A Guideline for Contextual Adaptation of Community-Based Health Interventions.

In Southeast Asia, community-based health interventions (CBHIs) are often used to target non-communicable diseases (NCDs). CBHIs that are tailored to sociocultural aspects of health and well-being: local language, religion, customs, traditions, individual preferences, needs, values, and interests, may promote health more effectively than when no attention is paid to these aspects. In this study, we aimed to develop a guideline for the contextual adaption of CBHIs. We developed the guideline in two stages: first, a checklist for contextual and cultural adaptation; and second, a guideline for adaptation. We performed participatory action research, and used the 'Appraisal of Guidelines for Research & Evaluation (AGREE) II' tool as methodological basis to develop the guideline. We conducted a narrative literature review, using a conceptual framework based on the six dimensions of 'Positive Health' and its determining contexts to theoretically underpin a checklist. we pilot tested a draft version of the guideline and included a total of 29 stakeholders in five informal meetings, two stakeholder meetings, and an expert review meeting. This yielded a guideline, addressing three phases: the preparation phase, the assessment phase, and the adoption phase, with integrated checklists comprising 34 cultural and contextual aspects for the adaption of CBHIs based on general health directives or health models. The guideline provides insight into how CBHIs can be tailored to the health perspectives of community members, and into the context in which the intervention is implemented. This tool can help to effect behavioral change, and improve the prevention and management of NCDs.

Spectral and spatial kernel water quality mapping.

An empirical approach through remote sensing generally produces a robust data model of water quality for inland and coastal water. Traditional regressions in water quality mapping fail because the bio-optical relationship of turbid water exhibits nonlinear and heterogeneous patterns. In addition, in situ data are generally insufficient in the water quality mapping. Mapping based on a relatively small amount of water quality samples is considered a practical issue in environmental monitoring. Learning-based algorithms that require a large amount of data are inapplicable in this case. According to the concept of Nadaraya-Watson estimator, the kernel model can estimate nonlinear and spatially varying water quality maps effectively in turbid water.Experiments indicate that the kernel estimator provides better goodness-of-fit between the observed and derived concentrations of water quality parameter, e.g., chlorophyll-a in turbid water. The kernel estimator is feasible for a relatively small size of ground observations. Approximately 30% improvement of cross-validation error was identified in this approach when compared with traditional regressions. The model offers a robust approach without further calibrations for estimating the spatial patterns of water quality by using remote sensing reflectance and a small set of observations, considering spatial and spectral information, e.g., multiple bands and band ratios.

Spectral Feature Selection Optimization for Water Quality Estimation.

The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg · m - 3 to 6.37 mg · m - 3 , and the Pearson's correlation coefficients between the predicted and in situ Chl- a improve from 0.56 to 0.89.