Markedly Divergent Tree Assemblage Responses to Tropical Forest Loss and Fragmentation across a Strong Seasonality Gradient

We examine the effects of forest fragmentation on the structure and composition of tree assemblages within three seasonal and aseasonal forest types of southern Brazil, including evergreen, Araucaria, and deciduous forests. We sampled three southernmost Atlantic Forest landscapes, including the largest continuous forest protected areas within each forest type. Tree assemblages in each forest type were sampled within 10 plots of 0.1 ha in both continuous forests and 10 adjacent forest fragments. All trees within each plot were assigned to trait categories describing their regeneration strategy, vertical stratification, seed-dispersal mode, seed size, and wood density. We detected differences among both forest types and landscape contexts in terms of overall tree species richness, and the density and species richness of different functional groups in terms of regeneration strategy, seed dispersal mode and woody density. Overall, evergreen forest fragments exhibited the largest deviations from continuous forest plots in assemblage structure. Evergreen, Araucaria and deciduous forests diverge in the functional composition of tree floras, particularly in relation to regeneration strategy and stress tolerance. By supporting a more diversified light-demanding and stress-tolerant flora with reduced richness and abundance of shade-tolerant, old-growth species, both deciduous and Araucaria forest tree assemblages are more intrinsically resilient to contemporary human-disturbances, including fragmentation-induced edge effects, in terms of species erosion and functional shifts. We suggest that these intrinsic differences in the direction and magnitude of responses to changes in landscape structure between forest types should guide a wide range of conservation strategies in restoring fragmented tropical forest landscapes worldwide

Radio Location of Partial Discharge Sources: A Support Vector Regression Approach

Partial discharge (PD) can provide a useful forewarning of asset failure in electricity substations. A significant proportion of assets are susceptible to PD due to incipient weakness in their dielectrics. This paper examines a low cost approach for uninterrupted monitoring of PD using a network of inexpensive radio sensors to sample the spatial patterns of PD received signal strength. Machine learning techniques are proposed for localisation of PD sources. Specifically, two models based on Support Vector Machines (SVMs) are developed: Support Vector Regression (SVR) and Least-Squares Support Vector Regression (LSSVR). These models construct an explicit regression surface in a high dimensional feature space for function estimation. Their performance is compared to that of artificial neural network (ANN) models. The results show that both SVR and LSSVR methods are superior to ANNs in accuracy. LSSVR approach is particularly recommended as practical alternative for PD source localisation due to it low complexity

Internet Use by Older Adults with Bipolar Disorder: International Survey Results

Background: The world population is aging and the number of older adults with bipolar disorder is increasing. Digital technologies are viewed as a framework to improve care of older adults with bipolar disorder. This analysis quantifies Internet use by older adults with bipolar disorder as part of a larger survey project about information seeking. Methods: A paper-based survey about information seeking by patients with bipolar disorder was developed and translated into 12 languages. The survey was anonymous and completed between March 2014 and January 2016 by 1222 patients in 17 countries. All patients were diagnosed by a psychiatrist. General estimating equations were used to account for correlated data. Results: Overall, 47% of older adults (age 60 years or older) used the Internet versus 87% of younger adults (less than 60 years). More education and having symptoms that interfered with regular activities increased the odds of using the Internet, while being age 60 years or older decreased the odds. Data from 187 older adults and 1021 younger adults were included in the analysis excluding missing values. Conclusions: Older adults with bipolar disorder use the Internet much less frequently than younger adults. Many older adults do not use the Internet, and technology tools are suitable for some but not all older adults. As more health services are only available online, and more digital tools are developed, there is concern about growing health disparities based on age. Mental health experts should participate in determining the appropriate role for digital tools for older adults with bipolar disorder

Airway sizes and proportions in children quantified by a video-bronchoscopic technique

Background: A quantitative understanding of airway sizes and proportions and a reference point for comparisons are important to a bronchoscopist. The aims of this study were to measure large airway areas, and define proportions and predictors of airway size in children. Methods: A validated videobronchoscope technique was used to measure in-vivo airway cross-sectional areas (cricoid, right (RMS) and left (LMS) main stem and major lobar bronchi) of 125 children. Airway proportions were calculated as ratios of airways to cricoid areas and to endotracheal tube (ETT) areas. Mann Whitney U, T-tests, and one-way ANOVA were used for comparisons and standard univariate and backwards, stepwise multivariate regression analyses were used to define airway size predictors. Results: Airways size increased progressively with increasing age but proportions remained constant. The LMS was 21% smaller than the RMS. Gender differences in airways' size were not significant in any age group or airway site. Cricoid area related best to body length (BL): cricoid area (mm2) = 26.782 + 0.254*BL (cm) while the RMS and LMS area related best to weight: RMS area (mm2) = 23.938 + 0.394*Wt (kg) and LMS area (mm2) = 20.055 + 0.263*Wt (kg) respectively. Airways to cricoid ratios were larger than airway to ETT ratios (p=0.0001). Conclusions: The cricoid and large airways progressively increase in size but maintain constant proportional relationships to the cricoid across childhood. The cricoid area correlates with body length while the RMS and LMS are best predicted by weight. These data provide for quantitative comparisons of airway lesions

Correction of second order converter dynamic errors implementing neural networks

W artykule przedstawiono wyniki wstępnych badań dotyczących możliwości wykorzystania jednokierunkowych sieci neuronowych do korekcji błędów dynamicznych wprowadzanych przez przetworniki opisane liniowym równaniem różniczkowym II-go rzędu. Oceniono zasadność stosowania tego rodzaju podejścia do zagadnienia korekcji błędów dynamicznych. Wnioski sformułowano w oparciu o wyniki badań symulacyjnych.Dynamic properties of second order transducers are usually modelled by the linear differential equation (1) which can be converted to the discrete equation of state (6). Recursive solving of this equation for the input quantity (Eqs. 8 and 9) is a dynamic error correction algorithm. This algorithm can be written in the form of equations (10 and 11) which can be solved by simple, feed-forward neural networks of structures shown in Fig.1. Fig. 2 illustrates the use of neural networks for realisation of the dynamic correction recursive algorithm. The possibility of applying neural networks to dynamic error correction was investigated by simulations in the Matlab Neural Toolbox environment. There were taken the following assumptions concerning the transducer model: , , and the discretization period . The network was learned using a 200 - element learning set generated on a basis of relation (14). The network was tested with a 200 000 - element testing set. The test results of both networks showed error - free implementation of (10) and (11) (errors of 10-15 order). At the next stage the learning sets were quantizied with 12 - bit resolution. The influence of the discretization period on the accuracy of correction realisation was also investigated. Fig. 7 presents the results as a dependency of the output results on the discretization period

Neural Approximation of Empirical Functions

The paper presents the results of simulation studies of selected neural network structures used for non-linear function approximation based on a limited accuracy data. There was performed the analysis of the interdependence of the network structure and the size of the set of learning patterns. The approximation inaccuracy was expressed by the uncertainty interval width. The approximation properties of the neural method were compared with those of the piece-wise linear and polynomial: "cubic" and "spline" methods

Random errors propagation in multiplicative algorithms of measurement data processing

W artykule opisano propagację błędów losowych w multiplikatywnych algorytmach przetwarzania, cechujących się mnożeniem danych pomiarowych przez siebie. Wyznaczono równania propagacji błędów dla dwóch algorytmów służących do obliczania wartości skutecznej i mocy elektrycznej na podstawie cyfrowych reprezentacji przebiegów. Przeprowadzono analizę propagacji błędów kwantowania i błędów spowodowanych drżeniem próbek przy użyciu równań propagacji błędów oraz metodą Monte Carlo wykorzystując niepewność wyników pomiaru do porównywania ich niedokładności.Multiplicative algorithm, used for example for calculation of electrical power on the basis of digital representations of a voltage and current signal, characterize occurrence of products of measurement results. Accuracy of the results in the output of the algorithm can be analyzed by using error propagation equations for different kinds of the algorithm input errors. The alternative way consist in application of Monte Carlo method especially in sophisticated measurement condition. The general form of the multiplication algorithms is described in the paper and, for two kinds of the algorithm, the propagation equations have been determined. Error analysis of the algorithms applied for calculation of effective value and electric power has been performed for two basic errors caused by sampling jitter and quantization of samples

Creation of segment-linear models for complex nonlinear transducers operating in dynamic state

W artykule przedstawiono ogólną koncepcję budowy modeli odcinkowo-liniowych dla złożonych nieliniowych przetworników realizujących pomiar w stanie dynamicznym, których błędy systematyczne korygowane są w sposób programowy na zasadzie odtwarzania. Rozważania teoretyczne zilustrowano wynikami uzyskanymi dla algorytmów korekcji błędów systematycznych mostka pelistorowego zastosowanego do pomiaru stężenia metanu w stanie dynamicznym po załączeniu napięcia zasilania.The paper presents a general concept of creation of segment-linear models for complex nonlinear transducers operating in dynamic state. The systematic errors of these transducers are corrected in a programmable way basing on the reconstruction principle. Theoretical considerations are illustrated by the results obtained for algorithms of systematic errors correction of the pellistor bridge which has been used for measuring of methane concentration in dynamic state after switching on the supply voltage

Correction of gas sensor dynamic errors by means of neural networks

The paper presents a method based on artificial neural network (ANN) technique applied for correction of dynamic error of gas concentration measuring transducer. Its response time is about 8 minutes. The results obtained in the research of this transducer were used for learning and testing ANN, which were implemented in the dynamic correction task. The described method allowed for significant reduction of the transducer’s response time – the output signal was practically fixed after a time equal to one sampling period of output signal provided that the stimulus is a step function. In addition, the use of ANN allows reducing the impact of the transducer dynamic non-linearity on the correction effectiveness

Neural Realisation of Dynamic Errors Correction in Sampling Transducer

W artykule przedstawiono wyniki badań zastosowania sieci neuronowych do korekcji błędów statycznych i dynamicznych wnoszonych przez analogową część toru przetwarzania przetwornika próbkującego opisaną modelem Wienera. Poddano analizie możliwość zastosowania dwóch niezależnych sieci neuronowych realizujących kolejno korekcję błędów statycznych i dynamicznych oraz możliwość zastosowania tylko jednej sieci do rozwiązania obu zadań jednocześnie.The paper presents the investigations results of application of neural networks to correction of static and dynamic errors caused by an analog part of the sampling transducer processing chain described by Wiener model. The possibility of using two independent neural networks realising correction of static and dynamic errors in turn as well as the possibility of application of one network only to solving the both tasks simultaneously are analysed