Nickel augmented biochar for sustaining produced water treatment to decarbonize oil and gas industrial waste using anaerobic-aerobic granular cylindrical periodic discontinuous batch reactors.

This study assessed the efficacy of granular cylindrical periodic discontinuous batch reactors (GC-PDBRs) for produced water (PW) treatment by employing eggshell and waste activated sludge (WAS) derived Nickel (Ni) augmented biochar. The synthesized biochar was magnetized to further enhance its contribution towards achieving carbon neutrality due to carbon negative nature, Carbon dioxide (CO2) sorption, and negative priming effects. The GC-PDBR1 and GC-PDBR2 process variables were optimized by the application of central composite design (CCD). This is to maximize the decarbonization rate. Results showed that the systems could reduce total phosphorus (TP) and chemical oxygen demand (COD) by 76-80% and 92-99%, respectively. Optimal organic matter and nutrient removals were achieved at 80% volumetric exchange ratio (VER), 5 min settling time and 3000 mg/L mixed liquor suspended solids (MLSS) concentration with desirability values of 0.811 and 0.954 for GC-PDBR1 and GC-PDBR2, respectively. Employing four distinct models, the biokinetic coefficients of the GC-PDBRs treating PW were calculated. The findings indicated that First order (0.0758-0.5365) and Monod models (0.8652-0.9925) have relatively low R2 values. However, the Grau Second-order model and Modified Stover-Kincannon model have high R2 values. This shows that, the Grau Second Order and Modified Stover-Kincannon models under various VER, settling time, and MLSS circumstances, are more suited to explain the removal of pollutants in the GC-PDBRs. Microbiological evaluation demonstrated that a high VER caused notable rises in the quantity of several microorganisms. Under high biological selective pressure, GC-PDBR2 demonstrated a greater percentage of nitrogen removal via autotrophic denitrification and a greater number of nitrifying bacteria. The overgrowth of bacteria such as Actinobacteriota spp. Bacteroidota spp, Gammaproteobacteria, Desulfuromonas Mesotoga in the phylum, class, and genus, has positively impacted on granule formation and stability. Taken together, our study through the introduction of intermittent aeration GC-PDBR systems with added magnetized waste derived biochar, is an innovative approach for simultaneous aerobic sludge granulation and PW treatment, thereby providing valuable contributions in the journey toward achieving decarbonization, carbon neutrality and sustainable development goals (SDGs).

Phase-shifting determination and pattern recognition using a modified Sagnac interferometer with multiple reflections.

This work has implemented a diverse modification of the Sagnac interferometer to accommodate various measurement requirements, including phase shifting, pattern recognition, and a morphological analysis. These modifications were introduced to validate the adaptability and versatility of the system. To enable phase shifting using the multiple light reflection technique, a half-wave plate (HWP) was utilized with rotations at 0, π/8, π/4, and 3π/8 radians, generating four interference patterns. It is possible to observe a distinct circular fringe width as the polarized light experiences diffraction at the interferometer's output as it travels through a circular aperture with various diameters ranging from 0.4 to 1 mm. Further modifications were made to the setup by inserting a pure glass and a fluoride-doped tin oxide (FTO) transparent substrate into the common path. This modification aimed to detect and analyze a horizontal fringe pattern. Subsequently, the FTO substrate was replaced with a bee leg to facilitate morphology recognition. A deep learning-based image processing technique was employed to analyze the bee leg morphology. The experimental results showed that the proposed scheme succeeded in achieving the phase shift, measuring hole diameters with errors smaller than 1.6%, separating distinct transparent crystals, and acquiring the morphological view of a bee's leg. The method also has successfully achieved an accurate surface area and background segmentation with an accuracy over 87%. Overall, the outcomes demonstrated the potential of proposed interferometers for various applications, and the advantages of the optical sensors were highlighted, particularly in microscopic applications.

NiO thickness measurement using a rectangular-type Sagnac interferometer as the material transport layer in a perovskite solar cell.

This work aims to utilize a phase-shifting technique in a rectangular-type Sagnac interferometer (RTSI) to measure the thickness of a thin film of nickel (II) oxide (NiO) in an electron transport layer (ETL) in perovskite solar cell preparation. The NiO layer is deposited on a fluorine-doped tin oxide (FTO) glass substrate. In the RTSI setup, the signal output from the interferometer is divided into the reference and testing arms using a nonpolarizing beam splitter (NPBS). The balanced photodetectors then detect the signal, with the FTO/NiO layer placed in the testing arm and pure FTO in the reference arm. By analyzing the signal intensities at polarization settings of 0° to 180°, the phase shift and thickness of the NiO layer can be determined. The thickness values of FTO and NiO films obtained through three different phase-shifting algorithms of three-, four-, and five-steps are calculated. The obtained NiO thickness values are validated against scanning electron microscopy (SEM). Finally, by considering the NiO thickness value that exhibits the lowest percentage error compared to one from SEM, it is confirmed that the three-step algorithm is the most suitable scheme for obtaining intensities at 0°, 45°, and 90°. Therefore, the proposed setup shows promise as a replacement for SEM in thickness measurements.

Chemometrics-based models hyphenated with ensemble machine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high-performance liquid chromatography.

In this research, two nonlinear models, namely; adaptive neuro-fuzzy inference system and feed-forward neural network and a classical linear model were employed for the prediction of retention time of isoquercitrin in Coriander sativum L. using the high-performance liquid chromatography technique. The prediction employed the use of composition of mobile phase and pH as the corresponding input parameters. The performance indices of the models were evaluated using root mean square error, determination co-efficient, and correlation co-efficient. The results obtained from the simple models showed that subclustering-adaptive-neuro fuzzy inference system gave the best results in both the training and testing phases and boosted the performance accuracy of the simple models. The overall comparison of the results showed that subclustering-adaptive-neuro fuzzy inference system ensemble demonstrated outstanding performance and increased the accuracy of the single models and ensemble models in the testing phase, up to 35% and 3%, respectively.

Trace metals geochemistry for health assessment coupled with adsorption remediation method for the groundwater of Lorong Serai 4, Hulu Langat, west coast of Peninsular Malaysia.

The research study was carried out to evaluate trace metals (Pb, Cd, Se, Al, Mn, Cu, Zn, Fe, As, Ni, Cr, and Ag) concentrations in groundwater of Lorong Serai 4, Hulu Langat, Selangor, Malaysia. Additionally, the research study focused on determining non-carcinogenic and carcinogenic health risks, sources of the contaminants, and effective remediation methods. The results show that the concentration levels of Pb, Cd, Se, Al, Cu, Zn, Ni, Cr, and Ag are lower than their corresponding permissible limits, while Fe, Mn, and As concentrations exceed their acceptable limit. The hazard index of the groundwater in the area exceeded the acceptable limit, showing the rate of carcinogenic and non-carcinogenic health effects associated with the water. The findings also indicate that the lifetime cancer risk is high compared to the maximum limits of lifetime cancer risk from the drinking water (10-6 to 10-4). The groundwater geochemical data of the area are used in establishing the source of Fe, Mn, and As metal ions. Evaluation of Fe2+/Fe3+ and S2-/SO42- redox couples and thermodynamic modelling indicates that the groundwater of the area is in redox disequilibrium. The groundwater samples contain aqueous iron sulphate, which is supersaturated, ferrous carbonate and aluminium sulphate that are saturated. The main state of redox disequilibrium is governed by mineral precipitation and dissolution. Aqueous arsenic and manganese are possibly derived from the dissolution of pyrite (arsenopyrite) and amorphous oxide-hydroxides, respectively. The high concentration of iron in the shallow groundwater in the area is primarily the result of silicate rock weathering of ferroan igneous and metamorphic minerals with a minor contribution from the oxidation of iron sulphides. Magnetite coated with graphene oxide (Fe3O4-GO) nanoparticles (NPs) was synthesized and characterized, and the adsorption preliminary experiments were carried out; and the Fe3O4-GO NPs show enhanced removal (Fe > As > Mn) capacity over graphene oxide (GO).

Case fatality and factors associated with Maternal mortality in patients with eclampsia: A retrospective review of 761 cases in a tertiary institution in Nigeria.

To determine the case fatality and factors associated with maternal mortality in patients with eclampsia in UMTH, we conducted a retrospective review of 761 consecutive cases of eclampsia managed UMTH, Nigeria, from 2005 to 2020. Case fatality was 7.6%, most being unbooked primigravidae with no formal education. Common causes of death identified were aspiration, cerebrovascular accident, and haemorrhage. Admission delayed >10 hours was found to be independently associated with maternal death, and Caesarean delivery.

Prediction of Cell Migration in MDA-MB 231 and MCF-7 Human Breast Cancer Cells Treated with Albizia Lebbeck Methanolic Extract Using Multilinear Regression and Artificial Intelligence-Based Models.

Breast cancer is a common cancer affecting women worldwide, and it progresses from breast tissue to other parts of the body through a process called metastasis. Albizia lebbeck is a valuable plant with medicinal properties due to some active biological macromolecules, and it's cultivated in subtropical and tropical regions of the world. This study reports the phytochemical compositions, the cytotoxic, anti-proliferative and anti-migratory potential of A. lebbeck methanolic (ALM) extract on strongly and weakly metastatic MDA-MB 231 and MCF-7 human breast cancer cells, respectively. Furthermore, we employed and compared an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multilinear regression analysis (MLR) to predict cell migration on the treated cancer cells with various concentrations of the extract using our experimental data. Lower concentrations of the ALM extract (10, 5 & 2.5 µg/mL) showed no significant effect. Higher concentrations (25, 50, 100 & 200 µg/mL) revealed a significant effect on the cytotoxicity and proliferation of the cells when compared with the untreated group (p < 0.05; n ≥ 3). Furthermore, the extract revealed a significant decrease in the motility index of the cells with increased extract concentrations (p < 0.05; n ≥ 3). The comparative study of the models observed that both the classical linear MLR and AI-based models could predict metastasis in MDA-MB 231 and MCF-7 cells. Overall, various ALM extract concentrations showed promising an-metastatic potential in both cells, with increased concentration and incubation period. The outcomes of MLR and AI-based models on our data revealed the best performance. They will provide future development in assessing the anti-migratory efficacies of medicinal plants in breast cancer metastasis.

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.

The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models-specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)-were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

Biochar-based geopolymer nanocomposite for COD and phenol removal from agro-industrial biorefinery wastewater: Kinetic modelling, microbial community, and optimization by response surface methodology.

Agro-industrial biorefinery effluent (AIBW) is considered a highly polluting source responsible for environmental contamination. It contains high loads of chemical oxygen demand (COD), and phenol, with several other organic and inorganic constituents. Thus, an economic treatment approach is required for the sustainable discharge of the effluent. The long-term process performance, contaminant removal and microbial response of AIBW to rice straw-based biochar (RSB) and biochar-based geopolymer nanocomposite (BGC) as biosorbents in an activated sludge process were investigated. The adsorbents operated in an extended aeration system with a varied hydraulic retention time of between 0.5 and 1.5 d and an AIBW concentration of 40-100% for COD and phenol removal under standard conditions. Response surface methodology was utilised to optimize the process variables of the bioreactor system. Process results indicated a significant reduction of COD (79.51%, 98.01%) and phenol (61.94%, 74.44%) for BEAS and GEAS bioreactors respectively, at 1 d HRT and AIBW of 70%. Kinetic model analysis indicated that the Stover-Kincannon model best describes the system functionality, while the Grau model was better in predicting substrate removal rate and both with a precision of between R2 (0.9008-0.9988). Microbial communities examined indicated the abundance of genera, following the biosorbent addition, while RSB and BGC had no negative effect on the bioreactor's performance and bacterial community structure of biomass. Proteobacteria and Bacteroidetes were abundant in BEAS. While the GEAS achieved higher COD and phenol removal due to high Nitrosomonas, Nitrospira, Comamonas, Methanomethylovorans and Acinetobacter abundance in the activated sludge. Thus, this study demonstrated that the combination of biosorption and activated sludge processes could be promising, highly efficient, and most economical for AIBW treatment, without jeopardising the elimination of pollutants or the development of microbial communities.

Prediction of cell migration potential on human breast cancer cells treated with Albizia lebbeck ethanolic extract using extreme machine learning.

Cancer is one of the major causes of death in the modern world, and the incidence varies considerably based on race, ethnicity, and region. Novel cancer treatments, such as surgery and immunotherapy, are ineffective and expensive. In this situation, ion channels responsible for cell migration have appeared to be the most promising targets for cancer treatment. This research presents findings on the organic compounds present in Albizia lebbeck ethanolic extracts (ALEE), as well as their impact on the anti-migratory, anti-proliferative and cytotoxic potentials on MDA-MB 231 and MCF-7 human breast cancer cell lines. In addition, artificial intelligence (AI) based models, multilayer perceptron (MLP), extreme gradient boosting (XGB), and extreme learning machine (ELM) were performed to predict in vitro cancer cell migration on both cell lines, based on our experimental data. The organic compounds composition of the ALEE was studied using gas chromatography-mass spectrometry (GC-MS) analysis. Cytotoxicity, anti-proliferations, and anti-migratory activity of the extract using Tryphan Blue, MTT, and Wound Heal assay, respectively. Among the various concentrations (2.5-200 µg/mL) of the ALEE that were used in our study, 2.5-10 µg/mL revealed anti-migratory potential with increased concentrations, and they did not show any effect on the proliferation of the cells (P < 0.05; n ≥ 3). Furthermore, the three data-driven models, Multi-layer perceptron (MLP), Extreme gradient boosting (XGB), and Extreme learning machine (ELM), predict the potential migration ability of the extract on the treated cells based on our experimental data. Overall, the concentrations of the plant extract that do not affect the proliferation of the type cells used demonstrated promising effects in reducing cell migration. XGB outperformed the MLP and ELM models and increased their performance efficiency by up to 3% and 1% for MCF and 1% and 2% for MDA-MB231, respectively, in the testing phase.

Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds.

Spirulina is a microalga and its phenolic compound is affected by growth mediums. In this study, Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference System (ANFIS) and Multilayer perceptron (MLP) models, and Step-Wise-Linear Regression (SWLR) were used to predict total phenolic compounds (TPC) of the spirulina algae. Spirulina productivity (P), extraction yield (EY), total flavonoids (TF), percent of flavonoid (%F) and percent of phenols (%P) are considered as input variables with the corresponding TPC as an output variable. From the result, TPC has a high positive correlation with the input variables with R = 0.99999. Also, the models showed that the ANFIS and SWLR gives superior result in the testing phase and increased its accuracy by 2% compared to MLP model in the prediction of TPC.

Clinical Modelling of RVHF Using Pre-Operative Variables: A Direct and Inverse Feature Extraction Technique.

Right ventricular heart failure (RVHF) mostly occurs due to the failure of the left-side of the heart. RVHF is a serious disease that leads to swelling of the abdomen, ankles, liver, kidneys, and gastrointestinal (GI) tract. A total of 506 heart-failure subjects from the Faculty of Medicine, Cardiovascular Surgery Department, Ege University, Turkey, who suffered from a severe heart failure and are currently receiving support from a ventricular assistance device, were involved in the current study. Therefore, the current study explored the application of both the direct and inverse modelling approaches, based on the correlation analysis feature extraction performance of various pre-operative variables of the subjects, for the prediction of RVHF. The study equally employs both single and hybrid paradigms for the prediction of RVHF using different pre-operative variables. The visualized and quantitative performance of the direct and inverse modelling approach indicates the robust prediction performance of the hybrid paradigms over the single techniques in both the calibration and validation steps. Whereby, the quantitative performance of the hybrid techniques, based on the Nash-Sutcliffe coefficient (NC) metric, depicts its superiority over the single paradigms by up to 58.7%/75.5% and 80.3%/51% for the calibration/validation phases in the direct and inverse modelling approaches, respectively. Moreover, to the best knowledge of the authors, this is the first study to report the implementation of direct and inverse modelling on clinical data. The findings of the current study indicates the possibility of applying these novel hybridised paradigms for the prediction of RVHF using pre-operative variables.

A Systematic Literature Review on Waste-to-Resource Potential of Palm Oil Clinker for Sustainable Engineering and Environmental Applications.

Several agro-waste materials have been utilized for sustainable engineering and environmental application over the past decades, showing different degrees of effectiveness. However, information concerning the wider use of palm oil clinker (POC) and its performance is still lacking. Therefore, as a solid waste byproduct produced in one of the oil palm processing stages, generating a huge quantity of waste mostly dumped into the landfill, the waste-to-resource potential of POC should be thoroughly discussed in a review. Thus, this paper provides a systematic review of the current research articles on the several advances made from 2005 to 2021 regarding palm oil clinker physical properties and performances, with a particular emphasis on their commitments to cost savings during environmental and engineering applications. The review begins by identifying the potential of POC application in conventional and geopolymer structural elements such as beams, slabs, and columns made of concrete, mortar, or paste for coarse aggregates, sand, and cement replacement. Aspects such as performance of POC in wastewater treatment processes, fine aggregate and cement replacement in asphaltic and bituminous mixtures during highway construction, a bio-filler in coatings for steel manufacturing processes, and a catalyst during energy generation are also discussed. This review further describes the effectiveness of POC in soil stabilization and the effect of POC pretreatment for performance enhancement. The present review can inspire researchers to find research gaps that will aid the sustainable use of agroindustry wastes. The fundamental knowledge contained in this review can also serve as a wake-up call for researchers that will motivate them to explore the high potential of utilizing POC for greater environmental benefits associated with less cost when compared with conventional materials.

Artificial intelligence-based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method.

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile , Annona squamosal , Camellia sinensis (tea), and coriander ( Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS-M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN-M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.

Measles in a tertiary institution in bida, niger state, Nigeria: prevalence, immunization status and mortality pattern.

OBJECTIVES: Measles is a highly infectious immunizable disease with potential for eradication but is still responsible for high mortality among children, particularly in developing nations like Nigeria. This study aims to determine the hospital based prevalence of measles, describe the vaccination status of children managed for measles at the Federal Medical Centre, Bida, Niger state and to identify the parental disposition to measles vaccination. METHODS: This is a cross-sectional study carried out over a period of 18 months beginning from July 2007. All children with a diagnosis of measles made clinically and reinforced with serological test in the WHO Measles, Rubella and Yellow Fever laboratory in Maitama District Hospital, Abuja were recruited. Informed consent was obtained from the parents/care givers. Structured questionnaire was used to obtain information and data analysis was by SPSS version 15. RESULTS: One hundred and nine children were managed for measles, constituting 8% of total admission over the study period. The male to female ratio was 1.2:1. Of the 109 children with measles, 90 (82%) did not receive measles vaccination. Eighty-eight (80%) of the parents or guardian felt vaccination was bad for various reasons. Of the 23 (21.1%) children whose parents or guardians were positively disposed to vaccination, one death was recorded while the remaining seven deaths were recorded among children whose parents were negatively disposed to vaccination. All the deaths were in the non-vaccinated group below 2 years of age. CONCLUSION: Measles is still a major health burden in our community. The majority of affected children were not vaccinated due to negative parental disposition. Continuous health education is required for change the disposition of the parents/guardian and improve vaccination coverage to minimize measles associated morbidity and mortality.

Amulets, Bands and Other Traditional Applications seen among Emergency and Neonatal Pediatric Admissions in a Tertiary Centre, Nigeria.

OBJECTIVES: This study aims to investigate the types and indications of amulets, bands and traditional applications among pediatric emergencies and neonatal admissions into the Federal Medical Centre, Bida, Nigeria. METHODS: This was a cross-sectional study conducted between January and July, 2008. Children admitted into the emergency and neonatal units of the institution with traditional applications were recruited. Information on demography, time the application was introduced, the reason, cost implication and belief about the efficacy were obtained using a semi-structured questionnaire. Socio-Economic Class was defined using the Oyedeji Classification. RESULTS: The study was based at Federal Medical Centre, Bida and consisted of 666 children admitted into the hospital via the Emergency pediatric and the Neonatal Unit, with a seven-month period prevalence of 11.4% (76 patients). Of the 76 cases screened for the use traditional applications, only 64 (84.2%) were recruited, the remaining did not agree to participate in the study. The mean age was 493.1±528.5 days. The study group included 37 males and 27 females (sex ratio 1.4:1) aged 11 days to 2920 days. The applications used included neck band (54.7%), ankle band (15.6%) and head paste (15.6%). Reasons for the applications included prevention of childhood illnesses (17.2%), sutures closure (15.6%), as well as warding off evil spirits and convulsions (12.5%). The socio-economic classification was I (1.6%), II (7.8%), III (15.6%), IV (45.3%), and V (29.7%). Sixty (93.0%) parents believed the applications cannot be used with conventional medicine, while 53.1% of the participants, obtained them free of charge. CONCLUSION: The use of traditional applications, often for preventive purposes, is common among pediatric patients, especially of low socio-economic classifications. The majority of users considered them incompatible with simultaneous conventional medical care. This has adverse implications for time to presentation for conventional care and treatment outcomes. Promotive and preventive healthcare interventions are needed to reverse this trend. Future broad-based research is warranted.