Indoor room optimal allocation is of great importance in geographic information science (GIS) applications because it can generate effective indoor spatial patterns that improve human behavior and efficiency. However, few research concerning indoor room optimal allocation has been reported. Using an office building as an example, this paper presents an integrative approach for indoor room optimal allocation, which includes an indoor room allocation optimization model, indoor connective map design, and a multiple ant colony optimization (MACO) algorithm. The mathematical optimization model is a minimized model that integrates three types of area-weighted costs while considering the minimal requirements of each department to be allocated. The indoor connective map, which is an essential data input, is abstracted by all floor plan space partitions and connectivity between every two adjacent floors. A MACO algorithm coupled with three strategies, namely, (1) heuristic information, (2) two-colony rules, and (3) local search, is effective in achieving a feasible solution of satisfactory quality within a reasonable computation time. A case study was conducted to validate the proposed approach. The results show that the MACO algorithm with these three strategies outperforms other types of ant colony optimization (ACO), Genetic Algorithm (GA), and particle swarm optimization (PSO) algorithms in quality and stability, which demonstrates that the proposed approach is an effective technique for generating optimal indoor room spatial patterns. Full article

Background: Folic acid supplementation is effective in reducing the risk of neural tube defects (NTDs). However, the use of folic acid is low among rural women in China. Nutrition education can provide information about folic acid and encourage its use. The primary objective of this study was to test the effectiveness of a village‐based nutrition intervention on folic acid use among rural women. Methods: Sixty villages were randomly selected using multiple‐stage sampling and were divided into control and intervention groups. The intervention included nutritional education at village clinics, written materials, and text messages (SMS). Folic acid use knowledge and behavior was assessed at baseline and after the intervention. Results: Self‐reported compliance with folic acid supplement use increased from 17.0%–29.2% at baseline to 41.7%-59.2% one year post‐intervention. During the same period, the folic acid knowledge score in the intervention group increased from 3.07 to 3.65, significantly higher than the control group (3.11 to 3.35). Multivariate binary logistic regression showed that the women who received folic acid education and SMS intervention were more likely to comply with folic acid supplement recommendations. Conclusions: The results indicated that an integrated village‐based folic acid education intervention may be an effective way of promoting folic acid use for the prevention of NTDs in rural women. Full article

Mixed pixels are common in hyperspectral remote sensing images. Endmember extraction is a key step in spectral unmixing. The linear spectral mixture model (LSMM) constitutes a geometric approach that is commonly used for this purpose. This paper introduces the use of artificial bee colony (ABC) algorithms for spectral unmixing. First, the objective function of the external minimum volume model is improved to enhance the robustness of the results, and then, the ABC-based endmember extraction process is presented. Depending on the characteristics of the objective function, two algorithms, Artificial Bee Colony Endmember Extraction-RMSE (ABCEE-R) and ABCEE-Volume (ABCEE-V) are proposed. Finally, two sets of experiment using synthetic data and one set of experiments using a real hyperspectral image are reported. Comparative experiments reveal that ABCEE-R and ABCEE-V can achieve better endmember extraction results than other algorithms when processing data with a low signal-to-noise ratio (SNR). ABCEE-R does not require high accuracy in the number of endmembers, and it can always obtain the result with the best root mean square error (RMSE); when the number of endmembers extracted and the true number of endmembers does not match, the RMSE of the ABCEE-V results is usually not as good as that of ABCEE-R, but the endmembers extracted using the former algorithm are closer to the true endmembers. Full article