Made available in DSpace on 2017-05-12T14:47:07Z (GMT). No. of bitstreams: 1 Joao Fernando _Apel Miguel.pdf: 2365694 bytes, checksum: 7d9d98b6d57859a97870ad98e9d6d788 (MD5) Previous issue date: 2014-08-15 / This trial aimed at determining the S index using a drip irrigated viticulture in a nitossol. Twenty soil samples were collected for chemical analyses at the same familiar farm in Salto do Lontra city, southwestern Paraná. Richards chamber methodology was applied to make the water retention curve. Statistical process control was chosen to check which elements were under statistical control. Only three elements of the chemical properties were within control chart limits: phosphorus, calcium and aluminum. The calculated values of S index were below the critical value of S = 0.035, which means good physical condition of the soil. / O objetivo deste trabalho foi determinar o índice S para um Nitossolo sob viticultura irrigada por gotejamento. Foram coletadas 20 amostras de solo para análises químicas na mesma propriedade agrícola, no município de Salto do Lontra, Sudoeste do Paraná. Foi utilizada a metodologia da câmara de Richards para a confecção da curva de retenção de água no solo. O controle estatístico de processo foi usado para verificar quais elementos químicos estavam sob controle estatístico. Os elementos das propriedades químicas que se apresentaram dentro dos limites dos gráficos de controle foram: fósforo, cálcio e alumínio. Os valores calculados do índice S ficaram abaixo do valor crítico de S = 0,035, o que significa boa condição física do solo.

Made available in DSpace on 2017-07-10T19:23:53Z (GMT). No. of bitstreams: 1 Joao Fernando _Apel Miguel.pdf: 2365694 bytes, checksum: 7d9d98b6d57859a97870ad98e9d6d788 (MD5) Previous issue date: 2014-08-15 / This trial aimed at determining the S index using a drip irrigated viticulture in a nitossol. Twenty soil samples were collected for chemical analyses at the same familiar farm in Salto do Lontra city, southwestern Paraná. Richards chamber methodology was applied to make the water retention curve. Statistical process control was chosen to check which elements were under statistical control. Only three elements of the chemical properties were within control chart limits: phosphorus, calcium and aluminum. The calculated values of S index were below the critical value of S = 0.035, which means good physical condition of the soil. / O objetivo deste trabalho foi determinar o índice S para um Nitossolo sob viticultura irrigada por gotejamento. Foram coletadas 20 amostras de solo para análises químicas na mesma propriedade agrícola, no município de Salto do Lontra, Sudoeste do Paraná. Foi utilizada a metodologia da câmara de Richards para a confecção da curva de retenção de água no solo. O controle estatístico de processo foi usado para verificar quais elementos químicos estavam sob controle estatístico. Os elementos das propriedades químicas que se apresentaram dentro dos limites dos gráficos de controle foram: fósforo, cálcio e alumínio. Os valores calculados do índice S ficaram abaixo do valor crítico de S = 0,035, o que significa boa condição física do solo.

How to quantify security is a classic question in the security community that until today has had no plausible answer. Unfortunately, current security evaluation models are often either quantitative but too specific (i.e., applicability is limited), or comprehensive (i.e., system-level) but qualitative. The importance of quantifying security cannot be overstated, but doing so is difficult and complex, for many reason: the “physics” of the amount of security is ambiguous; the operational state is defined by two confronting parties; protecting and breaking systems is a cross-disciplinary mechanism; security is achieved by comparable security strength and breakable by the weakest link; and the human factor is unavoidable, among others. Thus, security engineers face great challenges in defending the principles of information security and privacy. This thesis addresses model-based system-level security quantification and argues that properly addressing the quantification problem of security first requires a paradigm shift in security modeling, addressing the problem at the abstraction level of what defines a computing system and failure model, before any system-level analysis can be established. Consequently, we present a candidate computing systems abstraction and failure model, then propose two failure-centric model-based quantification approaches, each including a bounding system model, performance measures, and evaluation techniques. The first approach addresses the problem considering the set of controls. To bound and build the logical network of a security system, we extend our original work on the Information Security Maturity Model (ISMM) with Reliability Block Diagrams (RBDs), state vectors, and structure functions from reliability engineering. We then present two different groups of evaluation methods. The first mainly addresses binary systems, by extending minimal path sets, minimal cut sets, and reliability analysis based on both random events and random variables. The second group addresses multi-state security systems with multiple performance measures, by extending Multi-state Systems (MSSs) representation and the Universal Generating Function (UGF) method. The second approach addresses the quantification problem when the two sets of a computing system, i.e., assets and controls, are considered. We adopt a graph-theoretic approach using Bayesian Networks (BNs) to build an asset-control graph as the candidate bounding system model, then demonstrate its application in a novel risk assessment method with various diagnosis and prediction inferences. This work, however, is multidisciplinary, involving foundations from many fields, including security engineering; maturity models; dependability theory, particularly reliability engineering; graph theory, particularly BNs; and probability and stochastic models.

How to quantify security is a classic question in the security community that until today has had no plausible answer. Unfortunately, current security evaluation models are often either quantitative but too specific (i.e., applicability is limited), or comprehensive (i.e., system-level) but qualitative. The importance of quantifying security cannot be overstated, but doing so is difficult and complex, for many reason: the “physics” of the amount of security is ambiguous; the operational state is defined by two confronting parties; protecting and breaking systems is a cross-disciplinary mechanism; security is achieved by comparable security strength and breakable by the weakest link; and the human factor is unavoidable, among others. Thus, security engineers face great challenges in defending the principles of information security and privacy. This thesis addresses model-based system-level security quantification and argues that properly addressing the quantification problem of security first requires a paradigm shift in security modeling, addressing the problem at the abstraction level of what defines a computing system and failure model, before any system-level analysis can be established. Consequently, we present a candidate computing systems abstraction and failure model, then propose two failure-centric model-based quantification approaches, each including a bounding system model, performance measures, and evaluation techniques. The first approach addresses the problem considering the set of controls. To bound and build the logical network of a security system, we extend our original work on the Information Security Maturity Model (ISMM) with Reliability Block Diagrams (RBDs), state vectors, and structure functions from reliability engineering. We then present two different groups of evaluation methods. The first mainly addresses binary systems, by extending minimal path sets, minimal cut sets, and reliability analysis based on both random events and random variables. The second group addresses multi-state security systems with multiple performance measures, by extending Multi-state Systems (MSSs) representation and the Universal Generating Function (UGF) method. The second approach addresses the quantification problem when the two sets of a computing system, i.e., assets and controls, are considered. We adopt a graph-theoretic approach using Bayesian Networks (BNs) to build an asset-control graph as the candidate bounding system model, then demonstrate its application in a novel risk assessment method with various diagnosis and prediction inferences. This work, however, is multidisciplinary, involving foundations from many fields, including security engineering; maturity models; dependability theory, particularly reliability engineering; graph theory, particularly BNs; and probability and stochastic models.