Implementation of a probabilistic based framework for safety assessment of existing timber elements

The assessment of existing timber structures is often limited to information obtained from non or semi destructive testing, as mechanical testing is in many cases not possible due to its destructive nature. Therefore, the available data provides only an indirect measurement of the reference mechanical properties of timber elements, often obtained through empirical based correlations. Moreover, the data must result from the combination of different tests, as to provide a reliable source of information for a structural analysis. Even if general guidelines are available for each typology of testing, there is still a need for a global methodology allowing to combine information from different sources and infer upon that information in a decision process. In this scope, the present work presents the implementation of a probabilistic based framework for safety assessment of existing timber elements. This methodology combines information gathered in different scales and follows a probabilistic framework allowing for the structural assessment of existing timber elements with possibility of inference and updating of its mechanical properties, through Bayesian methods. The probabilistic based framework is based in four main steps: (i) scale of information; (ii) measurement data; (iii) probability assignment; and (iv) structural analysis. In this work, the proposed methodology is implemented in a case study. Data was obtained through a multi-scale experimental campaign made to old chestnut timber beams accounting correlations of non and semi-destructive tests with mechanical properties. Finally, different inference scenarios are discussed aiming at the characterization of the safety level of the elements.

Release of insulin from PLGA-alginate dressing stimulates regenerative healing of burn wounds in rats

Burn wound healing involves a complex set of overlapping processes in an environment conducive to ischemia, inflammation, and infection costing $7.5 billion/year in the US alone, in addition to the morbidity and mortality that occur when the burns are extensive. We previously showed that insulin, when topically applied to skin excision wounds, accelerates re-epithelialization, and stimulates angiogenesis. More recently, we developed an alginate sponge dressing (ASD) containing insulin encapsulated in PLGA microparticles that provides a sustained release of bioactive insulin for >20days in a moist and protective environment. We hypothesized that insulin-containing ASD accelerates burn healing and stimulates a more regenerative, less scarring, healing. Using a heat-induced burn injury in rats, we show that burns treated with dressings containing 0.04mg insulin/cm2, every three days for 9 days, have faster closure, faster rate of disintegration of dead tissue, and decreased oxidative stress.In addition, in insulin-treated wounds the pattern of neutrophil inflammatory response suggests faster clearing of the burn dead tissue. We also observe faster resolution of the pro-inflammatory macrophages. We also found that insulin stimulates collagen deposition and maturation with the fibers organized more like a basket weave (normal skin) than aligned and crosslinked (scar tissue). In summary , application of ASD-containing insulin-loaded PLGA particles on burns every three days stimulates faster and more regenerative healing. These results suggest insulin as a potential therapeutic agent in burn healing and, because of its long history of safe use in humans, insulin could become one of the treatments of choice when repair and regeneration are critical for proper tissue function.

A multi-criteria decision support system for a routing problem in waste collection

Autor proof

A decision support system for a waste collection vehicle routing problem

The selective collection of municipal solid waste for recycling is a very complex and expensive process, where a major issue is to perform cost-efficient waste collection routes. Despite the abundance of commercially available software for fleet management, they often lack the capability to deal properly with sequencing problems and dynamic revision of plans and schedules during process execution. Our approach to achieve better solutions for the waste collection process is to model it as a vehicle routing problem, more specifically as a team orienteering problem where capacity constraints on the vehicles are considered, as well as time windows for the waste collection points and for the vehicles. The final model is called capacitated team orienteering problem with double time windows (CTOPdTW).We developed a genetic algorithm to solve routing problems in waste collection modelled as a CTOPdTW. The results achieved suggest possible reductions of logistic costs in selective waste collection.

Decision methods applied to solve a safety problem on a waste treatment facility

To solve a health and safety problem on a waste treatment facility, different multicriteria decision methods were used, including the PROV Exponential decision method. Four alternatives and ten attributes were considered. We found a congruent solution, validated by the different methods. The AHP and the PROV Exponential decision method led us to the same options ordering, but the last method reinforced one of the options as being the best performing one, and detached the least performing option. Also, the ELECTRE I method results led to the same ordering which allowed to point the best solution with reasonable confidence. This paper demonstrates the potential of using multicriteria decision methods to support decision making on complex problems such as risk control and accidents prevention.

Safety classification using GIS in decision-making process to define priority road interventions

Given the current economic situation of the Portuguese municipalities, it is necessary to identify the priority investments in order to achieve a more efficient financial management. The classification of the road network of the municipality according to the occurrence of traffic accidents is fundamental to set priorities for road interventions. This paper presents a model for road network classification based on traffic accidents integrated in a geographic information system. Its practical application was developed through a case study in the municipality of Barcelos. An equation was defined to obtain a road safety index through the combination of the following indicators: severity, property damage only and accident costs. In addition to the road network classification, the application of the model allows to analyze the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents. This analysis can be further refined according to the nature of the accidents namely in collision, runoff and pedestrian crashes.

Indicadores de "Hidricidade" como ferramenta de avaliação da eficiência urbana

One of the biggest challenges for urban space planners is to conciliate the indispensable demands of life with the forms of occupation of the territory. Among these demands is water. Population growth and urban sprawl are challenging water availability. Thus, it is important to evaluate development trends to predict future scenarios, enabling guide preventive actions and decision making. It is essential to have a practical and direct tool that allows us to transmit the necessary information. The objective of this study is to present a method for evaluating the urban efficiency in the process of city expansion and the availability of water supply. To do so, the indicators will be defined, called indicators of "hidricidade". As a result, it is expected to be obtained a direct and practical tool, that is going to allow to transmit the necessary information to the harmonic urban process planning, applicable to medium size Brazilian cities, allowing decision making as a guarantee of water supply and urban sprawl.

A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"

Improving selective collection of urban waste using a business intelligence system

Today recovering urban waste requires effective management services, which usually imply sophisticated monitoring and analysis mechanisms. This is essential for the smooth running of the entire recycling process as well as for planning and control urban waste recovering. In this paper we present a business intelligence system especially designed and im- plemented to support regular decision-making tasks on urban waste management processes. The system provides a set of domain-oriented analytical tools for studying and characterizing poten- tial scenarios of collection processes of urban waste, as well as for supporting waste manage- ment in urban areas, allowing for the organization and optimization of collection services. In or- der to clarify the way the system was developed and the how it operates, particularly in process visualization and data analysis, we also present the organization model of the system, the ser- vices it disposes, and the interface platforms for exploring data.

Pervasive business intelligence platform to improve the quality of decision process in primary and secondary education – A Portuguese case study

Business Intelligence (BI) can be seen as a method that gathers information and data from information systems in order to help companies to be more accurate in their decision-making process. Traditionally BI systems were associated with the use of Data Warehouses (DW). The prime purpose of DW is to serve as a repository that stores all the relevant information required for making the correct decision. The necessity to integrate streaming data became crucial with the need to improve the efficiency and effectiveness of the decision process. In primary and secondary education, there is a lack of BI solutions. Due to the schools reality the main purpose of this study is to provide a Pervasive BI solution able to monitoring the schools and student data anywhere and anytime in real-time as well as disseminating the information through ubiquitous devices. The first task consisted in gathering data regarding the different choices made by the student since his enrolment in a certain school year until the end of it. Thereafter a dimensional model was developed in order to be possible building a BI platform. This paper presents the dimensional model, a set of pre-defined indicators, the Pervasive Business Intelligence characteristics and the prototype designed. The main contribution of this study was to offer to the schools a tool that could help them to make accurate decisions in real-time. Data dissemination was achieved through a localized application that can be accessed anywhere and anytime.

Step towards multiplatform framework for supporting pediatric and neonatology care unit decision process

Children are an especially vulnerable population, particularly in respect to drug administration. It is estimated that neonatal and pediatric patients are at least three times more vulnerable to damage due to adverse events and medication errors than adults are. With the development of this framework, it is intended the provision of a Clinical Decision Support System based on a prototype already tested in a real environment. The framework will include features such as preparation of Total Parenteral Nutrition prescriptions, table pediatric and neonatal emergency drugs, medical scales of morbidity and mortality, anthropometry percentiles (weight, length/height, head circumference and BMI), utilities for supporting medical decision on the treatment of neonatal jaundice and anemia and support for technical procedures and other calculators and widespread use tools. The solution in development means an extension of INTCare project. The main goal is to provide an approach to get the functionality at all times of clinical practice and outside the hospital environment for dissemination, education and simulation of hypothetical situations. The aim is also to develop an area for the study and analysis of information and extraction of knowledge from the data collected by the use of the system. This paper presents the architecture, their requirements and functionalities and a SWOT analysis of the solution proposed.

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Strategic planning of electricity systems: Integrating renewable energies

Tese de Doutoramento - Programa Doutoral em Engenharia Industrial e Sistemas (PDEIS)

Improving buildings energy performance: comparison between simple payback period and life cycle costs analysis

The building sector is one of the Europeâ s main energy consumer, making buildings an important target for a wiser energy use, improving indoor comfort conditions and reducing the energy consumption. To achieve the European Union targets for energy consumption and carbon reductions it is crucial to act in new, but also in existing buildings, which constitute the majority of the building stock. In existing buildings, the significant improvement of their efficiency requires important investments. Therefore, costs are a major concern in the decision making process and the analysis of the cost effectiveness of the interventions is an important path in the guidance for the selection of the different renovation scenarios. The Portuguese thermal legislation considers the simple payback method for the calculations of the time for the return of the investment. However, this method does not take into consideration inflation, cash flows and cost of capital, as well as the future costs of energy and the building elements lifetime as it happens in a life cycle cost analysis. In order to understand the impact of the economic analysis method used in the choice of the renovation measures, a case study has been analysed using simple payback calculations and life cycle costs analysis. Overall results show that less far-reaching renovation measures are indicated when using the simple payback calculations which may be leading to solutions less cost-effective in a long run perspective.

BeSmart2: A multicriteria decision aid application

This paper presents an improved version of an application whose goal is to provide a simple and intuitive way to use multicriteria decision methods in day-to-day decision problems. The application allows comparisons between several alternatives with several criteria, always keeping a permanent backup of both model and results, and provides a framework to incorporate new methods in the future. Developed in C#, the application implements the AHP, SMART and Value Functions methods.