Application of computational chemistry in chemical reactivity: a review

Molecular orbitals are vital to giving reasons several chemical reactions occur. Although, Fukui and coworkers were able to propose a postulate which shows that highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) is incredibly important in predicting chemical reactions. It should be kept in mind that this postulate could be a rigorous one therefore it requires an awfully serious attention in order to be understood. However, there has been an excellent breakthrough since the introduction of computational chemistry which is mostly used when a mathematical method is fully well built that it is automated for effectuation and intrinsically can predict chemical reactivity. At the cause of this review, we’ve reported on how HOMO and LUMO molecular orbitals may be employed in predicting a chemical change by the utilization of an automatic data processing (ADP) system through the utilization of quantum physics approximations.

Ridge Estimation's Effectiveness for Multiple Linear Regression with Multicollinearity: An Investigation Using Monte-Carlo Simulations

The goal of this research is to compare multiple linear regression coefficient estimations with multicollinearity. In order to quantify the effectiveness of estimations by the mean of average mean square error, the ordinary least squares technique (OLS), modified ridge regression method (MRR), and generalized Liu-Kejian method (LKM) are compared (AMSE). For this study, the simulation scenarios are 3 and 5 independent variables with zero mean normally distributed random error of variance 1, 5, and 10, three correlation coefficient levels; i.e., low (0.2), medium (0.5), and high (0.8) are determined for independent variables, and all combinations are performed with sample sizes 15, 55, and 95 by Monte Carlo simulation technique for 1,000 times in total. As the sample size rose, the AMSE decreased. The MRR and LKM both outperformed the LSM. At random error of variance 10, the MRR is the most suitable for all circumstances.

Characterization and Evaluation of Human Health Risk of Heavy Metals in Tin Mine Tailings in Selected Area of Plateau State, Nigeria

Tin mining tailings are unprocessed waste materials that overlie an ore which are displaced during mining activities. This research work is aimed at characterizing and evaluating the human health risk of heavy metals in tin mine tailings in Zabot (S3) and Tafan (S4) districts in Barkin Ladi Local Government Area of Plateau State, Nigeria. The samples were characterized using EDX-XRF and SEM. The concentrations of seven heavy metals (Pb, Cr, As, Ni, Cd, Cu and Zn) were determined in S3 and S4. The results showed that Cr, Ni, Cd, Cu and Zn were within the USEPA permissible limits, except for Pb and As with range of (270-300) mg/kg and (40-70) mg/kg respectively for both mining and control sites of S3 and S4. The SEM results revealed small particles size with fine porous structure, and rough areas with varying sizes and pores distributed over the surface for S3 and S4 respectively. Results of the risk assessment showed that the hazard quotient HQ and HI values were greater than 1 indicating high risk. The Carcinogenic and non-carcinogenic risks associated with Pb, Zn, Cd, Cr, Ni and As were evaluated for S3 and S4 for the three exposure pathway and it was found that the mining sites pose more risk than the control and the children were more exposed than the adults. The carcinogenicity of these samples were due to the high hazard quotient for ingestion and dermal exposure pathway. The R total results for As, Cr, Pb and Ni for mining site S3 were found to be (1.39 × 102 , 2.02 × 10-7 , 3.30 × 103 and 8.17 × 10-8 ), and control site (3.42 × 103 , 2.64 × 10-5 , 38.30 × 101 , 6.90 × 10-8 ) for As, Cr, Pb and Ni respectively. From the R total results As and Pb were more than the acceptable threshold, while Cr and Ni were below the threshold of 1 × 10-4 . For the mining site S4, the R total were found to be (5.70 × 102 , 1.82 × 10-7 , 3.63 × 104 and 9.64 × 10-9 ),and the control (1.16 × 103 , 1.71 × 10-7 , 31.1 × 102 and 1.51 × 10-8 ) for As, Cr, Pb and Ni respectively. From the results of the mining and control sites, As and Pb R total were higher than the acceptable threshold, while Cr and Ni were below the threshold of 1 × 10-4 .

Sentiment Analysis using various Machine Learning and Deep Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

Deposition Time induced Structural and Optical Properties of Lead Tin Sulphide Thin Films

Lead tin sulphide (Pb-Sn-S) thin films (TFs) were deposited on fluorine-doped tin oxide (FTO) substrates via the electrochemical deposition process using lead (II) nitrate [Pb(NO3)2], tin (II) chloride dehydrate [SnCl2.2H2O] and thiacetamide [C2H5NS] precursors as sources of lead (Pb), tin (Sn) and sulphur (S). The solution of all the compounds was harmonized with a stirrer (magnetic) at 300k. In this study, we reported on the improvements in the properties (structural and optical) of Pb-Sn-S TFs by varying the deposition time. We observed from X-ray diffractometer (XRD) that the prepared material is polycrystalline in nature. UV-Vis measurements were done for the optical characterizations and the band gap values were seen to be increasing from 1.52 to 1.54 eV with deposition time. In addition to this, the absorption coefficient and refractive index were also estimated and discussed.

COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.

Optimized Breast Cancer Classification using Feature Selection and Outliers Detection

Breast cancer is the second most commonly diagnosed cancer in women throughout the world. It is on the rise, especially in developing countries, where the majority of cases are discovered late. Breast cancer develops when cancerous tumors form on the surface of the breast cells. The absence of accurate prognostic models to assist physicians recognize symptoms early makes it difficult to develop a treatment plan that would help patients live longer. However, machine learning techniques have recently been used to improve the accuracy and speed of breast cancer diagnosis. If the accuracy is flawless, the model will be more efficient, and the solution to breast cancer diagnosis will be better. Nevertheless, the primary difficulty for systems developed to detect breast cancer using machine-learning models is attaining the greatest classification accuracy and picking the most predictive feature useful for increasing accuracy. As a result, breast cancer prognosis remains a difficulty in today's society. This research seeks to address a flaw in an existing technique that is unable to enhance classification of continuous-valued data, particularly its accuracy and the selection of optimal features for breast cancer prediction. In order to address these issues, this study examines the impact of outliers and feature reduction on the Wisconsin Diagnostic Breast Cancer Dataset, which was tested using seven different machine learning algorithms. The results show that Logistic Regression, Random Forest, and Adaboost classifiers achieved the greatest accuracy of 99.12%, on removal of outliers from the dataset. Also, this filtered dataset with feature selection, on the other hand, has the greatest accuracy of 100% and 99.12% with Random Forest and Gradient boost classifiers, respectively. When compared to other state-of-the-art approaches, the two suggested strategies outperformed the unfiltered data in terms of accuracy. The suggested architecture might be a useful tool for radiologists to reduce the number of false negatives and positives. As a result, the efficiency of breast cancer diagnosis analysis will be increased.

Performance Study of N-grams in the Analysis of Sentiments

In this work, a study investigation was carried out using n-grams to classify sentiments with different machine learning and deep learning methods. We used this approach, which combines existing techniques, with the problem of predicting sequence tags to understand the advantages and problems confronted with using unigrams, bigrams and trigrams to analyse economic texts. Our study aims to fill the gap by evaluating the performance of these n-grams features on different texts in the economic domain using nine sentiment analysis techniques and found more insights. We show that by comparing the performance of these features on different datasets and using multiple learning techniques, we extracted useful intelligence. The evaluation involves assessing the precision, recall, f1-score and accuracy of the function output of the several machine learning algorithms proposed. The methods were tested using Amazon, IMDB, Reuters, and Yelp economic review datasets and our comprehensive experiment shows the effectiveness of n-grams in the analysis of sentiments.

Eigensolution to Morse potential for Scandium and Nitrogen monoiodides

The solutions for Morse potential energy function under the influence of Schr¨odinger equation are examined using supersymmetric approach. The energy equation obtained was used to generate eigenvalues forX1 +state of scandium monoiodide (ScI) and X3 state of nitrogen monoiodide (NI) respectively were obtained by imputing their respective spectroscopic parameters. The calculated results for the two molecules aligned excellently with the predicted/observed values.

Waste glass: An excellent adsorbent for crystal violet dye, Pb2+ and Cd2+ heavy metals ions decontamination from wastewater

The suitability of waste glass as an eco-friendly adsorbent for the removal of crystal violet (CV) dye, Pb2+ and Cd2+ heavy metal ions in waste water samples was investigated in batch mode. Waste glass sample was pulverized and characterized by SEM/EDX, XRD, BET and FTIR. Effects of variation in temperature, pH, contact time and recyclability of the adsorbent were studied. FTIR spectra revealed major peaks around 491.53 and 3444.12 cm-1 corresponding to the bending vibrations of Si-O-Si and -OH groups respectively. SEM/EDX analysis showed a dense, coarse, porous morphology with predominantly silica component. The effective surface area and size of the adsorbent were 557.912 m2/g and 2.099 nm respectively. Increase in temperature, dosage, contact time resulted in increase in adsorption efficiency. Optimum adsorption efficiency of 94%, 97.5% and 89.1% was attained for Pb2+ , Cd2+ ions and CV dye respectively at 70?C. Adsorption process followed more accurately pseudo-first order model and isotherm fitted perfectly into Freundlich model indicating a multilayer adsorption mechanism for CV dye and the heavy metals. 89.87% reduction in Chemical Oxygen Demand (COD) level of wastewater was reported upon treatment with waste glass adsorbent affirming its efficiency for dye and heavy metal pollutants removal.