Great Circle Navigation with Vectorial Methods

The present paper is concerned with the solution of a series of practical problems relevant to great circle navigation, including the determination of the true course at any point on the great circle route and the determination of the lateral deviation from a desired great circle route. Intersection between two great circles or between a great circle and a parallel is also analyzed. These problems are approached by means of vector analysis, which yields solutions in a very compact form that can be computed numerically in a very straightforward manner. This approach is thus particularly appealing for performing computer-aided great circle navigation

Peanut leaf spot disease identification using pre-trained deep convolutional neural network

Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants

Cross-border Digital Platform for Transport Critical Infrastructure Resilience: Functionalities and Use-case

The resilience of increasingly interdependent Critical Infrastructure (CI) systems hugely depends on the stakeholder organizations’ ability to exchange information and coordinate, while CI’s cross-border dimension further increases the complexity and challenges. This paper presents the progress in the Lombardy Region (Italy) and Canton Ticino (Switzerland) on the joint capacity to manage disruptive events involving transportation CI between the two countries. We present a cross-border digital platform (Critical Infrastructure Platform – PIC) and its main functionalities for improved cross-border risk and resilience management of CI. A use case, based on a scenario of an intense snowfall along the transboundary motorway impacting both countries, demonstrates how PIC advances the exchange of information, its visualization and analysis in real-time. The use case also shows the practical value of the digital platform and its potential to support the management of cross-border events (and their cascading events) that require the cooperation of Italian and Swiss actors