Application of FAO-CROPWAT software for modelling irrigation schedule of rice in Rwanda

The overall objectives of the present research are the study of weather, soil, discharges in the irrigation channels crop water requirement and irrigation scheduling of rice in Muvumba-8 marshland of Nyagatare district in Rwanda. The specific objective is to study the crop water requirement and irrigation scheduling of rice in the marshland. The average infiltration rate of the soil in the experimental field was 12.8 mm/hour. The average discharge in the primary channel is 7.94 m3/sec. The average reference crop evapotranspiration for the site was 3.89 mm/day. It varies from 3.51 to 4.38 mm/day. The maximum reference crop evapotranspiration was recorded in August and the minimum was May. The difference between maximum and minimum of reference crop evapotranspiration was observed to be 0.87 mm/day. CROPWAT derived the maximum effective rainfall of 80.3 mm in the month of October and the minimum of 20.9 mm during July. The total irrigation water requirement for rice crop for the season from Sept. to March was 412.7 mm. This low water requirement for rice is mainly due to higher effective rain fall in the experimental site from Sept to Dec. It was also inferred that the higher irrigation is needed from Dec to Feb because effective rainfall is lesser during that period. The moisture depletion pattern during the irrigation schedule varies from 59 to 71% with an average depletion of 64.8%. The net irrigation supplied to the field varies from 11.3 to 14.7 mm with an average net irrigation requirement 13.2 mm. The gross irrigation water requirement was varying from 16.2 to 21 mm with an average gross irrigation requirement of 18.96 mm. The average flow rate of water to the field was worked out to be 0.6 liter/sec/ha and it varies from 0.33 to 0.78 0.6 liter/sec/ha. The total gross irrigation was 906.9 mm and the total net irrigation was 634.8 mm.Keywords: CROPWAT, rice field, water requirement, irrigation schedul

Studies on green house gas emissions from rice field in Rwanda

The overall objective of this research is to estimate the Green House Gases (GHGs) emissions in different time of the day and to bring out the time of maximum and minimum GHGs emissions from the rice field. An experiment was conducted to estimate the GHGs emission from the rice fields of Muvumba P-8. Gas collection chambers were installed in 9 plots to collect the greenhouse gases. The gas samples were analyzed in Gas Chromatography and converted its results in to usable form. There was marked difference in the mean CO2 gas emission among the plots. The overall mean of CO2 gas emission among the experimental plots was 1950521 μg m-2 h-1. CH4 gas emission was high at 9 am and the minimum is at 3 pm among the mean gas production. Maximum CH4 gas emission at 9 am is due to the fact that during night time rice plant takes more CH4 and release the same due to ambient temperature rise at 9 am. The minimum CH4 gas emission at 3 pm is due to the fact that rice plant released all its CH4 during day time around 9 am to 3 pm and there was less CH4 in the rice plant to release at 3 pm. The mean of N2O gas emission at 6 am, 9 am, 12 noon and 3 pm of all the experimental plots was found to be 960.86 μg m-2 h-1. The mean N2O gas emission at 9am was found to be 995.82μg m-2 h-1. The important conclusion from the study is that N2O gas emission at 6 am and 12 noon are behaving similarly with decreasing trend. It was also found that N2O gas emission at 9 am and 3 pm are behaving similarly with decreasing trend.Keywords: Rice field, Green House Gases, emission, marshlan

Water Demand Forecasting using Deep Learning in IoT Enabled Water Distribution Network

Most of the water losses occur during water distribution in pipelines during transportation. In order to eradicate the losses, an “IoT based water distribution system” integrated with “Fog and Cloud Computing" proposed for water distribution and underground health monitoring of pipes. For developing an effective water distribution system based on Internet of Things (IoT), the demand of the consumer should be analysed. So, towards predicting the water demand for consumers, Deep learning methodology called Long Short-Term Memory (LSTM) is compared with traditional Time Series methodology called Auto Regressive Integrated Moving Average (ARIMA) in terms of error and accuracy. Now based on demand prediction with higher accuracy, an IoT integrated “Water Distribution Network (WDN)” is designed using hydraulic engineering. This WDN design will ensure minimal losses during transportation and quality of water to the consumers. This will lead to development of a smart system for water distribution

Spin re-orientation induced anisotropic magnetoresistance switching in LaCo0.5_{0.5}Ni0.5_{0.5}O3−δ_{3-\delta} thin films

Realization of novel functionalities by tuning magnetic interactions in rare earth perovskite oxide thin films opens up exciting technological prospects. Strain-induced tuning of magnetic interactions in rare earth cobaltates and nickelates is of central importance due to their versatility in electronic transport properties. Here we reported the spin re-orientation induced switching of anisotropic magnetoresistance (AMR) and its tunability with strain in epitaxial LaCo$_{0.5}$Ni$_{0.5}$O$_{3-\delta}$ thin films across the ferromagnetic transition. Moreover, with strain tuning, we could observe a two-fold to four-fold symmetry crossover in AMR across the magnetic transition temperature. The magnetization measurements revealed an onset of ferromagnetic transition around 50 K, and a further reduction in temperature showed a subtle change in the magnetization dynamics, which reduced the ferromagnetic long-range ordering and introduced glassiness in the system. X-ray absorption and X-ray magnetic circular dichroism spectroscopy measurements over Co and Ni L edges revealed the Co spin state transition below the magnetic transition temperature leading to the AMR switching and also the presence of Ni$^{2+}$ and Co$^{4+}$ ions evidencing the charge transfer from Ni to Co ions. Our work demonstrated the tunability of magnetic interactions mediated electronic transport in cobaltate-nickelate thin films, which is relevant in understanding Ni-Co interactions in oxides for their technological applications such as in AMR sensors

Elucidation of Relaxation Dynamics Beyond Equilibrium Through AI-informed X-ray Photon Correlation Spectroscopy

Understanding and interpreting dynamics of functional materials \textit{in situ} is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales, however spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work we have developed an unsupervised deep learning (DL) framework for automated classification and interpretation of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system behavior. We demonstrate how this method can be used to rapidly explore large datasets to identify samples of interest, and we apply this approach to directly correlate bulk properties of a model system to microscopic dynamics. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery

Oral squamous cell carcinoma detection using EfficientNet on histopathological images

Introduction: Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model’s objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization. Methods: The research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies. Results: The customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model’s efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC. Discussion: This research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model’s ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC

Smart Agent Based Mobile Tutoring and Querying System

With our busy schedules today and the rising cost of education there is a need to find a convenient and cost effective means of maximizing our educational/training experiences. New trends in the delivery/access of information are becoming more technology based in all areas of society with education being no exception. The ubiquitous use of mobile devices has led to a boom in m-commerce. Mobile devices provide many services in commercial environments such as mobile banking, mobile purchasing, mobile learning, etc. It is therefore fitting that we seek to use mobile devices as a platform in delivering our convenient and cost effective solution. The proposed agent based Mobile tutoring system seeks to provide a student with a rich learning experience that will provide them with the relevant reading material based on their stage of development which allows them to move at their own pace. The system will allow the user to be able to ask certain questions and get explanations as if they were interacting with a human tutor but with the added benefit of being able to do this anytime in any location via their mobile phone

Oman Medical Specialty Board Proximal Fibular Osteochondroma Producing Common Peroneal Nerve Palsy in a Post-Cesarean Section Patient

Abstract Various causes of common peroneal palsy have been described in the English language medical literature in the past. Authors report a case of foot drop in a post cesarean section patient, due to osteochondroma of the proximal fibula, which recovered completely after excision of the offending mass