Smart nano generation of transgenic algae expressing white spot syndrome virus in shrimps for inner ear-oral infection treatments using the spotted hyena optimizer (SHO)-Long short-term memory algorithm.

Nanotechnology offers a promising avenue to amplify the effectiveness and precision of using transgenic algae in managing WSSV in shrimp by possibly crafting nano-carriers for targeted therapeutic agent delivery or modifying algae cells at a molecular level. Leveraging the capabilities of nano-scale interventions, this study could explore innovative means to manipulate cellular processes, control biological interactions, and enhance treatment efficacy while minimizing undesirable impacts in aquatic environments. The White Spot Syndrome Virus (WSSV) is a double-stranded DNA virus with a tail and rod form that belongs to theNimaviridaefamily. There is no workable way to manage this illness at the moment. This research proposes a new model based on the Long Short-Term Memory (LSTM) and Spotted Hyena Optimizer (SHO) method to control the inner ear-oral infection, utilizing transgenic algae (Chlamydomonas reinhardtii). It is pretty tricky to modify the weight matrix in LSTM. The output will be more accurate if the weight of the neurons is exact. Histological examinations and nested polymerase chain reaction (PCR) testing were performed on the challenged shrimp every 4 h to assess the degree of white spot disease. The SHO-LSTM has shown the highest accuracy and Roc value (98.12% and 0.93, respectively) and the lowest error values (MSE = 0.182 and MAE = 0.48). The hybrid optimized model improves the overall inner ear-oral linked neurological diseases detection ratio. Additionally, with the slightest technical complexity, it effectively controls the forecast factors required to anticipate the ENT. Algal cells were found to be particularly well-suited for inner ear-oral infections, and shrimps fed a transgenic line had the best survival ratio in WSSV infection studies, with 87% of the shrimp surviving. This shows that using this line would effectively stop the spread of WSSV in shrimp populations.

Utilizing nanotechnology to boost the reliability and determine the vertical load capacity of pile assemblies.

Because of their high electrocatalytic activity, sensitivity, selectivity, and long-term stability in electrochemical sensors and biosensors, numerous nanomaterials are being used as suitable electrode materials thanks to developments in nanotechnology. Electrochemical sensors and biosensors are two areas where two-dimensional layered materials (2DLMs) are finding increasing utility due to their unusual structure and physicochemical features. Nanosensors, by their unprecedented sensitivity and minute scale, can probe deeper into the structural integrity of piles, capturing intricacies that traditional tools overlook. These advanced devices detect anomalies, voids, and minute defects in the pile structure with unparalleled granularity. Their effectiveness lies in detection and their capacity to provide real-time feedback on pile health, heralding a shift from reactive to proactive maintenance methodologies. Harvesting data from these nanosensors, data was incorporated into a probabilistic model, executing the reliability index calculations through Monte Carlo simulations. Preliminary outcomes show a commendable enhancement in the predictability of vertical bearing capacity, with the coefficient of variation dwindling by up to 12%. The introduction of nanosensors facilitates instantaneous monitoring and fortifies the long-term stability of pile foundations. This study accentuates the transformative potential of nanosensors in geotechnical engineering.

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.

Spotlighting of microbial electrodeionization cells for sustainable wastewater treatment: Application of machine learning.

Microbial electrodeionization cells (MECs) have been investigated for various potential applications, including the elimination of persistent pollutants, chemical synthesis, the recovery of resources, and the development of biosensors. Nevertheless, MEC technology is still developing, and practical large-scale applications face significant obstacles. This review aims to investigate MEC implementations in sustainable wastewater treatment. Ideas and concepts of MEC technology, the setup of the electrodeionization component, the membranes of MECs, the working mechanism of MECs, and the various microorganisms used in MECs are discussed. Additionally, difficulties and prospective outcomes were discussed. The goal of this review is to support scientists and engineers in fully grasping the most recent developments in MEC technologies and applications.

Application of lipid and polymeric-based nanoparticles for treatment of inner ear infections via XGBoost.

Taking hearing loss as a prevalent sensory disorder, the restricted permeability of blood flow and the blood-labyrinth barrier in the inner ear pose significant challenges to transporting drugs to the inner ear tissues. The current options for hear loss consist of cochlear surgery, medication, and hearing devices. There are some restrictions to the conventional drug delivery methods to treat inner ear illnesses, however, different smart nanoparticles, including inorganic-based nanoparticles, have been presented to regulate drug administration, enhance the targeting of particular cells, and decrease systemic adverse effects. Zinc oxide nanoparticles possess distinct characteristics that facilitate accurate drug delivery, improved targeting of specific cells, and minimized systemic adverse effects. Zinc oxide nanoparticles was studied for targeted delivery and controlled release of therapeutic drugs within specific cells. XGBoost model is used on the Wideband Absorbance Immittance (WAI) measuring test after cochlear surgery. There were 90 middle ear effusion samples (ages = 1-10 years, mean = 34.9 months) had chronic middle ear effusion for four months and verified effusion for seven weeks. In this research, 400 sets underwent wideband absorbance imaging (WAI) to assess inner ear performance after surgery. Among them, 60 patients had effusion Otitis Media with Effusion (OME), while 30 ones had normal ears (control). OME ears showed significantly lower absorbance at 250, 500, and 1000 Hz than controls (p < 0.001). Absorbance thresholds >0.252 (1000 Hz) and >0.330 (2000 Hz) predicted a favorable prognosis (p < 0.05, odds ratio: 6). It means that cochlear surgery and WAI showed high function in diagnosis and treatment of inner ear infections. Regarding the R2 0.899 and RMSE 1.223, XGBoost shows excellent specificity and sensitivity for categorizing ears as having effusions absent or present or partial or complete flows present, with areas under the curve (1-0.944).

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.

Nano-integrating green and low-carbon concepts into ideological and political education in higher education institutions through K-means clustering.

Universities and colleges play a pivotal role in the pursuit of a future that is sustainable through their pedagogical efforts and the execution of state-of-the-art research endeavors aimed at mitigating the effects of climate change. Higher Education Institutions (HEIs) serve as crucial catalysts in advancing sustainable development. HEIs are increasingly embracing precise measures to reduce their carbon footprint (CF) while also educating students on global sustainability. These nano-methods provide a quantitative framework for assessing a campus's sustainability efforts in line with Green Campus (GC) initiatives to lower carbon emissions align with GC goals. This study employs K-means clustering to analyze the integration of green and low-carbon principles in higher education political and ideological studies. Its goal is to identify patterns, assess teaching effectiveness, and improve sustainability education, aligning with Green Campus initiatives to enhance institutional contributions to sustainable growth through informed pedagogical strategies. Input data includes curriculum content, teaching methods, student engagement, and institutional goals related to sustainability. Seeking to improve sustainability education align with Green Campus initiatives, higher education can strategically enhance their contributions to long-term sustainability and growth through effective pedagogical approaches. Cluster 3 has the lowest WCSS value of 1200, indicating tighter cohesion and less variability within this cluster compared to Cluster 1 (1500) and Cluster 2 (1800). Cluster 3 stands out with the highest silhouette score of 0.7, suggesting well-defined and distinct clusters, while Cluster 2 has the lowest score of 0.4, indicating some overlap or ambiguity in data points. Cluster 1 has the lowest Davies-Bouldin Index of 0.4, implying better separation between clusters compared to Cluster 2 (0.6) and Cluster 3 (0.5). Cluster 3 is well-defined and cohesive, showing strong integration of green practices. Cluster 1 displays good separation and cohesion, while Cluster 2 requires refinement due to potential overlap in sustainability integration.

Latest insights on separation and storage of carbon compounds in buildings towards sustainable environment: Recent innovations, challenges, future perspectives and application of machine learning.

Throughout the past few decades, scientific agencies have paid a lot of attention to environmental issues such as acid rain, water poisoning, and global warming. In order to solve these environmental problems, metal-organic frameworks (MOFs), which are made up of metal ions and/or clusters attached to organic ligands, have shown some promise. With a focus on the usage of MOFs, this paper examines the most recent developments, difficulties, and potential future directions in the separation and storage of carbon compounds in buildings for a sustainable environment. The importance of using MOFs in decarbonizing water systems and lowering environmental concerns in buildings is highlighted in the research. It addresses the most recent developments in the use of MOFs for renewable energy, such as the elimination of dangerous gases like CO2 and CH4 from water systems. The article also looks at how MOFs might be used to decarbonize water systems in structures, with a focus on how carbon-containing compounds are stored chemically and physically using artificial neural network models. MOFs are a potential solution for renewable energy and environmental remediation in buildings because they have special physical and chemical characteristics like adjustable pores, high porosity, and tiny pore size. The report offers insights into existing treatments and invites academics to investigate MOFs' potential for resolving environmental problems in order to create a sustainable environment in buildings.

Assessment of graphene-based polymers for sustainable wastewater treatment: Development of a soft computing approach.

Since graphene possesses distinct electrical and material properties that could improve material performance, there is currently a growing demand for graphene-based electronics and applications. Numerous potential applications for graphene include lightweight and high-strength polymeric composite materials. Due to its structural qualities, which include low thickness and compact 2D dimensions, it has also been recognized as a promising nanomaterial for water-barrier applications. For barrier polymer applications, it is usually applied using two main strategies. The first is the application of graphene, graphene oxide (GO), and reduced graphene oxide (rGO) to polymeric substrates through transfer or coating. In the second method, fully exfoliated GO or rGO is integrated into the material. This study provides an overview of the most recent findings from research on the use of graphene in the context of water-barrier applications. The advantages and current limits of graphene-based composites are compared with those of other nanomaterials utilized for barrier purposes in order to emphasize difficult challenges for future study and prospective applications.

Risk assessment of organophosphorus pesticide residues in drinking water resources: Statistical and Monte-Carlo approach.

A large part of the world's agricultural production, despite its adverse effects on human health and water resources, depends on the use of pesticides. Despite strict regulations, the use of pesticides continues around the world. This study aimed to determine the residual concentrations of malathion and diazinon in samples of drinking water resources. To achieve this goal, 384 samples from 8 various sites from January to December 2020 were analyzed using gas chromatography (GC) with an electron capture detector (ECD) and liquid-liquid extraction technique. Besides, statistical analysis and a risk-modeling approach supported by an automatic Monte-Carlo procedure were applied. The results showed that there is a high carcinogenic risk regarding malathion and that the low age population is at the most non-carcinogenic risk regarding diazinon.

Mechanical Characteristics and Self-Healing Soil-Cementitious Hydrogel Materials in Mine Backfill Using Hybridized ANFIS-SVM.

The compressive strength, shrinkage, elasticity, and electrical resistivity of the cement-soil pastes (slag, fly ash) of self-healing of cementitious concrete have been studied while adding hydrogels with nano silica (NSi) in this research. Defining the hydraulic and mechanical properties of these materials requires improvement to motivate more uptake for new buildings. Initially, examining the impact of different synthesized hydrogels on cement-soil pastes showed that solid particles in the mixtures highly affected the absorption capacity of NSi, representing the importance of direct interactions between solid particles and hydrogels in a cementitious matrix. All test results were analyzed by use of a hybridized soft computing model such as the adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) for precise studying and the avoidance of few empirical tests or error percentages. Subsequently, the best RMSE of ANFIS is 0.6568 and the best RMSE of SVM is 1.2564; the RMSE of ANFIS-SVM (0.5643) in the test phase is also close to zero, showing a better performance in hypothesizing self-healing soil-cementitious hydrogel materials in mine backfill. The R2 value for ANFIS-SVM is 0.9547, proving that it is a proper model for predicting the study's goal. Electrical resistivity and compressive strength declined in the cement-soil pastes including hydrogels according to experimental outcomes; it was lowered by the increase of NSi concentration in the hydrogel. There was a decrement in the autogenous shrinkage of cement-soil pastes while adding hydrogel, depending on the NSi concentration in the hydrogels. The findings of this research are pivotal for the internal curing of cementitious materials to define the absorption of hydrogels.