Brucellosis is reportedly endemic in ruminants in Pakistan. Both Brucella abortus and B. melitensis infections have been decumented in domestic animals and humans in the country. This study aimed to identify the burden of anti-Brucella antibodies in small ruminants as well as associated potential risk factors with its occurrence at nine institutional livestock farms in Punjab, Pakistan. The sera collected from equal number of sheep and goats (500 from each species) were screened by indirect-ELISA for anti-smooth-Brucella antibodies followed by a serial detection by real-time PCR. Overall, 5.1% (51/1000) seropositivity was registered corresponding to 5% (25/500) prevalence in goats and 5.2% (26/500) in sheep. Brucella-DNA could not be detected in any of the tested sera by real-time PCR. Multiple logistic regression model indicated that farm location (OR 34.05), >4 years of age (OR 2.88), with history of reproductive disorders (OR 2.69), and with BCS of ≤3 (OR 12.37) were more likely to test positive for brucellosis at these farms. A routine screening, stringent biosecurity, and quarantine measures are warranted for monitoring and eradication of the infection. Similarly, isolation and molecular investigation of the etiologic agent(s) are needed to understand the relationship of epidemiology and outbreaks of brucellosis in the country.
Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine (SVM) algorithm namely LungNet-SVM. The proposed model consists of seven convolutional layers, three pooling layers, and two fully connected layers used to extract features. Support vector machine classifier is applied for the binary classification of nodules into benign and malignant. The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016 (LUNA16). The proposed model has achieved 97.64% of accuracy, 96.37% of sensitivity, and 99.08% of specificity. A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung cancer. The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.
Synthetic aperture radar (SAR) is the most efficient tool to provide high-resolution data for Earth Observation (EO). Doppler centroid (DC) estimation is indispensable for high precision SAR data analysis such as extracting the ocean surface current, which is important for scientific pursuits. Correlation doppler estimation (CDE), and energy balancing (EB) based DC methods are implemented in this paper. A 2-D sliding window is deployed to estimate DC on small blocks of data while covering the whole scene so that all parts of the scene are potentially represented. We analyzed Sentinel-1 single look complex (SLC) data from the coastline of a non-homogeneous scene. The CDE method utilizes the azimuth shift in the time domain which is associated with the DC, and this fDc history is used to extract ocean surface current. We find the results of DC estimates are confined to primitive baseband (±PRF⁄2). Moreover, the corresponding retrieved ocean surface current component is reasonable, particularly values vary within the limit of error bounds. Finally, the parameters of ocean surface current are compared with ocean wave models reported in the literature. Efficacy and simulation of implemented methods are good fit for Sentinel-1 SAR data.
During the past decade, the schoolchildren faced many disasters and emergencies originating from natural and man-made sources. The safety of schoolchildren rests with school management and teachers. This study aimed to assess teachers' awareness with regard to disaster prevention and health, safety, security, and environment (HSSE) policies at primary schools in Pakistan. In addition, it explores the suitability of teachers to perform as emergency handlers in the absence of school nurses and resource officers (SROs). The study involved a qualitative study, based on open-ended interviews from a sample of 25 school teachers. Methodology triangulation was applied to reduce bias. Results revealed that the majority of teachers do not know about national policies related to school safety and security. Most of the teachers had been assigned additional roles as safety officers at the school, though they lack relevant training. Moreover, neither safety nor security aspects are incorporated into national educational policy. This study was the first study on Pakistani teachers’roles as emergency first responders. With the highest rate of attacks on educational institutions globally, the region still lacks a coherent policy structure. The study found numerous inconsistencies in public policies. It seeks to contribute to the literature to better understand the educational safety environment at both the ground and policy levels.
The spectral characteristics of single-look complex -interferometric wide (SLC-IW) swath, terrain observation by progressive scan (TOPS), are significantly different from those of strip-map (SM). Due to the burst mode and series of sub-swaths, the target area is scanned for a short period of time. Therefore, swath width comes at the expense of azimuth resolution. To eliminate quadratic phase drift and achieve SLC baseband, significant processing is required. De-ramping is a necessary step to compute ocean circulation parameters. In this work, we extract ocean parameters from the complex echo signal based on data driven Doppler centroid (f DC ) regardless of the OCN product information and geophysical f DC image. The radial surface velocity (RSV) is retrieved from Doppler history, and the significant wave height (SWH) is estimated with an empirical relationship of RSV. The results of ocean circulation parameters are promising when compared with benchmark and in-situ data. This work demonstrates the efficacy and necessity of de-ramping the TOPS data for subsequent use in a variety of ocean remote sensing applications.
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