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Improved approximation of spatial light distribution.

ABSTRACT: The rapid worldwide evolution of LEDs as light sources has brought new challenges, which means that new methods are needed and new algorithms have to be developed. Since the majority of LED luminaries are of the multi-source type, established methods for the design of light engines cannot be used in the design of LED light engines. This is because in the latter case what is involved is not just the design of a good reflector or projector lens, but the design of several lenses which have to work together in order to achieve satisfactory results. Since lenses can also be bought off the shelf from several manufacturers, it should be possible to combine together different off the shelf lenses in order to design a good light engine. However, with so many different lenses to choose from, it is almost impossible to find an optimal combination by hand, which means that some optimization algorithms need to be applied. In order for them to work properly, it is first necessary to describe the input data (i.e. spatial light distribution) in a functional form using as few as possible parameters. In this paper the focus is on the approximation of the input data, and the implementation of the well-known mathematical procedure for the separation of linear and nonlinear parameters, which can provide a substantial increase in performance.

SUBMITTER: Kaljun D

PROVIDER: S-EPMC5409530 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Improved approximation of spatial light distribution.

Kaljun David D Novak Tina T Žerovnik Janez J

PloS one 20170428 4

The rapid worldwide evolution of LEDs as light sources has brought new challenges, which means that new methods are needed and new algorithms have to be developed. Since the majority of LED luminaries are of the multi-source type, established methods for the design of light engines cannot be used in the design of LED light engines. This is because in the latter case what is involved is not just the design of a good reflector or projector lens, but the design of several lenses which have to work ...[more]

PMID: 28453546

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Project description:Background Plasmodium knowlesi, a simian malaria parasite infection, increases as Plasmodium falciparum and Plasmodium vivax infections decrease in Johor, Malaysia. Therefore, this study aimed to identify the distribution of vectors involved in knowlesi malaria transmission in Johor. This finding is vital in estimating hotspot areas for targeted control strategies. Methods Anopheles mosquitoes were collected from the location where P. knowlesi cases were reported. Cases of knowlesi malaria from 2011 to 2019 in Johor were analyzed. Internal transcribed spacers 2 (ITS2) and cytochrome c oxidase subunit I (COI) genes were used to identify the Leucosphyrus Group of Anopheles mosquitoes. In addition, spatial analysis was carried out on the knowlesi cases and vectors in Johor. Results One hundred and eighty-nine cases of P. knowlesi were reported in Johor over 10 years. Young adults between the ages of 20–39 years comprised 65% of the cases. Most infected individuals were involved in agriculture and army-related occupations (22% and 32%, respectively). Four hundred and eighteen Leucosphyrus Group Anopheles mosquitoes were captured during the study. Anopheles introlatus was the predominant species, followed by Anopheles latens. Spatial analysis by Kriging interpolation found that hotspot regions of P. knowlesi overlapped or were close to the areas where An. introlatus and An. latens were found. A significantly high number of vectors and P. knowlesi cases were found near the road within 0–5 km. Conclusions This study describes the distribution of P. knowlesi cases and Anopheles species in malaria-endemic transmission areas in Johor. Geospatial analysis is a valuable tool for studying the relationship between vectors and P. knowlesi cases. This study further supports that the Leucosphyrus Group of mosquitoes might be involved in transmitting knowlesi malaria cases in Johor. These findings may provide initial evidence to prioritize diseases and vector surveillance. Supplementary Information The online version contains supplementary material available at 10.1186/s12936-021-03963-0.

Dataset Information

Improved approximation of spatial light distribution.

Publications

Improved approximation of spatial light distribution.

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