A Graphical Adversarial Risk Analysis Model for Oil and Gas Drilling Cybersecurity

Oil and gas drilling is based, increasingly, on operational technology, whose cybersecurity is complicated by several challenges. We propose a graphical model for cybersecurity risk assessment based on Adversarial Risk Analysis to face those challenges. We also provide an example of the model in the context of an offshore drilling rig. The proposed model provides a more formal and comprehensive analysis of risks, still using the standard business language based on decisions, risks, and value.Comment: In Proceedings GraMSec 2014, arXiv:1404.163

A historical study of the missionary work of Dr. George W. Butler and an analysis of his influence on Brazil

In August of 1954,many people were astonished to read that the new president of Brazil, Cafe Filho, was a Protestant. When this occurred, it was scarcely known outside of Brazil that the influence o.f the Protestants there is greatly out of proportion to their numbers. It would be an almost impossible task to try to trace from the United States, the development of the whole Protestant movement in Brazil. Therefore, this writer has limited the scope or this paper to a scant survey of the beginning of the Protestant missionary work in Brazil, and more particularly to a thorough study of the life of Dr. George W. Butler who was one of the first missionaries of the North Brazil Presbyterian Mission in the state or Pernambuco. The purpose of this paper is to study Dr. Butler\u27s work and his influence on the people with whom he and his associates came in contact . It will attempt to show that his work met a great need and was extremely beneficial to the community and to the state

CONTROLLING NITRATE CONCENTRATIONS IN LARGE SEAWATER FACILITIES

Upravitelji velikih akvatorija gdje nema značajnije primarne proizvodnje i gdje su promjene vode nepraktične koriste biološku denitrifikaciju za kontrolu visokih koncentracija nitrata. Dva opisana sustava denitrifikacije u ovom radu funkcioniraju na različite načine: Sustav Živo more (Living Sea) koristi se serijskim sustavom (batch-system), dok državni akvarijum u New Jerseyu koristi protočni (flow-through) sustav. Brzina denitrifikacije kontrolira djelovanje sustava Living Sea, dok vremensko zadržavanje vode kontrolira rad sustava državnog akvarijuma New Jerseya.water changes are impractical have been using biological denitrification to control high nitrate concentrations. The two denitrification systems described in this study operate in different ways: the Living Seas uses a batch system, while the New Jersey State Aquarium uses a flow-through system. The rate of denitrification controls the operation of the Living Seas system, while water residence time controls the operation of the New Jersey State Aquarium system

Ensino Aprendizagem do Conceito de Limite

As dificuldades relativas ao ensino e à aprendizagem do conceito de limite são há muito conhecidas. As tentativas de simplificações, por vezes abusivas, de conceitos tão delicados arriscam-se a gerar polémica como o provam textos relativamente recentes publicados no Boletim da SPM

Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis

Tese de mestrado, Engenharia Informática (Engenharia de Software), Universidade de Lisboa, Faculdade de Ciências, 2021ML pipelines are composed of several steps that load data, clean it, process it, apply learning algorithms and produce either reports or deploy inference systems into production. In real-world scenarios, pipelines can take days, weeks, or months to train with large quantities of data. Unfortunately, current tools to design and orchestrate ML pipelines are oblivious to the semantics of each step, allowing developers to easily introduce errors when connecting two components that might not work together, either syntactically or semantically. Data scientists and engineers often find these bugs during or after the lengthy execution, which decreases their productivity. We propose a Visual Programming Language (VPL) enriched with semantic constraints regarding the behavior of each component and a verification methodology that verifies entire pipelines to detect common ML bugs that existing visual and textual programming languages do not. We evaluate this methodology on a set of six bugs taken from a data science company focused on preventing financial fraud on big data. We were able detect these data engineering and data balancing bugs, as well as detect unnecessary computation in the pipelines

Lumped damage mechanics as a diagnosis tool of reinforced concrete structures in service: case studies of a former bridge arch and a balcony slab

Reinforced concrete structures may need repair in order to ensure the designed durability. Such necessity vary in cause and effect, but the structural diagnosis serves as the basis for adopting intervention measures. The assessment of the structural condition usually is made in loco, but sometimes numerical analyses are required as a low cost and effective preliminary diagnosis. In general, numerical analyses use hundreds or thousands of finite elements and nonlinear theories that are not often used in engineering practice. As an alternative, lumped damage mechanics (LDM) uses key concepts of classic fracture and damage mechanics in plastic hinges throughout well-known quantities such as ultimate moment and cracking moment. Such theory describes the concrete cracking by a damage variable, which can be used as a diagnosis criterion. Therefore, this paper presents LDM as a diagnosis tool to analyse actual structures. The case studies presented in this paper are a former bridge arch tested in China and a balcony that collapsed in Brazil. The results show that LDM numerical response of those structures are quite close to laboratory observations (former bridge arch) and in loco measurements (balcony)

Fault management preventive maintenance approach in mobile networks using sequential pattern mining

Mobile networks' fault management can take advantage of Machine Learning (ML) algorithms making its maintenance more proactive and preventive. Currently, Network Operations Centers (NOCs) still operate in reactive mode, where the troubleshoot is only performed after the problem identification. The network evolution to a preventive maintenance enables the problem prevention or quick resolution, leading to a greater network and services availability, a better operational efficiency and, above all, ensures customer satisfaction. In this paper, different algorithms for Sequential Pattern Mining (SPM) and Association Rule Learning (ARL) are explored, to identify alarm patterns in a live Long Term Evolution (LTE) network, using Fault Management (FM) data. A comparative performance analysis between all the algorithms was carried out, having observed, in the best case scenario, a decrease of 3.31% in the total number of alarms and 70.45% in the number of alarms of a certain type. There was also a considerable reduction in the number of alarms per network node in a considered area, having identified 39 nodes that no longer had any unresolved alarm. These results demonstrate that the recognition of sequential alarm patterns allows taking the first steps in the direction of preventive maintenance in mobile networks.info:eu-repo/semantics/publishedVersio