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Unmanned Aerial Vehicle (UAV) communication networks are vulnerable to malicious jamming and co-channel interference, deteriorating the performance of the networks. Therefore, the exploration of anti-jamming methods to enhance communication security becomes a significant challenge. In this paper, we propose a novel anti-jamming channel selection scheme in a multi-channel multi-UAV network. We first formulate the anti-jamming problem as a Partially Observable Stochastic Game (POSG), where the UAV pairs with partial observability compete for a limited number of communication channels against a Markov jammer. To ensure rapid adaptation to the dynamic jamming environment, we propose a Meta-Mean-Field Q-learning (MMFQ) algorithm, which provides a Nash Equilibrium (NE) solution to the POSG problem. Furthermore, we derive the expressions of the upper bound for the loss function of MMFQ and prove the convergence of the proposed algorithm. Simulation results demonstrate that the proposed algorithm can achieve a superior average reward compared to the benchmark algorithms, facilitating throughput enhancement and resource utilization increase, especially for large-scale UAV communication networks.

Traditional semantic segmentation models often struggle with poor generalizability in zero-shot scenarios such as recognizing attributes unseen in the training labels. On the other hands, language-vision models (VLMs) have shown promise in improving performance on zero-shot tasks by leveraging semantic information from textual inputs and fusing this information with visual features. However, existing VLM-based methods do not perform as effectively on remote sensing data due to the lack of such data in their training datasets. In this paper, we introduce a two-stage fine-tuning approach for a VLM-based segmentation model using a large remote sensing image-caption dataset, which we created using an existing image-caption model. Additionally, we propose a modified decoder and a visual prompt technique using a saliency map to enhance segmentation results. Through these methods, we achieve superior segmentation performance on remote sensing data, demonstrating the effectiveness of our approach.

The ground fissures caused by coal mining have seriously affected the ecological environment of the land. Timely and accurate identification and landfill treatment of ground fissures can avoid secondary geological disasters in coal mine areas. At present, the fissure identification methods based on deep learning show excellent performance on roads and walls, etc. Nevertheless, the automatic and reliable segmentation of ground fissures in remote sensing images poses a challenge for deep learning networks, due to the diverse and complex texture information included in the mining ground fissures and background. To overcome these challenges, we propose an improved YOLOv8 instance segmentation network to automatically and efficiently segment the ground fissures in coal mining areas. In detail, a model called FS_YOLOv8 is proposed. The DSPP (Dynamic Snake convolutional Pyramid Pooling) module is incorporated into the FS_YOLOv8 model to establish a multi-scale dynamic snake convolution feature aggregation structure. This module replaces the conventional convolution found in the SPPF module of YOLOv8 and aims to enhance the model's ability to extract features related to fissures with tubular structures. Furthermore, the D-LKA (Deformable Large Kernel Attention) module is employed to autonomously collect fissure context information. To enhance the detection capability of challenging samples in remote sensing images with intricate background and fissure texture, we employ a Slide Loss function. Ultimately, the ground fissure dataset of unmanned aerial vehicle (UAV) images in coal mine areas is subjected to experimental analysis. The experimental findings demonstrate that FS_YOLOv8 exhibits exceptional proficiency in segmenting ground fissures within intricate and expansive mining areas.

To explore how trees optimize their structure, we developed a method based on Pareto optimality theory. This method consists of the following operations. Firstly, we utilize Quantitative Structure Models for Single Trees from Laser Scanner Data (TreeQSM) to extract tree structures from point clouds acquired through Light Detection and Ranging (LiDAR). Subsequently, we utilize a graph-theoretical model to characterize the natural tree structure networks and implement a greedy algorithm to generate Pareto optimal tree structure networks. Finally, based on the Pareto optimality theory, we explore whether tree structures are multi-objective optimized. This paper demonstrates that tree structures lie along the Pareto front between minimizing "transport distance" and minimizing "total length". The growth pattern of trees, which produces multi-objective optimized structures, is likely an intrinsic mechanism in the generation of tree structure networks. The location of tree structures along the Pareto front varies under different environmental conditions, reflecting their diverse survival strategies.

Light detection and ranging (LiDAR), as an innovative remote sensing tool, not only captures target reflectance but also provides its morphological parameters. Traditional single/multi-band LiDAR and multispectral LiDAR (MSL) are presently employed in applications such as 3D modeling and plant biochemical parameter inversion albeit with effectiveness limited. Moreover, hyperspectral LiDAR (HSL) distinguished by its expanded array of spectral detection channels and enhanced spectral resolution, has proven more effective in meeting these requirements and also exhibits superior capabilities in both feature and land cover classification tasks. Nevertheless, point clouds acquired through HSL frequently exhibit quality deficiencies, including uneven density and excessive noise. Meanwhile, there exists a notable absence of technical specifications and operational standards governing the measurement protocols for HSL systems globally. To address this gap, this study constructed a systematic analysis framework of data quality in hyperspectral point clouds and endeavors to qualitatively analyse 30 tree point clouds continuously scanned with Finnish Geospatial Research Institute (FGI) 8-band hyperspectral laser scanner. Furthermore, this research validated the theoretical feasibility of employing the 8-band HSL system for inversion processes aimed at quantifying chlorophyll leaf content. Apart from detecting the time-varying patterns of reflectance within birch canopy point clouds, the results of this study also effectively pinpointed the band exhibiting heightened noise level of the HSL system, demonstrating the efficacy of our proposed quality analysis methodology. The endeavor presented in this study can serve as a cornerstone for advancing hyperspectral LiDAR across a diverse array of related remote sensing and earth observation applications.

Sulphate plays a vital role in the growth and development of the foetus. Sodium sulphate (Na2SO4) is utilised as a dietary protein nutrient factor and helps replenish sulphur elements in livestock and poultry. Therefore, this study aimed to investigate the effects of Na2SO4 supplementation in mid to late pregnancy on bile acid metabolism, amino acid metabolism, placental vascular development and antioxidant capacity of sows. At day 1 of gestation (G1), a total of twenty-six primiparous sows were carefully chosen and randomised into two groups: (1) control group, (2) Na2SO4 group (1.40 g/kg). Blood samples and placentas from sows were collected to measure biochemistry parameters, antioxidant indexes, placental vascular density, and indicators related to bile acid metabolism and amino acid concentrations, respectively. We found that dietary supplementation with Na2SO4 had a tendency for a reduction of incidence of stillborn at farrowing. Further observation showed that sows supplemented with Na2SO4 had decreased total bile acid level in cord blood, and increased placental gene expression of sulphotransferase and organic anion transport peptide. Na2SO4 supplementation increased catalase and total superoxide dismutase activity in cord blood, decreased placental malondialdehyde content, and enhanced placental protein expression of Sirtuin 1. Moreover, Na2SO4 consumption resulted in increased vascular density of placental stroma and elevated amino acid levels in sows and cord blood. Furthermore, maternal Na2SO4 consumption reduced serum urea concentrations of sows and umbilical cord blood at G114. In addition, dietary supplementation with Na2SO4 activated the protein expression of the placental mechanistic target of rapamycin complex 1. Collectively, these findings indicated that maternal supplementation with Na2SO4 during mid-to-late gestation elevated foetal survival via improving placental angiogenesis, bile acid metabolism and amino acid utilisation.

We report the first measurements of cumulants, up to 4th order, of deuteron number distributions and proton-deuteron correlations in Au+Au collisions recorded by the STAR experiment in phase-I of Beam Energy Scan (BES) program at the Relativistic Heavy Ion Collider. Deuteron cumulants, their ratios, and proton-deuteron mixed cumulants are presented for different collision centralities covering a range of center-of-mass energy per nucleon pair sNN=7.7 to 200 GeV. It is found that the cumulant ratios at lower collision energies favor a canonical ensemble over a grand canonical ensemble in thermal models. An anti-correlation between proton and deuteron multiplicity is observed across all collision energies and centralities, consistent with the expectation from global baryon number conservation. The UrQMD model coupled with a phase-space coalescence mechanism qualitatively reproduces the collision-energy dependence of cumulant ratios and proton-deuteron correlations.

The chiral magnetic effect (CME) is a phenomenon that arises from the QCD anomaly in the presence of an external magnetic field. The experimental search for its evidence has been one of the key goals of the physics program of the Relativistic Heavy-Ion Collider. The STAR Collaboration has previously presented the results of a blind analysis of isobar collisions (_{44}^{96}Ru+_{44}^{96}Ru, _{40}^{96}Zr+_{40}^{96}Zr) in the search for the CME. The isobar ratio (Y) of CME-sensitive observable, charge separation scaled by elliptic anisotropy, is close to but systematically larger than the inverse multiplicity ratio, the naive background baseline. This indicates the potential existence of a CME signal and the presence of remaining nonflow background due to two- and three-particle correlations, which are different between the isobars. In this postblind analysis, we estimate the contributions from those nonflow correlations as a background baseline to Y, utilizing the isobar data as well as Heavy Ion Jet Interaction Generator simulations. This baseline is found consistent with the isobar ratio measurement, and an upper limit of 10% at 95% confidence level is extracted for the CME fraction in the charge separation measurement in isobar collisions at sqrt[s_{NN}]=200 GeV.

The differential cross section for Z0 production, measured as a function of the boson's transverse momentum (pT), provides important constraints on the evolution of the transverse momentum dependent parton distribution functions (TMDs). The transverse single spin asymmetry (TSSA) of the Z0 is sensitive to one of the polarized TMDs, the Sivers function, which is predicted to have the opposite sign in p+p →W/Z+X from that which enters in semi-inclusive deep inelastic scattering. In this Letter, the STAR Collaboration reports the first measurement of the Z0/γ⁎ differential cross section as a function of its pT in p+p collisions at a center-of-mass energy of 510 GeV, together with the Z0/γ⁎ total cross section. We also report the measurement of Z0/γ⁎ TSSA in transversely polarized p+p collisions at 510 GeV.

Due to a robust emission control policy, significant reductions in major air pollutants, such as PM2.5, SO2, NO2, and CO, were observed in China between 2015 and 2020. On the other hand, during the same period, there was a notable increase in ozone (O3) concentrations, making it a prominent air pollutant in eastern China. The annual mean concentration of maximum daily 8 h average (MDA8) O3 exhibited alarming linear increases of 2.4, 1.1, and 2.0 ppb yr−1 (ppb is for parts per billion) in three megacity clusters: Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), respectively. Meanwhile, there was a significant 3-fold increase in the number of O3-exceeding days, defined as MDA8 O3 > 75 ppb. Our analysis indicated that the upward increases in the annual mean concentration of MDA8 were primarily driven by the rise in consecutive O3-exceeding days. There were expansions of high O3 in urban centers to rural areas accompanied by a saturation effect so that MDA8 O3 concentrations at the high-O3 stations in 2015 remained nearly constant at 100 ppb. Last, we found a close association between O3 episodes with 4 or more consecutive O3-exceeding days and the position and strength of tropical cyclones (TCs) in the northwest Pacific and the West Pacific subtropical high (WPSH). The TC and WPSH contributed to meteorological conditions characterized by clear skies, subsiding air motion, high vertical stability in the lower troposphere, increased solar radiation, and a positive temperature anomaly at the surface. These favorable meteorological conditions greatly facilitated the formation of O3. Thus, we propose that the worsening O3 increases observed in the BTH, YRD, and PRD regions from 2015 to 2020 can be mostly attributed to enhanced photochemical O3 production resulting from an increased occurrence of meteorological conditions with high solar radiation and positive temperature anomalies under the influence of the WPSH and TCs.

Nitrogen dioxide (NO2) is a highly photochemically reactive gas, has a lifetime of only a few hours, and at high concentrations is harmful to human beings. Therefore, it is important to monitor NO2 with high-precision, time-resolved instruments. To this end, a Pandora spectrometer has been installed on the roof of the laboratory building of the Aerospace Information Research Institute of the Chinese Academy of Sciences in the Olympic Park, Beijing, China. The concentrations of trace gases (including NO2, HCHO, O3) measured with Pandora are made available through the open-access Pandora database (https://data.pandonia-global-network.org/Beijing-RADI/Pandora171s1/, last access: 11 July 2023). In this paper, an overview is presented of the Pandora total and tropospheric NO2 vertical column densities (VCDs) and surface concentrations collected during the first year of operation, i.e., from August 2021 to July 2022. The data show that NO2 concentrations were high in the winter and low in the summer, with a diurnal cycle where the concentrations reached a minimum during the daytime. The concentrations were significantly lower during the 2022 Winter Olympics in Beijing, showing the effectiveness of the emission control measures during that period. The Pandora observations show that during northerly winds, clean air is transported to Beijing with low NO2 concentrations, whereas during southerly winds, pollution from surrounding areas is transported to Beijing and NO2 concentrations are high. The contribution of tropospheric NO2 to the total NO2 VCD varies significantly on daily to seasonal timescales; i.e., monthly averages vary between 50 % and 60 % in the winter and between 60 % and 70 % in the spring and autumn. A comparison of Pandora-measured surface concentrations with collocated in situ measurements using a Thermo Scientific 42i-TL analyzer shows that the Pandora data are low and that the relationship between Pandora-derived surface concentrations and in situ measurements is different for low and high NO2 concentrations. Explanations for these differences are offered in terms of measurement techniques and physical (transport) phenomena. The use of Pandora total and tropospheric NO2 VCDs for validation of collocated TROPOspheric Monitoring Instrument (TROPOMI) data, resampled to 100 m × 100 m, shows that although on average the TROPOMI VCDs are slightly lower, they are well within the expected error for TROPOMI of 0.5 Pmolec.cm-2 + (0.2 to 0.5) ⋅ VCDtrop (1 Pmolec.cm-2 = 1 × 1015 molec cm−2). The location of the Pandora instrument within a sub-orbital TROPOMI pixel of 3.5 km × 5.5 km may result in an error in the TROPOMI-derived tropospheric NO2 VCD between 0.223 and 0.282 Pmolec.cm-2, i.e., between 1.7 % and 2 %. In addition, the data also show that the Pandora observations at the Beijing-RADI site are representative of an area with a radius of 10 km.

We report on the charged-particle multiplicity dependence of net-proton cumulant ratios up to sixth order from s=200 GeV p+p collisions at the Relativistic Heavy Ion Collider (RHIC). The measured ratios C4/C2, C5/C1, and C6/C2 decrease with increased charged-particle multiplicity and rapidity acceptance. Neither the Skellam baselines nor PYTHIA8 calculations account for the observed multiplicity dependence. In addition, the ratios C5/C1 and C6/C2 approach negative values in the highest-multiplicity events, which implies that thermalized QCD matter may be formed in p+p collisions.

We report results on an elastic cross section measurement in proton–proton collisions at a center-of-mass energy s=510 GeV, obtained with the Roman Pot setup of the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The elastic differential cross section is measured in the four-momentum transfer squared range 0.23≤−t≤0.67 GeV2. This is the only measurement of the proton-proton elastic cross section in this t range for collision energies above the Intersecting Storage Rings (ISR) and below the Large Hadron Collider (LHC) colliders. We find that a constant slope B does not fit the data in the aforementioned t range, and we obtain a much better fit using a second-order polynomial for B(t). This is the first measurement below the LHC energies for which the non-constant behavior B(t) is observed. The t dependence of B is also determined using six subintervals of t in the STAR measured t range, and is in good agreement with the phenomenological models. The measured elastic differential cross section dσ/dt agrees well with the results obtained at s=540 GeV for proton–antiproton collisions by the UA4 experiment. We also determine that the integrated elastic cross section within the STAR t-range is σelfid=462.1±0.9(stat.)±1.1(syst.)±11.6(scale) μb.

Multi-sensor image matching is a key technology to fully exploit complementary information on images from multiple sources. In particular, the nonlinear radiance distortion between optical and SAR images makes image matching very difficult. Focusing on satellite optical and SAR image matching, this paper proposed an oriented edge-based template image matching, namely Multiple Orientation Edge Feature Correlation (MOEFC). Firstly, the edge feature was extracted by the Sobel operator to construct oriented gradient channels on optical and SAR images. Then spatial filtering was performed separately by a two-dimensional and a one-dimensional kernel. Finally, the similarity was determined by stacking the normalized cross correlation (NCC) of each oriented gradient channel. To validate the new method, it was compared with other four edge-based template image matching methods detected by Sobel, Canny, Laplacian and phase congruency-based methods on satellite optical-SAR image pairs, which were tested in rural and urban areas. And the results show that the proposed MOEFC method can obtain the largest number of correct matching (NCM) with the least root mean square error (RMSE) in both areas. In the rural area, the NCM of the MOEFC method is higher than the other four methods, and the improvement ranges from 54.1% to 74.3%. And the RMSE of the MOEFC method is 0.645 pixel. In the urban area, the NCM of the MOEFC method can also be improved by 3.6% to 46.4%. And the RMSE of the MOEFC method equals 0.489 pixel.

Reconstructing the geometric curves of highways holds significant value for various tasks, such as highway design, traffic simulation, and road network planning. An essential step in highway curve reconstruction is fitting highway curve models to the extracted road markings from laser scanning point clouds. Existing methods for highway curve fitting typically handle one curve at a time. However, a stretch of highway often contains multiple curves. In this paper, we introduce a novel method capable of fitting multiple highway curves simultaneously. The method leverages a reinforcement learning (RL) algorithm to achieve this goal. Specifically, we design a unique RL environment that empowers the RL algorithm to fit multiple highway curves. Our experimental results demonstrate the superiority of our method over other methods.

Lakes are important components of the Earth's surface water bodies, and their fluctuating water levels and changing water volumes can have an important impact on ecological processes and landscape patterns within a watershed. Climate change and human activities are the main drivers of water volume changes in lakes. We applied a long time series analysis approach using classical statistics and geographic information system (GIS) technics to quantitatively attribute water volume variations in Lake Victoria from 2000 to 2019. We examined several drivers of lake water volume variations: meteorological factors including regional climate change (precipitation, evaporation) and global climate change (El Niño/Southern Oscillation events), and anthropogenic factors including socio-economic development and land use/cover change. The results show that regional climate change has a greater impact on lake water volume variations than global climate change; among them, the correlation coefficient between precipitation and lake water volume variations is the largest (0.410), with an impact lag of 1 month. For anthropogenic factors, variations in lake water volume are more pronounced in areas with rapid economic development and high population growth, with correlation coefficients greater than 0.440. Different types of land use/cover have varying impacts on Lake Victoria's water volume variations. Overall, the influence of anthropogenic factors is greater than that of meteorological factors. This study results not only provides a scientific basis for exploring the hydrological changes and their drivers in the world's large transboundary lakes, but also be great scientific significance to rational use of water resources and sustainable regional development.

Rapid access to the basic structure of village buildings is conducive to the investigation of the load-bearing bodies of village houses and provides data support for disaster assessment and post-disaster rescue and reconstruction. The development of computer vision technology provides new ideas and tools for identifying and extracting basic structures of housing buildings. Considering that the original Mask R-CNN ignores the spatial association and relationship of door and window elements, an advanced deep learning model based on Mask R-CNN network is proposed in this paper to detect and segment the door and window structure from the façade images. The improved network architectures integrate the attention mechanism with the original network, containing an improved Coordinate Attention(CA) module and a relationship module-based head network. The experimental results show that the Average Precision(AP) value of the backbone combined with the improved CA module is increased by 0.7% and 0.7% on regression and segmentation tasks respectively, compared with the original Mask R-CNN network. In the head network based on the relationship module, the calculation strategy of the relational module proposed in this paper increases the AP values of detection and segmentation from 76.7% and 77.7% to 80.6% and 80.0%, respectively.

This study focuses on the uncertainties that influence numerical simulation results of meteorological fields (horizontal resolution: 75, 15, and 3 km; vertical resolution: 48 and 62 levels; near-surface (N-S) scheme: MM5 and Eta schemes; initial and boundary conditions: Final (FNL) and European Center for Medium-Range Weather Forecasting (ECMWF) reanalysis data; underlying surface update: model default and latest updates; model version: version 3.6.1 and 3.9.1). By further evaluating and analyzing the uncertainty factors, it is expected to provide relevance for those scholars devoted to factor analysis in order to make the results closer to the observed values. In this study, a total of 12 experiments are set up to analyze the effects of the uncertainties mentioned above, and the following conclusions are drawn: (1) horizontal resolution has a greater effect than vertical resolution; (2) the simulated effects of temperature and wind speed in the N-S scheme are smaller than those in the planetary boundary layer (PBL) scheme; (3) the initial and boundary conditions of different products have the most remarkable effect on relative humidity, while the simulation results of ECMWF data are the best; (4) the updates with urban and water bodies as the underlying surface have a more significant contribution to the meteorological fields, especially on temperature; and (5) for the PBL parameterization schemes, the update of the model version has less impact on the simulation results because each update has small changes and no major changes overall. In general, the configuration of uncertainties needs to be considered comprehensively according to what you need in order to obtain the best simulation results.

The optimal choice of the planetary boundary layer (PBL) parameterization scheme is of particular interest and urgency to a wide range of scholars, especially for many works involving models. At present, there have been many works to evaluate the PBL schemes. However, little research has been conducted into a more comprehensive and systematic assessment of the performance capability of schemes in key regions of China, especially when it comes to the differences in the mechanisms of the schemes themselves, primarily because there is scarcely sufficient observational data, computer resources, and storage support to complete the work. In this companion paper (i.e., Part 1), four typical schemes (i.e., YSU, ACM2, BL, and MYJ) are selected to systematically analyze and evaluate near-surface meteorological parameters, PBL vertical structure, PBL height (PBLH), and turbulent diffusion coefficient (TDC) in five key regions of China (i.e., North China Plain, NCP; Yangtze River Delta, YRD; Sichuan Basin, SB; Pearl River Delta, PRD and Northwest Semi-arid region, NS) in different seasons (i.e., January, April, July, and October). The differences in the simulated 2 m temperatures between the nonlocal closure schemes are mainly affected by the downward shortwave radiation, but to compare the nonlocal closure schemes with the local closure schemes, the effect of sensible heat flux needs to be further considered. The 10 m wind speed is under the influence of factors like the momentum transfer coefficient and the integrated similarity functions at night. The wind speeds are more significantly overestimated in the plains and basin, while less overestimated or even underestimated in the mountains, as a result of the effect on topographic smoothing in the model. Moreover, the overestimation of small wind speeds at night is attributable to the inapplicability of the Monin–Obukhov similarity theory (MOST) at night. The model captures the vertical structure of temperature well, while the wind speed is outstandingly overestimated below 1000 m, largely because of the TDC. The difference between the MOST and the mixing length theory, PBLH, and Prandtl number is cited as the reason for the difference between the TDC of the YSU and ACM2 schemes. The TDCs of the BL and MYJ schemes are affected by the mixing length scale, which of BL is calculated on the basis of the effect of buoyancy, while MYJ calculates it with the consideration of the effect of the total turbulent kinetic energy. The PBLH of the BL scheme is better than the other schemes because of the better simulation results of temperature. In general, to select the optimal scheme, it is necessary to offer different options for different regions with different focuses (heat or momentum). The first focus is on the temperature field. The BL scheme is recommended for January in the NCP region, especially for Beijing, and the MYJ scheme is better for the other 3 months. The ACM2 scheme would be a good match for the YRD region, where the simulation differences between the four schemes are small. The topography of the SB region is more complex, but for most of the areas in the basin, the MYJ scheme is proposed, and if more stations outside the basin are involved, the BL scheme is recommended. The MYJ scheme is applied to the PRD region in January and April, and the BL scheme in July and October. The MYJ scheme is counseled for the NS region. The second focus is the wind field. The YSU scheme is recommended if the main concern is the near-surface layer, and the BL scheme is suggested if focusing on the variation in the vertical direction. The final evaluation of the parameterization scheme and uncertainties will lay the foundation for the improvement of the modules and forecasting of the GRAPES_CUACE regional model developed independently in China.

In the autumn of 2019, a 5 d long-lasting dust event was observed using a synergy of field measurement techniques in Shanghai. This particular dust event stood out from others due to its unique characteristics, including low wind speed, high relative humidity, elevated levels of gaseous precursors, and contrasting wind patterns at different altitudes. During this event, three distinct dust stages were identified. The first stage was a typical dust invasion characterized by high concentrations of particulate matters but relatively short duration. In contrast, the second stage exhibited an unusual enhancement of ozone, attributed to compound causes of a weak synoptic system, transport from the ocean, and subsidence of high-altitude ozone downdrafted by dust. Consequently, gas-phase oxidation served as the major formation pathway of sulfate and nitrate. In the third stage of dust, a noteworthy phenomenon known as dust backflow occurred. The dust plume originated from the Shandong Peninsula and slowly drifted over the Yellow Sea and the East China Sea before eventually returning to Shanghai. Evidence of this backflow was found through the enrichment of marine vessel emissions (V and Ni) and increased solubility of calcium. Under the influence of humid oceanic breezes, the formation of nitrate was dominated by aqueous processing. Additionally, parts of nitrate and sulfate were directly transported via sea salts, evidenced by their co-variation with Na+ and confirmed through thermodynamic modeling. The uptake of NH3 on particles, influenced by the contributions of alkali metal ions and aerosol pH, regulated the formation potential of secondary aerosol. By developing an upstream–receptor relationship method, the quantities of transported and secondarily formed aerosol species were separated. This study highlights that the transport pathway of dust, coupled with environmental conditions, can significantly modify the aerosol properties, especially at the complex land–sea interface.

Intense tropical cyclones (TCs) can cause catastrophic damage to coastal regions after landfall. Recent studies have linked the devastation associated with TCs to climate change, which induces favorable conditions, such as increasing sea-surface temperature, to supercharge storms. Meanwhile, environmental factors, such as atmospheric aerosols, also impact the development and intensity of TCs, but their effects remain poorly understood, particularly coupled with ocean dynamics. Here, we quantitatively assess the aerosol microphysical effects and aerosol-modified ocean feedbacks during Hurricane Katrina using a cloud-resolving atmosphere–ocean coupled model: Weather Research and Forecasting (WRF) in conjunction with the Regional Ocean Model System (ROMS). Our model simulations reveal that an enhanced storm destructive power, as reflected by larger integrated kinetic energy, heavier precipitation, and higher sea-level rise, is linked to the combined effects of aerosols and ocean feedbacks. These effects further result in an expansion of the storm circulation with a reduced intensity because of a decreasing moist static energy supply and enhancing vorticity Rossby wave outward propagation. Both accumulated precipitation and storm surge are enhanced during the mature stage of the TC with elevated aerosol concentrations, implying exacerbated flood damage over the polluted coastal region. The ocean feedback following the aerosol microphysical effects tends to mitigate the vertical mixing cooling in the ocean mixing layer and offsets the aerosol-induced storm weakening by enhancing cloud and precipitation near the eyewall region. Our results highlight the importance of accounting for the effects of aerosol microphysics and ocean-coupling feedbacks to improve the forecast of TC destructiveness, particularly near the heavily polluted coastal regions along the Gulf of Mexico.

To reveal the spatiotemporal distributions of atmospheric CO2 and CH4 mixing ratios and regulation mechanisms over the China shelf sea, two field surveys were conducted in the southern Yellow Sea in China in November 2012 and June 2013, respectively. The results observed showed that mean background atmospheric CO2 and CH4 mixing ratios were 403.94 (±13.77) ppm and 1924.8 (±27.8) ppb in November 2012 and 395.90 (±3.53) ppm and 1918.0 (±25.7) ppb in June 2013, respectively. An improved data-filtering method was optimised and established to flag atmospheric CO2 and CH4 emission from different sources in the survey area. We found that the spatiotemporal distributions of atmospheric CO2 and CH4 mixing ratios over the southern Yellow Sea were dominated by land–sea air mass transport, which was mainly driven by seasonal monsoon, while the influence of air–sea exchange was negligible. In addition, atmospheric CO2 and CH4 mixing ratios over the southern Yellow Sea could be elevated remarkably at a distance of approximately 20 km offshore by land-to-sea air mass transportation from the Asian continent during the early-winter monsoon.

Low-tannin sorghum is an excellent energy source in pig diets. However, sorghum contains several anti-nutritional factors that may have negative effects on nutrient digestibility. The impacts of proteases on growth performance, nutrient digestibility, blood parameters, and gut microbiota of growing pigs fed sorghum-based diets were studied in this study. Ninety-six pigs (20.66 ± 0.65 kg BW) were allocated into three groups (eight pens/group, four pigs/pen): (1) CON (control diet, sorghum-based diet included 66.98% sorghum), (2) PRO1 (CON + 200 mg/kg proteases), (3) PRO2 (CON + 400 mg/kg proteases) for 28 d. No differences were observed in growth performance and apparent total tract digestibility (ATTD) of nutrients between CON and PRO1 groups. Pigs fed PRO2 diet had increased (P

To develop effective strategies for controlling both PM2.5 and O3 levels, it is crucial to understand their synergistic mechanisms and key precursors and the atmospheric physiochemical processes involved. In this study, a wintertime co-occurring O3 and PM2.5 pollution event in a coastal city in southeast China was investigated based on high-time-resolution measurements of criteria air pollutants and chemical compositions of PM2.5, and O3 precursors, such as NOx, HCHO, and volatile organic compounds (VOCs). The results of this study revealed the characteristics of positively correlated PM2.5 and MDA8 O3 concentrations, and an increase in atmospheric oxidation capacity (AOC) during the cold seasons. Strong correlations (R2= 0.415–0.477) between HCHO, Fe, Mn, and sulfate concentrations were observed, suggesting the influence of catalyzed oxidation processes in the coastal city. Through an observation-based model (OBM) analysis coupled with the Regional Atmospheric Chemistry Mechanism, version 2 (RACM2) and the Chemical Aqueous-Phase Radical Mechanism, version 3.0 (CAPRAM 3.0), we found that high concentrations of precursors (SO2 and HCHO), high relative humidity, and moderately acidic pH conditions enhanced the heterogeneous formation of hydroxymethanesulfonate (HMS) in PM2.5. Furthermore, by employing an OBM coupled to the Master Chemical Mechanism (OBM-MCM), we verified that disabling the HCHO mechanism could decrease daytime net O3 production rates by reducing the production rates of HO2 + NO. These results were consistent with the daily values of AOC, OH, HO2, and RO2 concentrations. This study contributes to a better understanding of the significance of HCHO in photochemical reactions and the formation of HMS in a coastal city.

Exposure to elemental carbon (EC) and NOx is a public health issue that has been gaining increasing interest, with high exposure levels generally observed in traffic environments, e.g., roadsides. Shanghai, home to approximately 25 million in the Yangtze River Delta (YRD) region in eastern China, has one of the most intensive traffic activity levels in the world. However, our understanding of the trend in vehicular emissions and, in particular, in response to the strict Covid-19 lockdown is limited partly due to the lack of a long-term observation dataset and application of advanced mathematical models. In this study, NOx and EC were continuously monitored at a sampling site near a highway in western Shanghai for 5 years (2016–2020). The long-term dataset was used to train the machine learning model, rebuilding NOx and EC in a business-as-usual (BAU) scenario for 2020. The reduction in NOx and EC attributable to the lockdown was found to be smaller than it appeared because the first week of the lockdown overlapped with the Lunar New Year holiday, whereas, at a later stage of the lockdown, the reduction (50 %–70 %) attributable to the lockdown was more significant, consistent with the satellite monitoring of NO2 showing reduced traffic on a regional scale. In contrast, the impact of the lockdown on vehicular emissions cannot be represented well by simply comparing the concentration before and during the lockdown for conventional campaigns. This study demonstrates the value of continuous air pollutant monitoring at a roadside on a long-term basis. Combined with the advanced mathematical model, air quality changes due to future emission control and/or event-driven scenarios are expected to be better predicted.

Methionine is indispensable for growth and meat formation in pigs. However, it is still unclear that increasing dietary sulphur-containing amino acid (SAA) levels using different methionine sources affects the growth performance and meat quality of barrows and gilts. To investigate this, 144 pigs (half barrows and half gilts) were fed the control (100% SAA, CON), DL-Methionine (125% SAA, DL-Met)-supplemented, or OH-Methionine (125% SAA, OH-Met)-supplemented diets during the 11–110 kg period. The results showed that plasma methionine levels varied among treatments during the experimental phase, with increased plasma methionine levels observed following increased SAA consumption during the 25–45 kg period. In contrast, pigs fed the DL-Met diet had lower plasma methionine levels than those fed the CON diet (95–110 kg). Additionally, gilts fed the DL-Met or OH-Met diets showed decreased drip loss in longissimus lumborum muscle (LM) compared to CON-fed gilts. OH-Met-fed gilts had higher pH45min values than those fed the CON or DL-Met diets, whereas OH-Met-fed barrows had higher L45min values than those fed the CON or DL-Met diets. Moreover, increased consumption of SAA, regardless of the methionine source, tended to decrease the shear force of the LM in pigs. In conclusion, this study indicates that increasing dietary levels of SAA (+25%) appeared to improve the meat quality of gilts by decreasing drip loss and increasing meat tenderness.

Density fluctuations near the QCD critical point can be probed via an intermittency analysis in relativistic heavy-ion collisions. We report the first measurement of intermittency in Au+Au collisions at sNN = 7.7-200 GeV measured by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The scaled factorial moments of identified charged hadrons are analyzed at mid-rapidity and within the transverse momentum phase space. We observe a power-law behavior of scaled factorial moments in Au+Au collisions and a decrease in the extracted scaling exponent (ν) from peripheral to central collisions. The ν is consistent with a constant for different collisions energies in the mid-central (10-40%) collisions. Moreover, the ν in the 0-5% most central Au+Au collisions exhibits a non-monotonic energy dependence that reaches a minimum around sNN = 27 GeV. The physics implications on the QCD phase structure are discussed.

The deconfined quark-gluon plasma (QGP) created in relativistic heavy-ion collisions enables the exploration of the fundamental properties of matter under extreme conditions. Noncentral collisions can produce strong magnetic fields on the order of 10^{18} G, which offers a probe into the electrical conductivity of the QGP. In particular, quarks and antiquarks carry opposite charges and receive contrary electromagnetic forces that alter their momenta. This phenomenon can be manifested in the collective motion of final-state particles, specifically in the rapidity-odd directed flow, denoted as v_{1}(y). Here, we present the charge-dependent measurements of dv_{1}/dy near midrapidities for π^{±}, K^{±}, and p(p[over ¯]) in Au+Au and isobar (_{44}^{96}Ru+_{44}^{96}Ru and _{40}^{96}Zr+_{40}^{96}Zr) collisions at sqrt[s_{NN}]=200 GeV, and in Au+Au collisions at 27 GeV, recorded by the STAR detector at the Relativistic Heavy Ion Collider. The combined dependence of the v_{1} signal on collision system, particle species, and collision centrality can be qualitatively and semiquantitatively understood as several effects on constituent quarks. While the results in central events can be explained by the u and d quarks transported from initial-state nuclei, those in peripheral events reveal the impacts of the electromagnetic field on the QGP. Our data put valuable constraints on the electrical conductivity of the QGP in theoretical calculations.

ABSTRACT The Treponema pallidum membrane protein Tp47 induces immunocyte adherence to vascular cells and contributes to vascular inflammation. However, it is unclear whether microvesicles are functional inflammatory mediators between vascular cells and immunocytes. Microvesicles that were isolated from Tp47-treated THP-1 cells using differential centrifugation were subjected to adherence assays to determine the adhesion-promoting effect on human umbilical vein endothelial cells (HUVECs). Intercellular adhesion molecule 1 (ICAM-1) and vascular cell adhesion molecule 1 (VCAM-1) levels in Tp47-induced microvesicle (Tp47-microvesicle)-treated HUVECs were measured, and the related intracellular signaling pathways of Tp47-microvesicle-induced monocyte adhesion were investigated. Tp47-microvesicles promoted THP-1 cell adhesion to HUVECs (P

In this study, three methods, i.e., the random forest (RF) algorithm, boosted regression trees (BRTs) and the improved complete ensemble empirical-mode decomposition with adaptive noise (ICEEMDAN), were adopted for investigating emission-driven interannual variations in concentrations of air pollutants including PM2.5, PM10, O3, NO2, CO, SO2 and NO2 + O3 monitored in six cities in South China from May 2014 to April 2021. The first two methods were used to calculate the deweathered hourly concentrations, and the third one was used to calculate decomposed hourly residuals. To constrain the uncertainties in the calculated deweathered or decomposed hourly values, a self-developed method was applied to calculate the range of the deweathered percentage changes (DePCs) of air pollutant concentrations on an annual scale (each year covers May to the next April). These four methods were combined together to generate emission-driven trends and percentage changes (PCs) during the 7-year period. Consistent trends between the RF-deweathered and BRT-deweathered concentrations and the ICEEMDAN-decomposed residuals of an air pollutant in a city were obtained in approximately 70 % of a total of 42 cases (for seven pollutants in six cities), but consistent PCs calculated from the three methods, defined as the standard deviation being smaller than 10 % of the corresponding mean absolute value, were obtained in only approximately 30 % of all the cases. The remaining cases with inconsistent trends and/or PCs indicated large uncertainties produced by one or more of the three methods. The calculated PCs from the deweathered concentrations and decomposed residuals were thus combined with the corresponding range of DePCs calculated from the self-developed method to gain the robust range of DePCs where applicable. Based on the robust range of DePCs, we identified significant decreasing trends in PM2.5 concentration from 2014 to 2020 in Guangzhou and Shenzhen, which were mainly caused by the reduced air pollutant emissions and to a much lesser extent by weather perturbations. A decreasing or probably decreasing emission-driven trend was identified in Haikou and Sanya with inconsistent PCs, and a stable or no trend was identified in Zhanjiang with positive PCs. For O3, a significant increasing trend from 2014 to 2020 was identified in Zhanjiang, Shenzhen, Guangzhou and Haikou. An increasing trend in NO2 + O3 was also identified in Zhanjiang and Guangzhou and an increasing or probably increasing trend in Haikou, suggesting the contributions from enhanced formation of O3. The calculated PCs from using different methods implied that the emission changes in O3 precursors and the associated atmospheric chemistry likely played a dominant role than did the perturbations from varying weather conditions. Results from this study also demonstrated the necessity of combining multiple decoupling methods in generating emission-driven trends in atmospheric pollutants.

Abstract Partons traversing the strongly interacting medium produced in heavy-ion collisions are expected to lose energy depending on their color charge and mass. We measure the nuclear modification factors for charm- and bottom-decay electrons, defined as the ratio of yields, divided by the number of binary nucleon–nucleon collisions, in $$\sqrt{s_{\textrm{NN}}}=200$$ s NN = 200 GeV Au+Au collisions to p+p collisions ( $$R_{\textrm{AA}}$$ R AA ), or in central to peripheral Au+Au collisions ( $$R_{\textrm{CP}}$$ R CP ). We find the bottom-decay electron $$R_{\textrm{AA}}$$ R AA and $$R_{\textrm{CP}}$$ R CP to be significantly higher than those of charm-decay electrons. Model calculations including mass-dependent parton energy loss in a strongly coupled medium are consistent with the measured data. These observations provide evidence of mass ordering of charm and bottom quark energy loss when traversing through the strongly coupled medium created in heavy-ion collisions.

The wide spread of the coronavirus (COVID-19) has significantly impacted the global human activities. Compared to numerous studies on conventional air pollutants, atmospheric mercury that has matched sources from both anthropogenic and natural emissions is rarely investigated. At a regional site in eastern China, an intensive measurement was performed, showing obvious decreases in gaseous elemental mercury (GEM) during the COVID-19 lockdown, while it was not as significant as most of the other measured air pollutants. Before the lockdown, when anthropogenic emissions dominated, GEM showed no correlation with temperature and negative correlations with wind speed and the height of the boundary layer. In contrast, GEM showed significant correlation with temperature, while the relationship between GEM and the wind speed/boundary layer disappeared during the lockdown, suggesting the enhanced natural emissions of mercury. By applying a machine learning model and the SHAP (SHapley Additive exPlanations) approach, it was found that the mercury pollution episodes before the lockdown were driven by anthropogenic sources, while they were mainly driven by natural sources during and after the lockdown. Source apportionment results showed that the absolute contribution of natural surface emissions to GEM unexpectedly increased (44 %) during the lockdown. Throughout the whole study period, a significant negative correlation was observed between the absolute contribution of natural and anthropogenic sources to GEM. We conclude that the natural release of mercury could be stimulated to compensate for the significantly reduced anthropogenic GEM via the surface–air exchange in the balance of mercury.

Objective: The focus of this study was the six-transmembrane epithelial antigen of the prostate 4 (STEAP4) gene, on the basis of the cloned goat STEAP4 gene sequence. Its molecular and expression characteristics were analyzed, and its influence on the differentiation of goat subcutaneous adipocytes was explored through overexpression. Method: Reverse-transcription PCR (RT-PCR) was used to clone the goat STEAP4 sequence, and online tools were used to analyze the molecular characteristic. Real-time quantitative PCR (qPCR) was used to detect the expression level of STEAP4 in goat tissues and subcutaneous adipocyte differentiation. Liposome transfection, BODIPY, Oil Red O staining, and qPCR were used to explore the effect of overexpression of STEAP4 on adipocyte differentiation. Results: The cloned goat STEAP4 gene sequence was 1388 bp, and the complete coding sequence (CDS) region was 1197 bp, which encoded a total of 398 amino acids. Compared with the predicted sequence (XM_005679300.3), there were three base mutations in the CDS region of goat STEAP4, A188G, T281C, and A507G. Among them, A507G changed the amino acid at position 170 from Ile to Val. Analysis of the physical and chemical properties of the protein showed that STEAP4 was a stable hydrophilic basic protein. STEAP4 gene expression level was highest in goat liver tissue (P), followed by lung and back subcutaneous adipose tissue. STEAP4 showed different expression levels in goat subcutaneous adipocytes at different times during the induction of differentiation. The expression in the late stage of differentiation was higher than that before differentiation and lowest at 12 h (P). Overexpression of STEAP4 promoted the accumulation of intracellular lipid droplets; C/EBPβ (CCAAT enhancer binding protein) was extremely significantly up-regulated (P), and aP2 (fatty acid binding protein) was significantly up-regulated (P). Conclusion: Overexpression of STEAP4 could promote the differentiation of goat subcutaneous preadipocytes. This study lays the foundation for an in-depth study of the role of STEAP4 in goat lipid deposition.

In the study of urban sustainable development, accurate classification of land use has become an important basis for monitoring urban dynamic changes. Hence it is necessary to develop the appropriate recognition model for urban-rural land use. Although deep learning algorithms have become a research hotspot in image classification tasks in recent years, and many good results have been achieved. But other machine learning algorithms are not going away. Compared deep learning with machine learning, there are some advantages and disadvantages in data dependence, hardware dependence, feature processing, problem solving methods, execution time, and interpretability, etc. Especially in the classification for remote sensing images, the continuous research and development of traditional machine learning algorithms is still of great significance. In this paper, the performances of several SLFN-based classification algorithms were studied and compared, including ELM, RBF K-ELM, mixed K-ELM, A-ELM and SVM. Extreme Learning Machine (ELM) is a new algorithm for single-hidden-layer feedforward neural network (SLFN). It has simple structure, fast speed and is easy to train. In some applications, however, standard ELM is prone to be overfitting and its performance will be affected seriously when outliers exist. In order to explore the performance of ELM and its improved algorithm for urban-rural land use classification, comparative experiments between three improved ELM algorithms (RBF K-ELM, mixed K-ELM and A-ELM), ELM and SVM with image data from several study areas were performed, and the classification accuracy and efficiency were analysed. The results show that the three improved ELM algorithms perform better than the standard ELM and SVM both in overall accuracy and Kappa coefficient. However, it is worth noting that the computation efficiency of RBF K-ELM and mixed K-ELM decreases greatly with larger image, the time cost is much more than other algorithms. Compared with other algorithms, A-ELM has the advantages of higher Overall Accuracy and less classification time.

Siamese networks are widely used for remote sensing change detection tasks. A vanilla siamese network has two identical feature extraction branches which share weights, these two branches work independently and the feature maps are not fused until about to be sent to a decoder head. However we find that it is critical to exchange information between two feature extraction branches at early stage for change detection task. In this work we present Mutual-Attention Siamese Network (MASNet), a general siamese network with mutual-attention plug-in, so to exchange information between the two feature extraction branches. We show that our modification improve the performance of siamese networks on multi change detection datasets, and it works for both convolutional neural network and visual transformer.

Using images from UAV oblique ultra-long focal small view field whiskbroom camera (ULF-SVF-WC) system for object positioning and mapping is more difficult than conventional aerial photogrammetry, for the particularity of oblique ULF-SVF-WC imaging mode. Therefore, the precision and accuracy of its object positioning are also quite different from that of the conventional UAV photography. In this paper, we analysed the accuracy of real-time object positioning without ground control points (GCPs) for images from UAV oblique ULF-SVF-WC System. Firstly, we studied the imaging principles and characteristics of the oblique ULF-SVF-WC system. Then, we established the coordinate transformation relationship from the object point to the image point and constructed a strict imaging model for oblique ULF-SVF-WC image, which was used for real-time single-ray back-projection positioning assisted by DEM. Thirdly, we quantitatively analysed the distribution and variation of the oblique ULF-SVF-WC image single-ray back-projection errors in theory based on error propagation law and simulation data. Finally, we conducted the experiment of real oblique ULF-SVF-WC flight images for the actual positioning accuracy analysis. The experiment results showed that: the influence of system error on positioning generally conforms to the distribution and variation of theoretical precision, that is, the accuracy of scanning direction is much lower than that of the flight direction; while the accuracy of actual single-ray back-projection positioning is evidently lower than that of the theoretical analysis and there are obvious system errors in the positioning residuals. It indicates that the 6 external orientation elements calculated from POS data contains obvious system error whose influence is greater than random error and this should be eliminated in real-time single-ray back-projection for object positioning.

In the paper, Henan Province with prevalent straw burning was taken as the study case. Aimed at remote sensing recognition of the straw burning fire points in a large-scale area with complex terrain and climate conditions, an optimized recognition strategy of straw burning was proposed based on the MOD021KM data. In our new recognition strategy, the threshold determination method was optimised by systematic statistical analysis of spectral features. Then the non-straw burning fire points were eliminated by extracting the agricultural land from MCD12Q1 image. Thus, the true straw-burning fire points were determined. The results showed that the recognition accuracy of straw burning fire points was effectively improved by optimized threshold determination method.

A large fraction of annual precipitation over the western United States comes from wintertime orographic clouds associated with atmospheric rivers (ARs). Transported African and Asian dust and marine aerosols from the Pacific Ocean may act as ice-nucleating particles (INPs) to affect cloud and precipitation properties over the region. Here we explored the effects of INPs from marine aerosols on orographic mixed-phase clouds and precipitation at different AR stages for an AR event observed during the 2015 ACAPEX field campaign under low dust (<0.02 cm−3) conditions. Simulations were conducted using the chemistry version of the Weather Research and Forecasting Model coupled with the spectral-bin microphysics at 1 km grid spacing, with ice nucleation connected with dust and marine aerosols. By comparing against airborne and ground-based observations, accounting for marine INP effects improves the simulation of AR-precipitation. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds, as well as to a large spillover effect of precipitation after AR landfall. The responses of cloud and precipitation to marine INPs vary with the AR stages, with more significant effects before AR landfall and post-AR than after AR landfall, mainly because the moisture and temperature conditions change with the AR evolution. This work suggests weather and climate models need to consider the impacts of marine INPs since their contribution is notable under low dust conditions despite the much lower relative ice nucleation efficiency of marine INPs.

Severe regional haze events, which are characterized by exceedingly high levels of fine particulate matter (PM), occur frequently in many developing countries (such as China and India), with profound implications for human health, weather, and climate. The occurrence of the haze extremes involves a complex interplay between primary emissions, secondary formation, and conducive meteorological conditions, and the relative contributions of the various processes remain unclear. Here we investigated severe regional haze episodes in 2013 over the Northern China Plain (NCP), by evaluating the PM production and the interactions between elevated PM and the planetary boundary layer (PBL). Analysis of the ground-based measurements and satellite observations of PM properties shows nearly synchronized temporal PM variations among the three megacities (Beijing, Baoding, and Shijiazhuang) in this region and a coincidence of the aerosol optical depth (AOD) hotspots with the three megacities during the polluted period. During the clean-to-hazy transition, the measured oxygenated organic aerosol concentration ([OOA]) well correlates with the odd-oxygen concentration ([Ox]=[O3]+[NO2]), and the mean [OOA] / [Ox] ratio in Beijing is much larger than those in other megacities (such as Mexico City and Houston), indicating highly efficient photochemical activity. Simulations using the Weather Research and Forecasting (WRF) model coupled with an explicit aerosol radiative module reveal that strong aerosol–PBL interaction during the polluted period results in a suppressed and stabilized PBL and elevated humidity, triggering a positive feedback to amplify the haze severity at the ground level. Model sensitivity study illustrates the importance of black carbon (BC) in the haze–PBL interaction and the aerosol regional climatic effect, contributing to more than 30 % of the PBL collapse and about half of the positive radiative forcing on the top of the atmosphere. Overall, severe regional haze exhibits strong negative radiative forcing (cooling) of −63 to −88 W m−2 at the surface and strong positive radiative forcing (warming) of 57 to 82 W m−2 in the atmosphere, with a slightly negative net radiative forcing of about −6 W m−2 on the top of the atmosphere. Our work establishes a synthetic view for the dominant regional features during severe haze events, unraveling rapid in situ PM production and inefficient transport, both of which are amplified by atmospheric stagnation. On the other hand, regional transport sufficiently disperses gaseous aerosol precursors (e.g., sulfur dioxide, nitrogen oxides, volatile organic compounds, and ammonia) during the clean period, which subsequently result in rapid in situ PM production via photochemistry during the transition period and via multiphase chemistry during the polluted period. Our findings highlight the co-benefits for reduction in BC emissions, which not only improve local and regional air quality by minimizing air stagnation but also mitigate the global warming by alleviating the positive direct radiative forcing.

Both weather–climate models and ice cloud remote sensing applications need to obtain effective ice crystal scattering (ICS) properties and the parameterization scheme. An irregularly shaped Voronoi ICS model has been suggested to be effective in remote sensing applications for several satellite programs, e.g., Himawari-8, GCOM-C (Global Change Observation Mission–Climate) and EarthCARE (Earth Cloud Aerosol and Radiation Explorer). As continuation work of Letu et al. (2016), an ice cloud optical property parameterization scheme (Voronoi scheme) of the Voronoi ICS model is employed in the Community Integrated Earth System Model (CIESM) to simulate the optical and radiative properties of ice clouds. We utilized the single-scattering properties (extinction efficiency, single-scattering albedo and asymmetry factor) of the Voronoi model from the ultraviolet to the infrared, combined with 14 408 particle size distributions obtained from aircraft measurements to complete the Voronoi scheme. The Voronoi scheme and existing schemes (Fu, Mitchell, Yi and Baum-yang05) are applied to the CIESM to simulate 10-year global cloud radiative effects during 2001–2010. Simulated globally averaged cloud radiative forcings at the top of the atmosphere (TOA) for Voronoi and the other four existing schemes are compared to the Clouds and the Earth's Radiant Energy System Energy Balanced and Filled (EBAF) product. The results show that the differences in shortwave and longwave globally averaged cloud radiative forcing at the TOA between the Voronoi scheme simulations and EBAF products are 1.1 % and 1.4 %, which are lower than those of the other four schemes. Particularly for regions (from 30∘ S to 30∘ N) where ice clouds occur frequently, the Voronoi scheme provides the closest match with EBAF products compared with the other four existing schemes. The results in this study fully demonstrated the effectiveness of the Voronoi ICS model in the simulation of the radiative properties of ice clouds in the climate model.

Introduction Insomnia is a prevalent global health problem that affects 11.7% - 36% of the population (Grewal et al. Clinical Handbook of Insomnia Int 2017; 13 - 25). It is a risk factor for depression, poor quality of life, and accidents. Increasingly, insomnia has been identified as a suicide risk factor (Lin et al. BMC Psychiatry 2018: 18; 117) We present a case report of a 43-year-old male patient with insomnia-induced suicidal ideation (SI). Objectives 1. Learn the mechanism of insomnia-induced SI 2. Understand the current insomnia treatments 3. Discuss the possible mechanism of ECT treating insomnia-induced SI Methods A 43-year-old single male with past psychiatric diagnoses of social anxiety, borderline personality disorder, chronic SI, and severe recurrent depression was admitted to inpatient due to intractable SI from insomnia. He failed trials on SSRIs/SNRIs, bupropion, trazodone, lithium, vortioxetine, quetiapine, zolpidem, and ketamine. The patient was initiated on electroconvulsive therapy (ECT) three times a week with 20mg vortioxetine and 100mg quetiapine for sleep initiation. Results After 6-sessions, the patient’s mood, affect and sleep had improved considerably, and his suicidal ideations resolved. The patient was discharged with outpatient follow-up and ECT as rescue therapy. His sleep gradually improved to 4-6hrs/night and his mood was back at baseline. Conclusions ECT is an effective treatment for refractory insomnia-induced SI, likely due to persistent REM suppression and reduced dendritic arborization and excitatory synapses in the amygdala (Doghramji et al. Sleep 2000; 23 - S16 - S20; Lahmeyer et al. Sleep Respiratory 1989; 18 346). Possible mechanisms for insomnia-induced SI include impaired decision-making, abnormalities in 5-HT function, or HPA dysfunction leading to a hyperarousal state and cortisol release (Chatzittofis et al. Euro Neuropsychopharmacology 2013; Elmenhorst et al. Sleep 2012; 35 1615 - 1623; Keilp et al. Psychol Medicine 2013; 43 539-551; Novati et al. 2008 Sleep; 31 1579 - 1585). Current insomnia treatments address underlying medical/psychological problems and non-pharmacologic and pharmacologic strategies. The predominant non-pharmacologic approach is Cognitive Behavioral Therapy Insomnia (CBT-I), such as relaxation techniques, sleep hygiene education, cognitive structuring, and sleep restriction (Rossman et al. American Journal of Lifestyle Medicine 2019; 13 544 - 547). Pharmacologic options include benzodiazepines, non-benzodiazepine hypnotics, tricyclic antidepressants, trazodone, and antihistamines (Saddichha et al. Annals of Indian Academy of Neurology 2010; 13 94-102). ECT should be strongly considered for the treatment of refractory insomnia-induced SI, and its early application may avoid rapid deterioration improving the quality of life. Disclosure of Interest None Declared

Introduction Sexual and gender minority (SGM) college students endorsed higher psychological distress and worsened mental health outcomes than their cisgender heterosexual peers. Such disparity is exacerbated during the COVID-19 pandemic, during which SGM youth may be sent home to unaccepting environments or presented with fewer healthcare options. The “Black lives matter (BLM)” and “Anti-Asian Hate” also exposed college students disproportionally to more witnessed discrimination and poorer social cohesion, which in turn, might negatively affect the mental health outcomes. Objectives The present study aims to explore the mental health outcome profile within SGM college students by (1) identify mental health disparities across different sexual and gender identities and (2) evaluating the impacts of discrimination, social cohesion and other factors on mental health outcomes of college students with different sexual and gender identities. Methods The study utilizes the 2020-2021 Healthy Minds Study data with 139,470 college students across 60 U.S. campuses. Multivariable regression models are built with minority status to predict mental health outcome (depression, anxiety, and suicidal ideation). Results SGM students reported higher symptoms of depression, anxiety, and suicidal ideation. Besides, SGM individuals having experienced or witnessed discrimination or hostile behaviors due to their race/ethnicity also showed worse mental health outcomes. Noted, perceived stronger social cohesion is a protective factor for lower depression (OR: 0.59; 95%CI: 0.45, 0.78) and anxiety (OR: 0.69; 95%CI: 0.51, 0.93) symptoms in SGM, while perceived weaker social cohesion is a risk factor for depression (OR: 1.37; 95%CI: 1.14, 1.64) and anxiety symptoms (OR:1.32; 95%CI:1.09-1.59) in cisgender heterosexual individuals. Conclusions These findings acknowledge the negative impact of discrimination on mental health, highlight the importance of recognizing social cohesion affect differently in SGM and their peers, and enhance the understanding of differential impact of social cohesion to inform public policy and early intervention in vulnerable populations during COVID-19 pandemic. Disclosure of InterestNone Declared

Introduction While the role of benzodiazepines (BZDs) has been well established for anxiety and related disorders, there are significant concerns about BZD dependence, withdrawal, and tolerance. There is a lot of ambiguity regarding the potential long-term effects of BZDs on mental health. However, the risk of developing subsequent other substance use disorders is in question. Objectives In this electronic medical record (EMR) based retrospective cohort study, the study cohort was defined as patients between the ages of 18 and 65 with anxiety disorders (ICD-10-CM: F40-F48) prescribed with at least one BZD; the control cohort was defined as patients between the ages of 18 and 65 with anxiety disorders (ICD-10-CM: F40-F48) with no BZD prescription during the five-year timeframe examined. We excluded patients with pre-existing substance use disorders (ICD-10-CM: F10-F19), et al. Methods We collected data from TriNetX Research database, a real-time international EMR network, from September 2017 to September 2022. Patients in the two cohorts were matched by gender, age, race, ethnicity, and common medical conditions at a 1:1 ratio by propensity scoring and then underwent Kaplan–Meier analysis and association analysis. Results A total of 626,754 patients were identified and matched for analysis. Patients in the study cohort were more likely to be female (67.6% vs. 66.7%, p < 0.001), non-Hispanic (65.8% vs. 62.5%, p < 0.001) and white (72.8% vs. 69.1%, p < 0.001). Kaplan–Meier analysis showed the survival probability at the end of the time window was 94.1% for the control cohort and 89.5% for the study cohort (Hazard ratio, 2.20; 95% CI, 2.16-2.25; P < 0.001) in all type of substance use disorders. (Table 1) Table 1. Hazard ratio of substance use disorders difference in BZD cohort versus the control cohort. BZD Cohortn (risk%) Control Cohortn (risk%) Hazard Ratio(95% Cl) P value Substance Use Disorders* 26,569 (4.2) 11,976 (1.9) 2.20 (2.16- 2.25)

Abstract Accurate estimation of sea‐ice production (SIP) is crucial to understanding the formation of Antarctic Bottom Water (AABW). Existing SIP estimates depend on the heat‐budget method, using atmospheric reanalysis together with satellite‐derived sea‐ice thickness data, and make an unrealistic assumption of no ocean heat flux occurring below the ice. Here, we propose a new method to estimate SIP based on the sea‐ice volume‐conservation (VC) theory. This new method can consider cases with sea‐ice melting (negative SIP). For a latent‐heat polynya—the Ross Sea Polynya—this method captures the synoptic SIP variations affected by warm‐water intrusions, including small‐scale melting and freezing patterns. Using the new VC approach for a sensible‐heat polynya, the Maud Rise Polynya, the sub‐sea‐ice oceanic heat flux was indirectly estimated, and the sea‐ice melting rate induced by warm water uplifted by Ekman pumping was estimated to be 0.15 and 1.44 cm d−1 for 2016 and 2017, respectively.

We present UV and/or optical observations and models of SN 2023ixf, a type II supernova (SN) located in Messier 101 at 6.9 Mpc. Early time ( flash ) spectroscopy of SN 2023ixf, obtained primarily at Lick Observatory, reveals emission lines of H i , He i/ii , C iv , and N iii/iv/v with a narrow core and broad, symmetric wings arising from the photoionization of dense, close-in circumstellar material (CSM) located around the progenitor star prior to shock breakout. These electron-scattering broadened line profiles persist for ∼8 days with respect to first light, at which time Doppler broadened the features from the fastest SN ejecta form, suggesting a reduction in CSM density at r ≳ 10 ^15 cm. The early time light curve of SN 2023ixf shows peak absolute magnitudes (e.g., M _u = −18.6 mag, M _g = −18.4 mag) that are ≳2 mag brighter than typical type II SNe, this photometric boost also being consistent with the shock power supplied from CSM interaction. Comparison of SN 2023ixf to a grid of light-curve and multiepoch spectral models from the non-LTE radiative transfer code CMFGEN and the radiation-hydrodynamics code HERACLES suggests dense, solar-metallicity CSM confined to r = (0.5–1) × 10 ^15 cm, and a progenitor mass-loss rate of $\dot{M}={10}^{-2}\,{M}_{\odot }$ yr ^−1 . For the assumed progenitor wind velocity of v _w = 50 km s ^−1 , this corresponds to enhanced mass loss (i.e., superwind phase) during the last ∼3–6 yr before explosion.

A positioning system for ultra-wideband (UWB) electric micro-tillers suitable for complex greenhouse environments was designed as the basis for realizing unmanned micro-tillers to address the problem of the frequent safety accidents of existing micro-tillers. First, the positioning base station and tag were designed on the basis of the STM32 and DW1000 chip hardware. The real-time coordinate display of the micro-tiller in the greenhouse could also be realized with the design of the upper computer software. Then, the realization method of the positioning function, including the analysis of time difference of arrival (TDOA) and two-way ranging (TWR) positioning methods, and the selection of the best Taylor positioning algorithm through a MATLAB simulation were studied to reduce the influence of noise and measurement errors on measurement accuracy. Finally, a positioning system accuracy test was conducted. Test results revealed that the maximum positioning error of the micro-tiller positioning system is no more than 6 cm, and the average error is 4.4 cm. The optimized positioning error is reduced by 10.2 %, considering that the influence of the base station location on the accuracy. The positioning system of the greenhouse electric micro-tiller designed in this paper demonstrates a stable performance and positioning accuracy to meet the needs of greenhouse rotary tillage operations. This system has certain theoretical importance and reference value for the development of precise positioning technology for working tools in the greenhouse and the further realization of unmanned rotary tillage operations.

Aerosol particles are a complex component of the atmospheric system which influence climate directly by interacting with solar radiation, and indirectly by contributing to cloud formation. The variety of their sources, as well as the multiple transformations they may undergo during their transport (including wet and dry deposition), result in significant spatial and temporal variability of their properties. Documenting this variability is essential to provide a proper representation of aerosols and cloud condensation nuclei (CCN) in climate models. Using measurements conducted in 2016 or 2017 at 62 ground-based stations around the world, this study provides the most up-to-date picture of the spatial distribution of particle number concentration (Ntot) and number size distribution (PNSD, from 39 sites). A sensitivity study was first performed to assess the impact of data availability on Ntot's annual and seasonal statistics, as well as on the analysis of its diel cycle. Thresholds of 50 % and 60 % were set at the seasonal and annual scale, respectively, for the study of the corresponding statistics, and a slightly higher coverage (75 %) was required to document the diel cycle. Although some observations are common to a majority of sites, the variety of environments characterizing these stations made it possible to highlight contrasting findings, which, among other factors, seem to be significantly related to the level of anthropogenic influence. The concentrations measured at polar sites are the lowest (∼ 102 cm−3) and show a clear seasonality, which is also visible in the shape of the PNSD, while diel cycles are in general less evident, due notably to the absence of a regular day–night cycle in some seasons. In contrast, the concentrations characteristic of urban environments are the highest (∼ 103–104 cm−3) and do not show pronounced seasonal variations, whereas diel cycles tend to be very regular over the year at these stations. The remaining sites, including mountain and non-urban continental and coastal stations, do not exhibit as obvious common behaviour as polar and urban sites and display, on average, intermediate Ntot (∼ 102–103 cm−3). Particle concentrations measured at mountain sites, however, are generally lower compared to nearby lowland sites, and tend to exhibit somewhat more pronounced seasonal variations as a likely result of the strong impact of the atmospheric boundary layer (ABL) influence in connection with the topography of the sites. ABL dynamics also likely contribute to the diel cycle of Ntot observed at these stations. Based on available PNSD measurements, CCN-sized particles (considered here as either >50 nm or >100 nm) can represent from a few percent to almost all of Ntot, corresponding to seasonal medians on the order of ∼ 10 to 1000 cm−3, with seasonal patterns and a hierarchy of the site types broadly similar to those observed for Ntot. Overall, this work illustrates the importance of in situ measurements, in particular for the study of aerosol physical properties, and thus strongly supports the development of a broad global network of near surface observatories to increase and homogenize the spatial coverage of the measurements, and guarantee as well data availability and quality. The results of this study also provide a valuable, freely available and easy to use support for model comparison and validation, with the ultimate goal of contributing to improvement of the representation of aerosol–cloud interactions in models, and, therefore, of the evaluation of the impact of aerosol particles on climate.

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question.