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The blue light hazard (BLH) and luminous efficacy of a source ( η V ) are associated with the photobiological safety and energy saving of light-emitting diodes (LEDs), respectively. In this study, we used genetic algorithm (GA) to optimize the BLH, η V , and color rendering parameters of five-chip hybrid white LEDs. Based on the optimal results, we used five-chip LEDs to obtain the hybrid white light. Their peak wavelengths were 461.5 (blue), 523.9 (green), 588.2 (orange), 643.3 (red), and 694.2 nm (far-red), respectively. In practice, the blue light hazard efficiency of radiation (BLHER) of the hybrid white light was less than 0.10, which was lower than half of the most common light sources. The η V ranged from 51.6 to 115.6 lm/W at correlated color temperature (CCT) from 2700 to 6500 K. On average, the CIE general color rendering index (Ra), CIE special color rendering index for the ninth deep red test sample (R9), ANSI/IES TM-30 color fidelity index (Rf), and color gamut index (Rg) of hybrid white light were 91.3, 87.8, 83.3, and 96.7, respectively. The melanopic efficacy of luminous radiation ( K m e l , V ) was tunable in the range of 0.60 to 1.35 mW/lm. These results demonstrated that we used far-red LEDs to fabricate white light with low BLH and relatively high efficiency. [ABSTRACT FROM AUTHOR]

Introduction: Depressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA)., Results: Our results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%., Discussion: These findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment., Competing Interests: JJ and CF were employed by Beijing Wanling Pangu Science and Technology Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ji, Dong, Li, Peng, Feng, Liu, Shi and Ma.)