Short-, Medium-, and Long-Term Prediction of Carbon Dioxide Emissions using Wavelet-Enhanced Extreme Learning Machine

Carbon dioxide (CO2) is the main greenhouse gas responsible for global warming. Early prediction of CO2 is critical for developing strategies to mitigate the effects of climate change. A sophisticated version of the extreme learning machine (ELM), the wavelet enhanced extreme learning machine (W-EELM), is used to predict CO2 on different time scales (weekly, monthly, and yearly). Data were collected from the Mauna Loa Observatory station in Hawaii, which is ideal for global air sampling. Instead of the traditional method (singular value decomposition), a complete orthogonal decomposition (COD) was used to accurately calculate the weights of the ELM output layers. Another contribution of this study is the removal of noise from the input signal using the wavelet transform technique. The results of the W-EELM model are compared with the results of the classical ELM. Various statistical metrics are used to evaluate the models, and the comparative figures confirm the superiority of the applied models over the ELM model. The proposed W-EELM model proves to be a robust and applicable computer-based technology for modeling CO2concentrations, which contributes to the fundamental knowledge of the environmental engineering perspective. Doi: 10.28991/CEJ-2023-09-04-04 Full Text: PD

Novel deep eutectic solvent-functionalized carbon nanotubes adsorbent for mercury removal from water

Due to the interestingly tolerated physicochemical properties of deep eutectic solvents (DESs), they are currently in the process of becoming widely used in many fields of science. Herein, we present a novel Hg2+ adsorbent that is based on carbon nanotubes (CNTs) functionalized by DESs. A DES formed from tetra-n-butyl ammonium bromide (TBAB) and glycerol (Gly) was used as a functionalization agent for CNTs. This novel adsorbent was characterized using Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy, XRD, FESEM, EDX, BET surface area, and Zeta potential. Later, Hg2+ adsorption conditions were optimized using response surface methodology (RSM). A pseudo-second order model accurately described the adsorption of Hg2+. The Langmuir and Freundlich isotherms models described the absorption of Hg2+ on the novel adsorbent with acceptable accuracy. The maximum adsorption capacity was found to be 177.76 mg/g

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Adsorption of 2,4-dichlorophenol from water using deep eutectic solvents-functionalized carbon nanotubes

In this work, novel adsorbents for 2,4-dichlorphenol (DCP) were introduced using deep eutectic solvent (DES) as functionalization agent for multi-wall carbon nanotubes (MWCNTs). Choline chloride salt (ChCl) was mixed with ethylene glycol (EG) as hydrogen bond donor (HBD) at molar ratio of (1:2) to prepare DES. Three DES-based MWCNTs adsorbents were produced and their chemical, physical and morphological properties were investigated using, RAMAN, FTIR, FESEM, zeta potential, TGA, TEM and BET surface area. The capability of DES as non-destructive functionalization agent for MWCNTs was proved by the increase of the purity and the surface area of MWCNTs. Response surface methodology was used to define the optimum conditions for 2,4-DCP adsorption onto each adsorbent. The adsorption experimental data were well described by pseudo-second order kinetic mode land by Langmuir isotherm model. DES-acid treated MWCNTs showed the highest maximum adsorption capacity of 390.53 mg g −1

Data-Driven Model for the Prediction of Total Dissolved Gas : Robust Artificial Intelligence Approach

Saturated total dissolved gas (TDG) is recently considered as a serious issue in the environmental engineering field since it standsbehind the reasons for increasing the mortality rates of fish and aquatic organisms. +e accurate and more reliable prediction ofTDG has a very significant role in preserving the diversity of aquatic organisms and reducing the phenomenon of fish deaths.Herein, two machine learning approaches called support vector regression (SVR) and extreme learning machine (ELM) have beenapplied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. For theUSGS 14150000 station, the recorded samples from 13 October 2016 to 14 March 2019 (75%) were used for training set, and therest from 15 March 2019 to 13 October 2019 (25%) were used for testing requirements. Similarly, for USGS 14181500 station, thehourly data samples which covered the period from 9 June 2017 till 11 March 2019 were used for calibrating the models and from12 March 2019 until 9 October 2019 were used for testing the predictive models. Eight input combinations based on differentparameters have been established as well as nine statistical performance measures have been used for evaluating the accuracy ofadopted models, for instance, not limited, correlation of determination (R2), mean absolute relative error (MAE), and uncertaintyat 95% (U95). +e obtained results of the study for both stations revealed that the ELM managed efficiently to estimate the TDG incomparison to SVR technique. For USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported asR2 of 0.986 (0.986), MAE of 0.316 (0.441), and U95 of 3.592 (3.869). Lastly, for USGS 14181500 station, the statistical measures forELM (SVR) were, respectively, reported as R2 of 0.991 (0.991), MAE of 0.338 (0.396), and U95 of 0.832 (0.837). In addition, ELM’straining process computational time is stated to be much shorter than that of SVM. +e results also showed that the temperatureparameter was the most significant variable that influenced TDG relative to the other parameters. Overall, the proposed model(ELM) proved to be an appropriate and efficient computer-assisted technology for saturated TDG modeling that will contribute tothe basic knowledge of environmental considerations.Validerad;2021;Nivå 2;2021-04-19 (alebob);Finansiär: AlMaarif University College</p

Predicting Compressive Strength of Concrete Containing Industrial Waste Materials: Novel and Hybrid Machine Learning Model

Validerad;2022;Nivå 2;2022-03-25 (sofila);Funder: Al-Maarif University College </p

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.Validerad;2022;Nivå 2;2022-10-03 (hanlid);Funder: Al-Maarif University College, Ramadi, Iraq</p

Introducing high-order response surface method for improving scour depth prediction downstream of weirs

Scour depth downstream of weirs is considered one of the most important hydraulic problems, which greatly influences the stability of weirs. Recently, artificial intelligence (AI) methods have become increasingly popular in modeling hydraulic variables, especially scour depth, because they can capture nonlinear relationships between input variables and their associated objectives. Despite their importance, these models have problems with hyperparameter tuning in scour depth modeling due to their structures, so algorithms must be used to tune the hyperparameters. Moreover, these algorithms are usually tuned by using the trial-and-error method to select the hyperparameters such as the number of hidden nodes, transfer function, and learning rate, and in this case, the main problem is overfitting during the training phase. To solve these problems, the high-order response surface method (HORSM), an improved version of the response surface method (RSM), is used as an alternative approach for the first time in this study to predict the scour depth. The HORSM model is based on high-order polynomial functions (from two to six) compared with the artificial neural network model (ANN). The findings indicate that the fifth order of the HORSM polynomial function yields the most precise predictions, with a higher coefficient of determination (R2) of 0.912 and Willmott Index (WI) of 0.972 compared to the values obtained using ANN (R2 = 0.886 and WI = 0.927). Moreover, the accuracy of the predictions is represented by a reduction of the mean square error by up to 44.17 and 29.01% compared to the classical RSM and ANN, respectively. The suggested model established an excellent correlation and accuracy with experimental values.Full text license: CC BY 4.0; </p

Antimycobacterial Activity of <i>Rosmarinus officinalis</i> (Rosemary) Extracted by Deep Eutectic Solvents

Tuberculosis (TB) is a massive problem for public health and is the leading cause of illness and death worldwide. Rosemary (Rosmarinus officinalis) is used traditionally to treat many diseases, such as infections of the lungs including pulmonary TB. R. officinalis was collected from Al Anbar Governorate, Iraq, and was extracted with deep eutectic solvents (DESs) of many different kinds and with conventional water solvent. The antimycobacterial activities of the R. officinalis extracts were tested against multidrug-resistant (MDR) Mycobacterium tuberculosis by agar disc diffusion assay. Minimum inhibitory concentrations were measured spectrophotometrically at 570 nm. Then, a time-kill assay and cell membrane integrity analysis were conducted to investigate the effects of the most active extracts on cell growth. The in vitro cytotoxicity of the most active extracts was evaluated against Rat Embryonic Fibroblasts (REF) cell line by MTT assay. Liquid chromatography-mass spectrometry (LC-MS) was conducted to analyze the chemical components of the most active extracts. At 200 mg/mL concentration, a significant inhibition activity was seen in DES2: Tailor (DIZ = 17.33 ± 1.15 mm), followed by DES3: ChGl, DES1: LGH and DES4: ChXl. The best result was DES2: Tailor, which had a MIC of 3.12 mg/mL and an MBC of 12.5 mg/mL. The DES2 extract exhibited a high drop in the number of colonies over time, killing more than 80 colonies. The main phytochemical compounds of the R. officinalis extract were camphene, camphenilol, α-pinene, limonene, apigenin, camphor, carnosol, linalool and myrcene. R. officinalis extracts obtained by DESs have shown evident power in treating tuberculosis, and extraction by DES is a greener procedure than the methods involving conventional extraction solvents. As a result, additional research into the application of DES should be considered

Antimycobacterial Activity of Rosmarinus officinalis (Rosemary) Extracted by Deep Eutectic Solvents

Tuberculosis (TB) is a massive problem for public health and is the leading cause of illness and death worldwide. Rosemary (Rosmarinus officinalis) is used traditionally to treat many diseases, such as infections of the lungs including pulmonary TB. R. officinalis was collected from Al Anbar Governorate, Iraq, and was extracted with deep eutectic solvents (DESs) of many different kinds and with conventional water solvent. The antimycobacterial activities of the R. officinalis extracts were tested against multidrug-resistant (MDR) Mycobacterium tuberculosis by agar disc diffusion assay. Minimum inhibitory concentrations were measured spectrophotometrically at 570 nm. Then, a time-kill assay and cell membrane integrity analysis were conducted to investigate the effects of the most active extracts on cell growth. The in vitro cytotoxicity of the most active extracts was evaluated against Rat Embryonic Fibroblasts (REF) cell line by MTT assay. Liquid chromatography-mass spectrometry (LC-MS) was conducted to analyze the chemical components of the most active extracts. At 200 mg/mL concentration, a significant inhibition activity was seen in DES2: Tailor (DIZ = 17.33 &plusmn; 1.15 mm), followed by DES3: ChGl, DES1: LGH and DES4: ChXl. The best result was DES2: Tailor, which had a MIC of 3.12 mg/mL and an MBC of 12.5 mg/mL. The DES2 extract exhibited a high drop in the number of colonies over time, killing more than 80 colonies. The main phytochemical compounds of the R. officinalis extract were camphene, camphenilol, &alpha;-pinene, limonene, apigenin, camphor, carnosol, linalool and myrcene. R. officinalis extracts obtained by DESs have shown evident power in treating tuberculosis, and extraction by DES is a greener procedure than the methods involving conventional extraction solvents. As a result, additional research into the application of DES should be considered