Assessing nitrate contamination risks in groundwater : a machine learning approach

Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record*

Genetic diversity in threatened plant species Alnus nitida (Spach.) Endel

Alnus nitida (Spach) Endl. is an ethnobotanically important threatened plant species. The genetic diversity among the 50 different genotypes of Alnus nitida was carried out using sodium dodecyl sulfate poly acrylamide gel electrophoresis (SDS-PAGE) characterization. A considerable amount of genetic diversity (90%) was observed among the genotypes of A. nitida. The protein characterization was carried out on 12% gel electrophoresis. A total of 10 protein bands were detected in A. nitida genotypes. SDS-PAGE procedure is a useful method for the investigation of both genetic diversity and phylogenetic relationship. Especially, B-5 was monomorphic in A. nitida genotypes and was considered as species specific. All other bands/loci were polymorphic. These polymorphic bands displayed 12, 16, 72, 88, 2, 44, 84, 54 and 12 percent variation respectively. In the present examination, the high intra-specific diversity was observed representing SDS-PAGE is a powerful tool for determining the genetically diverse germplasms in A. nitida. The results obtained by this study could be helpful in the identification and selection of desired genotypes of Alnus nitida for conservation programmes in future. Today, there is still a need to assess genetic variation and protect genetic resources, especially of wild species for prospective benefits in plant conservation programmes

Preparation of Na2O supported CNTs nanocatalyst for efficient biodiesel production from waste-oil

The present work demonstrated the preparation of sodium oxide impregnated on carbon nanotubes (CNTs) and its application as a heterogeneous catalyst for transesterification of waste cooking oil. The catalyst was prepared by impregnation of metal oxide such as sodium oxide, Na2O on the CNTs by calcination at 500 °C for 3 h. It was assumed that the positive metal ion which is Na+ (cations) possess Lewis acidity, whereby, high negativity of oxygen ions can acts as the Brønsted bases, which could enhance the activity of the catalyst. The characterization of synthesized Na2O impregnated-CNTs nanocatalyst was performed using Temperature-programmed desorption of carbon dioxide (TPD-CO2), X-ray diffraction (XRD), infrared spectroscopy and Field emission scanning electron microscope (FESEM). Herein, the mechanism of the transesterification process assisted by the Lewis acidic metal oxide on carbon support was proposed and explained. Series of reactions were carried out to determine the performance of the catalyst. It was found that the prepared Na2O(20 wt%)/CNTs catalysts yielded above 97% of FAME yield at 65 °C assisted by 3 wt% of catalyst amount and 20:1 of methanol-to-oil molar ratio in 3 h of reaction time. Moreover, the results on catalyst’s reusability indicated that the catalyst could last for 3 subsequent reaction cycles due to deactivation of the catalyst caused by leaching of metal oxides and poisioning effect on the active sites. It can be concluded that the prepared Lewis acidic carbon catalyst has a potential to catalyse the production of biodiesel from waste cooking oil (WCO)

Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks

Recent Advances in Dye Sensitized Solar Cells

Solar energy is an abundant and accessible source of renewable energy available on earth, and many types of photovoltaic (PV) devices like organic, inorganic, and hybrid cells have been developed to harness the energy. PV cells directly convert solar radiation into electricity without affecting the environment. Although silicon based solar cells (inorganic cells) are widely used because of their high efficiency, they are rigid and manufacturing costs are high. Researchers have focused on organic solar cells to overcome these disadvantages. DSSCs comprise a sensitized semiconductor (photoelectrode) and a catalytic electrode (counter electrode) with an electrolyte sandwiched between them and their efficiency depends on many factors. The maximum electrical conversion efficiency of DSSCs attained so far is 11.1%, which is still low for commercial applications. This review examines the working principle, factors affecting the efficiency, and key challenges facing DSSCs

Activated carbon from various agricultural wastes by chemical activation with KOH : preparation and characterization

Activated carbons (AC) were prepared by pyrolysis from oil palm empty fruit bunch (EFB), bamboo stem (BS), and coconut shells (CNS) at 800 °C by using potassium hydroxide under nitrogen atmosphere. The influence of temperature and type of agricultural biomass on surface area and morphological properties investigated. Activated carbon produced from BS have a higher specific surface area (1212 m2 g−1) and microporosity percentage than those produced from oil palm EFB, and CNS lies in the range of commercial activated carbons. The morphological analysis of the samples was determined by scanning electron microscopy. The external surfaces are full of cavities and quite irregular as a result of activation. X-ray diffraction analysis showed degree of crystallinity 13.25% in case of AC-BS sample while AC-EFB and AC-CNS showed a crystallinity of 1.68% and 8.19%, respectivel

Mechanistic evaluation of a novel cyclohexenone derivative?s functionality against nociception and inflammation: An in-vitro, in-vivo and in-silico approach

The synthesis of a novel cyclohexanone derivative (CHD; Ethyl 6-(4-metohxyphenyl)-2-oxo-4-phenylcyclohexe-3-enecarboxylate) was described and the subsequent aim was to perform an in vitro, in vivo and in silico pharmacological evaluation as a putative anti-nociceptive and anti-inflammatory agent in mice. Initial in vitro studies revealed that CHD inhibited both cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) enzymes and it also reduced mRNA expression of COX-2 and the pro-inflammatory cytokines TNF-α and IL-1β. It was then shown that CHD dose dependently inhibited chemically induced tonic nociception in the abdominal constriction assay and also phasic thermal nociception (i.e. anti-nociception) in the hot plate and tail immersion tests in comparison with aspirin and tramadol respectively. The thermal test outcomes indicated a possible moderate centrally mediated anti-nociception which, in the case of the hot plate test, was pentylenetetrazole (PTZ) and naloxone reversible, implicating GABAergic and opioidergic mechanisms. CHD was also effective against both the neurogenic and inflammatory mediator phases induced in the formalin test and it also disclosed anti-inflammatory activity against the phlogistic agents, carrageenan, serotonin, histamine and xylene compared with standard drugs in edema volume tests. In silico studies indicated that CHD possessed preferential affinity for GABAA, opioid and COX-2 target sites and this was supported by molecular dynamic simulations where computation of free energy of binding also favored the formation of stable complexes with these sites. These findings suggest that CHD has prospective anti-nociceptive and anti-inflammatory properties, probably mediated through GABAergic and opioidergic interactions supplemented by COX-2 and 5-LOX enzyme inhibition in addition to reducing pro-inflammatory cytokine expression. CHD may therefore possess potentially beneficial therapeutic effectiveness in the management of inflammation and pain

Observation of the diphoton decay of the Higgs boson and measurement of its properties

Peer reviewe

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8 TeV

Peer reviewe