Factors associated with gestational diabetes among women registered at secondary hospitals in Karachi, Pakistan

Introduction: Few things are proven, there are modifiable and non-modifiable factors that could impact on the health of pregnant women who have Gestational Diabetes Millitus (GDM). However, case control studies are lacking that explore the modifiable factors and identify which modifiable factors are associated with GDM.Purpose: The aim of this study was to identify the modifiable associated risk factors of GDM among women at 32 to 40 weeks of gestation.Methodology: A case-control study design was conducted at secondary hospitals for women and children in Karachi, Pakistan. The data were collected from 100 cases and same number of controls, through a structured questionnaire. The data was analyzed by means of descriptive and inferential statistics, using Stata(TM) Version 12.0.Results: The majority of the study participants had a past history of GDM and had a Body Mass Index (BMI) greater than 25kg/m2. Most of the participants were graduates or post-graduates. The results of the study identified that the modifiable factors which were significantly associated with GDM included household physical activities, transportation related physical activities, recreational activities (i.e., walking, number of stairs climbed daily), use of fruits and eggs, and night time sleep duration. Only 12% of the participants reported that they spent greater than or equal to six hours in recreational physical activities. About one-third (35%) of the participants reported sleeping more than six hours a night.Conclusion: The present study identified the association of some modifiable factors with GDM. There is a dire need to develop preventive strategies that can promote a healthy lifestyle among pregnant women. Attention should be given to increasing physical activity, promoting a healthy diet, and having proper sleep. In light of the current study findings, a study with a large sample size, including multi-center settings, is needed

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence

Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%

Classification for ammonia in water by specific concentration using artificial neural network (ANN)

Water pollution caused by poor management of waste water release or dump need to be monitored. This paper present to monitor on ammonia release by industry which can caused death to plant worker. This monitoring was a combination between E-Nose and Classification techniques which is ANN. ANN the most common retrieval method that used in industry nowadays. Furthermore, ANN classification successful to classify 100% accuracy for specific concentration of Ammonia which is using Lavernberg-Marquardt (LM) algorithm with supervised learning and fast convergence Back Propagation (BP) method

The investigation of meat classification based on significant authentication features using odor-profile intelligent signal processing approach

Meat is the flesh or another edible part of an animal and includes uncooked meat prepared or otherwise but does not include meat products. Meat is the most valuable livestock product and for many people serves as their first-choice source of animal protein. Fraud meat products are causing annoyance to consumer’s, especially Muslim users. There are many cases that have been brought to the public attention regarding fraud on meat products such as incidences of meat that is labeled, certified or sold as halal may not be so. This project sets out to identify two types of different meat which is beef meat and pork meat. Therefore, the significant authentication features using odor-profile intelligent signal processing approach which is Electronic Nose (E-nose) was used to measure odor-profile from meat. E-nose is one of the chemical-based sensor arrays instruments which have a capability to measure odor-profile based sample data. The data measurement of odor-profile for different meat samples was collected based on the designated experimental procedure. Then, the normalized and their unique features were extracted using statistical tools for feature extraction. The input of features will be inserting into Case-Based Reasoning (CBR) library and intelligently classified using CBR method and will be validated based specific performance measure. From the CBR performance measures result, it is observed that the classification of CBR is 100%

Temporal Monitoring of the Soil Freeze-Thaw Cycles over a Snow-Covered Surface by Using Air-Launched Ground-Penetrating Radar

We tested an off-ground ground-penetrating radar (GPR) system at a fixed location over a bare agricultural field to monitor the soil freeze-thaw cycles over a snow-covered surface. The GPR system consisted of a monostatic horn antenna combined with a vector network analyzer, providing an ultra-wideband stepped-frequency continuous-wave radar. An antenna calibration experiment was performed to filter antenna and back scattered effects from the raw GPR data. Near the GPR setup, sensors were installed in the soil to monitor the dynamics of soil temperature and dielectric permittivity at different depths. The soil permittivity was retrieved via inversion of time domain GPR data focused on the surface reflection. Significant effects of soil dynamics were observed in the time-lapse GPR, temperature and dielectric permittivity measurements. In particular, five freeze and thaw events were clearly detectable, indicating that the GPR signals respond to the contrast between the dielectric permittivity of frozen and thawed soil. The GPR-derived permittivity was in good agreement with sensor observations. Overall, the off-ground nature of the GPR system permits non-invasive time-lapse observation of the soil freeze-thaw dynamics without disturbing the structure of the snow cover. The proposed method shows promise for the real-time mapping and monitoring of the shallow frozen layer at the field scale

Exploring designers’ cognitive abilities in the concept product design phase through traditional and digitally-mediated design environments

DESIGN2024 18th International Design Conference, May, 20-23, 2024, Cavtat, Dubrovnik, Croatia202405 bcchVersion of RecordRGCPublishedC

Thermal radiation effect on a mixed convection flow and heat transfer of the Williamson fluid past an exponentially shrinking permeable sheet with a convective boundary condition

The thermal radiation effect on a steady mixed convective flow with heat transfer of a nonlinear (non-Newtonian) Williamson fluid past an exponentially shrinking porous sheet with a convective boundary condition is investigated numerically. In this study, both an assisting flow and an opposing flow are considered. The governing equations are converted into nonlinear ordinary differential equations by using a suitable transformation. A numerical solution of the problem is obtained by using the Matlab software package for different values of the governing parameters. The results show that dual nonsimilar solutions exist for the opposing flow, whereas the solution for the assisting flow is unique. It is also observed that the dual nonsimilar solutions exist only if a certain amount of mass suction is applied through the porous sheet, which depends on the Williamson parameter, convective parameter, and radiation parameter

Dual solutions of an unsteady magnetohydrodynamic stagnation-point flow of a nanofluid with heat and mass transfer in the presence of thermophoresis

The unsteady two-dimensional magnetohydrodynamic stagnation point flow of a nanofluid with thermophoresis effect is investigated numerically. The technique of similarity transformation is implemented to obtain the self-similar ordinary differential equations and then the self-similar equations are solved numerically using shooting method. This analysis explores the conditions of the existence, non-existence, uniqueness, and duality of the solutions of self-similar equations numerically. Dual solutions of velocity, temperature and concentration profiles are reported for different values of the each parameter involved for two types of nanoparticles, namely copper (Cu) and gold (Au) in the water-based fluid. It is found that the dual solutions exist for negative values of unsteady parameter A, whereas for positive values of unsteady parameter, the solution is unique. The results also indicate that the nanoparticle volume fraction reduces the skin friction coefficient, the heat transfer rate as well as mass transfer rate. Further, due to increase of thermophoresis parameter, the concentration inside the boundary layer reduces and the mass transfer rate enhances. In addition, to validate the present numerical results, comparison with published results is made and found to be in excellent agreement