A latent profile analysis of sleep disturbance in relation to mental health among college students in China

AimsThis study aimed to examine the subtype classification characteristics of sleep disturbance (SD) in college students and their associations with sample characteristic factors and mental health outcomes.MethodsThe sample comprised 4,302 college students (Mean age = 19.92 ± 1.42 years, 58.6% females). The Youth Self-Rating Insomnia Scale, Beck Depression Inventory, 8-item Positive Subscale of the Community Assessment of Psychic Experiences, and 10-item Connor-Davidson Resilience Scale were used to assess adolescents’ sleep disturbance, depressive symptoms, psychotic-like experiences (PLEs), and resilience. Latent profile analysis, logistic regression, and liner regression analysis were used to analyze the data.ResultsThree subtypes of SD in college students were identified: the high SD profile (10.6%), the mild SD profile (37.5%), and the no SD profile (51.9%). Compared with college students in the “no SD” profile, risk factors for “high SD” include being male and poor parental marital status. Sophomores were found to predict the “high SD” profile or “mild SD” profile relative to the “no SD” profile. College students in the “mild SD” profile or “high SD” profile were more likely to have a higher level of depressive symptoms and PLEs, while a lower level of resilience.ConclusionThe findings highlighted that target intervention is urgently needed for male college students, sophomores, and those with poor parental marital status in the “mild SD” profile or “high SD” profile

Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model

With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing, re-analysis, and ground-based observational data, we established a machine learning (ML) model to analyze annual and seasonal changes in primary air pollutants in 2020 compared to 2018 and 2019 over central and eastern China. The root mean squared errors (RMSE) for the PM2.5, PM10, O3, and CO validation dataset were 9.027 μg/m3, 20.312 μg/m3, 10.436 μg/m3, and 0.097 mg/m3, respectively. The geographical random forest (RF) model demonstrated good performance for four main air pollutants. Notably, PM2.5, PM10, and CO decreased by 44.1%, 43.2%, and 35.9% in February 2020, which was likely influenced by the COVID-19 lockdown and primarily lasted until May 2020. Furthermore, PM2.5, PM10, O3, and CO decreased by 16.4%, 24.2%, 2.7%, and 19.8% in 2020 relative to the average values in 2018 and 2019. Moreover, the reduction in O3 emissions was not universal, with a significant increase (~20–40%) observed in uncontaminated areas

Remote Sensing of Global Sea Surface pH Based on Massive Underway Data and Machine Learning

Seawater pH is a direct proxy of ocean acidification, and monitoring the global pH distribution and long-term series changes is critical to understanding the changes and responses of the marine ecology and environment under climate change. Owing to the lack of sufficient global-scale pH data and the complex relationship between seawater pH and related environmental variables, generating time-series products of satellite-derived global sea surface pH poses a great challenge. In this study, we solved the problem of the lack of sufficient data for pH algorithm development by using the massive underway sea surface carbon dioxide partial pressure (pCO(2)) dataset to structure a large data volume of near in situ pH based on carbonate calculation between underway pCO(2) and calculated total alkalinity from sea surface salinity and relevant parameters. The remote sensing inversion model of pH was then constructed through this massive pH training dataset and machine learning methods. After several tests of machine learning methods and groups of input parameters, we chose the random forest model with longitude, latitude, sea surface temperature (SST), chlorophyll a (Chla), and Mixed layer depth (MLD) as model inputs with the best performance of correlation coefficient (R-2 = 0.96) and root mean squared error (RMSE = 0.008) in the training set and R-2 = 0.83 (RMSE = 0.017) in the testing set. The sensitivity analysis of the error variation induced by the uncertainty of SST and Chla (SST <= +/- 0.5 degrees C and Chla <= +/- 20%; RMSESST <= 0.011 and RMSEChla <= 0.009) indicated that our sea surface pH model had good robustness. Monthly average global sea surface pH products from 2004 to 2019 with a spatial resolution of 0.25 degrees x 0.25 degrees were produced based on the satellite-derived SST and Chla products and modeled MLD dataset. The pH model and products were validated using another independent station-measured pH dataset from the Global Ocean Data Analysis Project (GLODAP), showing good performance. With the time-series pH products, refined interannual variability and seasonal variability were presented, and trends of pH decline were found globally. Our study provides a new method of directly using remote sensing to invert pH instead of indirect calculation based on the construction of massive underway calculated pH data, which would be made useful by comparing it with satellite-derived pCO(2) products to understand the carbonate system change and the ocean ecological environments responding to the global change

Variation of water body in Dongting Lake from in situ measurements and MODIS observations in recent decades

Identifying the spatiotemporal dynamics of the water body in Dongting Lake, the second largest freshwater lake in China, is crucial for water resource management. In this study, the variations of the water body were comprehensively analyzed based on remote sensing images and in situ measurements from 2000 to 2019. Four breakpoint detection approaches were integrated to analyze the change trends and explore the related driving forces behind the changes. The results showed that significant intra– and inter–annual fluctuations of the water body were found from 2000 to 2019. The water area and volume decreased at rates of 1.26 km2/a and 16.65 × 106 m3/a, respectively. During the entire study period, the outflow at Chenglingji station (CLJ), the inflow from three outlets of the Yangtze River (Inflow2), and the inundation conditions during the last period (Arealag) made the largest relative contributions to the water area variation (around 25%, 27% and 24%, respectively). A breakpoint was detected around 2004, corresponding to the operation period of the Three Gorges Dam (TGD). The regulation of TGD profoundly affected the hydrological characteristics at the three outlets and CLJ, and may have indirectly caused the water area to expand by 2.41 km2/a during the dry seasons between 2004 and 2019. These results provide valuable insight into how natural and anthropogenic factors affect water body variation and may offer a practical reference for the local government to adjust management strategies

Lithium Phosphosulfide Electrolytes for Solid-State Batteries: Part I

A high performance and stable Li-ion conductive solid electrolyte is one of the key components for the future all-solid-state batteries with metallic lithium anodes. Phosphate, oxide and phosphosulfide-based inorganic solid electrolytes are currently under development. High ambient temperature Li-ion conductivities amounting up to 10−2 S cm−1 for the best performing electrolytes distinguish the phosphosulfides from the other material systems. Part I of the review starts with the motivation and background for the development of Li-phosphosulfide electrolytes followed by an overview of four different types of phosphosulfide electrolytes; the Li–P–S, thio-LiSICon, LGPS and the Argyrodite-type electrolytes. The core of part I is concerned with a detailed discussion of the phosphosulfide electrolyte types that have been under investigation already for a long time, the Li–P–S and the LiSICon. There is a multiplicity of different compositions within each of these types. The idea behind the outline of these sections is to point out the relations and differences between the different materials with respect to their chemistry related to the phase diagrams. Patterns for the relations among the materials identified in the phase diagrams are the base for a discussion of structure, processing and Li-ion conductivity within separate sections for each type and resulting in intra-type comparisons. The follow up part II will continue with a treatment of the more recently developed LGPS and Argyrodite-type electrolytes tracking the same concept, before addressing an inter-type comparison of ambient temperature Li-ion conductivities and the electrochemical stability of the electrolytes vs. metallic lithium. A final section in part II summarizes conclusions and provides perspectives for future research on Li-ion conductive phosphosulfide electrolytes

Fluorine-free water-in-ionomer electrolytes for sustainable lithium-ion batteries.

The continuously increasing number and size of lithium-based batteries developed for large-scale applications raise serious environmental concerns. Herein, we address the issues related to electrolyte toxicity and safety by proposing a "water-in-ionomer" type of electrolyte which replaces organic solvents by water and expensive and toxic fluorinated lithium salts by a non-fluorinated, inexpensive and non-toxic superabsorbing ionomer, lithium polyacrylate. Interestingly, the electrochemical stability window of this electrolyte is extended greatly, even for high water contents. Particularly, the gel with 50 wt% ionomer exhibits an electrochemical stability window of 2.6 V vs. platinum and a conductivity of 6.5 mS cm-1 at 20 °C. Structural investigations suggest that the electrolytes locally self-organize and most likely switch local structures with the change of water content, leading to a 50% gel with good conductivity and elastic properties. A LiTi2(PO4)3/LiMn2O4 lithium-ion cell incorporating this electrolyte provided an average discharge voltage &gt; 1.5 V and a specific energy of 77 Wh kg-1, while for an alternative cell chemistry, i.e., TiO2/LiMn2O4, a further enhanced average output voltage of 2.1 V and an initial specific energy of 124.2 Wh kg-1 are achieved

Fluorine-free water-in-ionomer electrolytes for sustainable lithium-ion batteries

The continuously increasing number and size of lithium-based batteries developed for large-scale applications raise serious environmental concerns. Herein, we address the issues related to electrolyte toxicity and safety by proposing a “water-in-ionomer” type of electrolyte which replaces organic solvents by water and expensive and toxic fluorinated lithium salts by a non-fluorinated, inexpensive and non-toxic superabsorbing ionomer, lithium polyacrylate. Interestingly, the electrochemical stability window of this electrolyte is extended greatly, even for high water contents. Particularly, the gel with 50 wt% ionomer exhibits an electrochemical stability window of 2.6 V vs. platinum and a conductivity of 6.5 mS cm−1 at 20 °C. Structural investigations suggest that the electrolytes locally self-organize and most likely switch local structures with the change of water content, leading to a 50% gel with good conductivity and elastic properties. A LiTi2(PO4)3/LiMn2O4 lithium-ion cell incorporating this electrolyte provided an average discharge voltage > 1.5 V and a specific energy of 77 Wh kg−1, while for an alternative cell chemistry, i.e., TiO2/LiMn2O4, a further enhanced average output voltage of 2.1 V and an initial specific energy of 124.2 Wh kg−1 are achieved