Improved Color Uniformity In White Light-Emitting Diodes Using LiLu(MoO4)2:Sm3+ Combined SiO2 Composite

The research herein concerns the composite of red phosphor of LiLu(MoO4)2:Sm3+ (LMOS), yellow phosphor YAG:Ce3+, SiO2 particles, and silicone gel. The LMOS phosphor is created via the sol-gel procedure and supposed to yields significant heat consistency. The concentration of this LMOS phosphor is fixed at around 10 wt.%  and the concentration of SiO2 particles is modifed. This is to influence the scattering performance of the composite to achieve the better color distribution. After sample creation, we analyzed the luminescence of the LMOS in the composite and the effects of the composite with different SiO2 dosages on the commercial light-emitting diode (LED). When excited via 405-nm ultraviolet, the samples generate red ray under 648 nm matching the shift between 4G5/2 and 6H9/2 for the ion of Sm3+. With high SiO2 amounts, the color difference is reduced, and the luminosity is enhanced. The correlated color temperature is also lower, resulting in a warmer white light for the packed LED. However, the color rendering index declines, which could be attributed to the green and blue color deficiency while the red color is dominant. From the tested outcomes, LiLu(MoO4)2:Sm3+@SiO2­ composite is validated to be effective at improving chromatic uniformity for white-ray diodes

A Cosine Similarity-based Method for Out-of-Distribution Detection

The ability to detect OOD data is a crucial aspect of practical machine learning applications. In this work, we show that cosine similarity between the test feature and the typical ID feature is a good indicator of OOD data. We propose Class Typical Matching (CTM), a post hoc OOD detection algorithm that uses a cosine similarity scoring function. Extensive experiments on multiple benchmarks show that CTM outperforms existing post hoc OOD detection methods.Comment: Accepted paper at ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability. 10 pages (4 main + appendix

PILOT SCALE STUDY ON AMMONIUM REMOVAL IN PHAP VAN WATER PLANT, HANOI CITY

Joint Research on Environmental Science and Technology for the Eart

The role of nutritional risk evaluation in predicting adverse outcomes among patients with severe COVID-19 in Vietnam

IntroductionAs sufficient nutrition helps alleviate catabolic stress and modulate the systemic inflammatory response of the body, it plays an indispensable role in the good prognosis of critically ill patients. Thus, this study aimed to investigate the malnutrition of patients with severe COVID-19 and its association with adverse treatment outcomes.MethodsWe conducted a retrospective cross-sectional study in two provincial hospitals in Hanoi from February to April 2022. Participants were patients with severe COVID-19 admitted to the Intensive Care Unit (ICU). Malnutrition risk were evaluated by Nutritional Risk Screening-2002 (NRS), Global Leadership Initiative on Malnutrition (GLIM), Prognostic Nutritional Index (PNI), and the adverse prognosis was assessed by Acute Physiology and Chronic Health Evaluation II (APACHE II). The multivariate receiver-operating characteristic (ROC) curve was applied to estimate the predictive ability of those criteria regarding worse treatment results.ResultsThe percentages of malnutrition measured by NRS, GLIM, PNI, and BMI were 62.6, 51.5, 42.9, and 16.6%, respectively. Patients with more severe malnutrition assessed by GLIM, PNI, and having above target fasting blood glucose (FBG) (≥10.0 mmol/L) were more likely to have higher APACHE scores. PNI had a better diagnostic performance than NRS and BMI (AUC = 0.84, 0.81, and 0.82, respectively). In addition, FBG revealed a good prognostic implication (AUC = 0.84).ConclusionA relatively high percentage of patients experienced moderate and severe malnutrition regardless of screening tools. Individuals at higher risk of malnutrition and high FBG were predicted to have more adverse treatment outcomes. It is recommended that nutritional screening should be conducted regularly, and personalizing nutritional care strategies is necessary to meet patients’ nutrient demands and prevent other nutrition-related complications

Effects of water scarcity awareness and climate change belief on recycled water usage willingness: Evidence from New Mexico, United States

The global water crisis is being exacerbated by climate change, even in the United States. Recycled water is a feasible alternative to alleviate the water shortage, but it is constrained by humans’ perceptions. The current study examines how residents’ water scarcity awareness and climate change belief influence their willingness to use recycled water directly and indirectly. Bayesian Mindsponge Framework (BMF) analytics was employed on a dataset of 1831 residents in Albuquerque, New Mexico, an arid inland region in the US. We discovered that residents’ willingness to use direct recycled potable water is positively affected by their awareness of water scarcity, but the effect is conditional on their belief in the impacts of climate change on the water cycle. Meanwhile, the willingness to use indirect recycled potable water is influenced by water scarcity awareness, and the belief in climate change further enhances this effect. These findings implicate that fighting climate change denialism and informing the public of the water scarcity situation in the region can contribute to the effectiveness and sustainability of long-term water conservation and climate change alleviation efforts

分光型熱ふく射センシングと加熱のための共鳴型マイクロ赤外吸収素子の研究 [全文の要約]

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