Crop-based irrigation operations in the North West Frontier Province of Pakistan. Vol.II: Research approach and interpretation. Final Report

Irrigation management / Crop-based irrigation / Research / Irrigation canals / Water demand / Performance evaluation / Agricultural production / Pakistan / North West Frontier Province

Compact relativistic geometries in f(R,G)f(R,G) gravity

One of the possible potential candidates for describing the universe's rapid expansion is modified gravity. In the framework of the modified theory of gravity $f(R,G)$, the present work features the materialization of anisotropic matter, such as compact stars. Specifically, to learn more about the physical behavior of compact stars, the radial, and tangential pressures as well as the energy density of six stars namely $Her X-1$, $SAXJ1808.4-3658$, $4U1820-30$, $PSR J 1614 2230$, $VELA X-1$, and $Cen X-3$ are calculated. Herein, the modified theory of gravity $f(R,G)$ is disintegrated into two parts i.e. the $\tanh$ hyperbolic $f(R)$ model and the three different $f(G)$ model. The study focuses on graphical analysis of compact stars wherein the stability aspects, energy conditions, and anisotropic measurements are mainly addressed. Our calculation revealed that, for the positive value of parameter n of the model $f(G)$, all the six stars behave normally.Comment: Some changes have been made. " To appear in International Journal of Geometric Methods in Modern Physics

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

The difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated. This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform was performed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputs; segmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%) using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

The difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated. This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform was performed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputs; segmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%) using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation

Chitosan biopolymer improves the fruit quality of litchi (Litchi chinensis Sonn.)

Chitosan (CHT) is a natural compound that has been used to control postharvest pathogenic diseases due to its capability of eliciting natural defense responses in plants. The aim of this study was to investigate the effect of foliar CHT application on yield and quality of the litchi fruit. Chitosan was applied by spraying on to fruit and foliage just after fruit set four times at 7-day intervals with four varying doses viz. 100, 250, 500, and 1,000 µg L−1 and a control (0 µg L−1). Although the application of CHT had no significant effect on the size of the fruits (length and width), the total contents of phenolics, flavonoids, and ascorbic acid and the antioxidant activity of litchi fruit arils were significantly increased in CHT-treated fruit compared with the untreated control. The highest phenolic, flavonoid, and ascorbic acid contents were 334 µg gallic acid g−1, 881 μg quercetin g−1, and 178 µg g−1, respectively, in fruits treated with 500 µg L−1 CHT. However, the highest antioxidant activity (622 μg butylated hydroxytoluene g−1) was recorded in 250 µg L−1 CHT-treated fruits. Our findings revealed that the application of low doses of CHT in a litchi orchard might improve fruit quality by increasing the content of antioxidants and antioxidant activities

Serological evidence of hepatitis E virus infection in pigs and jaundice among pig handlers in Bangladesh

Hepatitis E virus (HEV) is the most common cause of viral hepatitis in humans. Pigs may act as a reservoir of HEV, and pig handlers were frequently identified with a higher prevalence of antibodies to HEV. The objectives of this study were to identify evidence of HEV infection in pigs and compare the history of jaundice between pig handlers and people not exposed to pigs and pork. Blood and faecal samples were collected from 100 pigs derived from three slaughterhouses in the Gazipur district of Bangladesh from January to June, 2011. We also interviewed 200 pig handlers and 250 non-exposed people who did not eat pork or handled pigs in the past 2 years. We tested the pig sera for HEV-specific antibodies using a competitive ELISA and pig faecal samples for HEV RNA using real-time RT-PCR. Of 100 pig sera, 82% (n = 82) had detectable antibody against HEV. Of the 200 pig handlers, 28% (56/200) demonstrated jaundice within the past 2 years, whereas only 17% (43/250) of controls had a history of jaundice (p < .05). Compared to non-exposed people, those who slaughtered pigs (31% versus 15%, p < .001), reared pigs (37% versus 20%, p < .001), butchered pigs (35% versus 19%, p < .001) or involved in pork transportation (28% versus 13%, p < .001) were more likely to be affected with jaundice in the preceding 2 years. In multivariate logistic regression analysis, exposure to pigs (odds ratio [OR]: 2.2, 95% CI: 1.2–3.9) and age (OR: 0.97, 95% CI: 0.95–0.99) was significantly associated with jaundice in the past 2 years. Pigs in Bangladesh demonstrated evidence of HEV infection, and a history of jaundice was significantly more frequent in pig handlers. Identifying and genotyping HEV in pigs and pig handlers may provide further evidence of the pig's role in zoonotic HEV transmission in Bangladesh

Land Use Patterns and the Scale of Adoption of Agroforestry in the Rural Landscapes of Padma Floodplain in Bangladesh

This research was conducted in six typical villages of Northern Bangladesh. A sample of 170 farmers was selected. Research indicates that the farmers practising agroforestry own small farms and the income of agroforestry helps them to reduce their poverty, maintain their socio-economic needs and sustain their livelihoods. Agroforestry is not a new concept in the study area. The people have been practicing agroforestry traditionally in the form of home gardens, hedgerows and alley cropping. Homestead agroforestry is an age old practice. Alley cropping and hedgerow agroforestry systems are comparatively new. Yet alley cropping is now most popular and is widely accepted in the study area because of its socio-economic advantages and environmental sustainability

MAPPING OF HIGHLY HETEROGENEOUS URBAN STRUCTURE TYPE FOR FLOOD VULNERABILITY ASSESSMENT

Vulnerability plays an important role in risk assessment. For flood vulnerability assessment, the map and characteristics of elements-at-risk at different scales are strongly required depending on the risk and vulnerability assessment requirements. This study proposes a methodology to classify urban structure type by combining object-based image classification and different high resolution remote sensing data. In this study, a high resolution satellite image and LiDAR have been acquired over Kota Bharu, Kelantan which consists of highly heterogeneous urban structure type (UST) classes. The first stage is data pre-processing that includes orthorectification and pansharpening of Geoeye satellite image, image resampling for normalised Digital Surface Model (nDSM) and followed by image segmentation for creating meaningful objects. The second stage comprises of derivation of image features, generation of training and testing datasets, and classification of UST. The classification was based on three types of machine learning classifiers, i.e. Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Tree (CART). The results obtained from the classification processes were compared using individual omission and commission error, overcall accuracy and Kappa coefficient. The results show that Random Forest classifier with all image features achieved the highest overall accuracy (93.5%) and Kappa coefficient (0.94). This is followed by CART classifier with overall accuracy of 93.7% and Kappa coefficient of 0.92. Finally, SVM classifier produced the lowest overall accuracy and Kappa coefficient with 88.6% and 0.86, respectively. The UST classification result can be further used to assist detailed building characterisation for large scale flood vulnerability assessment

An embedded beamformer for a PID-based trajectory sensing for an autonomous vehicle

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