Assessment of knowledge, attitude, and practice on livestock traceability among cattle farmers and cattle traders in peninsular Malaysia and its impact on disease control.

The ability to trace the movement of animals and their related products is key to success in animal disease control. To ensure that a traceability system is optimized, livestock farmers and traders must have good appreciation and understanding about animal tracing. The present study examined the traceability of cattle in Malaysia vis-à-vis the domains of knowledge, attitude, and practice among cattle farmers and traders. A total of 543 farmers and traders in Peninsular Malaysia were interviewed. The results revealed that over 60% of the respondents had satisfactory knowledge and attitude about cattle movement and traceability. A lower proportion of the respondents (49%) were involved in appropriate practice that facilitated traceability of cattle. We found that the type of husbandry system and stakeholders' participation in livestock management-specific short courses were positively associated with satisfactory knowledge, attitude, and practice. A structured education and training program should be formulated to improve these domains so that the benefit of traceability becomes clear, paving the way to a successful traceability program.

Imaging appearances of hepatic tuberculosis: experience with 12 patients.

AIM: To review computed tomography (CT), ultrasound (US), magnetic resonance cholangiopancreatography (MRCP), endoscopic retrograde cholangiopancreatography (ERCP) and percutaneous transhepatic cholangiogram (PTC) appearances and their diagnostic value in hepatic tuberculosis. MATERIALS AND METHODS: The imaging studies for 12 patients with biopsy-proven hepatic tuberculosis from January 2012 till March 2014 were reviewed retrospectively. These cases were confirmed via ultrasound-guided biopsy. RESULTS: The patients were aged 24-72 years. Four patients had parenchymal tuberculosis only and eight patients had mixed parenchymal and biliary duct involvement. The parenchymal tuberculosis patients showed poorly enhancing, hypodense nodules on CT with central calcification and adjacent dilated intrahepatic ducts. Most patients had multiple lesions except for two patients with a single lesion. The size of the lesions ranged from 0.5 to 6 cm. Seven patients with biliary duct involvement showed a hilar strictures involving the intrahepatic ducts and common bile duct. Nine of the patients showed hilar stricture with atrophy of the ipsilateral lobe of the liver and compensatory hypertrophy of the contralateral lobe. Hepatolithiasis was seen in five patients. Tuberculous lung involvement was seen in seven patients. CONCLUSION: The presence of calcified and hypodense nodules with biliary duct dilatation associated with lobar atrophy were the most consistent features of hepatic tuberculosis, especially in the presence of active lung disease.

A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis.

Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.