Bayesian Causal Network for Discrete Variables

Ensuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 10.1007/978-981-16-8044-1_14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.

144 The Epidemiology of Hoof-related Lameness in Western Canadian Feedlot Cattle

The epidemiology of hoof-related lameness (HRL) in western Canadian feedlots, with a focus on digital dermatitis (DD), was described and analyzed to help inform recommendations on lameness control and prevention in western Canadian feedlot cattle. The retrospective data in this study were accessed from 28 western Canadian feedlots that placed cattle in 2014–2018, inclusive. The total population for this study was 1,796,176 cattle, with an annual placement average of 12,830 cattle per feedlot. These data were accessed through iFHMS Consolidated Database, provided by Feedlot Health Management Services by TELUS Agriculture, and manipulated using Microsoft® Office Access 365 ProPlus and Microsoft® Office Excel 365 ProPlus. Epidemiological analyses determined that lameness accounts for 25.7% of all treatments in western Canadian feedlots. Of those treatments, 71.7% are localized to the hoof, corresponding to 18.6% of all treatments. The most common HRL diseases are infectious bovine pododermatitis [foot rot (FR)]; digital dermatitis (DD), also known as hairy-heel wart or strawberry foot rot; and toe-tip necrosis syndrome (TTNS). These diseases account for 89.6%, 7.9% and 2.4% of HRL, respectively. Between 2014 and 2018, HRL prevalence ranged between 1.93% and 3.09% of the population, with FR consistently having the highest prevalence and TTNS the lowest. HRL and DD were tested for their associations with several animal-level risk factors using © Ausvet 2021 Epitools software. The resultant crude, univariate odds ratio values, evaluated at 95% confidence, are summarized in Table 1. Based on this analysis, acquisition source has the largest influence on the odds of developing HRL and DD, followed by population size, and placement quarter. Using SAS® (Version 9.4, SAS Institute Inc, Cary, North Carolina) statistical software, these preliminary findings will be subjected to a multivariate statistical model, which will provide adjusted OR values and statistical significance for the data in this study.

Prediction of Good Quality Blastocyst Formation by Metabolomic Profile of Spent Embryo Culture Media using FTIR Spectroscopy in Women undergoing IVF Cycle : A Cohort Prospective Study

Background: In this study, we evaluated the performance of a multivariate statistical model to predict good quality blastocyst formation by processing chemometric data from FTIR spectral of spent culture media at day 1 cultured.Methods: This study aimed to determine if metabolomic profile of spent embryo culture media using Fourier-transform infrared spectroscopy (FTIR) could predict good quality blastocyst formation using cohort prospective design. A total of 44 spent culture media from 8 patients were individually collected. Forty-four samples derived from day 1 cultured. All sample was known either form a good blastocyst or no on day 5 cultured. Samples were evaluated using FTIR spectroscopy. The spectra were analyzed using chemometric and multivariate statistical model to make group classification. K fold cross-validation was used, to avoid random correlation. AUC, sensitivity and accuracy of predicting good quality blastocyst were calculated.Results: FTIR spectroscopy predicted blastocyst formation with Area under the ROC curve (AUC) 0,752 sensitivity 73 % and accuracy 72% from day-1 spent culture media.Conclusions: Metabolomic profiling of spent embryo culture media using FTIR spectroscopy combined with bioinformatics has the potential to predict blastocyst formation.

A Multivariate Prediction Copula Model to Characterize the Expenditure Categories in Tourism

The aim of this article is to introduce a multivariate statistical model that represents the expenditure of tourists disaggregated by categories. The model is applied to study the distribution of the expenditure of cruise passengers in Uruguay, using data of the 2016-2017 cruise season survey (collected by the Ministry of Tourism). Given the mixed distribution in each component of the main variable, the model is implemented in two stages and using copulas to obtain a conditional distribution of the different items of expenditure, characterizing the dependence between them. The empirical results show that the key variables that determine the average spending of cruise tourists are their residence and the port of arrival of the cruise. The parameters representing dependence of the copula show moderate association between the different categories of expenditure, in particular for cruisers disembarking in Montevideo, the capital of Uruguay. In addition, it can be noted that the expenditure pattern in each item shows time dependence. In general, the empirical results show that a cruiser that spends more on one item is likely to spend more (less) on a complementary (noncomplementary) items of expense.

Coral Reef Community Changes in Karimunjawa National Park, Indonesia: Assessing the Efficacy of Management in the Face of Local and Global Stressors

Karimunjawa National Park is one of Indonesia’s oldest established marine parks. Coral reefs across the park are being impacted by fishing, tourism and declining water quality (local stressors), as well as climate change (global pressures). In this study, we apply a multivariate statistical model to detailed benthic ecological datasets collected across Karimunjawa’s coral reefs, to explore drivers of community change at the park level. Eighteen sites were surveyed in 2014 and 2018, before and after the 2016 global mass coral bleaching event. Analyses revealed that average coral cover declined slightly from 29.2 ± 0.12% (Standard Deviation, SD) to 26.3 ± 0.10% SD, with bleaching driving declines in most corals. Management zone was unrelated to coral decline, but shifts from massive morphologies toward more complex foliose and branching corals were apparent across all zones, reflecting a park-wide reduction in damaging fishing practises. A doubling of sponges and associated declines in massive corals could not be related to bleaching, suggesting another driver, likely declining water quality associated with tourism and mariculture. Further investigation of this potentially emerging threat is needed. Monitoring and management of water quality across Karimunjawa may be critical to improving resilience of reef communities to future coral bleaching.

Salivary Metabolomics Fingerprint of Chronic Apical Abscess with Sinus Tract: A Pilot Study

Chronic apical abscess (CAA) is a lesion of apical periodontitis mostly characterized by areas of liquefactive necrosis with disintegrating polymorphonuclear neutrophils surrounded by macrophages. Its presence leads to local bacterial infection, systemic inflammatory response, pain, and swelling. The use of a novel approach for the study of CAA, such as metabolomics, seems to be important since it has proved to be a powerful tool for biomarkers discovery which could give novel molecular insight on CAA. So, the aim of this study was to verify the possibility to identify the metabolic fingerprint of CAA through the analysis of saliva samples. Nineteen patients were selected for this study: eleven patients affected by CAA with a sinus tract constituted the study group whereas eight patients without clinical and radiographic signs of CAA formed the healthy control group. Saliva samples were collected from each subject and immediately frozen at −80°C. Metabolomic profiles were obtained using a gas chromatography/mass spectrometry instrument. Subsequently, in order to compare the two groups, a multivariate statistical model was built that resulted to be statistically significant. The class of metabolites characterizing the CAA patients was closely related to the bacterial catabolism, tissue necrosis, and presence of a sinus tract. These preliminary results, for the first time, indicate that saliva samples analyzed by means of GC/MS metabolomics may be useful for identifying the presence of CAA, leading to new insights into this disease.

A MULTIVARIATE STATISTICAL MODEL TO SIMULATE STORM EVOLUTION

The design and management of a coastal structure must take into account not only the different levels of damage along its useful life but also the construction, reparation and dismantling costs. Therefore, it should be addressed as an optimization problem that depends on random multivariate climate variables. In this context it is essential to develop tools that allow the simulation of storms taking into account all the main maritime variables and their evolution (Borgman, 1969). In general, most studies focusing on storm characterization and evolution use geometric shapes like the equivalent triangular storm (Bocotti, 2000; ROM-1.0; 2009) to characterize individual storms. Actual storms have, however, irregular and random histories. In this work, we present a simple and efficient methodology to simulate time-series of storm events including several maritime variables. This methodology includes the use of non-stationary parametric distributions (Solari, 2011) to characterize each variable, a vector autoregressive (VAR) model to describe the temporal dependence between variables, and a copula model to link the seasonal dependency of the storm duration and the interarrival time between consecutive storms.

Applications of Distress Prediction Models: What Have We Learned After 50 Years from the Z-Score Models?

Fifty years ago, I published the initial, classic version of the Z-score bankruptcy prediction models. This multivariate statistical model has remained perhaps the most well-known, and more importantly, most used technique for providing an early warning signal of firm financial distress by academics and practitioners on a global basis. It also has been used by scholars as a benchmark of credit risk measurement in countless empirical studies. Practical applications of the Altman Z-score model have also been numerous and can be divided into two main categories: (1) from an external analytical standpoint, and (2) from an internal to the distressed firm viewpoint. This paper discusses a number of applications from the former’s standpoint and in doing so, we hope, also provides a roadmap for extensions beyond those already identified.