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Vibrational dynamics of the a-polymorphic form of trans-1-4-polyisoprene is described using Higg's method and the Urey-Bradley force field. Characteristic features of dispersion profiles such as repulsion and bunching are reported. A comparison with the b-form is presented and possible reasons for variation in heat capacity are discussed.

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Today, agriculture is back bone of our country's economy. Agriculture is sometimes referred to as the art and science of raising crops and feeding domestic animals. Moreover, half of the country's GDP is contributed by the agricultural sector. The pace of output is influenced by the crops, fertilisers, and cultivation techniques. Unknown plant or crop diseases now have a significant impact on agricultural productivity. It may be difficult for a farmer to spot a plant disease, but it may also be difficult to do so without a microscope or even with our unaided eyes. To address this complex problem, we thus provide a methodology that uses machine learning and deep learning to identify the plant sickness. Convolution neural networks can be used in conjunction with deep learning and machine learning to identify plant diseases. Deep learning allows us to characterise the behaviour and symptoms of the plant in addition to detecting sickness. By employing various architectures, deep learning aids in the visualisation of the picture. There are several types of architecture, including AlexNet, VGG, ResNet, and CNN, among others. We have developed a model to identify plant diseases using the proper architecture. Finally, this work analyses and makes predictions on how image processing-based plant disease and pest detection may progress in the future

Plant diseases are a major threat to crop yield and food security. Early detection and diagnosis are crucial to prevent the spread of disease and minimize crop losses. Plant diseases can have devastating effects on crop yields and food security. Disease detection is crucial for effective disease management. In recent years, deep learning techniques, such as Convolutional Neural Networks (CNN), have shown promising results in disease detection. In this study, we propose a plant disease detection system using CNN and Arduino. The system involves capturing images of plant leaves using a camera module connected to an Arduino board. In recent years, deep learning techniques such as Convolutional Neural Networks (CNNs) have shown great promise in image-based disease detection. Additionally, the use of low-cost microcontrollers like Arduino can provide a costeffective solution for real-time disease detection in the field. This paper proposes a plant disease detection system that combines CNN and Arduino for early and accurate disease detection. The system utilizes a CNN model to classify plant disease images and an Arduino board to analyze the results and provide real-time feedback to the user. The proposed system is tested on a publicly available dataset of plant disease images, achieving high accuracy, and demonstrating its potential for practical use. The results show that the proposed system can provide an affordable and efficient solution for early plant disease detection, facilitating prompt action and reducing crop losses.