Udayanath College of Science and Technology

Background:Household air pollution from biomass cookstoves is a major concern in low- and middle-income countries because it may be linked with increasing rates of metabolic disorders such as diabetes. We assessed cross-sectional associations between household air pollution concentrations and glycated hemoglobin (HbA1c) levels. Methods:We analyzed data from 346 women 40 to < 80 years of age who cooked with biomass fuel and were enrolled in the Household Air Pollution Intervention Network (HAPIN) Trial in Guatemala, India, Peru, and Rwanda. We explored associations of 24-h average personal exposure to fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter ( PM 2.5 )], black carbon (BC), and carbon monoxide (CO) with HbA1c through individual pollutant linear models adjusted for potential confounders. We examined the effect modification of age, body mass index (BMI), and research site on the associations. Results:We did not observe evidence of associations between HbA1c (percentage points) and 1-unit increases in log-transformed PM 2.5 [ - 0.07 ; 95% confidence interval (CI): - 0.18 , 0.05], BC (0.01; 95% CI: - 0.15 , 0.13), or CO (0.07; 95% CI: - 0.24 , 0.10). Effect modification of the BC associations with HbA1c was observed for BMI and research site. An association in the hypothesized direction was observed among women with high BMI ( ≥ 25   kg / m 2 ): 0.13 (95% CI: - 0.06 , 0.31) compared with low BMI ( < 25   kg / m 2 ): - 0.17 (95% CI: - 0.38 , 0.04; p interaction = 0.04 ). In the Guatemala research site, there was an association in the hypothesized direction with HbA1c and log-transformed BC (0.36; 95% CI: 0.03, 0.70) that was countered by an association in the opposite direction as that hypothesized for the India site ( - 0.21 ; 95% CI: - 0.45 , 0.02) and associations consistent with the null association in the Peru and Rwanda sites ( p interaction = 0.05 ). No other evidence of effect modification was observed. Conclusions:Evidence suggests a need for further research to better understand household air pollution's influence on HbA1c, with particular attention on potential effect modifiers. https://doi.org/10.1289/JHP1053.

Background:Exposure to household air pollution from the combustion of solid fuels is a leading risk factor for death and disease in low- and middle-income countries, where cleaner cooking and lighting options are often unavailable. Few studies have measured personal exposure during pregnancy, a sensitive period of development, particularly in Africa. Objective:We aimed to characterize exposure during early to midpregnancy among women in Rwanda and to assess predictors of personal exposure, including stove and fuel type, cooking behaviors, housing conditions, sociodemographic characteristics, and other potential sources of exposure. Methods:We assessed 24-h baseline personal exposure data among 798 pregnant women in the Household Air Pollution Intervention Network (HAPIN) trial in Rwanda, including 717 with fine particulate matter ( PM 2.5 ), 569 with black carbon (BC), and 716 with carbon monoxide (CO) samples. Best subsets regression identified key predictors of personal PM 2.5 , BC, and CO exposure, defined by maximizing adjusted R 2 values and minimizing prediction errors (Mallow's CP and the Bayesian information criterion). Results:The 24-h median concentrations at baseline were 88.9   μ g / m 3 [ interquartile range  ( IQR ) = 85.0 ], 10.9   μ g   / m 3 ( IQR = 7.6), and 1.12  ppm ( IQR = 1.9) for PM 2.5 , BC, and CO, respectively. Households using kerosene as a primary lighting source had higher PM 2.5 levels ( median = 116   μ g / m 3 , IQR = 107) than those using electricity ( 64   μ g / m 3 , IQR = 69). Women in households with modified biomass stoves with a chimney had lower median values ( 48   μ g / m 3 , IQR = 52) for PM 2.5 , compared with those in households using open fires ( 113   μ g / m 3 , IQR = 74) and other traditional stove types ( 155   μ g / m 3 , IQR = 43) that yielded the highest values. Consensus models from the best subsets' regression explained 26% of the variation in PM 2.5 , 36% in BC, and 31% in CO concentrations. Conclusions:Based on a unique and large dataset describing personal exposure among pregnant women in rural Rwanda, lighting and cooking practices described some variability in household PM 2.5 concentrations, but overall, substantial unexplained variability remained. https://doi.org/10.1289/JHP1049.

This study assesses groundwater Quality and subsurface lithology in a residential area built on a reclaimed municipal waste dumpsite in Enugu, Nigeria. It addresses the potential environmental and health impacts of such sites, aligning with SDG 6 (Clean Water and Sanitation) and SDG 11 (Sustainable Cities and Communities). The objectives include groundwater Quality evaluation, subsurface characterization, and groundwater suitability assessment. Water samples from hand-dug wells were collected during both rainy and dry seasons over 2 years. Electrical Resistivity Tomography (ERT) was employed to identify subsurface leachate pathways, while Atomic Absorption Spectrometry (AAS) was used to analyze eight heavy metals and 13 physicochemical parameters for groundwater quality index (WQI) calculation. ERT results revealed zones of low resistivity (0.5–7 Ωm) at depths of 5–16 m, indicating leachate presence. These zones were bordered by moderately resistive lateritic materials (58–199 Ωm). Most parameters fell within WHO permissible limits, except for cadmium (dry, 0.02 mg/L; wet, 0.17 mg/L), cobalt (dry, 0.02 mg/L; wet, 0.04 mg/L), temperature (dry, 28 °C; wet, 25 °C), and TSS (dry, 290 mg/L; wet, 110 mg/L). During the dry season, lead (0.15 mg/L) and total solids (TS) (720 mg/L) exceeded acceptable limits. pH values were slightly acidic, ranging from 5.0 (dry) to 5.5 (wet). The WQI scores of 987 (dry) and 3005 (wet) indicated high contamination in both seasons. Statistical analysis showed no significant seasonal variation in contaminant levels. Overall, geophysical and laboratory findings confirm that the groundwater is highly contaminated and poses serious health risks to residents.

Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

The 3-factor authentication control system is aim at providing maximum security in company’s stores, special utilities and premises where valuables are stored. A developed security system with automatic sensing was introduced using the integration of Radio Frequency Identification (RFID) card tagging system, fingerprint sensing biometric security system and Global System for Mobile communication (GSM) to send token generated by the system to the authorized user’s access cell-phone. The accuracy of the system was measured virtually in a simulation screen. Results and performance evaluation test shows over 98% accuracy in access granted to access denial, for both right and wrong RFID card and correct to incorrect fingerprint scanning respectively. This consequently, shows satisfaction in the performance of the system test, which proves the validity and efficiency of the system by ensuring full integrity of the door lock in any premises as the case may be.