Water treatment in rural Guatemala: factors associated with the use of biosand water filters

Point-of-use water filters are a means to provide clean water vital to the health of people in developing countries. The factors that influence the adoption of this technology include hygiene knowledge, health beliefs related to the use of new technology, and technical issues with using the filter (e.g., water taste and breakage). This study examines how people in Mayan communities in rural Guatemala perceived biosand filters they had received and what factors related to their filter use. Based on the survey and interviewer observations, approximately 53% were regular filter users, 28% were irregular filter users, and 19.4% were non-filter users. The observational data revealed that actual filter use is lower than self-reported use, reflecting complexities in the adoption of technology. One such complexity can be seen in the connection between health beliefs and behavior. The belief that believing drinking filtered water is salubrious does not necessarily coincide with filter use, but education and hygienic practices correlated with regular filter use. Furthermore, regular users typically depend on family members for a daily reminder to use the filter, suggesting that education should foster peer support as well as imparting knowledge.

Microwave Filter Modeling and Intelligent Tuning

Traditional filter tuning methods mainly entail tuning by electromagnetic simulation technology, which treats the tuned filter as an ideal model. However, the structure of the actual filter is relatively complex, and filter tuning becomes affected by the loss of resonant cavity, phase loading and high-order mode. In this study, to solve these problems, the tuning process was divided into four stages. First, the passband and suppression of the filter could be tuned to a reasonable range by using the phase attribute of the reflection characteristics. Secondly, the tuning model parameters (coupling matrix) were extracted by curve fitting and the improved Cauchy method. Thirdly, the tuning model of the actual filter was established by a complex neural network. Finally, the mapping relationship between the surrogate model and the actual tuning model was established by the improved space mapping algorithm. By optimizing the parameters of surrogate model, we quickly obtained the optimal position of the screws. The results of the tuning experiment with the eighth coaxial cavity filter revealed that the method had high accuracy and fast convergence speed.

Improved total atmospheric water vapour amount determination from near-infrared filter measurements with sun photometers

In this work we explore the effect of the contribution of the solar spectrum to the recorded signal in wavelengths outside the typical 940-nm filter's bandwidth. We use gaussian-shaped filters as well as actual filter transmission curves to study the implications imposed by the non-zero out-of-band contribution to the coefficients used to derive precipitable water from the measured water vapour band transmittance. The moderate-resolution SMARTS radiative transfer code is used to predict the incident spectrum outside the filter bandpass for different atmospheres, solar geometries and aerosol optical depths. The high-resolution LBLRTM radiative transfer code is used to calculate the water vapour transmittance in the 940 nm band. The absolute level of the out-of-band transmittance has been chosen to range from 10−6 to 10−4, and typical response curves of commercially available silicon photodiodes are included into the calculations. It is shown that if the out-of-band transmittance effect is neglected, as is generally the case, then the derived columnar water vapour is systematically underestimated by a few percents. The actual error depends on the specific out-of-band transmittance, optical air mass of observation and water vapour amount. We apply published parameterized transmittance functions to determine the filter coefficients. We also introduce an improved, three-parameter, fitting function that can describe the theoretical data accurately, with significantly less residual effects than with the existing functions. Further investigations will use experimental data from field campaigns to validate these findings.

Changes to itk::PathToImageFilter.

I have made some changes to make itk::PathToImageFilter to make it a usable base class. There were some problems with the CVS version, including: (1) this ase classhad actual filter functionality, (2) the mechanism for specifying the size, spacing, origin, etc., of the output image was incomplete, contrary to ITK convention, and hard to extend, and (3) the documentation specified that the filter would calculate the size of the output image from the path, but it did not.I solved these problems by (1) moving the filter functionality to itk::TracePathOnImageFilter, (2) requiring that filter->GetOutput()->Set…(…) should be used to set the information for the output image, and (3) providing methods to calculate the correct image region a path inhabits, and a boolean flag to specify whether that calculation should be done (default true).

Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning

The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.

What Affects the Cost of Hot Gas Filter Stations?

This technical paper examines the various aspects of hot gas filter station design that ultimately affect both initial and operational costs. Process conditions such as temperature and pressure, and design constraints such as face velocity, are discussed with respect to their bearing on filter station costs. More subtle parameters, such as pulse gas cleaning requirements and filter element geometry, also directly impact filter design and hence, cost. As all of the information presented is based upon actual filter applications, it will provide useful insight for those involved in filter designing and recommendations.

What Affects the Cost of Hot Gas Filter Stations?

This technical paper examines the various aspects of hot gas filter station design which ultimately affect both initial and operational costs. Process conditions such as temperature and pressure, and design constraints such as face velocity are discussed with respect to their bearing on filter station costs. More subtle parameters such as pulse gas cleaning requirements and filter element geometry also directly impact filter design and hence, cost. As all of the information presented is based upon actual filter applications, it will provide useful insight for those involved in filter designing and recommendations.