Wave overtopping pressures and spatial distribution behind rubble mound breakwaters

Currently, there is no widely accepted method to determine the pressure profiles induced by wave overtopping behind the crest of a breakwater other than physical modelling. In this experimental study, the spatial distribution of overtopping pressures on a vertical structure is investigated at various distances behind a rubble mound breakwater with a crown wall. A 2D physical modelling study in presented in an attempt to derive a practical method for estimating these overtopping pressures. The variability of overtopping wave pressures behind the crest of a breakwater is also discussed. Rule of thumb guidelines are proposed which will contribute to better concept and schematic structural designs in advance of physical model testing

An Artificial Immune Network for Distributed Demand-Side Management in Smart Grids

In this work we present a Distributed Demand-Side Management system based on the Artificial Immune Network algorithm. It implements an intelligent, distributed and autonomous control of the customer's Air Conditioning devices in order to meet the desired demand. The system is particularly adapted to tackle the Peak Load problem that appears in Tropical and Subtropical climates due to the use of thousands of these devices at the same time. The design follows the guidelines set by the Smart Grid paradigm, in the sense that it is fault tolerant, distributed and self-controlled. It requires minimal communication infrastructure when compared to a centralized system. The algorithm was evaluated using synthetic and real data. We define Maximal and Average Tolerance as performance metrics, and show that the system keeps the consumption within 1% of the given load limit in all 5 cases

Process mining dashboard in operating rooms: Analysis of staff expectations with analytic hierarchy process

The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining-based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc.), distinguishing the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (n = 10) was classified into three groups: Technical, clinical, and managerial staff according to their responsibilities. Results showed different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms. This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018