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AbstractHospital observation units (OUs) are meant to host patients for relatively short periods of time, during which healthcare providers can observe a patient (usually following an emergency department encounter) and assess the need for an inpatient hospital admission. The initial placement of a patient in a unit (inpatient or observation) can have consequences for the care the patient receives and the patient's length of stay in the hospital, as well as financial implications for both the patient and the hospital. We propose a practical real‐time optimization algorithm that balances the need to place a patient in the OU with considerations for available capacity. Through computational experiments with inputs derived from real data, we study the benefit of using the algorithm for placing the right type of patients in the OU. We discuss the role of data‐driven decision support tools like the proposed algorithm for anchoring the decision‐making process in a hospital setting and for incorporating the viewpoints of multiple stakeholders by defining appropriate "rewards" for available alternatives.

Evaluation of the performance of Human-Machine Teaming brings two substantial values to military effectiveness: (1) enhancing the design quality of cognitive aircraft systems, (2) synchronizing the behavior of the virtual assistant with the cockpit needs during fights. This paper presents "MOHICAN", a system-of-systems approach for monitoring the performance of Human-Machine Teaming in combat aircraft cockpits. MOHICAN will include a method, its tools, and a model addressing a multirole aircraft. Those principles developed for the cockpit may be extended to more complex systems of systems by expanding measure criteria, and by integrating collective teaming contribution to global performance of the system as a whole (e.g., military air operations).

Microgrids and smart grids are largely accepted concepts in power energy systems. They bring an innovative and distributed view to the old centralized system. This demands a more active participation from end-consumers, that can be achieved by using demand response and demand side management. In this paper it is proposed a solution for demand side management involving and agent-based architectures that was deployed in a small office. The deployment integrated an algorithm for generation and consumption balance with real-time contextual resources' priorities. The deployment's overall results, from a winter and a summer day, are presented in this paper. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019 and SFRH/BD/109248/2015.

This contribution presents an innovative and integrated framework for real-time-process reconciliation and optimization (RTRO) in large continuous open pit coal mines. RTRO-Coal is currently developed, validated, tested and implemented as part of a multi-national multi-partner European Union funded R& ; D project. The key concept is to promote a shift in paradigm from intermittent discontinuous to a continuous process monitoring and quality management system in large scale coal mining operations. The framework is based on a real-time feedback control loop linking online data acquired during extraction rapidly with a sequentially up-datable resource model. The up-to-date model is integrated with a real-time optimization of short-term sequencing and production control decisions. Improved decisions are expected to lead to increased resource-and process efficiency and support a sustainable extraction of natural resources. This contribution introduces to the framework, discusses main building blocks and illustrates the value added by the means of selected examples.

This contribution presents an innovative and integrated framework for real-time-process reconciliation and optimization (RTRO) in large continuous open pit coal mines. RTRO-Coal is currently developed, validated, tested and implemented as part of a multi-national multi-partner European Union funded R&D project. The key concept is to promote a shift in paradigm from intermittent discontinuous to a continuous process monitoring and quality management system in large scale coal mining operations. The framework is based on a real-time feedback control loop linking online data acquired during extraction rapidly with a sequentially up-datable resource model. The up-to-date model is integrated with a real-time optimization of short-term sequencing and production control decisions. Improved decisions are expected to lead to increased resource-and process efficiency and support a sustainable extraction of natural resources. This contribution introduces to the framework, discusses main building blocks and illustrates the value added by the means of selected examples. ; Geoscience & Engineering ; Civil Engineering and Geosciences

Efficient use of energy is currently a very important issue. As conventional energy resources are limited, improving energy efficiency is, nowadays, present in any government policy. Railway systems consume a huge amount of energy, during normal operation, some routes working near maximum energy capacity. Therefore, maximizing energy efficiency in railway systems has, recently, received attention from railway operators, leading to research for new solutions that are able to reduce energy consumption without timetable constraints. In line with these goals, this paper proposes a Simulated Annealing optimization algorithm that minimizes train traction energy, constrained to existing timetable. For computational effort minimization, re-annealing is not used, the maximum number of iterations is one hundred, and generation of cruising and braking velocities is carefully made. A Matlab implementation of the Simulated Annealing optimization algorithm determines the best solution for the optimal speed profile between stations. It uses a dynamic model of the train for energy consumption calculations. Searching for optimal speed profile, as well as scheduling constraints, also uses line shape and velocity limits. As results are obtained in seconds, this new algorithm can be used as a real-time driver advisory system for energy saving and railway capacity increase. For now, a standalone version, with line data previously loaded, was developed. Comparison between algorithm results and real data, acquired in a railway line, proves its success. An implementation of the developed work as a connected driver advisory system, enabling scheduling and speed constraint updates in real time, is currently under development.