Conditional probability applied to the definition of limestone geochemical facies: A new Methodological Approach

A new method, based on linear correlation and phase diagrams was successfully developed for processes like the sedimentary process, where the deposition phase can have different time duration - represented by repeated values in a series - and where the erosion can play an important rule deleting values of a series. The sampling process itself can be the cause of repeated values - large strata twice sampled - or deleted values: tiny strata fitted between two consecutive samples. What we developed was a mathematical procedure which, based upon the depth chemical composition evolution, allows the establishment of frontiers as well as the periodicity of different sedimentary environments. The basic tool isn't more than a linear correlation analysis which allow us to detect the existence of eventual evolution rules, connected with cyclical phenomena within time series (considering the space assimilated to time), with the final objective of prevision. A very interesting discovery was the phenomenon of repeated sliding windows that represent quasi-cycles of a series of quasi-periods. An accurate forecast can be obtained if we are inside a quasi-cycle (it is possible to predict the other elements of the cycle with the probability related with the number of repeated and deleted points). We deal with an innovator methodology, reason why it's efficiency is being tested in some case studies, with remarkable results that shows it's efficacy. Keywords: sedimentary environments, sequence stratigraphy, data analysis, time-series, conditional probability.

Algorithm-oriented design of efficient many-core architectures applied to dense matrix multiplication

Recent integrated circuit technologies have opened the possibility to design parallel architectures with hundreds of cores on a single chip. The design space of these parallel architectures is huge with many architectural options. Exploring the design space gets even more difficult if, beyond performance and area, we also consider extra metrics like performance and area efficiency, where the designer tries to design the architecture with the best performance per chip area and the best sustainable performance. In this paper we present an algorithm-oriented approach to design a many-core architecture. Instead of doing the design space exploration of the many core architecture based on the experimental execution results of a particular benchmark of algorithms, our approach is to make a formal analysis of the algorithms considering the main architectural aspects and to determine how each particular architectural aspect is related to the performance of the architecture when running an algorithm or set of algorithms. The architectural aspects considered include the number of cores, the local memory available in each core, the communication bandwidth between the many-core architecture and the external memory and the memory hierarchy. To exemplify the approach we did a theoretical analysis of a dense matrix multiplication algorithm and determined an equation that relates the number of execution cycles with the architectural parameters. Based on this equation a many-core architecture has been designed. The results obtained indicate that a 100 mm(2) integrated circuit design of the proposed architecture, using a 65 nm technology, is able to achieve 464 GFLOPs (double precision floating-point) for a memory bandwidth of 16 GB/s. This corresponds to a performance efficiency of 71 %. Considering a 45 nm technology, a 100 mm(2) chip attains 833 GFLOPs which corresponds to 84 % of peak performance These figures are better than those obtained by previous many-core architectures, except for the area efficiency which is limited by the lower memory bandwidth considered. The results achieved are also better than those of previous state-of-the-art many-cores architectures designed specifically to achieve high performance for matrix multiplication.

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

An uncertainty and sensitivity analysis applied to the prioritisation of pharmaceuticals as surface water contaminants from wastewater treatment plant direct emissions

In this study, the concentration probability distributions of 82 pharmaceutical compounds detected in the effluents of 179 European wastewater treatment plants were computed and inserted into a multimedia fate model. The comparative ecotoxicological impact of the direct emission of these compounds from wastewater treatment plants on freshwater ecosystems, based on a potentially affected fraction (PAF) of species approach, was assessed to rank compounds based on priority. As many pharmaceuticals are acids or bases, the multimedia fate model accounts for regressions to estimate pH-dependent fate parameters. An uncertainty analysis was performed by means of Monte Carlo analysis, which included the uncertainty of fate and ecotoxicity model input variables, as well as the spatial variability of landscape characteristics on the European continental scale. Several pharmaceutical compounds were identified as being of greatest concern, including 7 analgesics/anti-inflammatories, 3 β-blockers, 3 psychiatric drugs, and 1 each of 6 other therapeutic classes. The fate and impact modelling relied extensively on estimated data, given that most of these compounds have little or no experimental fate or ecotoxicity data available, as well as a limited reported occurrence in effluents. The contribution of estimated model input variables to the variance of freshwater ecotoxicity impact, as well as the lack of experimental abiotic degradation data for most compounds, helped in establishing priorities for further testing. Generally, the effluent concentration and the ecotoxicity effect factor were the model input variables with the most significant effect on the uncertainty of output results.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Autonomous guided vehicles applied to industrial engineering and management studies

This article presents a framework to an Industrial Engineering and Management Science course from School of Management and Industrial Studies using Autonomous Ground Vehicles (AGV) to supply materials to a production line as an experimental setup for the students to acquire knowledge in the production robotics area. The students must be capable to understand and put into good use several concepts that will be of utmost importance in their professional life such as critical decisions regarding the study, development and implementation of a production line. The main focus is a production line using AGVs, where the students are required to address several topics such as: sensors actuators, controllers and an high level management and optimization software. The presented framework brings to the robotics teaching community methodologies that allow students from different backgrounds, that normally don’t experiment with the robotics concepts in practice due to the big gap between theory and practice, to go straight to ”making” robotics. Our aim was to suppress the minimum start point level thus allowing any student to fully experience robotics with little background knowledge.

Exploraty Multivariate Statistical Methods Applied to Pharmaceutical Industry CRM Data

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

WebGIS applied to improve spatial knowledge of high school students

This research seeks to design and implement a WebGIS application allowing high school students to work with information related to the disciplinary competencies of the competency-teaching model, in Mexico. This paradigm assumes knowledge to be acquired through the application of new technologies and to link it with everyday life situations of students. The WebGIS provides access to maps regarding natural risks in Mexico, e.g. volcanism, seismic activities, or hurricanes; the prototype's user interface was designed with special emphasis on scholar needs for high school students.

Modulation of electrical stimulation applied to human physiology and clinical diagnostic

The use, manipulation and application of electrical currents, as a controlled interference mechanism in the human body system, is currently a strong source of motivation to researchers in areas such as clinical, sports, neuroscience, amongst others. In electrical stimulation (ES), the current applied to tissue is traditionally controlled concerning stimulation amplitude, frequency and pulse-width. The main drawbacks of the transcutaneous ES are the rapid fatigue induction and the high discomfort induced by the non-selective activation of nervous fibers. There are, however, electrophysiological parameters whose response, like the response to different stimulation waveforms, polarity or a personalized charge control, is still unknown. The study of the following questions is of great importance: What is the physiological effect of the electric pulse parametrization concerning charge, waveform and polarity? Does the effect change with the clinical condition of the subjects? The parametrization influence on muscle recruitment can retard fatigue onset? Can parametrization enable fiber selectivity, optimizing the motor fibers recruitment rather than the nervous fibers, reducing contraction discomfort? Current hardware solutions lack flexibility at the level of stimulation control and physiological response assessment. To answer these questions, a miniaturized, portable and wireless controlled device with ES functions and full integration with a generic biosignals acquisition platform has been created. Hardware was also developed to provide complete freedom for controlling the applied current with respect to the waveform, polarity, frequency, amplitude, pulse-width and duration. The impact of the methodologies developed is successfully applied and evaluated in the contexts of fundamental electrophysiology, psycho-motor rehabilitation and neuromuscular disorders diagnosis. This PhD project was carried out in the Physics Department of Faculty of Sciences and Technology (FCT-UNL), in straight collaboration with PLUX - Wireless Biosignals S.A. company and co-funded by the Foundation for Science and Technology.

The value creation radar applied to the space engineering sector

This case study illustrates the application of the Value Creation Radar (VCR) to SenSyF, an Earth Observation (EO) system which was developed by Deimos Engenharia S.A. (DME), the Portuguese affiliate of Elecnor Deimos. It describes how a team of consultants adopted the VCR in order to find new market applications for SenSyF, selected the one with the highest potential, and defined a path to guarantee a sustainable market launch. This case study highlights the main challenges of bringing a technology-driven company closer to the market in the pursuit of long-term sustainability, while not compromising its technological capabilities

The Beglocal framework applied to B2C E-commerce

This case-study examined the use of the BeGloCal Framework applied to B2C E-commerce, for a fast moving consumer goods European manufacturing firm. It explains how the framework supported the team within the company to identify the right local market as to where to start the project, the problem for the company was to find the most appealing area to invest resources. By going through all the steps of the framework the findings led the company to London (Kensington and Chelsea). It shows how managers should act when they have to find a trade-off between standardization and adaptation.

The global radar applied to the precision farming sector

The case describes the development of MyFARM’s internationalization plan, a service of Deimos Engenharia, under the GloCal Radar. This space engineering company hired Lisbon Consulting Company to undertake the project to overcome its lack of market orientation. The consultants’ analysis revealed Stevens County, Kansas, as the market with the highest potential for MyFARM. A suitable entry strategy and adaptation of the service for the local market was proposed. The case culminates with the Board of Directors discussing the viability of implementing the consultants’ recommendations to start diversifying their sources of revenue streams.

Proceedings of the 1st International Conference on Applied Innovations in IT

This volume contains publications of the 1st International Conference on Applied Innovations in IT (ICAIIT), which took place in Koethen March 25th 2013. The conference is devoted to problems of applied research in the fields of mechanical and economical engineering, auotmation and communications as well as of data mining. The research results can be of interest for researchers and development engineers, who deal with theoretical base and the application of the knowledge in the respective areas.

Proceedings of the 2nd International Conference on Applied Innovations in IT

This volume contains publications of the 2nd International Conference on Applied Innovations in IT (ICAIIT), which took place in Koethen March 27th 2014. The conference is devoted to problems of applied research in the fields of automation and communications. The research results can be of interest for researchers and development engineers, who deal with theoretical base and the application of the knowledge in the respective areas.