Bifunctional TiO - cellulose based nanocomposites for synergistic adsorptive-photocatalytic removal of methyl orange: Response modelling and optimization.

Removal of azo dyes from aquatic environments represents a global challenge. Herein, by utilizing the waste of mandarin peels as a cellulose source (MP500), a bifunctional adsorbent-photocatalyst, TiO@MP500, has been prepared via a one-pot hydrothermal synthesis. Taking advantage of this dual role, remediation of methyl orange (MO) has been successfully addressed.

Mandarin Peels-Derived Carbon Dots: A Multifaceted Fluorescent Probe for Cu(II) Detection in Tap and Drinking Water Samples.

Carbon dots (CDs) derived from mandarin peel biochar (MBC) at different pyrolysis temperatures (200, 400, 600, and 800 °C) have been synthesized and characterized. This high-value transformation of waste materials into fluorescent nanoprobes for environmental monitoring represents a step forward towards a circular economy. In this itinerary, CDs produced via one-pot hydrothermal synthesis were utilized for the detection of copper (II) ions.

Data-driven water quality prediction for wastewater treatment plants.

Monitoring and managing wastewater treatment plants (WWTPs) is crucial for environmental protection. The presection of the quality of treated water is essential for energy efficient operation. The current research presents a comprehensive comparison of machine learning models for water quality parameter prediction in WWTPs.

Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater.

To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to make an estimation of the performance of reduction catalysis in the context of ecologically detrimental nitrophenols and azo dyes contaminants. The catalyst utilized in the experiment was Ag@CMC, which proved to be highly effective in eliminating various contaminants found in water, like 4-nitrophenol (4-NP).

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction.

Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge-Kutta optimizer (RUN).

Crosslinked chitosan-montmorillonite composite and its magnetized counterpart for the removal of basic fuchsin from wastewater: Parametric optimization using Box-Behnken design.

Treating wastewater polluted with organic dyestuffs is still a challenge. In that vein, facile synthesis of a structurally simple composite of chitosan with montmorillonite (CS-MMT) using glutaraldehyde as a crosslinker and the magnetized analogue (MAG@CS-MMT) was proposed as versatile adsorbents for the cationic dye, basic Fuchsin (FUS). Statistical modeling of the adsorption process was mediated using Box-Behnken (BB) design and by varying the composite dose, pH, [FUS], and contact time.

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach.

Examining driving behaviour is crucial for traffic operations because of its influence on driver safety and the potential for increased risk of accidents, injuries, and fatalities. Approximately 95% of severe traffic collisions can be attributed to human error. With the progress in artificial intelligence in recent decades, notable advancements have been achieved in computer capabilities, communication systems and data collection technology.

Groundwater level forecasting with machine learning models: A review.

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results.

Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system.

A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration.

Techno-economic assessment of waste mandarin biochar as a green adsorbent for binary dye wastewater effluents of methylene blue and basic fuchsin: Lab- and large-scale investigations.

Treating polluted wastewater effluents on a large-scale requires the development of high performance and cost-effective adsorbents. The recycling of waste mandarin peels, an environmentally friendly, and copiously available waste biomass into biochar (MRBC), has been approached. In the context of finding affordable and effective solutions for depollution of wastewater, MRBC was used for the adsorption of two dyes: methylene blue (MB) as well as basic fuchsin (BF) from their individual solutions and binary combinations.

Turning waste avocado stones and montmorillonite into magnetite-supported nanocomposites for the depollution of methylene blue: adsorbent reusability and performance optimization.

The existence of methylene blue (MB) in wastewater even as traces is raising environmental concerns. In this regard, the performances of four adsorbents, avocado stone biochar (AVS-BC), montmorillonite (MMT), and their magnetite FeO-derived counterparts, were compared. Results showed the superior performance of FeO@AVS-BC and FeO@MMT nanocomposites with removal percentages (%R) of 95.

Streamflow classification by employing various machine learning models for peninsular Malaysia.

Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level.

Predicting sea levels using ML algorithms in selected locations along coastal Malaysia.

In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model.

Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study.

The impact of the suspended sediment load (SSL) on environmental health, agricultural operations, and water resources planning, is significant. The deposit of SSL restricts the streamflow region, affecting aquatic life migration and finally causing a river course shift. As a result, data on suspended sediments and their fluctuations are essential for a number of authorities especially for water resources decision makers.

Competitive adsorptive removal of promazine and promethazine from wastewater using olive tree pruning biochar: operational parameters, kinetics, and equilibrium investigations.

This research aims to remove two phenothiazines, promazine (PRO) and promethazine (PMT), from their individual and binary mixtures using olive tree pruning biochar (BC-OTPR). The impact of individual and combinatory effects of operational variables was evaluated for the first time using central composite design (CCD). Simultaneous removal of both drugs was maximized utilizing the composite desirability function.

Enhanced adsorptive removal of rifampicin and tigecycline from single system using nano-ceria decorated biochar of mango seed kernel.

Pharmaceutically active compounds (PhACs) represent an emerging class of contaminants. With a potential to negatively impact human health and the ecosystem, existence of pharmaceuticals in the aquatic systems is becoming a worrying concern. Antibiotics is a major class of PhACs and their existence in wastewater signifies a health risk on the long run.

Performance of Pristine Magnetized Orange Peels Biochar Adapted to Adsorptive Removal of Daunorubicin: Eco-Structuring, Kinetics and Equilibrium Studies.

Drugs and pharmaceuticals are an emergent class of aquatic contaminants. The existence of these pollutants in aquatic bodies is currently raising escalating concerns because of their negative impact on the ecosystem. This study investigated the efficacy of two sorbents derived from orange peels (OP) biochar (OPBC) for the removal of the antineoplastic drug daunorubicin (DNB) from pharmaceutical wastewater.

Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique.

To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD).

Water budget-salt balance model for calculating net water saving considering different non-conventional water resources in agricultural process.

Iraq is facing a dire water crisis due to the decrease in water quantities flow in Tigris and Euphrates Rivers. Due to population growth, several studies estimated the water shortage in 2035 to be 44 Billion Cubic Meter (BCM). Thus, Water Budget-Salt Balance Model (WBSBM) has been developed, applied and examined for the Euphrates River basin to compute the net water saving from Non-Conventional Water Resources (NCWRs).

Application of Plackett-Burman Design for Spectrochemical Determination of the Last-Resort Antibiotic, Tigecycline, in Pure Form and in Pharmaceuticals: Investigation of Thermodynamics and Kinetics.

Tigecycline (TIGC) reacts with 7,7,8,8-tetracyanoquinodimethane (TCNQ) to form a bright green charge transfer complex (CTC). The spectrum of the CTC showed multiple charge transfer bands with a major peak at 843 nm. The Plackett-Burman design (PBD) was used to investigate the process variables with the objective being set to obtaining the maximum absorbance and thus sensitivity.

Rainfall prediction using multiple inclusive models and large climate indices.

Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall.

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction.

Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN).

Sofosbuvir/ledipasvir in combination or nitazoxanide alone are safe and efficient treatments for COVID-19 infection: A randomized controlled trial for repurposing antivirals.

Background And Study Aims: Currently, there is no therapy approved for COVID-19. We evaluated the efficacy and safety of sofosbuvir/ledipasvir and nitazoxanide for the treatment of patients with COVID-19 infection.

Patients And Methods: A multicenter, open-label randomized controlled trial included one hundred and ninety patients with non-severe COVID-19 infection.

An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models.

Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR-vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover.

Dapagliflozin cost-effectiveness analysis in heart failure patients in Egypt.

Aims: In Egypt, cardiovascular (CV) diseases are not only the cause of 33% of disability-adjusted life years but are also a leading cause of death. This study aimed to evaluate dapagliflozin's cost-effectiveness as an add-on to the standard of care (SOC) for the treatment of heart failure with reduced ejection fraction (HF-rEF) from the Egyptian healthcare system perspective.

Materials And Methods: A state transition model was utilized to assess the cost-effectiveness of dapagliflozin as an add-on to the SOC and a cost-minimization analysis was performed to compare dapagliflozin to sacubitril/valsartan, as they have had similar efficacy.