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In a MOOC environment, each student's interaction with the course content is a crucial clue for learning analytics, which offers an opportunity to record learner activity of unprecedented scale. In online learning, the educators and the administrators need to get informed with students' learning states since the performance of unsupervised learning style is difficult to control. Learning analytics considered as a key process is to provide students and educators with evidence-based, analytical and contextual outcomes in a way of making sense of their learning engagements. In this conceptual framework, this manuscript per the authors intends to adopt sequential analysis method to exploit students' learning behavior patterns in Cloud classroom (an online course platform based on MOOC). Moreover, this research also compares the behavioral patterns of four grade levels in a university, with the purpose of finding the most key behavioral patterns of each grade group.

Nowadays, Massive Open Online Courses (MOOCs) have obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data in education. To mine useful information from these data, we need to use educational data mining and learning analysis technique to study the learning feelings and discussed topics among learners. This paper aims to mine and analyze topic information hidden in the unstructured reviews data in MOOCs; a novel author topic model based on an unsupervised learning idea is proposed to extract learning topics for each learner. According to the experimental results, we will analyze and focus on interests of learners, which facilitates further personalized course recommendation and improves the quality of online courses.

Three new species of the genera Thiania C. L. Koch, 1846 and Yaginumaella Prószyński, 1979 are described and named as T. bamian sp. nov. (♂♀), T. flacata sp. nov. (♀) and Y. curvata sp. nov. (♂♀), from Hunan Province, China. Detailed descriptions, photos of somatic features and copulatory organs, as well as a distribution map are provided. Nucleotide data for the barcoding gene, cytochrome c oxidase subunit I (COI) of T. bamian sp. nov. (♂♀) and Y. curvata sp. nov. (♀) are provided. [ABSTRACT FROM AUTHOR]

Rationally adjusting the interface between SnO2 electron transport layer (ETL) and the perovskite (PVK) buried surface plays a key role in achieving efficient and stable PVK solar cells (PSCs). Herein, a cost‐effective organic salt, 5‐bromo‐6‐chloro‐3‐indolyl sulfate potassium salt (SPS), used as an interface modifier is deposited on the SnO2 ETL. SPS has played a multifunctional role in ETL and PVK layer: 1) the highly electronegative SO4− group of SPS can be adsorbed on the surface of SnO2 films, resulting in the energy levels of the PVK layer and SnO2 ETL becomes more compatible. 2) The lower contact angle of the modified SnO2 film is conducive to the formation of pinhole‐free PVK films. 3) The functional groups in SPS are helpful for the decrease of defect density in the PVK films. As a consequence, the defect density reduces from 8.17 × 1015 to 5.57 × 1015 cm−3. The power conversion efficiency (PCE) of modified PSCs increases from 19.82% to 21.33%. Furthermore, the unencapsulated modified devices retain 91.7% of the initial PCE after 624 h. These results indicate that using SPS as a multifunctional interface modifier is a promising strategy to achieve efficient and stable PSCs. [ABSTRACT FROM AUTHOR]

Wind reversals resulting in southerly flow along the California coast are not well understood in terms of how aerosol and cloud characteristics change. This gap is addressed using airborne field measurements enhanced with data from space-borne remote sensing (Moderate Resolution Imaging Spectroradiometer), surface stations Interagency Monitoring of Protected Visual Environments), and models (Navy Aerosol Analysis and Prediction System and Coupled Ocean/Atmosphere Mesoscale Prediction System), with a focus on sub- and supermicron aerosol, and cloud microphysical variables: cloud droplet number concentration (Nd), cloud optical thickness (COT), and cloud droplet effective radius (re). Southerly flow coincided with higher values of submicron aerosol concentration (Na) and mass concentrations of species representative of fine aerosol pollution (NO3- and nss-SO42-) and shipping/continental emissions (V, oxalate, NH4+, Ni, OC, and EC). Supermicron Na did not change, however, heightened levels of acidic species in southerly flow coincided with reduced Cl-:Na+ suggestive of Cl- depletion in salt particles. Clouds responded correspondingly in southerly flow, with more acidic cloud water, higher levels of similar species as in the aerosol phase (e.g., NO3-, nss-SO42-, NH4+, V), along with elevated values of Nd and COT and reduced re during campaigns with similar cloud liquid water paths. Case study flights help to visualize offshore pollution gradients and highlight the sensitivity of the results to the presence of widespread smoke coverage including how associated plumes have enhanced supermicron Na. These results have implications for aerosol-cloud interactions during wind reversals, and have relevance for weather, public welfare, and aviation. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs) provide learners with high-quality learning resources, but learners drop out frequently. Learners' concerns (e.g. the topics in course content or logistics) and cognitive engagement patterns (e.g. tentative or certain) are considered the essential factors affecting learners' course completions. However, it is still unclear what different learning achievement groups focus on in each cognitive engagement pattern. In this study, we adopted an unsupervised computational model, the temporal cognitive topic model (TCTM), to automatically investigate learners' cognitive engagement patterns in discussing different topics, as well as the changes under each cognitive engagement pattern over time. A data experiment of 4080 learners enrolled in a Modern Etiquette course revealed that the high-achievement group preferred to discuss on-task topics in an exclusive cognitive engagement pattern; the low-achievement group preferred to discuss off-task topics in a tentative pattern, including certificate acquisition and examination grades; the medium-achievement group showed less variation in different cognitive engagement patterns. Additionally, a moderation analysis showed that there was a significant moderating effect of discussion guidance, especially for instructor-led guidance, between the salient cognitive topics and learning achievements. The analytical results can help instructors to conduct (e.g. feedback and guidance) and timely intervention of cognitive knowledge construction in MOOCs. [ABSTRACT FROM AUTHOR]

The emergence of aerosol reanalyses in recent years has facilitated a comprehensive and systematic evaluation of Aerosol Optical Depth (AOD) trends and attribution over multi-decadal timescales. Notable aerosol reanalyses currently available include NAAPS-RA from the U.S. Naval Research Laboratory; the NASA MERRA-2; JRAero from the Japan Meteorological Agency (JMA); and CAMSRA from Copernicus/ECMWF. These aerosol reanalyses are based on differing underlying meteorology models, representations of aerosol processes, and data assimilation methods and treatment of AOD observations. This study presents the basic verification characteristics of these four reanalyses versus both AERONET and MODIS retrievals in monthly AOD properties and identifies the strength of each reanalysis and the regions where diversity and challenges are prominent. Regions with high pollution and often mixed fine-coarse mode aerosol environments such as South Asia, East Asia, Southeast Asia, and the Maritime Continent pose significant challenges, as indicated by higher monthly AOD root mean square error. Moreover, regions that are distant from major aerosol source areas, including the polar regions, and remote oceans exhibit large relative differences in speciated AODs and fine-mode vs coarse-mode AODs among the four reanalyses. To ensure consistency across the globe, a multi-reanalysis-consensus (MRC) approach was developed similar to the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME). Like the ICAP-MME, while the MRC does not consistently rank first among the reanalyses for individual regions, it performs well by ranking first or second globally in AOD correlation and RMSE, making it a suitable candidate for climate studies that require robust and consistent assessments.   [ABSTRACT FROM AUTHOR]

Abstract Pyrosomes represent a group of marine holozooplankton. They do not exist as individual entities but as intricate colonies comprising numerous zooids, well-known for their bioluminescent properties. Pyrosomes inhabit most oceanic waters spanning tropical to temperate regions. They serve as significant consumers of phytoplankton, playing a crucial role in transport of organic carbon and sedimentation of organic matter debris. Nonetheless, the knowledge about this group remains limited, particularly concerning genomic and gene aspects. To further investigate the pyrosome at the molecular level, we used Pyrosoma atlanticum, which is the best known of pyrosome, for investigation in this study. We performed PacBio Iso-Seq and Illumina RNA-seq to generate high-quality and full-length transcript data from P. atlanticum. The systematic gene functional annotation was performed by integrated data. The full-length transcriptome produced in this study represents the inaugural dataset of transcriptome within the class Thaliacea, serving as a reference for future investigations. Additionally, as a stem group for chordates, the information of pyrosomes can provide a valuable research foundation for the evolution of vertebrates.

Abstract Vascular endothelial injury initiates atherosclerosis (AS) progression. N-Acetylneuraminic acid (Neu5Ac) metabolic disorder was found to intensify endothelial mitochondrial damage. And GLS2-associated glutaminolysis disorder contributed to mitochondrial dysfunction. However, mechanisms underlying Neu5Ac-associated mitochondrial dysfunction as well as its association with GLS2 remains unclear. In this study, we constructed GLS2−/−ApoE−/− mice by using HBLV-GLS2 shRNA injection. And methods like immunofluorescence, western blotting, transmission electron microscopy were applied to detect profiles of endothelial injury and AS progression both in vivo and in vitro. We demonstrated that Neu5Ac accumulation increased GLS2 expression and promoted glutaminolysis disorder, which further induced endothelial mitochondrial dysfunction via a pyroptosis-dependent pathway in vivo and in vitro. Mechanically, Neu5Ac interacted with SIRT3 and led to FOXO3a deacetylation and phosphorylation, further facilitated c-Myc antagonism and ultimately increased GLS2 levels. Inhibition of GLS2 could improve mitochondrial function and mitigate pyroptosis process. In addition, blocking Neu5Ac production using neuraminidases (NEUs) inhibitor could rescue endothelial damage and alleviate AS development in ApoE−/− mice. These findings proposed that Neu5Ac induced GLS2-mediated glutaminolysis disorder and then promoted mitochondrial dysfunction in a pyroptosis-dependent pathway. Targeting GLS2 or inhibiting Neu5Ac production could prevent AS progression.