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The widespread utilization of groundwater in various sectors, including households for drinking purposes and the agricultural and industrial domains, has elevated its status as an indispensable and crucial natural resource. Groundwater has seen significant changes in both quantity and quality factors. Water Quality Index (WQI), which is dependent on a number of factors, is still a crucial gauge of water quality (WQ) and a key component of efficient water management. If there is an automated method for forecasting WQ, the administration will benefit. The main goal of this project is to develop a machine learning (ML) model to forecast the quality of groundwater in several areas of Tamil Nadu (TN), India. The available dataset encompasses comprehensive data groundwater attributes, encompassing parameters such as pH, electrical conductivity (EC), total hardness (TH), calcium (Ca${}^{2+})$, magnesium (Mg${}^{2+})$, sodium (Na${}^{+})$, bicarbonate (HCO${}^{3-})$, nitrate (NO${}^{3-})$, sulfate (SO${}_4^{2-})$, and chloride (Cl${}^{-})$. In this study, various ML regression algorithms such as linear, least angle, random forest and support vector regressor models and their comparison with the ensemble model (EM) were depicted to predict WQI, and the results were evaluated using performance metrics. It is found that the EM has a lower RMSE in the order of $2.4\times 10^{-6}$. Further, the predicted WQI values are used to classify the districts of TN.

Arterial stiffness is a strong determinant of cardiovascular risk. Pulse wave velocity (PWV) is an index of arterial stiffness, and its prognostic value has been repeatedly emphasized. The work presented in this paper is concerned with the design of a new system for measurement of the PWV and analysis. It is in fact related to the description of the hardware setup and the software development in order to measure and analyze the PWV. In the proposed system, the determination of the PWV is carried out through the measurement of the pulse wave transit time (PWTT) using the electrocardiogram (ECG) and the photoplethysmogram (PPG) and the distance separating the site of ejection of the systolic pulse and the site of measuring the PPG signal. The hardware setup therefore consists of an optical device to detect the PPG and electrodes to detect ECG, and different boards to process and digitalize these signals to be acquired in the PC and analyzed. The developed software is concerned with first, the acquisition and processing of both ECG and PPG signals then the determination of the PWV and finally its analysis for different subjects and conditions. The analysis of the PWV is carried out for subjects of different ages in different physiological conditions according to heart activity. The obtained results show that there is a high correlation ($r=0.88$), between heart rate variability (HRV) and PWV. They also show that PWV increases with age. The analysis of the PWV variations with age is also carried out through different regression models. The obtained result shows that the cubic regression model best fits these variations.

COVID-19 pandemic is one of the worst global disasters in the last century. Its pandemic spread and influence in everyday social life, economics and health is in central interest of concern for all governments in the world. North Macedonia is one of the countries with very high percentage of COVID-19 deaths. The health system in a few periods was before collapse. In this paper, we analyze the COVID-19 epidemic situation in North Macedonia from its beginning. We make analysis and comparisons of the situation in different time epidemic periods. We use regression models and machine learning algorithms in order to make predictions, which can be used as an efficient tool to give directions of the authorities to deal with COVID-19 challenges.

This paper makes a theoretical contribution by presenting a detailed derivation of a zero-inflated Poisson (ZIP) model, and then deriving the parameters of the ZIP model using a fishing data set. This model has several practical applications, and is largely performed to model count data that have an excess number of zero counts. In the scope of the paper, we introduce the complete formulae, the likelihood and log-likelihood functions and the estimating equation of the ZIP model. We then investigate the theory of large sample properties of this model under some regularity conditions. A simulation study and a fishing data set are studied for the ZIP model. The results in the actual application in this work are meaningful, useful and crucial in reality. The results also provide reliable evidence for obtaining the largest number of fish while fishing. This is the contribution of this research in terms of applications. Finally, the important applications of this model in practice, some conclusions, and future work is also presented for consideration.

Polycyclic aromatic hydrocarbons (PAHs) have distinctive chemical structures and are well known for their wide range of uses and environmental relevance. This work explores the impact of these structural characteristics on eccentricity-based topological indices offering information about the arrangement of atoms within the molecules. This study uses quantitative structure-property relationship (QSPR) analysis to construct prediction models for understanding and forecasting specific PAHs including Dibenzo[e,l]Pyrene, Heptacence, Heptaphene, Naphthacene, Naphthalene, Naphto[1,2a]pyrene, Naphtho[2,3a]pyrene, perylene, Perylene, Picene, Phenanthrene, Pyrene, Tetraphene, Tribenzo[b,n,pqr]perylene, Tribenzo[a,fg,op]tetracene and Triphenylene. Furthermore, regression analysis applies to clarify the quantitative correlations between the factors under study and improves the interpretability of the data produced. The combined use of these diverse approaches advances a thorough comprehension of the mutual influence of chemical structure, topological indices and predictive modeling about PAHs.