues facing people worldwide is liver fibrosis. Liver fibrosis may be brought on by long-term exposure to harmful substances, medicines, and microorganisms. The development of liver fibrosis in children was particularly worrying due to their longer life-span, which was possibly related to a great risk of developing long-term complications. Marine algae species have provided a biological variety in the research phase of novel approaches to the treatment of numerous ailments. Marine macroalgae have recently been the subject of research due to their rich bioactive chemical composition and potential for the production of various nutraceuticals. Macroalgae are potentially excellent sources of bioactive substances with particular and distinct biological activity when compared to their terrestrial equivalents. Macroalgae in diverse marine cases offer a few biologically active metabolites, comprising sterols, polyunsaturated fatty acids, carotenoids, oligosaccharides, polysaccharides, proteins, polyphenols, vitamins, and minerals. Accordingly, there is great interest in their high potential for supporting immunomodulatory, antimicrobial, antidiabetic, antitumoral, anti-inflammatory, antiangiogenic, and neuroprotective properties. Using an experimental model, the current research intends to collect data on the therapeutic value of macroalgae nanoparticles for fatty liver disease. The researchers' goal of predicting illnesses from the extensive medical datasets is quite difficult. The purpose of this research is to assess the protective effects of a seaweed, Padina pavonia (PP), on liver fibrosis caused by carbon tetrachloride (CCl4). This research presents two models of logistic regression (LR) and support vector machines (SVM) for predicting the likelihood of liver disease incidence. The performance of the model was evaluated using a dataset. PP macro-algae considerably reduce the high blood concentrations of aminotransferases, alkaline phosphatases, and lactate dehydrogenases, as well as causing a considerable (p < 0.05) decrease in serum bilirubin levels. In addition to improving kidney function (urea and creatinine), algal extracts enhance fat metabolism (triglycerides and cholesterol). With an accuracy rate of 70.2%, a sensitivity of 92.3%, a specificity of 74.7%, a type I error of 9.1%, and a type II error of 21.0%, the predictive model has demonstrated excellent performance. The model validated laboratory tests' ability to predict illness (age; direct bilirubin (DB), total proteins (TP), and albumin (ALB). These classifier methods are compared on the basis of their execution time and classification accuracy.

Application of logistic regression, support vector machine and random forest on the effects of titanium dioxide nanoparticles using macroalgae in treatment of certain risk factors associated with kidney injuries.

The use of titanium dioxide (TiO2) nanoparticles in many biological and technical domains is on the rise. There hasn't been much research on the toxicity of titanium dioxide nanoparticles in biological systems, despite their ubiquitous usage. In the current investigation, samples were exposed to various dosages of TiO2 nanoparticles for 4 days, 1 month, and 2 months following treatment. ICP-AES was used to dose TiO2 into the tissues, and the results showed that the kidney had a significant TiO2 buildup. On the other hand, apoptosis of renal tubular cells is one of the most frequent cellular processes contributing to kidney disease (KD). Nevertheless, the impact of macroalgal seaweed extract on KD remains undetermined. In this work, machine learning (ML) approaches have been applied to develop prediction algorithms for acute kidney injury (AKI) by use of titanium dioxide and macroalgae in hospitalized patients. Fifty patients with (AKI) and 50 patients (non-AKI group) have been admitted and considered. Regarding demographic data, and laboratory test data as input parameters, support vector machine (SVM), and random forest (RF) are utilized to build models of AKI prediction and compared to the predictive performance of logistic regression (LR). Due to its strong antioxidant and anti-inflammatory powers, the current research ruled out the potential of using G. oblongata red macro algae as a source for a variety of products for medicinal uses. Despite a high and fast processing of algorithms, logistic regression showed lower overfitting in comparison to SVM, and Random Forest. The dataset is subjected to algorithms, and the categorization of potential risk variables yields the best results. AKI samples showed significant organ defects than non-AKI ones. Multivariate LR indicated that lymphocyte, and myoglobin (MB) ≥ 1000 ng/ml were independent risk parameters for AKI samples. Also, GCS score (95% CI 1.4-8.3 P = 0.014) were the risk parameters for 60-day mortality in samples with AKI. Also, 90-day mortality in AKI patients was significantly high (P < 0.0001). In compared to the control group, there were no appreciable changes in the kidney/body weight ratio or body weight increases. Total thiol levels in kidney homogenate significantly decreased, and histopathological analysis confirmed these biochemical alterations. According to the results, oral TiO2 NP treatment may cause kidney damage in experimental samples.

Seaweed biomass as a sustainable resource for synthesis of ZnO nanoparticles using Sargassum wightii ethanol extract and their environmental and biomedical applications through Gaussian mixture model.

Zinc oxide nanoparticles (ZnO) possess unique features that mak them a common matter among different industries. Nevertheless, traditional models of synthesizing ZnO-NPs are related with health and environmental and risks due to harmful chemicals. The biosynthesis of zinc oxide nanoparticles was achieved using the hot water extract of Sargassum wightii (SW), which serves as a reducing agent. This extract is mixed with zinc precursors, initiating a bio-reduction process. UV-vis, FTIR, XRD, Raman, DLS, SEM, EDX, TEM imaging, and XPS analysis are used. The novelty of this research lies in utilizing a bio-reduction process involving hot water extract of SW to synthesize zinc oxide nanoparticles, providing a safer and eco-friendly alternative to traditional chemical methods. Here, the zinc oxide nanoparticles produced through the biosynthesis process effectively addressed oral infections (Streptococcus mutans) due to their ability to disrupt the integrity of bacterial cell membranes, interfere with cellular processes, and inhibit the growth and proliferation of bacteria responsible for oral infections. Gaussian Mixture Models (GMMs) uncover intricate patterns within medical data, enabling enhanced diagnostics, treatment personalization, and patient outcomes. This study aims to apply Gaussian Mixture Models (GMMs) to medical data for subpopulation identification and disease subtyping, contributing to personalized treatment strategies and improved patient care. With a dataset comprising 300 samples, the application of GMM showed lower BIC and AIC values (2500, 3200), a high Silhouette Score (0.65 from -1 to 1) reflecting well-defined clusters, Calinski-Harabasz (120) and Davies-Bouldin Indices (0.45). These metrics collectively underscored the model's success in revealing distinct patterns within the data. ZnO-nanocoated aligners were effective against Streptococcus mutans, with the maximum antibacterial effect observed for 2 days and lasting for 7 days.

Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability.

Nanoparticles (NPs) are a promising alternative to antibiotics for targeting microorganisms, especially in the case of difficult-to-treat bacterial illnesses. Antibacterial coatings for medical equipment, materials for infection prevention and healing, bacterial detection systems for medical diagnostics, and antibacterial immunizations are potential applications of nanotechnology. Infections in the ear, which can result in hearing loss, are extremely difficult to cure. The use of nanoparticles to enhance the efficacy of antimicrobial medicines is a potential option. Various types of inorganic, lipid-based, and polymeric nanoparticles have been produced and shown beneficial for the controlled administration of medication. This article focuses on the use of polymeric nanoparticles to treat frequent bacterial diseases in the human body. Using machine learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), this 28-day study evaluates the efficacy of nanoparticle therapy. An innovative application of advanced CNNs, such as Dense Net, for the automatic detection of middle ear infections is reported. Three thousand oto-endoscopic images (OEIs) were categorized as normal, chronic otitis media (COM), and otitis media with effusion (OME). Comparing middle ear effusions to OEIs, CNN models achieved a classification accuracy of 95%, indicating great promise for the automated identification of middle ear infections. The hybrid CNN-ANN model attained an overall accuracy of more than 0.90 percent, with a sensitivity of 95 percent and a specificity of 100 percent in distinguishing earwax from illness, and provided nearly perfect measures of 0.99 percent. Nanoparticles are a promising treatment for difficult-to-treat bacterial diseases, such as ear infections. The application of machine learning models, such as ANNs and CNNs, can improve the efficacy of nanoparticle therapy, especially for the automated detection of middle ear infections. Polymeric nanoparticles, in particular, have shown efficacy in treating common bacterial infections in children, indicating great promise for future treatments.

Green synthesis of Fe3O4 nanoparticles using Alliaceae waste (Allium sativum) for a sustainable landscape enhancement using support vector regression.

The synthesis of metal nanoparticles using green chemistry methods has gained significant attention in the field of landscape enhancement. Researchers have paid close attention to the development of very effective green chemistry approaches for the production of metal nanoparticles (NPs). The primary goal is to create an environmentally sustainable technique for generating NPs. At the nanoscale, ferro- and ferrimagnetic minerals such as magnetite exhibit superparamagnetic properties (Fe3O4). Magnetic nanoparticles (NPs) have received increased interest in nanoscience and nanotechnology due to their physiochemical properties, small particle size (1-100 nm), and low toxicity. Biological resources such as bacteria, algae, fungus, and plants have been used to manufacture affordable, energy-efficient, non-toxic, and ecologically acceptable metallic NPs. Despite the growing demand for Fe3O4 nanoparticles in a variety of applications, typical chemical production processes can produce hazardous byproducts and trash, resulting in significant environmental implications. The purpose of this study is to look at the ability of Allium sativum, a member of the Alliaceae family recognized for its culinary and medicinal benefits, to synthesize Fe3O4 NPs. Extracts of Allium sativum seeds and cloves include reducing sugars like glucose, which may be used as decreasing factors in the production of Fe3O4 NPs to reduce the requirement for hazardous chemicals and increase sustainability. The analytic procedures were carried out utilizing machine learning as support vector regression (SVR). Furthermore, because Allium sativum is widely accessible and biocompatible, it is a safe and cost-effective material for the manufacture of Fe3O4 NPs. Using the regression indices metrics of root mean square error (RMSE) and coefficient of determination (R2), the X-ray diffraction (XRD) study revealed the lighter, smoother spherical forms of NPs in the presence of aqueous garlic extract and 70.223 nm in its absence. The antifungal activity of Fe3O4 NPs against Candida albicans was investigated using a disc diffusion technique but exhibited no impact at doses of 200, 400, and 600 ppm. This characterization of the nanoparticles helps in understanding their physical properties and provides insights into their potential applications in landscape enhancement.

Potential of Waste Marble Sludge for Repressing Alkali-Silica Reaction in Concrete with Reactive Aggregates.

The continuous development of the marble industry has led to an increase in the accumulation of waste marble sludge causing landfilling and health-associated issues. The intention of the current study is to explore the potential of waste marble sludge powder (MS) utilization as a means of controlling alkali-silica reaction (ASR) in concrete. Specimen (cubes, prisms, and mortar bars) were prepared to incorporate reactive aggregates and various proportions of MS ranging from 5% to 40% as a replacement for aggregates. Expansion and mechanical strength characteristics were determined to investigate the effectiveness of MS to control ASRfor up to 150 days. Results revealed that on replacing aggregates in the control specimen with 25% MS, the ASR expansion at 14 days reduced from 0.23% to 0.17%, and the expansion at 28 days reduced from 0.28% to 0.17% which is within limits as per American Standard for Testing of Materials (ASTM) C1260. Furthermore, specimens incorporating MS exhibited improved compressive and flexural strength as compared to the identical specimen without MS. Microstructural analysis using Scanning electron microscopy (SEM) revealed micro-cracks in the control specimen while the specimen incorporating MS was found intact. Thus, it can be foreseen that the use of MS as a partial replacement of aggregates can control ASR in concrete as well as reduce the dumping and harmful emissions issue.