GESTÃO DO PROCESSO DE CAPTAÇÃO DE LEITE: UM ESTUDO DE CASO NA COOPERATIVA AGRÃCOLA ALTO RIO GRANDE LTDA (CAARG)

The aim of this work was to assess the milking process in an agricultural cooperative in Lavras, Minas Gerais state. The study was done between August and November/2004, employing a qualitative research methodology that involved the researchers and the cooperative’s employees. Process management promotes the organization and management of information in the company, favoring cost administration and control. The study presented some lines with average costs above R$ 1.51/Km, and others below. This fact could impair the efficiency of the process. To ensure quality, legal parameters were defined, through laboratory analysis, to classify the milk. The observance of the strong and weak points are the best way to maximize the organization’s efficiency. However, these weak points deserve special attention from the CAARG’s decision makers in order to guarantee the cooperative’s performance. The main strategies implemented were financing and allowance of the outgoing tanks, combined with payment according to the volume and quality of the milk. Nevertheless, vertical integration should be adopted through supply contracts or quota definition, which is extremely important in guaranteeing not only the supply, but mainly the quality of the raw-material.competitive Strategy, Agricultural Cooperative, Vertical Integration, and costs of milk transportation, Agribusiness, Agricultural Finance, Industrial Organization,

An automated and distributed machine learning framework for telecommunications risk management

Automation and scalability are currently two of the main challenges of Machine Learning. This paper proposes an automated and distributed ML framework that automatically trains a supervised learning model and produces predictions independently of the dataset and with minimum human input. The framework was designed for the domain of telecommunications risk management, which often requires supervised learning models that need to be quickly updated by non-ML-experts and trained on vast amounts of data. Thus, the architecture assumes a distributed environment, in order to deal with big data, and Automated Machine Learning (AutoML), to select and tune the ML models. The framework includes several modules: task detection (to detect if classification or regression), data preprocessing, feature selection, model training, and deployment. In this paper, we detail the model training module. In order to select the computational technologies to be used in this module, we first analyzed the capabilities of an initial set of five modern AutoML tools: Auto-Keras, Auto-Sklearn, Auto-Weka, H2O AutoML, and TransmogrifAI. Then, we performed a benchmarking of the only two tools that address distributed ML (H2O AutoML and TransmogrifAI). Several comparison experiments were held using three real-world datasets from the telecommunications domain (churn, event forecasting, and fraud detection), allowing us to measure the computational effort and predictive capability of the AutoML tools.This work was executed under the project IR-MDA - Intelligent Risk Management for the Digital Age, Individual Project, NUP: POCI-01-0247-FEDER-038526, co-funded by the Incentive Systemfor Research and Technological Development, fromthe Thematic Operational Program Competitivenessof the national framework program - Portugal2020

A scalable and automated machine learning framework to support risk management

Due to the growth of data and wide spread usage of Machine Learning (ML) by non-experts, automation and scalability are becoming key issues for ML. This paper presents an automated and scalable framework for ML that requires minimum human input. We designed the framework for the domain of telecommunications risk management. This domain often requires non-ML-experts to continuously update supervised learning models that are trained on huge amounts of data. Thus, the framework uses Automated Machine Learning (AutoML), to select and tune the ML models, and distributed ML, to deal with Big Data. The modules included in the framework are task detection (to detect classification or regression), data preprocessing, feature selection, model training, and deployment. In this paper, we focus the experiments on the model training module. We first analyze the capabilities of eight AutoML tools: Auto-Gluon, Auto-Keras, Auto-Sklearn, Auto-Weka, H2O AutoML, Rminer, TPOT, and TransmogrifAI. Then, to select the tool for model training, we performed a benchmark with the only two tools that address a distributed ML (H2O AutoML and TransmogrifAI). The experiments used three real-world datasets from the telecommunications domain (churn, event forecasting, and fraud detection), as provided by an analytics company. The experiments allowed us to measure the computational effort and predictive capability of the AutoML tools. Both tools obtained high- quality results and did not present substantial predictive differences. Nevertheless, H2O AutoML was selected by the analytics company for the model training module, since it was considered a more mature technology that presented a more interesting set of features (e.g., integration with more platforms). After choosing H2O AutoML for the ML training, we selected the technologies for the remaining components of the architecture (e.g., data preprocessing and web interface).This work was executed under the project IRMDA - Intelligent Risk Management for the Digital Age, Individual Project, NUP: POCI-01-0247-FEDER-038526, co- funded by the Incentive System for Research and Technological Development, from the Thematic Operational Program Competitiveness of the national framework program - Portugal2020

Flood Risk Assessment in the Lisbon Metropolitan Area

Flood processes are one of the most challenging to risk assessment and management. In many situations, peak flows are generated kilometers away from the places where inundation is observed. Scale in flood risk assessments is a fundamental factor when estimating hazard, exposure, and vulnerability. Municipal, civil parish, and building-level information are used to construct flood risk indexes and profiles. It is observed that, depending on the scale at which it is represented, the same root information provides distinct insights into flood risk expression in the Lisbon Metropolitan Area. When compared with the Flood Directive critical areas, the results show they are mostly consistent with the results at the different scales, identifying the same hotspots of flood risk (in the Loures, V. F. Xira, and Setúbal municipalities) as those selected during the Directive’s implementation. Flood loss reduction implies the involvement of distinct risk practitioners and decision-makers, acting at distinct scales and sectors related to risk governance. Interconnections between flood risk components and between flood processes and other potential cascading processes are still insufficiently known and require the priority of society.info:eu-repo/semantics/publishedVersio

Effects of different slipping methods on the mortality of sardine, Sardina pilchardus, after purse-seine capture off the Portuguese Southern coast (Algarve)

The effects of two different slipping methods on the survival, physical and physiological response of sardines, Sardina pilchardus, captured in a purse-seine fishery were investigated in southern Portugal. Sardines were collected and transferred into holding tanks onboard a commercial fishing vessel after being captured, crowded and deliberately released using two slipping procedures: standard and modified. The standard slipping procedure aggregated fish at high densities and made them "roll over" the floatline, while the modified procedure aggregated the fish at moderate densities and enabled them to escape through an opening created by adding weights to the floatline. Both slipping methods were compared with minimally harmed non-slipped sardines (sardines collected from the loose pocket of the purse seine). Survival rates were monitored in captivity over 28 days using three replicates for each treatment. The estimated survival of sardines was 43.6% for the non-slipped fish, 44.7% for the modified slipping and 11.7% for the standard slipping treatments. Scale loss indicated the level of physical impact experienced, with dead fish from the non-slipped and modified slipping technique showing significantly lower scale loss than those fish from the standard slipping treatment within the same period. Of the physiological indicators of stress measured, cortisol, glucose, lactate and osmolality attained peak values during slipping and up to the first hours after introduction to captivity. This work indicates that although delayed mortality after release may be substantial, appropriately modified slipping techniques significantly enhance survival of slipped sardines.FCT [SFRH/BPD/116307/2016]; European Commission's Horizon 2020 Research and Innovation Programme [634495

The Portuguese National Registry for Hemophilia: Developing of a Web-based Technological Solution

AbstractThe crucial role that patient records have in the management of the rare and chronic diseases greatly increases the need to create mechanisms to facilitate the identification and management of the patient's data. Hemophilia is an X-linked congenital bleeding disorder caused by a deficiency of coagulation factor that affects the population on a ratio of 1 case for 10,000 people born. Currently, there are several countries with technological platforms to support the National Patients’ Registries (NPR) of Hemophilia and other Congenital Coagulopathies (HoCC), due to its benefits in the management of the disease. This work presents the technological platform developed in a joint initiative between the University of Aveiro (UA) and the Portuguese Association of Congenital Coagulopathies (PACC), with the purpose of creating the first NPR with HoCC in Portugal. This web application is hosted in the data center of the University of Aveiro, and is being used by the clinicians of the different Hemophilia Treatment Centers (HTC) across the country