Effects and acceptability of implementing improved cookstoves and heaters to reduce household air pollution: a FRESH AIR study

The objective was to evaluate the effectiveness and acceptability of locally tailored implementation of improved cookstoves/heaters in low- and middle-income countries. This interventional implementation study among 649 adults and children living in rural communities in Uganda, Vietnam and Kyrgyzstan, was performed after situational analyses and awareness programmes. Outcomes included household air pollution (PM2.5 and CO), self-reported respiratory symptoms (with CCQ and MRC-breathlessness scale), chest infections, school absence and intervention acceptability. Measurements were conducted at baseline, 2 and 6-12 months after implementing improved cookstoves/heaters. Mean PM2.5 values decrease by 31% (to 95.1 µg/m3) in Uganda (95%CI 71.5-126.6), by 32% (to 31.1 µg/m3) in Vietnam (95%CI 24.5-39.5) and by 65% (to 32.4 µg/m3) in Kyrgyzstan (95%CI 25.7-40.8), but all remain above the WHO guidelines. CO-levels remain below the WHO guidelines. After intervention, symptoms and infections diminish significantly in Uganda and Kyrgyzstan, and to a smaller extent in Vietnam. Quantitative assessment indicates high acceptance of the new cookstoves/heaters. In conclusion, locally tailored implementation of improved cookstoves/heaters is acceptable and has considerable effects on respiratory symptoms and indoor pollution, yet mean PM2.5 levels remain above WHO recommendations.European Union’s Horizon 2020 programme under grant agreement no. 680997, TRIAL ID NTR5759, http://www.trialregister.nl/trialreg/admin/rctsearch.asp?Term=23332. The devices, measuring the personal HAP, were funded by Netherlands Enterprise Agency (RVO

COPD’s early origins in low-and-middle income countries: what are the implications of a false start?

[Excerpt] The Global Initiative for chronic Obstructive Lung disease (GOLD)guideline of 2018 describes COPD as‘the result of a complexinterplay of long-term cumulative exposure to noxious gases andparticles, combined with a variety of host factors includinggenetics, airway hyper-responsiveness and poor lung growthduring childhood’.1Tobacco smoking is traditionally viewed as themain contributing factor to the development of COPD. However,COPD also occurs among non-smokers, especially in low-incomeand middle-income countries (LMICs).2,3Notably, more than 90%of COPD-related deaths occur in LMICs.4For these countries, otherrisk factors, such as ambient, occupational and household airpollution play a significant role in the development of COPD.1,2,5–7Does COPD in these settings have a different pathophysiologicaltrajectory compared to COPD in high-income countries, and if so:what does this imply?In normal lung development, airway branching is completed bythe 17th week of gestation, after which airways increase in volumeuntil young adulthood. Alveoli are present at birth and developfurther during childhood. Lung volume and airflow continue toincrease as the thorax grows, influenced by age, sex, and ethnicity,reaching a peak at young adulthood. Lung function then remainsconstant for about 10 years (the plateau phase), after which itgradually declines.8In the‘classic’COPD patient, the decline inlung function is more rapid than in healthy individuals. However,in a considerable proportion of COPD patients, lung function doesnot decline rapidly, but reaches a lower plateau phase in earlyadulthood instead. For these patients, a completely differentpathophysiological trajectory seems to lead to the diagnosis ofCOPD: the decline in lung function follows a normal pattern, yetthey seem to have a‘false start’by attaining a lower maximumlung function. [...

Support Vector Regression Machines

A new regression technique based on Vapnik's concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space. 1. Introduction In the following, lower case bold characters represent vectors and upper case bold characters represent matrices. Superscript "t" represents the transpose of a vector. y represents either a vector (in bold) or a single observance of the dependent variable in the presence of noise. y (p) indicates a predicted value due to the input vector xx (p) not seen in the training set. Suppose we have an unknown function G(xx) (the "truth") which is a function of a vector xx (termed input space). The vector xx t = [x 1 , x 2 , ..., x d ] has ..

Detecting hidden messages using higher-order statistics and support vector machines

www.cs.dartmouth.edu/~{lsw,farid} Abstract. Techniques for information hiding have become increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages has become considerably more difficult. This paper describes an approach to detecting hidden messages in images that uses a wavelet-like decomposition to build higher-order statistical models of natural images. Support vector machines are then used to discriminate between untouched and adulterated images.

An evolutionary approximation for the coefficients of decision functions within a support vector machine learning strategy

Support vector machines represent a state-of-the-art paradigm, which has nevertheless been tackled by a number of other approaches in view of the development of a superior hybridized technique. It is also the proposal of present chapter to bring support vector machines together with evolutionary computation, with the aim to offer a simplified solving version for the central optimization problem of determining the equation of the hyperplane deriving from support vector learning. The evolutionary approach suggested in this chapter resolves the complexity of the optimizer, opens the ’blackbox’ of support vector training and breaks the limits of the canonical solving component