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The scale height (SH) represents the height increment for a certain parameter to decrease to 36.7% (1/e) of its value at a certain height. Here we present ERA5-SH, a gridded dataset containing the SH values of six troposphere key parameters (PWV, WVD, Tm, ZTD, ZHD and ZWD) based on ERA5 reanalysis from 2013 to 2022, with a temporal resolution of 1 hour and a spatial resolution of 1°. The dataset was generated using numerical integral and exponential fitting, and exhibits high reliability with mean coefficients of determination being 0.991, 0.957, 0.980, 0.999, 0.999, and 0.995, respectively. Using the global distributed radiosonde sites as references, the mean RMSE for the six parameters were 0.243 km, 0.189 km, 3.290 km, 0.879 km, 0.681 km, and 0.263 km, respectively. This dataset will contribute to a deeper understanding of the tropospheric vertical distribution and to improve the accuracy of atmospheric delay modeling, which are vital for the advancement of the Earth observation technologies with high precision. [ABSTRACT FROM AUTHOR]

A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). First, the weather factors less relevant to photovoltaic (PV) power generation are filtered using the Spearman correlation coefficient. Historical data are then clustered into three categories—sunny, cloudy, and rainy days—using the K-means algorithm. Next, the original PV power data are decomposed through VMD. A DHKELM-based combined prediction model is developed for each component of the decomposition, tailored to different weather types. The model's hyperparameters are optimized using the IDBO. The final power forecast is determined by combining the outcomes of each individual component. Validation is performed using actual data from a PV power plant in Australia and a PV power station in Kashgar, China demonstrates. Numerical evaluation results show that the proposed method improves the Mean Absolute Error (MAE) by 3.84% and the Root-Mean-Squared Error (RMSE) by 3.38%, confirming its accuracy. [ABSTRACT FROM AUTHOR]

The potential health risks posed by the coexistence of nanoplastics (NPs) and triclosan (TCS) have garnered significant attention. However, the effects and underlying mechanisms of NPs and TCS on key functional proteins at the molecular level remain poorly understood. This study reports the effect of polystyrene nanoplastics (PSNPs) on the binding of TCS to human serum albumin (HSA) using multispectral methods and molecular simulation systems. The experimental results show that TCS significantly inhibits HSA esterase activity, with exacerbating inhibition in the presence of PSNPs, which is attributed to the alteration of HSA conformation and microenvironment of the amino acid residues induced by PSNPs. Molecular docking and site marker competitive studies indicate that TCS predominantly binds to site I of subdomain Sudlow II and the presence of PSNPs does not affect the binding sites. Spectra analyses indicate that the quenching mechanism between TCS and HSA belongs to the static quenching type and the presence of PSNPs does not change the fluorescence quenching type. The HSA fluorescence quenching and the conformational alterations induced by TCS are further enhanced in the presence of PSNPs, indicating that PSNPs enhance the binding of TCS to HSA by making TCS more accessible to the binding sites. This study provides valuable information about the toxicity of PSNPs and TCS in case of co-exposure. [ABSTRACT FROM AUTHOR]

Background: The impact of acute mountain sickness (AMS) on individuals ascending to plateaus, soon after exposure to high altitudes, is well-documented. However, the specific relationship between AMS and alterations in blood parameters remains unclear. Methods: A total of 40 healthy volunteers were recruited. Following their arrival at an altitude of 3,300 m, an AMS questionnaire survey was administered 48 h later. Based on the AMS scores obtained, participants were categorized into three groups: non-AMS, mild AMS, and moderate/severe AMS (encompassing both moderate and severe cases). Blood routine tests were performed on all groups at 3-, 7-, and 30-days post-arrival at the plateau, with blood oxygen saturation tests conducted at 3 and 30 days after rapidly entering the plateau. Results: In the current investigation, a total of 40 participants were stratified into non-AMS (n = 24), mild-AMS (n = 8), and moderate/severe-AMS (n = 8) cohorts subsequent to rapid ascension to an altitude of 3,300 m. The incidence of AMS in this study was 40%. Noteworthy elevations in red blood cells (RBC), hemoglobin (Hb), and hematocrit (HCT) levels were noted at the 3-day mark post-ascent across all delineated groups. By the 7th day, the moderate/severe-AMS cohort displayed sustained increments in Hb and HCT levels, whereas solely HCT levels rose in the mild-AMS and non-AMS cohorts. Upon reaching the 30-day milestone, the moderate/severe-AMS group demonstrated a reduction in RBC, Hb, and HCT levels, while only HCT levels decreased in the mild-AMS and non-AMS groups. Furthermore, it was observed that all groups exhibited notable reductions in oxygen saturation (SpO2) at 3 days post-ascent, followed by a partial recovery at 30 days, albeit remaining below baseline levels. The correlation analysis results indicated that RBC, Hb, and HCT exhibited a positive correlation with the severity of AMS after a 7-day acclimatization period at high altitude. Conversely, SpO2 demonstrated a negative correlation with the severity of AMS following the same duration at high altitude. The findings of the study suggest a strong association between alterations in RBC, Hb, and HCT levels and AMS, particularly among individuals in the moderate/severe-AMS category who displayed more significant fluctuations in these parameters. Conclusion: Individuals suffering from moderate to severe AMS demonstrated increased levels of RBC, Hb, and HCT, as well as reduced SpO2, indicating a greater need for oxygen adaptation to high-altitude hypoxia. These findings emphasize the physiological adjustments to high altitudes and their potential implications for the treatment of AMS. [ABSTRACT FROM AUTHOR]

The real-time tracking of moving objects has extensive applications in various domains. Existing tracking methods typically utilize video image processing, but their performance is limited due to the high information throughput and computational requirements associated with processing continuous images. Additionally, imaging in certain spectral bands can be costly. This paper proposes a non-imaging real-time three-dimensional tracking technique for distant moving targets using single-pixel LiDAR. This novel approach involves compressing scene information from three-dimensional to one-dimensional space using spatial encoding modulation and then obtaining this information through single-pixel detection. A LiDAR system is constructed based on this method, where the peak position of the detected full-path one-dimensional echo signal is used to obtain the target distance, while the peak intensity is used to obtain the azimuth and pitch information of the moving target. The entire process requires minimal data collection and a low computational load, making it feasible for the real-time three-dimensional tracking of single or multiple moving targets. Outdoor experiments confirmed the efficacy of the proposed technology, achieving a distance accuracy of 0.45 m and an azimuth and pitch angle accuracy of approximately 0.03° in localizing and tracking a flying target at a distance of 3 km. [ABSTRACT FROM AUTHOR]

Cord blood (CB) CD34+ cells have the potential to be used to achieve artificial hematopoiesis because of their ability to expand and differentiate in multiple directions. However, the mechanism and molecular changes underlying such differentiation are still unclear. The differentiation of CB CD34+ cells is generally driven by subtle changes in gene expression. A crucial method for examining gene expression is quantitative real-time polymerase chain reaction, but the accuracy of the results is dependent on the use of reliable reference genes. Here, the transcription levels of 10 novel candidate reference genes (EIF4G2, DYNC1H1, LUC7L3, CD46, POLR1D, WSB1, GAPVD1, HGS, LGALS8, and RBM5) and 8 traditional reference genes (GAPDH, YWHAZ, ACTB, B2MG, TBP, HMBS, PPIA, HPRT1) in CB CD34+ cells under different oxygen concentrations were screened and evaluated by using the geNorm and NormFinder algorithms. Comprehensive analysis conducted by RefFinder online tool showed that TBP (a traditional reference gene) and EIF4G2 (a novel reference gene) had the most stable expression, whereas GAPDH and HMBS were the least suitable reference genes under these conditions. These results may serve as a basis for selecting reference genes with stable expression for more accurate normalization under different oxygen concentration stimulation during CB CD34+ cells differentiation. [ABSTRACT FROM AUTHOR]

We report a new horizontal Global Positioning System velocity field in the Chinese mainland from the data analysis of about 2000 GPS sites observed in 2009, 2011 and 2013 through three campaigns of the CMONOC project. Assuming the crustal block to characterize their kinematic behaviors, we estimate parameters of 22 crustal blocks to fit the GPS-derived velocity by using GIPSY software. We restrict us to compare two competing models in which the rigid blocks and the deforming blocks are involved. Our modeling suggests that the most crustal blocks characterized by coherent movement and internal strain may be better in describing the kinematics of crustal deformation in the Chinese mainland.

Summary: Based on the failure of critical parameter sensors at nuclear power plants (NPPs) during accidents, a prediction model for critical parameter prediction during accidents was developed utilizing a long short‐term memory (LSTM) neural network and historical‐critical parameter operation sequences. The validation results show that the critical parameters model built with the LSTM neural network accurately predicts nuclear power, pressurizer pressure, pressurizer water level, coolant flow rate, coolant average temperature, and steam generator water level under loss of coolant accident and steam generator tube rupture conditions, and can help in the event of a sensor failure of critical operating parameters. This means that NPP operators will be able to better control the unit's status and improve safety in the event of a major operating parameter sensor failure. [ABSTRACT FROM AUTHOR]

The weighted mean temperature (Tm) is a function of atmospheric temperature and vertical humidity profiles. It plays a crucial role in the progress of retrieving water vapor information from the tropospheric delay of GNSS signals. The Tm estimated by the empirical models is always used to convert the zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. However, these empirical Tm models used trigonometric functions, making it difficult to describe Tm in detail and leading to an obvious accuracy difference with latitude changes. Thus, a global latitude zone augmentation mode was adopted for the empirical Tm models; the augmentation coefficients for each latitude zone were obtained by introducing the measured surface temperature and using the least-squares method. Using the Tm data of 2011–2015 derived from radiosonde, the GPT3 model, UNB3m model, and GWTMD model were augmented and analyzed. The results show that all augmentation models can improve the accuracy of the estimated Tm compared with their corresponding original models, and their levels of improvement are different. The three augmentation models achieved an average RMSE of 2.79 K, 3.47 K, and 3.22 K, which correspond to 22%, 49%, and 8% improvement against the GPT3 model, UNB3m model, and GWTMD model. In addition, the comparisons with the Tm linear formula were carried out and showed the superiority of the augmentation models. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the effects of a traditional Chinese herbal medicine complex (TCHMC) on the productive performance of periparturient dairy cows. Eighteen non-lactating pregnant Holstein dairy cows with similar body conditions with 1 to 2 parity were randomly divided into three groups (n = 6), receiving a basal diet with 0 (CON group), 200 (T-200 group), and 300 (T-300 group) g TCHMC per day from 14 to 9 days prepartum. The results demonstrated that TCHMC treatments decreased the days of gestation, calving to first service, and calving to first visible estrus. Compared with CON at specific time points, TCHMC treatments increased the concentrations of follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2), whereas progesterone (P4) and E2 concentrations decreased. Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and creatine kinase (CK) concentrations were downregulated, whereas that of globulin (GLB) and immunoglobulin G (IgG) were upregulated by TCHMC treatments around the time of calving. Compared with CON and T-200 treatments, the T-300 treatment increased the serum concentrations of glutathione peroxidase (GSH-Px), superoxide dismutase (SOD), catalase (CAT) and total antioxidant capacity (T-AOC) and decreased the malondialdehyde (MDA) concentration from 7 d prepartum to 21 d postpartum when. In addition, although TCHMC treatment had no effect on average birth weight, heart rate, respiratory rate, and body temperature of calves, the T-300 treatment increased serum albumin (ALB) and IgG concentrations in calves from 3 to 14 days postpartum. The addition of TCHMC used in the present study could serve as a potential effective strategy to improve the health and productive performance of periparturient dairy cows, and the optimal dose should be set at 300 g per day. [ABSTRACT FROM AUTHOR]