Abstract
Models-at different levels of abstraction and pertaining to different engineering views-are central in the design of railway networks, in particular signalling systems. The design of such systems must follow numerous strict rules, which may vary from project to project and require information from different views. This renders manual verification of railway networks costly and error-prone. This paper presents EVEREST, a tool for automating the verification of railway network models that preserves the loosely coupled nature of the design process. To achieve this goal, EVEREST first combines two different views of a railway network model-the topology provided in signalling diagrams containing the functional infrastructure, and the precise coordinates of the elements provided in technical drawings (CAD)-in a unified model stored in the railML standard format. This railML model is then verified against a set of user-defined infrastructure rules, written in a custom modal logic that simplifies the specification of spatial constraints in the network. The violated rules can be visualized both in the signalling diagrams and technical drawings, where the element(s) responsible for the violation are highlighted. EVEREST is integrated in a long-term effort of EFACEC to implement industry-strong tools to automate and formally verify the design of railway solutions.

Abstract
Purpose This study contributes to the understanding of how cultural organizations are using digital technologies to redesign their business models and enable sustainable and impactful audiovisual digital archives. Design/methodology/approach An inductive multiple case research design was used. Five cases of audiovisual digital archives of independent films were selected. Data collected was based on desk research, onsite visits, interviews with top managers responsible for the digitalization of some of the archives and experimentation with the services provided. Data was collected and analyzed based on a theoretical framework defined from the literature for business models of cultural organizations. Findings The archives analyzed faced the challenge of aligning the commercial viability with a mission of making content available to increase cultural knowledge. A sustainable business model may be achieved by using different revenue models, while guaranteeing to offer a value proposition carefully aligned with stakeholders' expectations. Moreover, an impactful business model, i.e. a business model that enhances the creation of cultural value for customers and reaches wider audiences, requires careful audience management and the use of data analysis about audience behavior to adjust the offering. Finally, the business model must consider the resources, activities and infrastructure that ensure critical capabilities for the business and must be designed to ensure financial resilience of the organization. Originality/value This study contributes with a holistic analysis of business models for the digital transformation of cultural organizations, detailing alternative configurations for the most relevant components of a digital business model for audiovisual archives.

Abstract
Machine Learning has been evolving rapidly over the past years, with new algorithms and approaches being devised to solve the challenges that the new properties of data pose. Specifically, algorithms must now learn continuously and in real time, from very large and possibly distributed sets of data. In this paper we describe a learning system that tackles some of these novel challenges. It learns and adapts in realtime by continuously incorporating user feedback, in a fully autonomous way. Moreover, it allows for users to manage features (e.g. add, edit, remove), reflecting these changes on-the-fly in the Machine Learning pipeline. The paper describes some of the main functionalities of the system, which despite being of general-purpose, is being developed in the context of a project in the domain of financial fraud detection.

Abstract
Nowadays, manufacturing companies present complex and robust in-house logistics operations that support production lines, where high system efficiency is the primary goal. However, to achieve the desired degree of efficiency, the use of tools that can help decision-makers to identify the improved set of operations is required. This need is explored in this work through the development of a simulation model. The model is inspired by a real automotive plant, where a segment of a mixed-model assembly line composed by a supermarket, diverse kits, human pickers and automated guided vehicles (AGV) is explored. Different scenarios are studied to analyse the potential for production support operation improvement, where the introduction of automated technologies, like robots, is explored. Results show that the system, through the addition of intelligent dynamic carrier robots, can significantly improve efficiency while reducing resources deployed. Furthermore, sizing the human workforce at the supermarket is the key to having a well-balanced production system.

Abstract

Abstract
In this paper, a multi-temporal optimal power flow (OPF) model for radial networks is proposed. The mathematical problem formulation is presented as a mixedinteger quadratically constrained programming (MIQCP) problem. The main core of the developed OPF problem is benefiting from the second-order conic programming (SOCP) approach while the quadratic constraints of the power flow equations have been efficiently handled. In the developed model, the dynamic behaviour of the electrical energy storage (EES) has been addressed for the day-ahead operation problem. In addition, the developed model is tested and verified for both normal and contingent events and the obtained results are satisfactory in terms of feasibility and optimality. In the islanded operation, a grid-forming unit is the main responsible for maintaining the voltage reference while other units behave as slave. The model is tested on the modified IEEE 33-bus network to verify the performance of the developed tool.