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Rapid developments in the electric industry have promoted an increasing demand for lithium resources. Lithium in salt lake brines has emerged as the main source for industrial lithium extraction, owing to its low cost and extensive reserves. The effective separation of Mg2+ and Li+ is critical to achieving high recovery efficiency and purity of the final lithium product. This paper summarizes Mg2+/Li+ separation materials and methods in the field of lithium recovery from salt lake brines. The review begins with an introduction to the global distribution and demand for lithium resources, followed by a description of the materials used in various separation techniques, including precipitation, adsorption, solvent extraction, nanofiltration membrane, electrodialysis, and electrochemical methods. A comparison, analysis, and outlook of such methods are comprehensively discussed in terms of principles, mechanisms, synthesis/operation, development, and industrial applications. We conclude with a presentation of challenges and insights into the future directions of lithium extraction from salt lake brines. A combination of the advantages of various materials is the most logical step toward developing novel methods for extracting lithium from brines with high separation selectivity, stability, low cost, and environmentally friendly characteristics.

We propose a single low-profile skin metasurface carpet cloak to hide objects with arbitrary shape and size under three different waves, i.e., electromagnetic (EM) waves, acoustic waves and water waves. We first present a metasurface which can control the local reflection phase of these three waves. By taking advantage of this metasurface, we then design a metasurface carpet cloak which provides an additional phase to compensate the phase distortion introduced by a bump, thus restoring the reflection waves as if the incident waves impinge onto a flat mirror. The finite element simulation results demonstrate that an object can be hidden under these three kinds of waves with a single metasurface cloak.

Mapping global shipping density, including vessel density and traffic density, is important to reveal the distribution of ships and traffic. The Automatic Identification System (AIS) is an automatic reporting system widely installed on ships initially for collision avoidance by reporting their kinematic and identity information continuously. An algorithm was created to account for errors in the data when ship tracks seem to ‘jump’ large distances, an artefact resulting from the use of duplicate identities. The shipping density maps, including the vessel and traffic density maps, as well as AIS receiving frequency maps, were derived based on around 20 billion distinct records during the period from August 2012 to April 2015. Map outputs were created in three different spatial resolutions: 1° latitude by 1° longitude, 10 minutes latitude by 10 minutes longitude, and 1 minute latitude by 1 minute longitude. The results show that it takes only 56 hours to process these records to derive the density maps, 1·7 hours per month on average, including data retrieval, computation and updating of the database.

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of patients is crucial to optimize treatment and improve prognosis under the new global definition of ARDS. Our study aimed to use machine learning models to develop models that can effectively predict the in-hospital mortality of patients with sepsis related ARDS, calculate the mortality, and to identify related risk factors under the new global definition of ARDS. Based on MIMIC database, our study included 3470 first-time admission records of patients with sepsis related ARDS. After excluding 4 patients under the age of 18, 75 patients with less than 24 h stay in ICU, and 5 cases with missing indicators > 30%, finally 3386 cases were retained. The variance inflation factor (VIF) analysis was used to test the collinearity of the explanatory variables. The data were divided into the training set and the test set according to the ratio of 7:3. Six models, extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), random forest (RF), classification and regression tree (CART), naive bayes (NB) and logistic regression (LR), were designed for training and testing. In the training set, XGBoost (AUROC = 0.951, 95% CI 0.942–0.961), LR (AUROC = 0.835, 95% CI 0.817–0.854), RF (AUROC = 1.0, 95% CI 1.0–1.0), LightGBM (AUROC = 1.0, 95% CI 1.0–1.0), CART (AUROC = 0.831, 95% CI 0.811–0.852), NB (AUROC = 0.793, 95% CI 0.772–0.814). In the test set, XGBoost (AUROC = 0.833, 95% CI 0.804–0.861), LR (AUROC = 0.82695% CI 0.796–0.856), RF (AUROC = 0.846, 95% CI 0.818–0.874), LightGBM (AUROC = 0.827, 95% CI 0.798–0.856), CART (AUROC = 0.753, 95% CI 0.718–0.787), NB (AUROC = 0.799, 95% CI 0.768–0.831). The RF model has the best performance on the test set. Further analyze the feature importance ranking and partial dependence plots of random forest model. Acute physiology and chronic health evaluation III (APACHE III), bicarbonate, anion gap and non-invasive blood pressure systolic were identified as the four most important risk characteristics. In this study, a variety of machine learning models have been successfully constructed to predict the in-hospital mortality of patients with sepsis related ARDS, among which the RF model performs well. Key risk factors identified include APACHE III, bicarbonate, anion gap and non-invasive blood pressure systolic. The identification of these factors helps clinicians to assess patients’ conditions more accurately and develop personalized treatment plans, thereby improving the survival rate and prognosis quality of patients under the new global definition of ARDS.

It is important to distinguish between two common defects, fatigue cracks and reflective cracks, and determine their locations (the top and bottom) in the highway pavement because they require individually targeted treatment measures. Ground Penetrating Radar (GPR) has the potential to detect cracks in the highway pavement due to the change of the electromagnetic properties of highway-pavement media, arising from the existences of cracks. By using a theoretical analysis and a numerical simulation, we compare the characteristics of corresponding radargrams, including the amplitude variation of diffracted waves with various models of presetting cracks inside the layered homogeneous media. For those fatigue cracks and reflective cracks extending to the road surface, the amplitude curves of direct ground wave can intuitively indicate the locations of the top of the cracks and qualitatively compare the width of these cracks. Furthermore, we find that the shape and pattern of diffraction hyperbolas of both types of cracks with bottoms at different locations are quite similar, but their amplitudes are significantly different. To be specific, for those cracks with the same width, the amplitude of diffracted waves generated by fatigue cracks is slightly higher than that generated by reflective cracks at the interface between the asphalt surface and the semi-rigid base layer. In contrast, the amplitude of the former is significantly lower than the latter at the interface between the semi-rigid base and the roadbed. We applied these findings to the interpretation of the field GPR data of a highway pavement in China, and successfully identified the locations of the cracks and corresponding types. Our model results and field results clearly show the efficiency of our findings in the detection of cracks for highway-pavement rehabilitation.

Background To examine the relationship between social capital and depression among community-dwelling older adults in Anhui Province, China. Methods A cross-sectional study was conducted among older people selected from three cities of Anhui Province, China using a multi-stage stratified cluster random sampling method. Data were collected through questionnaire interviews and information on demographic characteristics, social capital, and depression was collected. The generalized linear model and classification and regression tree model were employed to assess the association between social capital and depression. Results Totally, 1810 older people aged ≥60 years were included in the final analysis. Overall, all of the social capital dimensions were positively associated with depression: social participation (coefficient: 0.35, 95% CI: 0.22–0.48), social support (coefficient:0.18, 95% CI:0.07–0.28), social connection (coefficient: 0.76, 95% CI:0.53–1.00), trust (coefficient:0.62, 95% CI:0.33–0.92), cohesion (coefficient:0.31, 95% CI:0.17–0.44) and reciprocity (coefficient:0.30, 95% CI:0.11–0.48), which suggested that older people with higher social capital had a smaller chance to develop depression. A complex joint effect of certain social capital dimensions on depression was also observed. The association with depression and the combinative effect of social capital varied among older adults across the cities. Conclusions Our study suggests that improving social capital could aid in the prevention of depression among older adults.

Increasing epidemiological studies have shown that a rapid temperature change within 1 day is an independent risk factor for human health. This paper aimed to systematically review the epidemiological evidence on the relationship between diurnal temperature range (DTR) and human health and to propose future research directions. A literature search was conducted in October 2013 using the databases including PubMed, ScienceDirect, and EBSCO. Empirical studies regarding the relationship between DTR and mortality and morbidity were included. Twenty-five relevant studies were identified, among which, 11 investigated the relationship between DTR and mortality and 14 examined the impact of DTR on morbidity. The majority of existing studies reported that DTR was significantly associated with mortality and morbidity, particularly for cardiovascular and respiratory diseases. Notably, compared with adults, the elderly and children were more vulnerable to DTR effects. However, there were some inconsistencies regarding the susceptible groups, lag time, and threshold of DTR. The impact of DTR on human health may be confounded or modified by season, socioeconomic, and educational status. Further research is needed to further confirm the adverse effects of DTR in different geographical locations; examine the effects of DTR on the health of children aged one or under; explore extreme DTR effects on human health; analyze the difference of DTR effects on human health in different locations and the modified effects of potential confounding factors; and develop detailed preventive measures against large DTR, particularly for susceptible groups.

We aim to estimate the total factor productivity and analyze factors related to the Chinese government's health care expenditure in each of its provinces after its implementation of new health care reform in the period after 2009. We use the Malmquist DEA model to measure efficiency and apply the Tobit regression to explore factors that influence the efficiency of government health care expenditure. Data are taken from the China statistics yearbook (2004-2020). We find that the average TFP of China's 31 provincial health care expenditure was lower than 1 in the period 2009-2019. We note that the average TFP was much higher after new health care reform was implemented, and note this in the eastern, central and western regions. But per capita GDP, population density and new health care reform implementation are found to have a statistically significant impact on the technical efficiency of the provincial government's health care expenditure (P<0.05); meanwhile, region, education, urbanization and per capita provincial government health care expenditure are not found to have a statistically significant impact. Although the implementation of the new medical reform has improved the efficiency of the government's health expenditure, it is remains low in 31 provinces in China. In addition, the government should consider per capita GDP, population density and other factors when coordinating the allocation of health care input. This study systematically analyzes the efficiency and influencing factors of the Chinese government's health expenditure after it introduced new health care reforms. The results show that China's new medical reform will help to improve the government's health expenditure. The Chinese government can continue to adhere to the new medical reform policy, and should pay attention to demographic and economic factors when implementing the policy.

Long noncoding RNAs have been identified as key regulators in the progression of various cancers. LINC00261 has been reported as a tumor suppressor in multiple cancers. However, its function and underlying mechanisms in pancreatic cancer remain largely unclear. Quantitative real-time PCR was performed to detect RNA expression. In situ hybridization was used to discover the subcellular location. The direct binding of LINC00261 to miR-222-3p was verified using a dual-luciferase reporter assay and RNA immunoprecipitation. LINC00261-binding proteins were detected using an RNA pulldown assay. LINC00261 was downregulated in pancreatic cancer tissues and cell lines. Its reduced expression was correlated with advanced pathological stage and poor prognosis. Forced expression of LINC00261 suppressed pancreatic cancer glycolysis and proliferation and induced cell cycle arrest and apoptosis. Mechanistically, downregulation of LINC00261 was caused by hypermethylation of the CpG island in the promoter region and EZH2-mediated histone H3 lysine 27 trimethylation. Moreover, LINC00261 exerted its biological function by binding to miR-222-3p to activate the HIPK2/ERK/c-myc pathway. In addition, LINC00261 could also reduce c-myc expression by sequestering IGF2BP1. Our study suggests that LINC00261 functions as a tumor suppressor in pancreatic cancer and identifies novel epigenetic and posttranscriptional regulatory mechanisms of LINC00261, which contribute to the targeted therapy of pancreatic cancer.

Asthma is a chronic inflammatory disease that affects millions of people worldwide. Obesity, particularly visceral adipose tissue (VAT), is known to secrete adipokines and pro-inflammatory factors, which are closely associated with various metabolic and cardiovascular diseases. Research indicates that these metabolic disturbances can exacerbate inflammatory conditions, contributing to both cardiovascular and respiratory diseases, including asthma. Despite these associations, studies on the specific relationship between VAT and asthma remain limited and warrant further investigation. Utilizing the NHANES database from 2011 to 2018, we included a total of 11,137 participants. Multivariable regression analysis was performed, stratifying subjects based on VAT levels and adjusting for various confounders. Subgroup interaction analysis and nonlinear analysis were conducted to explore potential effect modifiers and nonlinear associations. In this study, 11,137 participants were included, with 49.74% being female. Among the 509 asthma patients, 69.35% were female. The number of asthma patients among Non-Hispanic Whites was 212, representing 41.65% of the total, the highest proportion among the studied groups. The VAT for asthma patients was 529 g, significantly higher than the 455 g in the non-asthma group ( P  < 0.001). Multivariable regression analysis showed that for every 200 g increase in VAT, the risk of asthma increased by 10.4% ( P  = 0.032), 20.8% ( P  < 0.001), and 20.3% ( P  = 0.004) across three models (unadjusted, adjusted for demographic factors, and fully adjusted). Subgroup analysis indicated a stronger association between VAT and asthma risk in females and individuals over 40 years old. Nonlinear analysis uncovers a J-shaped relationship between VAT and asthma, with the lowest risk observed at 464.57 g ( P  < 0.001). The study findings suggest that increased VAT is associated with elevated asthma risk, particularly among females and older individuals. These results underscore the importance of considering VAT in asthma risk assessment and highlight potential targeted interventions to reduce asthma risk associated with excess visceral adiposity.