Abstract
This paper presents a pricing optimisation framework for energy, reserve, and load scheduling of a power system considering demand response (DR). The proposed scheduling framework is formulated as a reliability constrained unit commitment program to minimise the power system operation costs by finding optimal electricity prices and optimal incentives while guaranteeing the reliability of the system during contingencies. Moreover, customers' attitude toward the electricity price and incentive adjustment and the effect of their preferences on load scheduling and operation of the system are investigated in various DR programs. The proposed scheme is implemented on an IEEE test system, and the scheduling process with and without DR implementation is discussed in detail by a numerical study. The proposed method helps both the system operators and customers to reliably schedule generation and consumption units and select the proper DR program according to defined prices and incentives in the case of an emergency.

Abstract
Centrality measures are used in network science to assess the centrality of vertices or the position they occupy in a network. There are a large number of centrality measures according to some criterion. However, the generalizations of the most well-known centrality measures for weighted networks, degree centrality, closeness centrality and betweenness centrality have solely assumed the edge weights to be constants. This article proposes a methodology to generalize degree, closeness and betweenness centralities taking into account the variability of edge weights in the form of closed intervals (interval-weighted networks, IWN). We apply our centrality measures approach to two real-world IWN. The first is a commuter network in mainland Portugal, between the 23 NUTS 3 Regions. The second focuses on annual merchandise trade between 28 European countries, from 2003 to 2015.

Abstract
Scientific advances in recent decades have provided universal access to a variety of new digital technologies. These technologies are used by the vast majority of today's university students. Therefore, the incorporation of innovative methods and technologies is a must in order to actively engage students in the learning process. In this paper, a selection of techniques that can be considered 'outside of the box' are examined in the context of the application of teaching/learning methods in control engineering and industrial automation education. © 2022 IEEE.

Abstract
In this article, we present solutions to visualize and interact with linked data in historical archives considering three different scenarios: search, individual record view, and creation of relationships. The created solutions were designed using nonfunctional mockups and were based on the CIDOC-CRM model, a model created and applied in the museums community liable to be extended to other cultural heritage institutions, being our solutions an application of this model to archives. A sample of 20 archival professionals was selected to evaluate the elements included in the proposed solutions. The evaluation sessions consisted in structured interviews supported by an introductory video and a survey. The think-aloud protocol was applied during the sessions. We conducted both a quantitative and qualitative analysis to the collected answers. From this analysis, we conclude that the majority of the participants showed great receptivity to the solutions presented and recognized many benefits in the application of linked data. Our contributions also include an exploratory study of some existing linked data systems, giving particular attention to their visualization and interaction modes.

Abstract
This paper presents a two-layer framework for improving the resilience of a 118-bus active distribution network consisting of four microgrids, which includes hybrid storage systems, electric buses (EBs), and the direct load control (DLC) program. In the proposed model, the uncertainties of RESs generation, demand, and EBs’ mobility are considered, and the robust optimization approach is used to tackle them. In the first layer, the planning of each microgrid is done separately and the energy purchase/sale request is sent to the control center. Then in the second layer, the control center performs the planning of the main network according to the requested program of the microgrids. Note that in this layer, the control center is able to rearrange the distribution feeder and send EBs to vital points of the network. Finally, the validity of the proposed model is evaluated through the implementation on seven case studies and the results show that the presence of hydrogen and electrical storage devices reduces forced load shedding (FLS) by 45.03% and 12.19%, respectively, during emergency situations. In addition, the results indicate that robust planning and the use of EBs for network recovery increase the resilience index by 3.35% and 3.98%, respectively.

Abstract
Health Remote Monitoring Systems (HRMS) offer the ability to address health-care human resource concerns. In developing nations, where pervasive mobile networks and device access are linking people like never before, HRMS are of special relevance. A fundamental aim of this research work is the realization of technological-based solution to triage and follow-up people living with dementias so as to reduce pressure on busy staff while doing this from home so as to avoid all unnecessary visits to hospital facilities, increasingly perceived as dangerous due to COVID-19 but also raising nosocomial infections, raising alerts for abnormal values. Sensing approaches are complemented by advanced predictive models based on Machine Learning (ML) and Artificial Intelligence (AI), thus being able to explore novel ways of demonstrating patient-centered predictive measures. Low-cost IoT devices composing a network of sensors and actuators aggregated to create a digital experience that will be used and exposure to people to simultaneously conduct several tests and obtain health data that can allow screening of early onset dementia and to aid in the follow-up of selected cases. The best ML for predicting AD was logistic regression with an accuracy of 86.9%. This application as demonstrated to be essential for caregivers once they can monitor multiple patients in real-time and actuate when abnormal values occur.