Vehicle Dynamics, Lateral Forces, Roll Angle, Tire Wear and Road Profile States Estimation - A Review

Estimation of vehicle dynamics, tire wear, and road profile are indispensable prefaces in the development of automobile manufacturing due to the growing demands for vehicle safety, stability, and intelligent control, economic and environmental protection. Thus, vehicle state estimation approaches have captured the great interest of researchers because of the intricacy of vehicle dynamics and stability control systems. Over the last few decades, great enhancement has been accomplished in the theory and experiments for the development of these estimation states. This article provides a comprehensive review of recent advances in vehicle dynamics, tire wear, and road profile estimations. Most relevant and significant models have been reviewed in relation to the vehicle dynamics, roll angle, tire wear, and road profile states. Finally, some suggestions have been pointed out for enhancing the performance of the vehicle dynamics models

Potencies of Justicia adhatoda L. for its possible phytotoxic activity

The phytotoxic effects of Justicia adhatoda L. were investigated on cauliflower, broccoli, tomato, foxtail millet and barnyard grass. The experiments were carried out under laboratory and in pot experiments. Six different aqueous methanol extract concentrations (control, 0.001, 0.003, 0.01, 0.03 and 0.1 g DW equivalent mL-1 extract) were tested in the laboratory and six aqueous extract concentrations (control, 1.0, 2.0, 3.0, 4.0 and 5.0 g DW mL-1 extract equivalent) were evaluated in the pot experiment. Results showed a reduction in germination and growth (shoot length, root length and biomass weight) at higher extract concentration compared to control. The leaf extracts from J. adhatoda showed that the foxtail millet and barnyard grass are germinating below 50 % both in the laboratory condition and in the pot experiment at their maximum concentration. When maximum extracts have been applied, we have found less than 0.5 cm of shoot and root of foxtail millet and barnyard grass. Maximum dry weight reduction was observed in foxtail millet and barnyard grass at the same concentration. The findings show that J. adhatoda may have phytotoxic potential and thus contains phytotoxins. Therefore, J. adhatoda can be used in sustainable crop production as a mulch or soil additive to suppress weeds

Development and validation of a modified QuEChERS method coupled with LC-MS/MS for simultaneous determination of difenoconazole, dimethoate, pymetrozine, and chlorantraniliprole in brinjal collected from fields and markets places to assess human health risk

An effective and sensitive analytical method was developed to quantify the most common pesticide residues (difenoconazole, dimethoate, pymetrozine, and chlorantraniliprole) used for brinjal cultivation in Bangladesh. The quantification of the analytes was done using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The samples were extracted using a modified QuEChERS method and followed by purification with dispersive solid phase extraction (d-SPE) sorbents (PSA, GCB, and C18). Matrix-matched calibration with a regression coefficient R2 ≥ 0.9964 were used to minimize the brinjal matrix effect. The method was validated in quintuple (n = 5) at five different spiked levels (8–400 μg/kg) having recoveries in the range of 70.3–113.2% with relative standard deviations RSDs ≤6.8%, limits of detection (LOD) and limits of quantification (LOQ) was in the range of 0.15–0.66 μg/kg and 0.4–2.0 μg/kg, respectively, for the four analytes. A total 100 samples (50 samples directly from fields of Jessore district, Bangladesh and 50 samples from local market of Dhaka, Bangladesh) were collected to analyse the pesticides residue. The result showed that pesticides residue was found in both the field and market collected samples, 54% and 38%, respectively. The overall mean residue levels of four pesticides in field samples were significantly higher than those of market samples. Moreover, 20% of the field samples and 10% of the market samples had dimethoate residues, which were the most abundant among the four analytes and it ranged from 0.017 to 0.252 mg/kg. In terms of health risk assessments, dimethoate showed the highest estimated daily intake (EDI) and hazard quotient (HQ) values that are 3.02 × 10−5 mg/kg/day and 1.51%, respectively, in field samples. Till now, there have been no regulations or guidelines for the maximum admissible pesticide residue in Bangladesh. Therefore, the above findings will be an initial step for the regulatory authorities of Bangladesh to implement regulations and guidelines for pesticide usage

Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL

Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL

The Influence of Knowledge Sharing on Sustainable Performance: A Moderated Mediation Study

The past few decades showed inadequate discussion of the impact of employees’ knowledge sharing and its diffusion on advancing banks’ long-term sustainability. The objective of the study is to examine the role of employees’ knowledge sharing on the sustainable performance of the banks operating in Bangladesh. Furthermore, this study tested the “moderated mediation model” of knowledge hiding and employees’ ambidexterity on the association above. The researchers applied the deductive reasoning method through the application of quantitative techniques, using structural equation modeling. Finally, 287 respondents from different banks were chosen through a self-administered questionnaire survey in the capital city of Dhaka. The findings indicated that all the predictor variables significantly explain the outcome variable, except the influence of knowledge sharing. Mediation analysis showed that employees’ ambidexterity mediated the association between knowledge sharing and sustainable performance. Surprisingly, moderation analysis revealed that the influence of knowledge sharing on employees’ ambidexterity is not affected by knowledge hiding. This study adds to the existing literature by demonstrating the importance of knowledge hiding, along with explaining how knowledge sharing can motivate and influence employees to achieve sustainable performances. In addition, the main contribution of this study is to advance knowledge and add values in the forms of knowledge creation, preservation, and dissemination among practitioners, banking professionals, and academics for utilizing their domain-specific areas to increase long-term sustainability