Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method

This article discusses the assessment of groundwater quality using a hybrid technique that would aid in the convenience of groundwater (GW) quality monitoring. Twenty eight (28) GW samples representing 62 barangays in Calapan City, Oriental Mindoro, Philippines were analyzed for their physicochemical characteristics and heavy metal (HM) concentrations. The 28 GW samples were collected at suburban sites identified by the coordinates produced by Global Positioning System Montana 680. The analysis of heavy metal concentrations was conducted onsite using portable handheld X-Ray Fluorescence (pXRF) Spectrometry. Hybrid machine learning—geostatistical interpolation (MLGI) method, specific to neural network particle swarm optimization with Empirical Bayesian Kriging (NN-PSO+EBK), was employed for data integration, GW quality spatial assessment and monitoring. Spatial map of metals concentration was produced using the NN-PSO-EBK. Another, spot map was created for observed metals concentration and was compared to the spatial maps. Results showed that the created maps recorded significant results based on its MSEs with values such as 1.404 × 10−4, 5.42 × 10−5, 6.26 × 10−4, 3.7 × 10−6, 4.141 × 10−4 for Ba, Cu, Fe, Mn, Zn, respectively. Also, cross-validation of the observed and predicted values resulted to R values range within 0.934–0.994 which means almost accurate. Based on these results, it can be stated that the technique is efficient for groundwater quality monitoring. Utilization of this technique could be useful in regular and efficient GW quality monitoring.

DETECTION OF A HARMONIC SIGNAL AGAINST THE BACKGROUND OF A NONSTATIONARY GAUSSIANINTERFERENCE WITH COMPLEX SPECTRUM

Background. Modern radar stations for various purposes operate in the conditions of interference created by the imprints of the radar signal from the background surface, from metrological formations (precipitation, clouds, etc.) and artificial radiation sources. Ensuring the operation of the radar in such difficult conditions requires the construction of adaptive signal processing algorithms that have high efficiency and maintain them when changing signal-to-noise situations. Objective. The purpose of the paper is creation of an adaptive algorithm for detecting a harmonic signal against the background of spatially correlated interference and estimating its parameters. Methods. Construction of a two-dimensional autoregressive model of a mixture of correlated spatial noise and harmonic signal and application of the empirical Bayesian approach to the synthesis of an adaptive algorithm for detecting and evaluating signal and noise parameters. Results. A two-dimensional adaptive space-time algorithm for detecting a radar signal reflected from a moving target against the background of a space-correlated interference is synthesized. The analysis of the efficiency of the algorithm by the Monte Carlo method is carried out. Conclusions. It is shown that the empirical Bayesian approach is an effective working methodology in solving the problem of detecting a harmonic signal and estimating its parameters under conditions of interference with a complex frequency spectrum under different conditions of a priori uncertainty of their parameters.

High-throughput mediation analysis of human proteome and metabolome identifies mediators of post-bariatric surgical diabetes control

To improve the power of mediation in high-throughput studies, here we introduce High-throughput mediation analysis (Hitman), which accounts for direction of mediation and applies empirical Bayesian linear modeling. We apply Hitman in a retrospective, exploratory analysis of the SLIMM-T2D clinical trial in which participants with type 2 diabetes were randomized to Roux-en-Y gastric bypass (RYGB) or nonsurgical diabetes/weight management, and fasting plasma proteome and metabolome were assayed up to 3 years. RYGB caused greater improvement in HbA1c, which was mediated by growth hormone receptor (GHR). GHR’s mediation is more significant than clinical mediators, including BMI. GHR decreases at 3 months postoperatively alongside increased insulin-like growth factor binding proteins IGFBP1/BP2; plasma GH increased at 1 year. Experimental validation indicates (1) hepatic GHR expression decreases in post-bariatric rats; (2) GHR knockdown in primary hepatocytes decreases gluconeogenic gene expression and glucose production. Thus, RYGB may induce resistance to diabetogenic effects of GH signaling.Trial Registration: Clinicaltrials.gov NCT01073020.

On Reliability Estimation of Lomax Distribution under Adaptive Type-I Progressive Hybrid Censoring Scheme

Bayesian estimates involve the selection of hyper-parameters in the prior distribution. To deal with this issue, the empirical Bayesian and E-Bayesian estimates may be used to overcome this problem. The first one uses the maximum likelihood estimate (MLE) procedure to decide the hyper-parameters; while the second one uses the expectation of the Bayesian estimate taken over the joint prior distribution of the hyper-parameters. This study focuses on establishing the E-Bayesian estimates for the Lomax distribution shape parameter functions by utilizing the Gamma prior of the unknown shape parameter along with three distinctive joint priors of Gamma hyper-parameters based on the square error as well as two asymmetric loss functions. These two asymmetric loss functions include a general entropy and LINEX loss functions. To investigate the effect of the hyper-parameters’ selections, mathematical propositions have been derived for the E-Bayesian estimates of the three shape functions that comprise the identity, reliability and hazard rate functions. Monte Carlo simulation has been performed to compare nine E-Bayesian, three empirical Bayesian and Bayesian estimates and MLEs for any aforementioned functions. Additionally, one simulated and two real data sets from industry life test and medical study are applied for the illustrative purpose. Concluding notes are provided at the end.

Zika virus infection and microcephaly: spatial analysis and socio-environmental determinants in a region of high Aedes aegypti infestation in the Central-West Region of Brazil

Background More than 5 years after the Zika virus (ZIKV) epidemic, Zika infection remains a major concern in regions with high Aedes infestation. The objectives of this study were (i) to identify clusters of ZIKV infection and microcephaly, and/or central nervous system (CNS) alterations associated with congenital infection during the epidemic peak in 2016 and subsequently, in 2017 and 2018; (ii) to measure the non-spatial correlation between ZIKV infection and microcephaly and/or CNS alterations associated with congenital infection; and (iii) to analyse the sociodemographic/economic, health, and environmental determinants associated with the incidence of ZIKV in a region of high infestation by Aedes aegypti in the Central-West Region of Brazil. Methods This ecological study analysed 246 municipalities in the state of Goiás (6.9 million inhabitants). The data were obtained from the Information System for Notifiable Diseases (ZIKV cases) and the Public Health Event Registry (microcephaly and/or CNS alterations associated with congenital infection). Incidence rates and prevalence of ZIKA infection were smoothed by an empirical Bayesian estimator (LEbayes), producing the local empirical Bayesian rate (LEBR). In the spatial analysis, ZIKV infection and microcephaly cases were georeferenced by the municipality of residence for 2016 and grouped for 2017 and 2018. Global Moran's I and the Hot Spot Analysis tool (Getis-Ord Gi* statistics) were used to analyse the spatial autocorrelation and clusters of ZIKV infection and microcephaly, respectively. A generalised linear model from the Poisson family was used to assess the association between ecological determinants and the smoothing incidence rate of ZIKV infection. Results A total of 9892 cases of acute ZIKV infection and 121 cases of microcephaly were confirmed. The mean LEBR of the ZIKV infection in the 246 municipalities was 22.3 cases/100,000 inhabitants in 2016, and 10.3 cases/100,000 inhabitants in 2017 and 2018. The LEBR of the prevalence rate of microcephaly and/or CNS alterations associated with congenital infection was 7 cases/10,000 live births in 2016 and 2 cases/10,000 live births during 2017–2018. Hotspots of ZIKV infection and microcephaly cases were identified in the capital and neighbouring municipalities in 2016, with new clusters in the following years. In a multiple regression Poisson analysis, ZIKV infection was associated with higher population density, the incidence of dengue, Aedes larvae infestation index, and average rainfall. The important determinant of ZIKV infection incidence reduction was the increase in households attended by endemic disease control agents. Conclusions Our analyses were able to capture, in a more granular way, aspects that make it possible to inform public managers of the sentinel areas identified in the post-epidemic hotspots.

Urban Road Accident Black Spot Identification and Classification Approach: A Novel Grey Verhuls–Empirical Bayesian Combination Method

The identification and classification of accident black spots on urban roads is a key element of road safety research. To solve the problems caused by the randomness of accident occurrences and the unclear classification of accident black spots by the traditional model, we propose a method that can quickly identify and classify accident black spots on urban roads: a combined grey Verhuls–Empirical Bayesian method. The grey Verhuls model is used to obtain the predicted/expected numbers of accidents at accident hazard locations, and the empirical Bayesian approach is used to derive two accident black spot discriminators, a safety improvement space and a safety index (SI), and to classify the black spots into two, three, four and five levels according to the range of the SI. Finally, we validate this combined method on examples. High-quality and high-accuracy data are obtained from the accident collection records of the Ningbo Jiangbei District from March to December 2020, accounting for 90.55% of the actual police incidents during this period. The results show that the combined grey Verhuls–Empirical Bayesian method can identify accident black spots quickly and accurately due to the consideration of accident information from the same types of accident locations. The accident black point classification results show that the five-level rating of accident black points is most reasonable. Our study provides a new idea for accident black spot identification and a feasible method for accident black spot risk level classification.

Prevalence of Obesity and Overweight Stratified by Age Group of the 34 Provinces in Indonesia: Local Empirical Bayesian Estimation

Background: Indonesia is one of the countries most affected by a high prevalence of obesity and overweight among other lower-middle-income countries. Most studies, however, are not concerned with the geographical dependence of the obesity and prevalence of overweight. The purpose of this study was to describe the prevalence of obesity and overweight stratified by age group among 34 provinces in Indonesia. Methods: This study used data from the Report on Basic Health Research 2018 of the Indonesian Ministry of Health. We calculated data to find the estimated prevalence of obesity and overweight in each age group across 34 provinces (n=427,675) by using the local empirical Bayesian estimation. The mapping was used to show the phenomenon of obesity and overweight in different age groups and areas. Results: In each group, the overweight prevalence was much higher than the obesity prevalence. Age group 18+ had the highest prevalence of obesity and overweight (obesity: 10.4–30.1%, overweight: 19.2–46.3%) followed by the age group of 5–12 years (obesity: 2.4–15.0%, overweight: 6.1–30.3%). The province with the highest prevalence of obesity among the age group of 5–12 years was Papua province and for the 18+ age group it was North Sulawesi province. The province with the largest number of obesity and overweight was West Java province for each group. Conclusion: Prevalence of obesity and overweight differed by age group and by 34 provinces.