The need for standardized tests to evaluate the reliability of data transport in wireless medical systems

Wireless medical systems are comprised of four stages, namely the medical device, the data transport, the data collection and the data evaluation stages. Whereas the performance of the first stage is highly regulated, the others are not. This paper concentrates on the data transport stage and argues that it is necessary to establish standardized tests to be used by medical device manufacturers to provide comparable results concerning the communication performance of the wireless networks used to transport medical data. Besides, it suggests test parameters and procedures to be used to produce comparable communication performance results.

Fatigue Prediction on Fibrin Poly-ε-caprolactone Macroporous Scaffolds

Tissue engineering applications rely on scaffolds that during its service life, either for in-vivo or in vitro applications, are under mechanical solicitations. The variation of the mechanical condition of the scaffold is strongly relevant for cell culture and has been scarcely addressed. Fatigue life cycle of poly-ε-caprolactone, PCL, scaffolds with and without fibrin as filler of the pore structure were characterized both dry and immersed in liquid water. It is observed that the there is a strong increase from 100 to 500 in the number of loading cycles before collapse in the samples tested in immersed conditions due to the more uniform stress distributions within the samples, the fibrin loading playing a minor role in the mechanical performance of the scaffolds

Prediction of genetic gain from selection indices for disease resistance in papaya hybrids

In order to select superior hybrids for the concentration of favorable alleles for resistance to papaya black spot, powdery mildew and phoma spot, 67 hybrids were evaluated in two seasons, in 2007, in a randomized block design with two replications. Genetic gains were estimated from the selection indices of Smith & Hazel, Pesek & Baker, Williams, Mulamba & Mock, with selection intensity of 22.39%, corresponding to 15 hybrids. The index of Mulamba & Mock showed gains more suitable for the five traits assessed when it was used the criterion of economic weight tentatively assigned. Together, severity of black spot on leaves and on fruits, characteristics considered most relevant to the selection of resistant materials, expressed percentage gain of -44.15%. In addition, there were gains for other characteristics, with negative predicted selective percentage gain. The results showed that the index of Mulamba & Mock is the most efficient procedure for simultaneous selection of papaya hybrid resistant to black spot, powdery mildew and phoma spot.

Predicting the mechanical behavior of amorphous polymeric materials under strain through multi-scale simulation

Polymeric materials have become the reference material for high reliability and performance applications. However, their performance in service conditions is difficult to predict, due in large part to their inherent complex morphology, which leads to non-linear and anisotropic behavior, highly dependent on the thermomechanical environment under which it is processed. In this work, a multiscale approach is proposed to investigate the mechanical properties of polymeric-based material under strain. To achieve a better understanding of phenomena occurring at the smaller scales, the coupling of a finite element method (FEM) and molecular dynamics (MD) modeling, in an iterative procedure, was employed, enabling the prediction of the macroscopic constitutive response. As the mechanical response can be related to the local microstructure, which in turn depends on the nano-scale structure, this multiscale approach computes the stress-strain relationship at every analysis point of the macro-structure by detailed modeling of the underlying micro- and meso-scale deformation phenomena. The proposed multiscale approach can enable prediction of properties at the macroscale while taking into consideration phenomena that occur at the mesoscale, thus offering an increased potential accuracy compared to traditional methods.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Assessment and prediction of asthma and its severity in the pediatric community

Seventy four asthmatic children aged 7 to 11 years were examined along with controls matched by age and sex. Clinical and laboratory investigations preceded a 28-day follow-up where data about morning and evening peak expiratory flow rate (PEF), symptoms and treatment were recorded. The coefficient of variation of PEF was found to be an objective measurement of asthma severity that has statistically significant correlation with both symptoms (r s= .36) and treatment (r s= .60). Moreover, it separates mild and severe asthmatics, as confirmed by statistically significant differences (p= .008 or less) in symptoms, treatment, skin allergy and airways response to exercise. Skin allergy and airways responsiveness to exercise were found to be predictors of both disease and severity. By means of logistic regression analysis it was possible to establish the probabilities for both asthma and severe asthma when children presenting and not presenting these characteristics are compared. One single positive skin test represent a probability of 88% for the development of asthma and a probability of 70% for severe disease. A PEF reduction of 10% after an exercise test implies a probability of 73% for disease and a probability of 64% for severe disease. Increases in these variables imply geometrically increased risks and their presence together have a multiplicative effect in the final risk.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

Reliability of self-reported weight and height among State bank employees

INTRODUCTION: Self-reported weight and height were compared with direct measurements in order to evaluate the agreement between the two sources. METHOD: Data were obtained from a cross-sectional study on health status from a probabilistic sample of 1,183 employees of a bank, in Rio de Janeiro State, Brazil. Direct measurements were made of 322 employees. Differences between the two sources were evaluated using mean differences, limits of agreement and intraclass correlation coefficient (ICC). RESULTS AND CONCLUSIONS: Men and women tended to underestimate their weight while differences between self-reported and measured height were insignificant. Body mass index (BMI) mean differences were smaller than those observed for weight. ICC was over 0.98 for weight and 0.95 for BMI, expressing close agreement. Combining a graphical method with ICC may be useful in pilot studies to detect populational groups capable of providing reliable information on weight and height, thus minimizing resources needed for field work.

A model to forecast financial failure, in non financial galician SMES

We are concerned with providing more empirical evidence on forecast failure, developing forecast models, and examining the impact of events such as audit reports. A joint consideration of classic financial ratios and relevant external indicators leads us to build a basic prediction model focused in non-financial Galician SMEs. Explanatory variables are relevant financial indicators from the viewpoint of the financial logic and financial failure theory. The paper explores three mathematical models: discriminant analysis, Logit, and linear multivariate regression. We conclude that, even though they both offer high explanatory and predictive abilities, Logit and MDA models should be used and interpreted jointly.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Reliability of nasal auscultation and their relation to middle ear condition – a clinical interest to pediatrics respiratory physiotherapy

Background: Acute respiratory infections are usual in children under three years old occurring in upper respiratory tract, having an impact on child and caregiver’s quality of life predisposing to otitis media or bronchiolitis. There are few valid and reliable measures to determine the child’s respiratory condition and to guide the physiotherapy intervention. Aim: To assess the intra and inter rater reliability of nasal auscultation, to analyze the relation between sounds’ classification and middle ear’s pressure and compliance as well as with the Clinical Severity Score. Methods: A cross-sectional observational study was composed by 125 nursery children aged up to three years old. Tympanometry, pulmonary and nasal auscultation and application of Clinical Severity Score were performed to each child. Nasal auscultation sounds’ were recorded and sent to 3 blinded experts, that classified, as “obstructed” and “unobstructed”, with a 48 hours interval, in order to analyze inter and intra rater reliability. Results: Nasal auscultation revealed a substantial inter and intra rater reliability (=0,749 and evaluator A - K= 0,691; evaluator B - K= 0,605 and evaluator C - K= 0,724, respectively). Both ears’ pressure was significantly lower in children with an "unobstructed" nasal sound when compared with an “obstructed” nasal sound (t=-3,599, p<0,001 in left ear; t=-2,258, p=0,026 in right ear). Compliance in both ears was significantly lower in children with an "obstructed" nasal sound when compared with “unobstructed” nasal sound (t=-2,728, p=0,007 in left ear; t=-3,830, p<0,001 in right ear). There was a statistically significant association between sounds’ classification and tympanograms types in both ear’s (=11,437, p=0,003 in left ear; =13,535, p=0,001 in right ear). There was a trend to children with an "unobstructed" nasal sound that had a lower clinical severity score when compared with “obstructed” children. Conclusion: It was observed a good intra and substantial inter reliability for nasal auscultation. Nasal auscultation sounds’ classification was related to middle ears’ pressure and compliance.

The hospital survey on patient safety culture in Portuguese hospitals: instrument validity and reliability

Purpose – Quantitative instruments to assess patient safety culture have been developed recently and a few review articles have been published. Measuring safety culture enables healthcare managers and staff to improve safety behaviours and outcomes for patients and staff. The study aims to determine the AHRQ Hospital Survey on Patient Safety Culture (HSPSC) Portuguese version's validity and reliability. Design/methodology/approach – A missing-value analysis and item analysis was performed to identify problematic items. Reliability analysis, inter-item correlations and inter-scale correlations were done to check internal consistency, composite scores. Inter-correlations were examined to assess construct validity. A confirmatory factor analysis was performed to investigate the observed data's fit to the dimensional structure proposed in the AHRQ HSPSC Portuguese version. To analyse differences between hospitals concerning composites scores, an ANOVA analysis and multiple comparisons were done. Findings – Eight of 12 dimensions had Cronbach's alphas higher than 0.7. The instrument as a whole achieved a high Cronbach's alpha (0.91). Inter-correlations showed that there is no dimension with redundant items, however dimension 10 increased its internal consistency when one item is removed. Originality/value – This study is the first to evaluate an American patient safety culture survey using Portuguese data. The survey has satisfactory reliability and construct validity.

Fuzzy Monte Carlo model for transmission power systems reliability based decision making

This thesis presents the Fuzzy Monte Carlo Model for Transmission Power Systems Reliability based studies (FMC-TRel) methodology, which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states. This is followed by a remedial action algorithm, based on Optimal Power Flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. For the system states that cause load curtailment, an optimization approach is applied to reduce the probability of occurrence of these states while minimizing the costs to achieve that reduction. This methodology is of most importance for supporting the transmission system operator decision making, namely in the identification of critical components and in the planning of future investments in the transmission power system. A case study based on Reliability Test System (RTS) 1996 IEEE 24 Bus is presented to illustrate with detail the application of the proposed methodology.