Role of Mothers\u27 Nutritional Knowledge, Nutritional Factors on the School Performance

A cross sectional study was carried out to investigate the effects of mothers\u27 nutritional knowledge, health and nutritional factors and socio-economic parameters on school performance among class five students of University Laboratory School, Dhaka. All of the eighty students were selected for this study. This study found there is a strong relationship between mother\u27s knowledge score and school performance. It was found that mothers\u27 knowledge score was responsible for 91.1 percent change in school performance. The mean BMI of the mothers was 20.44. We found that the school performance measured by class roll number of the students is significantly related with mothers BMI. There was an imperfect negative association between socio-economic parameters and school performance. But the relationship between the school performances with socio-economic parameters was strongly significant. This study also observed the relationship between Individual Dietary Diversity Score (IDDS) of respondent and marks achieved in class 4 final exam. It is alarming that consumption percentage were low for eggs (30) and milk and milk products (37.5), but majority of the students who consumed milk and milk products (63.3%) and eggs (66.7%) got the highest marks

Natural convection in a porous trapezoidal enclosure with magneto-hydrodynamic effect

The effects of magnetic force, acting vertically downward on natural convection within a porous trapezoidal enclosure saturated with an electrically conducting fluid have been investigated numerically. The bottom wall of the enclosure is subjected to a constant hot temperature and the top wall experiences a constant cold temperature whereas the remaining sidewalls are kept adiabatic. The physical problems are represented mathematically by different sets of governing equations along with the corresponding boundary conditions. By using Galerkin weighted residual method of finite element formulation, the non-dimensional governing equations are discritized. For natural convection in a porous medium the influential parameters are the modified Rayleigh number Ram, the fluid Rayleigh number Raf , the inclination angle of the sidewalls of the cavity γ, the rotational angle of the enclosure Φ and the Hartmann number Ha, through which different thermo-fluid characteristics inside the enclosure are obtained. In the present study, the obtained results are presented in terms of streamlines, isotherms and average Nusselt number along the hot wall. The result shows that with increasing Ha, the diffusive heat transfer become prominent even though the modified Rayleigh number increases. Optimum heat transfer rate is obtained at higher values of Ram in the absence of magnetic force

Using Deep Learning Model to Identify Iron Chlorosis in Plants

Iron deficiency in plants causes iron chlorosis which frequently occurs in soils that are alkaline (pH greater than 7.0) and that contain lime. This deficiency turns affected plant leaves to yellow, or with brown edges in advanced stages. The goal of this research is to use the deep learning model to identify a nutrient deficiency in plant leaves and perform soil analysis to identify the cause of the deficiency. Two pre-trained deep learning models, Single Shot Detector (SSD) MobileNet v2 and EfficientDet D0, are used to complete this task via transfer learning. This research also contrasts the architecture and performance of the models at each stage and freezes the models for future use. Classification accuracy ranged from 93% to 98% for the SSD Mobilenet v2 model. Although this model took less time to process, its accuracy level was lower. While the EfficientDet D0 model required more processing time, it provided very high classification accuracy for the photos, ranging from 87% to 98.4%. These findings lead to the conclusion that both models are useful for real-time classifications, however, the EfficientDet D0 model may perform significantly better

Prevalence and factors associated with depression and anxiety among first-year university students in Bangladesh: a cross-sectional study

University students, especially those in their first year, not only face challenges related to their personal life but also to their academic performance. Such unfavorable conditions predispose them to psychological illnesses such as depression and anxiety. The present study surveyed first-year undergraduate students to assess the prevalence of depression and anxiety and their potential underlying reasons. A cross-sectional questionnaire was conducted with 400 first-year university students aged between 18 and 23 years residing at Jahangirnagar University (in Bangladesh). After providing informed consent, the participants completed a survey examining socio-demographic variables and behavioral variables, along with the PHQ-9 scale, and the GAD-7 scale. Binary logistic regression was performed to determine the significant associations between the variables under examination. The prevalence rates of moderate to extremely severe levels of depression and anxiety were 69.5% and 61%, respectively, with no significant gender differences. The main risk factors for depression were unsatisfactory sleep quality and lack of physical exercise. The main risk factor for anxiety was excessive internet use. The prevalence of depression and anxiety levels among first-year university students was high in the present study and appears to confirm the notion that first-year university students are a vulnerable group for mental illnesses. The findings suggest the need for intervention programs, alongside adequate and appropriate supportive services for Bangladeshi university students

Embedded programmable web-based ECG monitoring & detection system using a fast algorithm

This paper presents the design of a complete portable package for a low cost embedded programmable ECG measurement and monitoring system implemented by a fast algorithm in detecting ECG characteristics points. This proposed system is expected to monitor the electrical activity of heart of the patient under critical care more conveniently and accurately for diagnosing which can be interfaced with computer to bring it under a network system widely for the doctor to monitor the patient's condition sitting in his own office without being physically present near to the patient's bed

Fatigue crack analysis of ferrite material by acoustic emission technique

Among various methods of Non-destructive techniques (NDT), analysis using released acoustic emission (AE) waves due to crack propagation is very effective due to its dynamic monitoring features. In fragmentation theory for AE there are some proportional relationships among the AE parameters i.e. AE event, AE energy, area and volume of cracks etc., which are calculated from the released AE waves from the dynamic crack inside any material. The necessity of calculating the fractal dimension has been found in such relationships and the value is emphasized for determining the geometry of the irregularity in crack surface and crack volume. In this paper a novel approach for evaluating that value based on image processing by MATLAB is proposed. The images of the cracks during propagation are preserved and utilized to find out the fractal dimension for analyzing the crack propagation characteristics. The AE energy is also estimated from the received AE waves. The positioning of the sensors plays a great impact on this calculation. Finally, the theoretical proportionality relations of AE parameters are interpreted experimentally during crack propagation behavior in ferrite cast iron under fatigue loading

Microwave breast imaging using compressed sensing approach of iteratively corrected delay multiply and sum beamforming

Microwave imaging (MI) is a consistent health monitoring technique that can play a vital role in diagnosing anomalies in the breast. The reliability of biomedical imaging diagnosis is substantially dependent on the imaging algorithm. Widely used delay and sum (DAS)-based diagnosis algorithms suffer from some significant drawbacks. The delay multiply and sum (DMAS) is an improved method and has benefits over DAS in terms of greater contrast and better resolution. However, the main drawback of DMAS is its excessive computational complexity. This paper presents a compressed sensing (CS) approach of iteratively corrected DMAS (CS-ICDMAS) beamforming that reduces the channel calculation and computation time while maintaining image quality. The array setup for acquiring data comprised 16 Vivaldi antennas with a bandwidth of 2.70-11.20 GHz. The power of all the channels was calculated and low power channels were eliminated based on the compression factor. The algorithm involves data-independent techniques that eliminate multiple reflections. This can generate results similar to the uncompressed variants in a significantly lower time which is essential for real-time applications. This paper also investigates the experimental data that prove the enhanced performance of the algorithm. 2021 by the authors. Licensee MDPI, Basel, Switzerland.Acknowledgments: This work was supported by Grant NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar and the internal grant of Qatar University QUST-1-CENG-2021-6 and the claims made herein are solely the responsibility of the authors. This work was supported by the Ministry of Higher Education of Malaysia (MOHE), grant code No. FRGS/1/2018/TK04/UKM/01/3. This work was supported by Grant NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar and the claims made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.Scopu

Predicting Temperature of Major Cities Using Machine Learning and Deep Learning

Currently, the issue that concerns the world leaders most is climate change for its effect on agriculture, environment and economies of daily life. So, to combat this, temperature prediction with strong accuracy is vital. So far, the most effective widely used measure for such forecasting is Numerical weather prediction (NWP) which is a mathematical model that needs broad data from different applications to make predictions. This expensive, time and labor consuming work can be minimized through making such predictions using Machine learning algorithms. Using the database made by University of Dayton which consists the change of temperature in major cities we used the Time Series Analysis method where we use LSTM for the purpose of turning existing data into a tool for future prediction. LSTM takes the long-term data as well as any short-term exceptions or anomalies that may have occurred and calculates trend, seasonality and the stationarity of a data. By using models such as ARIMA, SARIMA, Prophet with the concept of RNN and LSTM we can, filter out any abnormalities, preprocess the data compare it with previous trends and make a prediction of future trends. Also, seasonality and stationarity help us analyze the reoccurrence or repeat over one year variable and removes the constrain of time in which the data was dependent so see the general changes that are predicted. By doing so we managed to make prediction of the temperature of different cities during any time in future based on available data and built a method of accurate prediction. This document contains our methodology for being able to make such predictions.Comment: 15 pages, 31 figure

Detecting Natural Language Biases with Prompt-based Learning

In this project, we want to explore the newly emerging field of prompt engineering and apply it to the downstream task of detecting LM biases. More concretely, we explore how to design prompts that can indicate 4 different types of biases: (1) gender, (2) race, (3) sexual orientation, and (4) religion-based. Within our project, we experiment with different manually crafted prompts that can draw out the subtle biases that may be present in the language model. We apply these prompts to multiple variations of popular and well-recognized models: BERT, RoBERTa, and T5 to evaluate their biases. We provide a comparative analysis of these models and assess them using a two-fold method: use human judgment to decide whether model predictions are biased and utilize model-level judgment (through further prompts) to understand if a model can self-diagnose the biases of its own prediction