A possibility for non-invasive diagnosis of superficial scald in 'Rocha' pear based on chlorophyll a fluorescence, colorimetry, and the relation between alpha-farnesene and conjugated trienols

This study aimed to identify physiological markers in superficially scalded 'Rocha' pear (Pyrus communis L 'Rocha') that would relate to chlorophyll a fluorescence (CF), allowing a non-invasive diagnosis of the disorder. Conditions chosen before shelf life provided two fruit groups with different developing patterns and severity of superficial scald: T fruit fully developed the disorder in storage, while C fruit developed it progressively throughout shelf life. Principal component analysis (PCA) of all the measured variables, and simple linear correlations among several major parameters and scald index (SI)/shelf life showed that scald and ripening/aging were concurring processes, and that it was not possible to isolate a particular variable that could deliver a direct non-invasive diagnosis of the disorder. For both fruit groups the SI resulted from the balance between the reducing power (OD200) and the content of conjugated trienols (CTos) and alpha-farnesene (alpha-Farn) in the fruit peel. At OD200 > 150 there was a linear relationship between CTos and OD200, suggesting that the level of antioxidants was self-adjusted in order to compensate the CTos level. However, at OD200 < 150 this relationship disappeared. A consistent linear relationship between dos and alpha-Farn existed throughout shelf life in both fruit groups, contrarily to the early storage stage, when those compounds do not relate linearly. The CF variables F-0, F-v/F-m, and the colorimetric variables, L* and h degrees were used in multi-linear regressions with other physiological variables. The regressions were made on one of the fruit groups and validated through the other. Reliable regressions to alpha-Farn and CTos were obtained (R approximate to 0.6; rmsec approximate to rmsep). Our results suggest that a model based on CF and colorimetric parameters could be used to diagnose non-invasively both the contents and the relationship between alpha-Farn and CTos and hence the stage of scald development. (C) 2011 Elsevier By. All rights reserved.

A possibility for non-invasive diagnosis of superficial scald in 'Rocha' pear based on chlorophyll a fluorescence, colorimetry, and the relation between alpha-farnesene and conjugated trienols

This study aimed to identify physiological markers in superficially scalded 'Rocha' pear (Pyrus communis L 'Rocha') that would relate to chlorophyll a fluorescence (CF), allowing a non-invasive diagnosis of the disorder. Conditions chosen before shelf life provided two fruit groups with different developing patterns and severity of superficial scald: T fruit fully developed the disorder in storage, while C fruit developed it progressively throughout shelf life. Principal component analysis (PCA) of all the measured variables, and simple linear correlations among several major parameters and scald index (SI)/shelf life showed that scald and ripening/aging were concurring processes, and that it was not possible to isolate a particular variable that could deliver a direct non-invasive diagnosis of the disorder. For both fruit groups the SI resulted from the balance between the reducing power (OD200) and the content of conjugated trienols (CTos) and alpha-farnesene (alpha-Farn) in the fruit peel. At OD200 > 150 there was a linear relationship between CTos and OD200, suggesting that the level of antioxidants was self-adjusted in order to compensate the CTos level. However, at OD200 < 150 this relationship disappeared. A consistent linear relationship between dos and alpha-Farn existed throughout shelf life in both fruit groups, contrarily to the early storage stage, when those compounds do not relate linearly. The CF variables F-0, F-v/F-m, and the colorimetric variables, L* and h degrees were used in multi-linear regressions with other physiological variables. The regressions were made on one of the fruit groups and validated through the other. Reliable regressions to alpha-Farn and CTos were obtained (R approximate to 0.6; rmsec approximate to rmsep). Our results suggest that a model based on CF and colorimetric parameters could be used to diagnose non-invasively both the contents and the relationship between alpha-Farn and CTos and hence the stage of scald development. (C) 2011 Elsevier By. All rights reserved.

Examining the use of Simarouba glauca Seed Oil as a Feedstock for the Production of Biodiesel using a Small Scale Model Developed in India

Simarouba glauca, a non-edible oilseed crop native to South Florida, is gaining popularity as a feedstock for the production of biodiesel. The University of Agriculture Sciences in Bangalore, India has developed a biodiesel production model based on the principles of decentralization, small scales, and multiple fuel sources. Success of such a program depends on conversion efficiencies at multiple stages. The conversion efficiency of the field-level, decentralized production model was compared with the in-laboratory conversion efficiency benchmark. The study indicated that the field-level model conversion efficiency was less than that of the lab-scale set up. The fuel qualities and characteristics of the Simarouba glauca biodiesel were tested and found to be the standards required for fuel designation. However, this research suggests that for Simarouba glauca to be widely accepted as a biodiesel feedstock further investigation is still required.

Development and validation of a computationally efficient pseudo 3D model for planar SOFC integrated with a heating furnace

Efficient numerical models facilitate the study and design of solid oxide fuel cells (SOFCs), stacks, and systems. Whilst the accuracy and reliability of the computed results are usually sought by researchers, the corresponding modelling complexities could result in practical difficulties regarding the implementation flexibility and computational costs. The main objective of this article is to adapt a simple but viable numerical tool for evaluation of our experimental rig. Accordingly, a model for a multi-layer SOFC surrounded by a constant temperature furnace is presented, trained and validated against experimental data. The model consists of a four-layer structure including stand, two interconnects, and PEN (Positive electrode-Electrolyte-Negative electrode); each being approximated by a lumped parameter model. The heating process through the surrounding chamber is also considered. We used a set of V-I characteristics data for parameter adjustment followed by model verification against two independent sets of data. The model results show a good agreement with practical data, offering a significant improvement compared to reduced models in which the impact of external heat loss is neglected. Furthermore, thermal analysis for adiabatic and non-adiabatic process is carried out to capture the thermal behaviour of a single cell followed by a polarisation loss assessment. Finally, model-based design of experiment is demonstrated for a case study.

Applicability of an assessment model for healthcare information systems in a public hospital

Assessment processes are essential to guarantee quality and continuous improvement of software in healthcare, as they measure software attributes in their lifecycle, verify the degree of alignment between the software and its objectives and identify unpredicted events. This article analyses the use of an assessment model based on software metrics for three healthcare information systems from a public hospital that provides secondary and tertiary care in the region of Ribeirão Preto. Compliance with the metrics was investigated using questionnaires in guided interviews of the system analysts responsible for the applications. The outcomes indicate that most of the procedures specified in the model can be adopted to assess the systems that serves the organization, particularly in the attributes of compatibility, reliability, safety, portability and usability.

A multiagent based strategy for detecting attacks in databases in a distributed mode

This paper presents a distributed hierarchical multiagent architecture for detecting SQL injection attacks against databases. It uses a novel strategy, which is supported by a Case-Based Reasoning mechanism, which provides to the classifier agents with a great capacity of learning and adaptation to face this type of attack. The architecture combines strategies of intrusion detection systems such as misuse detection and anomaly detection. It has been tested and the results are presented in this paper.

One-dimensional Model for Fluids of Third-grade in Tubes with Constant Radius

Our goal in this paper is to extend previous results obtained for Newtonian and secondgrade fluids to third-grade fluids in the case of an axisymmetric, straight, rigid and impermeable tube with constant cross-section using a one-dimensional hierarchical model based on the Cosserat theory related to fluid dynamics. In this way we can reduce the full threedimensional system of equations for the axisymmetric unsteady motion of a non-Newtonian incompressible third-grade fluid to a system of equations depending on time and on a single spatial variable. Some numerical simulations for the volume flow rate and the the wall shear stress are presented.

A Case-Based Approach to Nosocomial Infection Detection

The nosocomial infections are a growing concern because they affect a large number of people and they increase the admission time in healthcare facilities. Additionally, its diagnosis is very tricky, requiring multiple medical exams. So, this work is focused on the development of a clinical decision support system to prevent these events from happening. The proposed solution is unique once it caters for the explicit treatment of incomplete, unknown, or even contradictory information under a logic programming basis, that to our knowledge is something that happens for the first time.

Waiting Time Screening in Diagnostic Medical Imaging - A Case-Based View

Due to the high standards expected from diagnostic medical imaging, the analysis of information regarding waiting lists via different information systems is of utmost importance. Such analysis, on the one hand, may improve the diagnostic quality and, on the other hand, may lead to the reduction of waiting times, with the concomitant increase of the quality of services and the reduction of the inherent financial costs. Hence, the purpose of this study is to assess the waiting time in the delivery of diagnostic medical imaging services, like computed tomography and magnetic resonance imaging. Thereby, this work is focused on the development of a decision support system to assess waiting times in diagnostic medical imaging with recourse to operational data of selected attributes extracted from distinct information systems. The computational framework is built on top of a Logic Programming Case-base Reasoning approach to Knowledge Representation and Reasoning that caters for the handling of in-complete, unknown, or even self-contradictory information.

A Case Based approach to assess Waiting Time Prediction at an Intensive Care Unity

Waiting time at an intensive care unity stands for a key feature in the assessment of healthcare quality. Nevertheless, its estimation is a difficult task, not only due to the different factors with intricate relations among them, but also with respect to the available data, which may be incomplete, self-contradictory or even unknown. However, its prediction not only improves the patients’ satisfaction but also enhance the quality of the healthcare being provided. To fulfill this goal, this work aims at the development of a decision support system that allows one to predict how long a patient should remain at an emergency unit, having into consideration all the remarks that were just stated above. It is built on top of a Logic Programming approach to knowledge representation and reasoning, complemented with a Case Base approach to computing.

Classi cation of new electricity customers based on surveys and smart metering data

This paper proposes a process for the classifi cation of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classifi cation task. Firstly, the normalized representative consumption profi les of the population are derived through the clustering of data from households. Secondly, new customers are classifi ed using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of diff erent consumption profi les, enabling the extraction of appropriate models. The results of a case study show that the use of survey data signi ficantly increases accuracy of the classifi cation task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classifi ed with only one week of metering data, with more weeks the accuracy is signifi cantly improved. The use of model-based feature selection resulted in the use of a signifi cantly lower number of features allowing an easy interpretation of the derived models.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

A Case Based Methodology for Problem Solving aiming at Knee Osteoarthritis Detection

Knee osteoarthritis is the most common type of arthritis and a major cause of impaired mobility and disability for the ageing populations. Therefore, due to the increasing prevalence of the malady, it is expected that clinical and scientific practices had to be set in order to detect the problem in its early stages. Thus, this work will be focused on the improvement of methodologies for problem solving aiming at the development of Artificial Intelligence based decision support system to detect knee osteoarthritis. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory information.

How well can models predict changes in species distributions? A 13-year-old otter model revisited

Species distribution and ecological niche models are increasingly used in biodiversity management and conservation. However, one thing that is important but rarely done is to follow up on the predictive performance of these models over time, to check if their predictions are fulfilled and maintain accuracy, or if they apply only to the set in which they were produced. In 2003, a distribution model of the Eurasian otter (Lutra lutra) in Spain was published, based on the results of a country-wide otter survey published in 1998. This model was built with logistic regression of otter presence-absence in UTM 10 km2 cells on a diverse set of environmental, human and spatial variables, selected according to statistical criteria. Here we evaluate this model against the results of the most recent otter survey, carried out a decade later and after a significant expansion of the otter distribution area in this country. Despite the time elapsed and the evident changes in this species’ distribution, the model maintained a good predictive capacity, considering both discrimination and calibration measures. Otter distribution did not expand randomly or simply towards vicinity areas,m but specifically towards the areas predicted as most favourable by the model based on data from 10 years before. This corroborates the utility of predictive distribution models, at least in the medium term and when they are made with robust methods and relevant predictor variables.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.