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Background: Social annotation (SA) allows users to collaboratively highlight important texts, make comments and discuss with each other on the same online document. This would not only accelerate and deepen learners' cognitive understanding of information, but also help build a sense of rapport, which is critical especially because of the worldwide shift from face-to-face class to remote education as a response to the COVID-19 pandemic. Objective: To provide a systematic review of empirical SA studies, so that current development as well as issues in SA practices and research are identified. Methods: A total of 32 studies were identified and bibliometrical, instructional, and methodological analysis were conducted. Results and Conclusions: The United States has published the most SA research and technology-related journals are most receptive of SA research; one-shot quantitative designs with a sample size between 30 and 100 have been adopted most often; there is a lack of theoretical support for SA studies; higher education settings have been more frequently researched than other educational levels; SA technological features and activities have focused more on student uses and outcomes than on those of instructors; self-designed technologies were more preferred than commercial ones; both cognitive and affective outcomes were emphasized and nearly all studies reported positive findings. Implications: Future SA studies may conduct blended designs with larger sample sizes that is grounded upon solid theoretical frameworks; more customized and affordable SA technologies that support both students and teachers should be developed. Learning analytics and emotional design may be capitalized more to meet the demand of remote education during the pandemic.

Play is crucial to children's development. With the prevalent use of digital technologies, children and young adults are seen playing games more often and longer on digital devices. How should teachers capitalize the advantages of games in class, without inducing more health risks or problems for children? To achieve this goal, teachers need empirical details and conclusive results in the application of game-based learning (GBL) in primary education, so that they can design GBL experiences that cater to the specific needs of pupils. The present paper provides a systematic review for 35 experimental studies that substantially integrated gaming elements in primary school lessons. Our major findings include that (1) most studies were published in technology-assisted learning journals, focusing on higher-grade students' natural science learning, through one GBL lesson conducted within one week; (2) theoretical foundations were absent in the majority of studies, with the rest implicitly or explicitly drawing upon Constructivist premises; (3) adopted GBL technologies were mostly self-designed, and used for formal learning; (4) Gamification was the most frequently used game genre and Intellectual Problem-Solving was the top gaming element to be incorporated and (5) there were more positive outcomes than mixed findings and more cognitive results reported than affective or behavioral dimensions. [ABSTRACT FROM AUTHOR]

Abstract: Tetragonal phase NaLnW2O8 (Ln=Y, Yb, Er, Tm, Eu, Gd, Ho, Ce, and Sm) nanocrystals were successfully prepared via a hydrothermal method. It is found that large ionic radii of lanthanide ions and high reaction temperature are beneficial for the formation of NaLnW2O8. The upconversion luminescence properties of NaYW2O8:Yb3+/Er3+ and NaYW2O8:Yb3+/Tm3+ were studied under 980nm excitation. It is found that the luminescence intensity of 2H11/2 → 4I15/2 is much stronger than that of 4S3/2 → 4I15/2 as well as 4F9/2 → 4I15/2 in NaYW2O8:Yb3+/Er3+. Interestingly, there is no apparent change in the intensity ratio of 2H11/2/4S3/2 → 4I15/2 to 4F9/2 → 4I15/2 on Er3+ concentration. By analyzing the Raman spectrum, we suggested that the nonradiative relaxation of 2H11/2 → 4S3/2 is inefficient in NaYW2O8:Yb3+/Er3+, resulting in the strong 2H11/2 → 4I15/2 emission. The CIE coordinates of the green luminescence changed with excitation power. While for NaYW2O8:Yb3+/Tm3+, the CIE coordinates of the blue luminescence (0.14, 0.10) is almost unchanged with excitation power. In addition, the photoluminescence of NaYW2O8:Eu3+ excited at different wavelengths was studied in detail. [Copyright &y& Elsevier]

Daocai is a traditional salted pickle in the southeastern region of Guizhou with a unique aroma, color, and taste. The quality of Daocai is greatly influenced by the fermentation temperature. In this study, high-throughput sequencing and headspace-gas chromatography-ion mobility spectrometry were used to investigate the changes in microbial community succession and volatile flavor compounds during Daocai fermentation under temperature-controlled (D group) and non-temperature-controlled (C group).We found that the predominant genera in the C group samples were Latilactobacillus (40.57 %), Leuconostoc (21.25 %), Cystofilobasidium (22.12 %), Vishniacozyma (23.89 %), and Leucosporidium (24.95 %), whereas Weissella (29.39 %), Lactiplantibacillus (45.61 %), Mucor (68.26 %), and Saccharomyces (23.94 %) were the predominant genera in the D group. A total of 92 VFCs were detected in Daocai samples, including 5 isothiocyanates, 16 esters, 14 alcohols, 24 aldehydes, 17 ketones, 3 acids, 2 pyrazines, 1 pyridines, 1 thiazoles, 3 furans, 4 alkenes, and 2 nitriles. Further analysis revealed Latilactobacillus , Leuconostoc , Lactococcus , Cystofilobasidium , Leucosporidium , Holtermanniella , and Dioszegia as key bacteria involved in flavor formation. They are closely related to the formation of flavors such as aldehydes, furans, pyridines, and alkenes. This study contributes to our understanding of the relationship between bacterial communities and the flavor formation during Daocai fermentation., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.