Displaying publications 1 - 20 of 33 in total

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  1. Afan HA, Allawi MF, El-Shafie A, Yaseen ZM, Ahmed AN, Malek MA, et al.
    Sci Rep, 2020 03 13;10(1):4684.
    PMID: 32170078 DOI: 10.1038/s41598-020-61355-x
    In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.
  2. Al Zand AW, Ali MM, Al-Ameri R, Badaruzzaman WHW, Tawfeeq WM, Hosseinpour E, et al.
    Materials (Basel), 2021 Oct 23;14(21).
    PMID: 34771860 DOI: 10.3390/ma14216334
    The flexural strength of Slender steel tube sections is known to achieve significant improvements upon being filled with concrete material; however, this section is more likely to fail due to buckling under compression stresses. This study investigates the flexural behavior of a Slender steel tube beam that was produced by connecting two pieces of C-sections and was filled with recycled-aggregate concrete materials (CFST beam). The C-section's lips behaved as internal stiffeners for the CFST beam's cross-section. A static flexural test was conducted on five large scale specimens, including one specimen that was tested without concrete material (hollow specimen). The ABAQUS software was also employed for the simulation and non-linear analysis of an additional 20 CFST models in order to further investigate the effects of varied parameters that were not tested experimentally. The numerical model was able to adequately verify the flexural behavior and failure mode of the corresponding tested specimen, with an overestimation of the flexural strength capacity of about 3.1%. Generally, the study confirmed the validity of using the tubular C-sections in the CFST beam concept, and their lips (internal stiffeners) led to significant improvements in the flexural strength, stiffness, and energy absorption index. Moreover, a new analytical method was developed to specifically predict the bending (flexural) strength capacity of the internally stiffened CFST beams with steel stiffeners, which was well-aligned with the results derived from the current investigation and with those obtained by others.
  3. Alavi J, Ewees AA, Ansari S, Shahid S, Yaseen ZM
    PMID: 34741267 DOI: 10.1007/s11356-021-17190-2
    Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044.
  4. Alkhabet MM, Yaseen ZM, Eldirderi MMA, Khedher KM, Jawad AH, Girei SH, et al.
    Materials (Basel), 2022 Nov 17;15(22).
    PMID: 36431654 DOI: 10.3390/ma15228167
    Gaseous pollutants such as hydrogen gas (H2) are emitted in daily human activities. They have been massively studied owing to their high explosivity and widespread usage in many domains. The current research is designed to analyse optical fiber-based H2 gas sensors by incorporating palladium/graphene oxide (Pd/GO) nanocomposite coating as sensing layers. The fabricated multimode silica fiber (MMF) sensors were used as a transducing platform. The tapering process is essential to improve the sensitivity to the environment through the interaction of the evanescent field over the area of the tapered surface area. Several characterization methods including FESEM, EDX, AFM, and XRD were adopted to examine the structure properties of the materials and achieve more understandable facts about their functional performance of the optical sensor. Characterisation results demonstrated structures with a higher surface for analyte gas reaction to the optical sensor performance. Results indicated an observed increment in the Pd/GO nanocomposite-based sensor responses subjected to the H2 concentrations increased from 0.125% to 2.00%. The achieved sensitivities were 33.22/vol% with a response time of 48 s and recovery time of 7 min. The developed optical fiber sensors achieved excellent selectivity and stability toward H2 gas upon exposure to other gases such as ammonia and methane.
  5. Bhagat SK, Tiyasha T, Awadh SM, Tung TM, Jawad AH, Yaseen ZM
    Environ Pollut, 2021 Jan 01;268(Pt B):115663.
    PMID: 33120144 DOI: 10.1016/j.envpol.2020.115663
    Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9-5 parameters without losing their learned information over the models' training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost-SVM, XGBoost-ANN, XGBoost-Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost-Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.
  6. Bhagat SK, Pyrgaki K, Salih SQ, Tiyasha T, Beyaztas U, Shahid S, et al.
    Chemosphere, 2021 Aug;276:130162.
    PMID: 34088083 DOI: 10.1016/j.chemosphere.2021.130162
    Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.
  7. Bhagat SK, Tiyasha T, Kumar A, Malik T, Jawad AH, Khedher KM, et al.
    J Environ Manage, 2022 Feb 16;309:114711.
    PMID: 35182982 DOI: 10.1016/j.jenvman.2022.114711
    Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.
  8. Bobaker AM, Alakili I, Sarmani SB, Al-Ansari N, Yaseen ZM
    PMID: 31159472 DOI: 10.3390/ijerph16111957
    Henna and walnut tree bark are widely used by Libyan women as cosmetics. They may contain lead (Pb), cadmium (Cd) and arsenic (As), which, in turn, pose a high risk to their health. This study aims to determine the levels of Pb, Cd and As in henna and walnut tree bark products sold in Libyan markets. The products were analyzed for their Pb, Cd and As content by using inductively coupled plasma mass spectrometry (ICP-MS) after a microwave acid digestion. The results showed a significant difference between the henna and walnut tree bark samples in terms of their heavy metals content (p < 0.05). The highest heavy metal concentrations were observed in the walnut tree bark samples whereas the lowest was observed in the henna samples. In addition, 60% of the henna and 90% of the walnut tree bark samples contained Pb levels and approximately 80% of the henna and 90% the walnut tree bark samples contained Cd levels, which are much higher than the tolerance limit. However, As concentrations in all the samples were lower. The results indicated that such cosmetics expose consumers to high levels of Pb and Cd and hence, to potential health risks. Thus, studying the sources and effects of heavy metals in such cosmetics is strongly recommended.
  9. Dastan D, Mohammed MKA, Al-Mousoi AK, Kumar A, Salih SQ, JosephNg PS, et al.
    Sci Rep, 2023 Jun 05;13(1):9076.
    PMID: 37277466 DOI: 10.1038/s41598-023-36427-3
    According to recent reports, planar structure-based organometallic perovskite solar cells (OPSCs) have achieved remarkable power conversion efficiency (PCE), making them very competitive with the more traditional silicon photovoltaics. A complete understanding of OPSCs and their individual parts is still necessary for further enhancement in PCE. In this work, indium sulfide (In2S3)-based planar heterojunction OPSCs were proposed and simulated with the SCAPS (a Solar Cell Capacitance Simulator)-1D programme. Initially, OPSC performance was calibrated with the experimentally fabricated architecture (FTO/In2S3/MAPbI3/Spiro-OMeTAD/Au) to evaluate the optimum parameters of each layer. The numerical calculations showed a significant dependence of PCE on the thickness and defect density of the MAPbI3 absorber material. The results showed that as the perovskite layer thickness increased, the PCE improved gradually but subsequently reached a maximum at thicknesses greater than 500 nm. Moreover, parameters involving the series resistance as well as the shunt resistance were recognized to affect the performance of the OPSC. Most importantly, a champion PCE of over 20% was yielded under the optimistic simulation conditions. Overall, the OPSC performed better between 20 and 30 °C, and its efficiency rapidly decreases above that temperature.
  10. Fu M, Le C, Fan T, Prakapovich R, Manko D, Dmytrenko O, et al.
    Environ Sci Pollut Res Int, 2021 Dec;28(45):64818-64829.
    PMID: 34318419 DOI: 10.1007/s11356-021-15574-y
    The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.
  11. Guangnan Z, Tao H, Rahman MA, Yao L, Al-Saffar A, Meng Q, et al.
    Work, 2021;68(3):871-879.
    PMID: 33612530 DOI: 10.3233/WOR-203421
    BACKGROUND: An isolated robot must take account of uncertainty in its world model and adapt its activities to take into account such as uncertainty. In the same way, a robot interaction with security and privacy issues (RISAPI) with people has to account for its confusion about the human internal state, as well as how this state will shift as humans respond to the robot.

    OBJECTIVES: This paper discusses RISAPI of our original work in the field, which shows how probabilistic planning and system theory algorithms in workplace robotic systems that work with people can allow for that reasoning using a security robot system. The problem is a general way as an incomplete knowledge 2-player game.

    RESULTS: In this general framework, the various hypotheses and these contribute to thrilling and complex robot behavior through real-time interaction, which transforms actual human subjects into a spectrum of production systems, robots, and care facilities.

    CONCLUSION: The models of the internal human situation, in which robots can be designed efficiently, are limited, and achieve optimal computational intractability in large, high-dimensional spaces. To achieve this, versatile, lightweight portrayals of the human inner state and modern algorithms offer great hope for reasoning.

  12. Halder B, Ahmadianfar I, Heddam S, Mussa ZH, Goliatt L, Tan ML, et al.
    Sci Rep, 2023 May 17;13(1):7968.
    PMID: 37198391 DOI: 10.1038/s41598-023-34774-9
    Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50-60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.
  13. Hameed MM, Razali SFM, Mohtar WHMW, Rahman NA, Yaseen ZM
    PLoS One, 2023;18(10):e0290891.
    PMID: 37906556 DOI: 10.1371/journal.pone.0290891
    The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.
  14. Hameed MM, Mohd Razali SF, Wan Mohtar WHM, Ahmad Alsaydalani MO, Yaseen ZM
    Heliyon, 2024 Jan 15;10(1):e22942.
    PMID: 38187234 DOI: 10.1016/j.heliyon.2023.e22942
    Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.
  15. Hashim BM, Al-Naseri SK, Al Maliki A, Sa'adi Z, Malik A, Yaseen ZM
    Environ Sci Pollut Res Int, 2021 Sep;28(36):50344-50362.
    PMID: 33956319 DOI: 10.1007/s11356-021-13812-x
    At the end of 2019, a novel coronavirus COVID-19 emerged in Wuhan, China, and later spread throughout the world, including Iraq. To control the rapid dispersion of the virus, Iraq, like other countries, has imposed national lockdown measures, such as social distancing, restriction of automobile traffic, and industrial enterprises. This has led to reduced human activities and air pollutant emissions, which caused improvement in air quality. This study focused on the analysis of the impact of the six partial, total, and post-lockdown periods (1st partial lockdown from March 1 to16, 2020, 1st total lockdown from March 17 to April 21, 2nd partial lockdown from April 22 to May 23, 2nd total lockdown from May 24 to June 13, 3rd partial lockdown from June 14 to August 19, and end partial lockdown from August 20 to 31) on the average of daily NO2, O3, PM2.5, and PM10 concentrations, as well as air quality index (AQI) in 18 Iraqi provinces during these periods (from March 1st to August 31st, 2020). The analysis showed a decline in the average of daily PM2.5, PM10, and NO2 concentrations by 24%, 15%, and 8%, respectively from March 17 to April 21, 2020 (first phase of total lockdown) in comparison to the 1st phase of partial lockdown (March 1 to March 16, 2020). Furthermore, the O3 increased by 10% over the same period. The 2nd phase of total lockdown, the 3rd partial lockdown, and the post-lockdown periods witnessed declines in PM2.5 by 8%, 11%, and 21%, respectively, while the PM10 increases over the same period. Iraqi also witnessed improvement in the AQI by 8% during the 1st phase of total lockdown compared to the 1st phase of partial lockdown. The level of air pollutants in Iraq declined significantly during the six lockdown periods as a result of reduced human activities. This study gives confidence that when strict measures are implemented, air quality can improve.
  16. Hashim BM, Al-Naseri SK, Hamadi AM, Mahmood TA, Halder B, Shahid S, et al.
    Int J Disaster Risk Reduct, 2023 Aug;94:103799.
    PMID: 37360250 DOI: 10.1016/j.ijdrr.2023.103799
    The COVID-19 pandemic was a serious global health emergency in 2020 and 2021. This study analyzed the seasonal association of weekly averages of meteorological parameters, such as wind speed, solar radiation, temperature, relative humidity, and air pollutant PM2.5, with confirmed COVID-19 cases and deaths in Baghdad, Iraq, a major megacity of the Middle East, for the period June 2020 to August 2021. Spearman and Kendall correlation coefficients were used to investigate the association. The results showed that wind speed, air temperature, and solar radiation have positive and strong correlations with the confirmed cases and deaths in the cold season (autumn and winter 2020-2021). The total COVID-19 cases negatively correlated with relative humidity but were not significant in all seasons. Besides, PM2.5 strongly correlated with COVID-19 confirmed cases for the summer of 2020. The death distribution by age group showed the highest deaths for those aged 60-69. The highest number of deaths was 41% in the summer of 2020. The study provided useful information about the COVID-19 health emergency and meteorological parameters, which can be used for future health disaster planning, adopting prevention strategies and providing healthcare procedures to protect against future infraction transmission.
  17. Jamei M, Ahmadianfar I, Karbasi M, Jawad AH, Farooque AA, Yaseen ZM
    J Environ Manage, 2021 Dec 15;300:113774.
    PMID: 34560461 DOI: 10.1016/j.jenvman.2021.113774
    The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.
  18. Jawad AH, Abdulhameed AS, Reghioua A, Yaseen ZM
    Int J Biol Macromol, 2020 Nov 15;163:756-765.
    PMID: 32634511 DOI: 10.1016/j.ijbiomac.2020.07.014
    In this research, an attempt to develop zwitterion composite adsorbent is conducted by modifying chitosan (CHS) with a covalent cross-linker (epichlorohydrin, ECH) and an aluminosilicate mineral (zeolite, ZL). The zwitterion composite adsorbent of chitosan-epichlorohydrin/zeolite (CHS-ECH/ZL) is performed multifunctional tasks by removing two structurally different cationic (methylene blue dye, MB), and anionic (reactive red 120 dye, RR120) dyes from aqueous solutions. The surface property, crystallinity, morphology, functionality, and charge of the CHS-ECH/ZL are analyzed using BET, XRD, SEM, FTIR, and pHpzc, analyses, respectively. The influence of pertinent parameters namely CHS-ECH/ZL dosage (0.02-0.5 g), solution pH (4-10), temperature (303-323K), initial dye concentration (30-400 mg/L), and contact time (0-600 min) on the MB and RR120 removal are tested. The research findings revealed that the adsorption isotherm at equilibrium well explained in according to the Freundlich isotherm model, and the recorded adsorption capacities of CHS-ECH/ZL are 156.1 and 284.2 mg/g for MB and RR120 respectively at 30 °C. The mechanism of MB and RR120 adsorption onto the CHS-ECH/ZL indicates various types of interactions namely, electrostatic interaction, hydrogen bonding, and Yoshida H-bonding in addition to n-π interaction. Overall, this research introduces CHS-ECH/ZL composite as an eco-friendly zwitterion adsorbent with good applicability towards the two structurally different cationic and anionic dyes from aqueous environment.
  19. Kashi E, Surip SN, Khadiran T, Nawawi WI, De Luna Y, Yaseen ZM, et al.
    Int J Biol Macromol, 2024 Feb;259(Pt 1):129147.
    PMID: 38181921 DOI: 10.1016/j.ijbiomac.2023.129147
    A composite of chitosan biopolymer with microalgae and commercial carbon-doped titanium dioxide (kronos) was modified by grafting an aromatic aldehyde (salicylaldehyde) in a hydrothermal process for the removal of brilliant green (BG) dye. The resulting Schiff's base Chitosan-Microalgae-TiO2 kronos/Salicylaldehyde (CsMaTk/S) material was characterised using various analytical methods (conclusive of physical properties using BET surface analysis method, elemental analysis, FTIR, SEM-EDX, XRD, XPS and point of zero charge). Box Behnken Design was utilised for the optimisation of the three input variables, i.e., adsorbent dose, pH of the media and contact time. The optimum conditions appointed by the optimisation process were further affirmed by the desirability test and employed in the equilibrium studies in batch mode and the results exhibited a better fit towards the pseudo-second-order kinetic model as well as Freundlich and Langmuir isotherm models, with a maximum adsorption capacity of 957.0 mg/g. Furthermore, the reusability study displayed the adsorptive performance of CsMaTk/S remains effective throughout five adsorption cycles. The possible interactions between the dye molecules and the surface of the adsorbent were derived based on the analyses performed and the electrostatic attractions, H-bonding, Yoshida-H bonding, π-π and n-π interactions are concluded to be the responsible forces in this adsorption process.
  20. Mhana KH, Norhisham SB, Katman HYB, Yaseen ZM
    Heliyon, 2023 Sep;9(9):e19413.
    PMID: 37809986 DOI: 10.1016/j.heliyon.2023.e19413
    Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991-2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.
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