Health status of the over 60 years of age population and its relationship with sociodemographic factors (ANCO Project)

OBJECTIVE: To describe the health status of a population over 60 years and to study their relationship with several socio-demographic variables. DESIGN: A cross-sectional study, population based. SETTINGS: A community. PARTICIPANTS: A randomized sample of 1,103 non institutionalized people over 60 years living in the city of Cordoba (Spain). MEASUREMENTS AND MAIN RESULTS: By mean of a personal interview at home we used the OARS-MFAQ-VE questionnaire. Low self-rated health was associated with the age, to be female sex, a low cultural background, and a low income. Only 5.2% of the study people do not suffered any illness and 56% state that their health problems are major problems for doing their current activities. 4.9% declared to have some degree of physical incapacity. 3.7% of elderly population has an important cognitive deficit. CONCLUSIONS: The majority of elderly people has good health. Age is related with a poor health. Women have more health problems than men.     

Adequacy of medical referrals to hospital emergency service

OBJECTIVE: To assess the correctness and diagnostic concordance in referrals of patients to hospital Casualty Departments (CD); and the possible differences between rural and urban health centres (HC) as well as between general practitioners (GP) and family and community medicine (FCM) specialists. DESIGN: Observational study of a crossover nature. SETTING: Health area 1 in the province of Badajoz. PATIENTS AND OTHER PARTICIPANTS: 800 patients, referred to their local hospital Casualty Department. MEASUREMENTS AND MAIN RESULTS: 50.5% of all referrals were considered justified. 15.1% of the patients referred were admitted to hospital. 50.7% of referrals from GPs were found to be correct, against 47% from FCM specialists; and 49% of those referred from rural HCs were correct, as against 45.9% of those referred from urban HCs (no significant differences). Only 54% of referrals had a diagnostic hypothesis, with diagnostic concordance at 50%. There was diagnostic concordance in 28.1% of GP referrals, against 33.3% of FCM specialists; and in 27.5% of those from rural HCs against 26.1% from urban HCs (no significant differences). CONCLUSIONS: We found no differences in the correctness of referrals or diagnostic concordance of patients seen in the CD of our city's health area, neither between patients referred from rural and urban HCs, nor between patients referred by GPs and FCM specialists.     

New thermosets obtained by cationic copolymerization of DGEBA with γ‐caprolactone with improvement in the shrinkage. II. Time–temperature–transformation (TTT) cure diagram

Mixtures of diglycidylether of bisphenol A (DGEBA) with different proportions of γ‐caprolactone (γ‐CL) were cured with ytterbium triflate as initiator. The curing was studied with differential scanning calorimetry (DSC) and thermo mechanical analysis (TMA). The results are presented in the form of a time–temperature–transformation diagram. The kinetic analysis was performed by means of the isoconversional integral procedure and the kinetic model was also determined using the Coats–Redfern method. Gelation was determined by means of combined experiences of DSC and TMA. The relationship between the glass transition temperature (T_g) and the degree of conversion α was determined by DSC. Using the isoconversional lines and the T_g‐α relationship, the vitrificacion curve was obtained. The methodology developed makes it possible to obtain the TTT diagram using only no‐isothermal experiments with equivalent results to those using classical isothermal procedures. The addition of γ‐CL accelerates the curing and reduces the shrinkage after gelation and consequently the internal stresses in the material. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry

In the context of process industries, online monitoring of quality variables is often restricted by inadequacy of measurement techniques or low reliability of measuring devices. Therefore, there has been a growing interest in the development of inferential sensors to provide frequent online estimates of key process variables on the basis of their correlation with real-time process measurements. Representation of multi-modal processes is one of the challenging issues that may arise in the design of inferential sensors. In this paper, Bayesian procedures for the development and implementation of adaptive multi-model inferential sensors are presented. It is shown that the application of a Bayesian scheme allows for accommodating the overlapping operating modes and facilitating the inclusion of prior knowledge. The effectiveness of the proposed procedures are first demonstrated through a simulation case study. The efficacy of the method is further highlighted by a successful industrial application of an adaptive multi-model inferential sensor designed for real-time monitoring of a key quality variable in an oil sands processing unit.     

A bio-inspired two-layer mixed-signal flexible programmable chip for early vision.

A bio-inspired model for an analog programmable array processor (APAP), based on studies on the vertebrate retina, has permitted the realization of complex programmable spatio-temporal dynamics in VLSI. This model mimics the way in which images are processed in the visual pathway, what renders a feasible alternative for the implementation of early vision tasks in standard technologies. A prototype chip has been designed and fabricated in 0.5 /spl mu/m CMOS. It renders a computing power per silicon area and power consumption that is amongst the highest reported for a single chip. The details of the bio-inspired network model, the analog building block design challenges and trade-offs and some functional tests results are presented in this paper.     

Just-in-time learning for the prediction of oil sands ore characteristics using GPS data in mining applications

For the mining based oilsands industry, it is desirable to determine the quality of the ore delivered to the extraction processes in real-time to make optimal operational decisions such as optimal ore blending to achieve maximal bitumen recovery. Currently, the industry determines the real-time ore characteristics for any given shovel Global Positioning System (GPS) location by first determining the shovel elevation from the topological mine map and then using the determined geological coordinates in the 3D geological block model. It should be noted that the block model is built based on the widely spaced core hole samples, and it is updated only on a yearly basis due to high cost of narrower core hole sampling. Thus, the block model predictions are often inaccurate in between the core hole spacing. On the other hand, mining operations data are available that contain accurate ore characteristics information in the already mined area. Therefore, in this work, we present a just-in-time based data-driven modelling strategy that utilizes the recently available mining operations data to obtain reliable ore characteristics given the GPS data. The prediction capability of ore characteristics using the proposed modelling strategy is validated at core hole locations. Further, the prediction of ore characteristics at non-core hole points demonstrate promising results.     

Relating indices of knowledge structure coherence and accuracy to skill-based performance: Is there utility in using a combination of indices?

The authors examined the relative criterion-related validity of knowledge structure coherence and two accuracy-based indices (closeness and correlation) as well as the utility of using a combination of knowledge structure indices in the prediction of skill acquisition and transfer. Findings from an aggregation of 5 independent samples (N = 958) whose participants underwent training on a complex computer simulation indicated that coherence and the accuracy-based indices yielded comparable zero-order predictive validities. Support for the incremental validity of using a combination of indices was mixed; the most, albeit small, gain came in pairing coherence and closeness when predicting transfer. After controlling for baseline skill, general mental ability, and declarative knowledge, only coherence explained a statistically significant amount of unique variance in transfer. Overall, the results suggested that the different indices largely overlap in their representation of knowledge organization, but that coherence better reflects adaptable aspects of knowledge organization important to skill transfer. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reconciling continuum and non-continuum data with industrial application

In order to perform data reconciliation, it is important that noises in the data have well-defined distributions. The motivation behind this study was to enable the comparison between a discrete and continuous data set so that means can be compared for gross error over the short term; this required that local variables exhibit similar distributions.A case study was done on a system where non-continuum loads from a dump truck were to be reconciled with two downstream continuum weightometers. An algorithm was developed using the binomial distribution and time delay in order to simulate the effect of the dump pocket.Regression analysis based on principal components was used to evaluate the performance of the smoothing algorithm and to determine the most likely maximum hopper capacity that locates between the two weightometers.     

Architectural and Basic Circuit Considerations for a Flexible 128 × 128 Mixed-Signal SIMD Vision Chip

From a system level perspective, this paper presents a 128 × 128 flexible and reconfigurable Focal-Plane Analog Programmable Array Processor, which has been designed as a single chip in a 0.35 m standard digital 1P-5M CMOS technology. The core processing array has been designed to achieve high-speed of operation and large-enough accuracy (7 bits) with low power consumption. The chip includes on-chip program memory to allow for the execution of complex, sequential and/or bifurcation flow image processing algorithms. It also includes the structures and circuits needed to guarantee its embedding into conventional digital hosting systems: external data interchange and control are completely digital. The chip contains close to four million transistors, 90% of them working in analog mode. The chip features up to 330 GOPs (Giga Operations per second), and uses the power supply (180 GOP/Joule) and the silicon area (3.8 GOPS/mm²) efficiently, and is able to maintain VGA processing throughputs of 100 Frames/s with about 10–20 basic image processing tasks on each frame.     

Development and industrial application of soft sensors with on-line Bayesian model updating strategy

This paper deals with the issues associated with the development of data-driven models as well as model update strategy for soft sensor applications. A practical yet effective solution is proposed. Key process variables that are difficult to measure are commonly encountered in practice due to limitations of measurement techniques. Even with appropriate instruments, some measurements are only available through off-line laboratory analysis with typical sampling intervals of several hours. Soft sensors are inferential models that can provide continuous on-line prediction of hidden variables; such models are capable of combining real-time measurements with off-line lab data. Due to the prevalence of plant-model mismatch, it is important to update the model using the latest reference data. In this paper, parameters of data-driven models are estimated using particle filters under the framework of expectation–maximization (EM) algorithms. A Bayesian methodology for model calibration strategy is formulated. The proposed framework for soft sensor development is applied to an industrial process to provide on-line prediction of a quality variable.