Estimated relative permittivity of contaminated laterite soil: An empirical model for GPR waves

Estimated relative permittivity performed on soil is essential for forecasting the performance of Ground Penetrating Radar (GPR) in an in-depth manner. This study investigated and verified the empirical relationship model between relative permittivity and volumetric water content in soil to predict the relative permittivity of contaminated laterite soil. In this study, a 24-hour measurement involving 800 MHz shielded antenna GPR was carried out in a concrete simulation field tank filled with Terap Red soil (1.5 m x 2.6 m x 1.5 m) at UiTM Perlis, Malaysia. Embedded moisture content probe was simultaneously measured to monitor the response of volumetric water content in contaminated soil in order to formulate an empirical relationship between relative permittivity and moisture content. The GPR data were pre-processed and filtered with Reflexw 7.5, while regression analysis was performed to evaluate the empirical relationship model. The model outcomes were retrieved from a number of cross-validation schemes, including correlation analysis (R2), root mean square error (RMSE), and calibrated Agilent Technologies Automated Vector Analyser (VNA). A third-order polynomial for analysis of variance (ANOVA) best fitted the model with positively strong correlation (R2=0.989, N=24, P < 0.01) and RMSE 0.003< RMSEpredicted < 0.19. Verification of the proposed model using calibrated VNA displayed exceptional agreement between 0.06% comparisons

Retrieving and modelling dielectric permittivity of diesel contamination in Terap Red and sandy soil using ground penetrating radar

Ground Penetrating Radar (GPR) is a non-destructive, full-wave electromagnetic (EM) measurement tool for quantitative imaging to describe dielectric permeability distributions. It is an efficient technique for detecting diesel contamination in soil tomography problems. However, dielectric permittivity relies entirely on variance moisture content facilitated by diesel fuel reaction soil, which determines GPR velocity. Difficulties in interpreting GPR reflection configuration are complex qualitative features limited to noisy or nonlinear relations problems. Consequently, positioning and depth determination would be misleading due to severe polarization and velocity mismatch in traveling-wave typically in Terap Red soil as silty-clay soil. Therefore, this study aims to determine the mathematical model for dielectric permittivity prediction and investigate the GPR signal segmentation algorithm model to map the diesel contamination plume in Terap Red soil. The calibration icon function of the GPR signal was quantified by dielectric permittivity prediction. The research approach was divided into 4 phases. The investigation commenced with an evaluation of the GPR propagation signal from a simulated diesel contamination plume of Terap Red and sandy soils concerning the results of geotechnical measurements using BS 1337: 1992. Next, the dielectric permittivity using the GPR velocity in modeling the empirical relationship between dielectric permittivity and moisture content was determined using statistical analysis. Additionally, cross-validation was performed using existing literature, Vector Network Analyzer (VNA) and in-situ measurements before the GPR signal images were segmented and categorized using a Support Vector Machine (SVM). Finally, ten-fold cross-validation and Logistic Regression (LR) classification were used to evaluate the spatial distribution classification mapping of GPR signals. The result shows the best prediction on Terap Red soil from third-order polynomial using ANOVA yielded a strong positive correlation (R2=0.9892, N=24, P <0.05) and a standard error of 0.076. The accuracy of dielectric permittivity in terms of root mean square error (RMSE) and mean absolute error (MAE) was obtained at 9.772E-14 and 0.049, respectively. The best-fitting relationship does exhibit some degree of textural bias that should be considered in the choice of petrophysical relationship with uncertainty mean differences via VNA validation for Terap Red and sandy soil were only 2.706 % and 1.985 %, compared to over 3.608% and 15.990 % for the existing model. The accuracy of the spatial distribution classification map generated by the SVM classifier is encouraging, with RMSE of 0.139, kappa statistics of 0.888, and correct instances classified (CIC) of nearly 100 % for both SVM and LR. In conclusion, the study results on dielectric permittivity prediction of contaminated soils for Terap Red and sandy soils indicate that the empirical relationship model is only applicable to specific soils with similar properties. Additional supervised data is recommended to achieve better classification outputs

The assessment of relative permittivity on diesel vapour in the moisture content of Terap Red soil by ground penetrating radar

In a common agriculture resource, soil contamination monitoring is a prominent area of study. Nowadays, it is crucial to provide a database for the interpretation of ground penetrating radar (GPR) field data in monitoring soil contamination, such as diesel scatter migration. This study aims to assess the association between permittivity properties and soil water content (θw) for diesel contamination in Terap Red soil, which is classified as lateritic soil. Terap Red soil is an agro potential soil and available in more than 40% of distribution areas in Northern Malaysia (Agro-based State). In this research, 800 MHz shielded antenna GPR was applied for 24 hour measurement in a concrete simulation field tank, which was filled with Terap Red soil (1.5 m x 2.6 m x 1.5 m) located at Universiti Teknologi MARA (UiTM) Perlis, Malaysia. Embedded moisture content probe was simultaneously measured to monitor the response of volumetric water content in the contaminated soil. The GPR data were pre-processed and filtered by Reflexw 7.5. The calibrated Agilent Technologies Automated Vector Analyser (VNA) was used to verify the independent relative permittivity value from GPR. As a result, the evaluation of velocities and reflection of GPR data were influenced by the presence of diesel and contaminated vapour. A positive and significant correlation was obtained between relative permittivity and moisture content in the diesel-contaminated soil. In addition, a positive and strong linear regression analysis was also found between relative permittivity and moisture content. This analysis included an accurate total difference of root mean square error (RMSE) difference, which amounted to 0.04, with calibrated dielectric permittivity

Abstracts of the International Conference on Business, Accounting and Finance 2023: Embracing New Business Paradigm Shifts

This book presents the abstracts of the selected contributions to the second International Academic Conference 2023, held on 25-26 February 2023 by the International University of Malaya-Wales (IUMW), Kuala Lumpur, Malaysia. IAC 2023 is the coming together of researchers and industry. It’s a place to gather and share groundbreaking ideas, discoveries, and experiences on a variety of thought leadership topics covered under this year’s conference theme, “Embracing New Business Paradigm Shifts". Conference Title: International Academic Conference 2023Conference Acronym: IAC 2023Conference Theme: Embracing New Business Paradigm ShiftsConference Date: 25-26 February 2023Conference Venue: IUMW, MalaysiaConference Organizer: International University of Malaya-Wales, Kuala Lumpur, Malaysi

Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients

International audienceThe aim of this study was to estimate the incidence of COVID-19 disease in the French national population of dialysis patients, their course of illness and to identify the risk factors associated with mortality. Our study included all patients on dialysis recorded in the French REIN Registry in April 2020. Clinical characteristics at last follow-up and the evolution of COVID-19 illness severity over time were recorded for diagnosed cases (either suspicious clinical symptoms, characteristic signs on the chest scan or a positive reverse transcription polymerase chain reaction) for SARS-CoV-2. A total of 1,621 infected patients were reported on the REIN registry from March 16th, 2020 to May 4th, 2020. Of these, 344 died. The prevalence of COVID-19 patients varied from less than 1% to 10% between regions. The probability of being a case was higher in males, patients with diabetes, those in need of assistance for transfer or treated at a self-care unit. Dialysis at home was associated with a lower probability of being infected as was being a smoker, a former smoker, having an active malignancy, or peripheral vascular disease. Mortality in diagnosed cases (21%) was associated with the same causes as in the general population. Higher age, hypoalbuminemia and the presence of an ischemic heart disease were statistically independently associated with a higher risk of death. Being treated at a selfcare unit was associated with a lower risk. Thus, our study showed a relatively low frequency of COVID-19 among dialysis patients contrary to what might have been assumed