An active machine learning technique for monitoring the voltage stability in transmission systems is presented. It has been shown that machine learning algorithms may be used to supplement the traditional simulation approach, but they suffer from the difficulties of online machine learning model update and offline training data preparation. We propose an active learning solution to enhance existing machine learning applications by actively interacting with the online prediction and offline training process. The technique identifies operating points where machine learning predictions based on power system measurements contradict with actual system conditions. By creating the training set around the identified operating points, it is possible to improve the capability of machine learning tools to predict future power system states. The technique also accelerates the offline training process by reducing the amount of simulations on a detailed power system model around operating points where correct predictions are made. Experiments show a significant advantage in relation to the training time, prediction time, and number of measurements that need to be queried to achieve high prediction accuracy.

This research describes the use of high-performance computing (HPC) and deep learning to create prediction models that could be deployed on edge AI devices equipped with camera and installed in poultry farms. The main idea is to leverage an existing IoT farming platform and use HPC offline to run deep learning to train the models for object detection and object segmentation, where the objects are chickens in images taken on farm. The models can be ported from HPC to edge AI devices to create a new type of computer vision kit to enhance the existing digital poultry farm platform. Such new sensors enable implementing functions such as counting chickens, detection of dead chickens, and even assessing their weight or detecting uneven growth. These functions combined with the monitoring of environmental parameters, could enable early disease detection and improve the decision-making process. The experiment focused on Faster R-CNN architectures and AutoML was used to identify the most suitable architecture for chicken detection and segmentation for the given dataset. For the selected architectures, further hyperparameter optimization was carried out and we achieved the accuracy of AP = 85%, AP50 = 98%, and AP75 = 96% for object detection and AP = 90%, AP50 = 98%, and AP75 = 96% for instance segmentation. These models were installed on edge AI devices and evaluated in the online mode on actual poultry farms. Initial results are promising, but further development of the dataset and improvements in prediction models is needed.

Blockchain offers decentralized, trustworthy and immutable data storage based on distributed ledger technology. Blockchain technology is recognized as an innovation enabler in many areas, with the food supply chain being one of them. This paper provides a systematic literature review of the current state of adoption of blockchain technology in the agri-food sector, specifically with a focus on the wine supply chain. Blockchain has the potential to improve the traceability and authenticity of the data provenance of wine products, increase consumer trust, and reduce fraud and errors. With these goals in mind, this study identifies the main research questions pertinent to the value proposition and competitive advantage of blockchain technology in the wine value chain and key players involved in the authentication and value chain recording. The PRISMA methodology was adopted to identify, screen and select only the relevant studies that were included in the analysis. This study also addresses the limitations and challenges for adoption, such as high implementation cost and lack of competences, and the need for standardized protocols and regulations. Finally, this systematic literature review includes an analysis of reports of blockchain applications in the wine sector and outlines the recommendations for future research to further explore the potential of blockchain-based solutions that could benefit all stakeholders across the wine value chain.

This study evaluates the impact of web-based blended learning in the physiology course at the Faculty of Medicine, University of Montenegro. The two main goals of the study were: to determine the impact of e-learning on student success in mastering the course, and to assess user satisfaction after the introduction of e-learning. The study compared a group of students who attended the physiology course before, with a group of students who attended the physiology course after the Moodle platform was fully implemented as an educational tool. Formative and summative assessment scores were compared between these two groups. The impact of high vs. low Moodle use on the assessment scores was analyzed. The satisfaction among Moodle users was assessed by the survey. The study found that attendance of face-to-face lectures had a positive impact on academic performance. The introduction of Moodle in the presented model of teaching increased interest of students, attendance of face-to-face lectures, as well as formative and summative scores. High frequency of Moodle use was not always associated with better academic performance, suggesting that the introduction of a new method of teaching was most likely equally accepted by low- and high-achieving students. Most of the students agreed that Moodle was easy to use and it complemented traditional teaching very well, but it could not completely replace traditional face-to-face lectures. The study supports continuing the use of web-based learning in a form of blended learning for physiology, as well as for other courses in medical education.