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A new incoherent Doppler lidar scheme is proposed using a high resolution Mach-Zehnder interferometer discriminator with sinusoidal transmission functions. A two-channel differential discrimination technique is developed which provides high sensitive velocity measurement. The aerosol and molecular backscatter signals can be separately measured and the backscatter ratio obtained. Principle of the measurement is described and the characteristics of this technique are analyzed and compared. Numerical calculation for a moderate size 1.064 μ lidar shows that an accuracy better than 1 m/s for the velocity measurement and 18% for the backscatter ratio measurement can be obtained up to a height of 10 km by a 500 shot average. [ABSTRACT FROM AUTHOR]

To comparatively study the size of and variation in the 'brain-haematoma' pressure gradient for different surgical methods for hypertensive intracerebral haemorrhage (HICH) and analyse the gradient's influence on surgical procedures and effects of the haemorrhage. Seventy-two patients with HICH treated from 1/2019 to 12/2019 were randomly divided into two groups, namely, the keyhole endoscopy and large trauma craniotomy groups, according to different operative methods. Intraoperative changes in intracranial pressure (ICP) were monitored to calculate intraoperative alterations in the 'brain-haematoma' pressure gradient. Intraoperative characteristics (operative time, bleeding volume, volume of blood transfusion, and haematoma clearance rate) and postoperative characteristics (oedema, postoperative activities of daily living (ADL) scores, mortality rate and rebleeding rate) were compared between the two groups. In the keyhole endoscopy group, ICP decreased slowly; the 'brain-haematoma' pressure gradient was large, averaging 251.1 ± 20.6 mmH2O, and slowly decreased. The mean operative time was 83.6 ± 4.3 min, the mean bleeding volume was 181.2 ± 13.6 ml, no blood transfusions were given, the average postoperative haematoma clearance rate was 95.6%, the rate of severe oedema was 10.9%, and the average postoperative ADL score was 85.2%. In the large trauma craniotomy group, ICP rapidly decreased after craniotomy. When the haematoma was removed, the 'brain-haematoma' pressure gradient was small, averaging 132.3 ± 10.5 mmH2O, and slowly decreased. The mean operative time was 232 ± 26.1 min, the mean bleeding volume was 412.6 ± 35.2 ml, the average volume of blood transfusion was 281.3 ± 13.6 ml, and the average postoperative haematoma clearance rate was 82.3%; moreover, the rate of severe oedema was 72.1%, and the average postoperative ADL score was 39.0%. These differences were statistically significant (P < 0.05). Neither the death rate (P > 0.05, 2.7% VS 2.8%) nor rebleeding rate (P > 0.05, 2.7% VS 2.8%) showed any obvious changes. The magnitude and variation in the 'brain-haematoma' pressure gradient for different surgical methods significantly influence surgical procedures and effects of HICH. During keyhole endoscopy surgery, this gradient was relatively large and slowly decreased; the haematoma was therefore easier to remove. Advantages of this approach include a high haematoma clearance rate, decreased bleeding volume, decreased operative time, reduced trauma, decreased postoperative brain oedema and improved postoperative recovery of neurological function. Chinese Clinical Trial Register: ChiCTR1900020655 registration in 12/01/02,019 registration in 28/02/02,020 Number: NCOMMS-20–08,091. [ABSTRACT FROM AUTHOR]

Cytoprotective mechanisms of heme oxygenases function by derivatizing heme to generate carbon monoxide, ferrous iron, and isomeric biliverdins, followed by rapid NAD(P)H-dependent biliverdin reduction to the antioxidant bilirubin using two non-overlapping biliverdin reductases that display biliverdin isomer-restricted redox activity. Although cytoprotective functions of heme oxygenases are widely recognized, concomitant effects of downstream biliverdin reductases remain incomplete. A computational model predicated on murine hematopoietic single-cell transcriptomic data identified Blvrb as a biological driver linked to the tumor necrosis factor stress pathway as a predominant source of variation defining hematopoietic cell heterogeneity. In vivo studies using Blvrb -deficient mice established the dispensable role of Blvrb in steady-state hematopoiesis, although model validation using aged Blvrb -deficient mice established an important cytoprotective function in stress hematopoiesis with dichotomous megakaryocyte-biased hematopoietic recovery. Defective stress erythropoiesis was evident in Blvrb −/− spleens and in bone marrow erythroid development, occurring in conjunction with defective lipid peroxidation as a marker of oxidant mishandling. Cell autonomous effects on megakaryocyte lineage bias were documented using multipotential progenitor assays. These data provide the first physiological function of murine Blvrb in a non-redundant pathway of stress cytoprotection. Divergent effects on erythroid/megakaryocyte lineage speciation impute a novel redox-regulated mechanism for lineage partitioning. Image 1 • Blvrb is dispensable for normal organ, hematopoietic, and erythrocyte/platelet function in mice. • Blvrb/Hmox pathway is physiologically-relevant for regulating lineage-restricted stress hematopoiesis. • Biliverdin IXβ reductase is required for hematopoetic stress cytoprotection in vivo. [ABSTRACT FROM AUTHOR]

Aims: Evidence is limited regarding the long-term impact of dietary iron intake on the development of hypertension. We investigated the association between dietary intakes of total, nonheme, and heme iron and hypertension risk in a large prospective cohort of Chinese populations over 26 years. Methods: A total of 16,122 adults (7810 men and 8312 women) who participated in the China Health and Nutrition Survey (1989–2015) were included. Dietary intake was repeatedly assessed by combining three consecutive 24‑h individual dietary recalls with household food inventory weighing at each survey round. Incident hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, diagnosis by physicians, or current use of anti-hypertensive drugs. Results: During a median follow‑up of 11.1 years, 2863 men and 2532 women developed hypertension. After adjustment for non-dietary and dietary factors, a lower risk of hypertension was found in men and women with higher intakes of total, nonheme, or heme iron. The adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the highest vs. lowest quartiles were 0.76 (0.67, 0.87) in men and 0.85 (0.74, 0.97) in women for total iron intake, 0.77 (0.67, 0.87) in men and 0.85 (0.74, 0.98) in women for nonheme iron intake, and 0.73 (0.62, 0.87) in men and 0.69 (0.58, 0.82) in women for heme iron intake. Dose–response analyses further revealed a U-shaped association of total and nonheme iron intake and an L-shaped association of heme iron intake with hypertension risk in both men and women (all P for non-linearity < 0.001). Conclusions: Our findings emphasize the importance of maintaining moderate iron intake in the prevention of hypertension. Both insufficient and excess intake of iron might increase the risk of hypertension. [ABSTRACT FROM AUTHOR]

This study applies fuzzy k -means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds. [ABSTRACT FROM AUTHOR]

Since the COVID-19 pandemic, telemedicine or non-face-to-face medicine has increased significantly. In practice, various types of medical images are essential to achieve effective telemedicine. Medical image encryption algorithms play an irreplaceable role in the fast and secure transmission and storage of these medical images. However, most of the existing medical image encryption algorithms are full encryption algorithms, which are inefficient and time-consuming, so they are not suitable for emergency medical scenarios. To improve the efficiency of encryption, a small number of works have focused on partial or selective encryption algorithms for medical images, in which different levels of encryption strategies were adopted for different information content regions of medical images. However, these encryption algorithms have inadequate security more or less. In this paper, based on the Logistic map, we designed an improved variable dimension map. Then, an encryption algorithm for medical images was proposed based on it. This algorithm has two modes: (1) full encryption mode and (2) semi-full encryption mode, which can better adapt to different medical scenarios, respectively. In full encryption mode, all pixels of medical images are encrypted by using the confusion-diffusion structure. In semi-full encryption mode, the region of interest of medical images is extracted. The confusion was first adopted to encrypt the region of interest, and then, the diffusion was adopted to encrypt the entire image. In addition, no matter which encryption mode is used, the algorithm provides the function of medical image integrity verification. The proposed algorithm was simulated and analyzed to evaluate its effectiveness. The results show that in semi-full encryption mode, the algorithm has good security performance and lower time consumption; while in full encryption mode, the algorithm has better security performance and is acceptable in time. [ABSTRACT FROM AUTHOR]

This paper describes the development and validation of a method to accurately identify snow/ice cover, surface melting, land surface and open water in polar regions using polar-orbiting Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar measurements from the Cloud and Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) mission. The technique is based on the relationship between integrated attenuated backscatter color ratio and integrated depolarization ratio, and is proven to efficiently separate snow/ice cover and surface melting from open water and land surfaces. The method has been applied to 10 years (2006–2016) of CALIOP data to study the seasonal and inter-annual variability of Arctic sea ice cover and its declining trend. Results show that the area fraction of snow cover over land at latitudes > 60°N varied between 0.9 during winter and 0.1 in summer. The CALIOP observations of Arctic sea ice cover exhibit a strong seasonal cycle and significant inter-annual variability, which are consistent with the passive microwave-based sea ice results. The > 10 years of CALIOP continuous observations of the snow/ice cover will benefit the communities modeling snow/ice melting and climate change. [ABSTRACT FROM AUTHOR]

Rapid disaster assessment is critical for public security and rescue. As a secondary disaster of large-scale meteorological disasters, power outages cause severe outcomes and thus need to be monitored efficiently and without being costly. Power outage detection from space-borne remote sensing imagery offers a broader coverage and is more temporally sensitive than ground-based surveys are. However, it is challenging to determine the affected area accurately and quantitatively evaluate its severity. Therefore, a new method is proposed to solve the above problems by building a power outage detection model (PODM) and drawing a power outage spatial distribution map (POSDM). This paper takes the winter storm Uri, of 2021, as the meteorological disaster background and Harris County, Texas, which was seriously affected, as the research object. The proposed method utilises the cloud-free VIIRS DNB nadir and close nadir images (<60 degrees) collected during the 3 months before and 15 days after Uri. The core idea beneath the proposed method is to compare the radiance difference in the affected area before and after the disaster, and a large difference in radiance indicates the happening of power outages. The raw radiance of night light measurement is first corrected to remove lunar and atmospheric effects to improve accuracy. Then, the maximum and minimum pixels in the target area of the image are considered outliers and iteratively eliminated until the standard deviation change before and after elimination is less than 1% to finalize the outlier removals. The case study results in Harris show that the PODM detects 28% of outages (including traffic area) compared to 17% of outages (living area only) reported by ground truth data, indicating general agreement with the proposed method. [ABSTRACT FROM AUTHOR]

Nighttime PM2.5 detection by remote sensing can expand understanding of PM2.5 spatiotemporal patterns due to wider coverage compared to ground monitors and by supplementing traditional daytime detection. However, using remote sensing data to invert PM2.5 at night is still challenging. Compared with daytime detection, which operates on sunlight, nighttime detection operates on much weaker moonlight and artificial light sources, complicating signal extraction. Moreover, as the attempts to sense PM2.5 remotely using satellite data are relatively recent, the existing nighttime models are still not mature, overlooking many important factors such as stray light, seasonality in meteorological effects, and observation angle. This paper attempts to improve the accuracy of nighttime PM2.5 detection by proposing an inversion model that takes these factors into consideration. The Visible Infrared Imaging Radiometer Suite/Day/Night Band (VIIRS/DNB) on board the polar-orbiting Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic Atmospheric Administration-20 (NOAA-20) was used to establish a nighttime PM2.5 inversion model in the Beijing area from 1 March 2018 to 28 February 2019. The model was designed by first studying the effects of these factors through a stepwise regression, then building a multivariate regression model to compensate for these effects. The results showed that the impact of satellite viewing zenith angle (VZA) was strongest, followed by seasonality and moonlight. Total accuracy was measured using correlation coefficient (R) compared to ground measurements, achieving 0.87 over the urban area and 0.74 over the suburbs. Specifically, the proposed method works efficiently at subsatellite points, which in this case correspond to VZA from 0 and 5°. In spring, summer, autumn, and winter, the R reached 0.95, 0.93, 0.94, and 0.97 at subsatellite points in the urban area, while it was 0.88, 0.82, 0.85, and 0.77 in the suburbs. [ABSTRACT FROM AUTHOR]

The features detected in monolayer atmospheric columns sounded by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and classified as cloud or aerosol layers by the CALIOP version 4 (V4) cloud and aerosol discrimination (CAD) algorithm are reassessed using perfectly collocated brightness temperatures measured by the Imaging Infrared Radiometer (IIR) aboard the same satellite. Using the IIR's three wavelength measurements of layers that are confidently classified by the CALIOP CAD algorithm, we calculate two-dimensional (2-D) probability distribution functions (PDFs) of IIR brightness temperature differences (BTDs) for different cloud and aerosol types. We then compare these PDFs with 1-D radiative transfer simulations for ice and water clouds and dust and marine aerosols. Using these IIR 2-D BTD signature PDFs, we develop and deploy a new IIR-based CAD algorithm and compare the classifications obtained to the results reported by the CALIOP-only V4 CAD algorithm. IIR observations are shown to be able to identify clouds with a good accuracy. The IIR cloud identifications agree very well with layers classified as confident clouds by the V4 CAD algorithm (88 %). More importantly, simultaneous use of IIR information reduces the ambiguity in a notable fraction of "not confident" V4 cloud classifications. 28 % and 14 % of the ambiguous V4 cloud classifications are reclassified more appropriately as confident cloud layers through the use of the IIR observations in the tropics and in the midlatitudes, respectively. IIR observations are of relatively little help in deriving high-confidence classifications for most aerosols, as the low altitudes and small optical depths of aerosol layers yield IIR signatures that are similar to those of clear skies. However, misclassifications of aerosol layers, such as dense dust or elevated smoke layers, by the V4 CAD algorithm can be corrected to cloud layer classification by including IIR information. 10 %, 16 %, and 6 % of the ambiguous V4 dust, polluted dust, and tropospheric elevated smoke, respectively, are found to be misclassified cloud layers by the IIR measurements. [ABSTRACT FROM AUTHOR]

Abstract: The performance of a space-based 2.1-μm coherent Doppler wind lidar (CDWL) measurement at a single laser shot in clear-air conditions is computer simulated, based on the coherent Doppler lidar theory developed in the recent decades, and using the global aerosol distribution derived from one year (March 2007–February 2008) of the CALIPSO lidar measurements. The accuracy of radial wind velocity good estimates and the fraction of good estimates, depending on backscattered signals from aerosols, generally decrease with altitude. A critical altitude is defined as the altitude below which the good estimate fraction of velocity estimates is larger than 90.0%. With a laser pulse energy of 250mJ at an off-nadir pointing angle of 45°, a telescope of 1m in diameter and a vertical range resolution of ∼800m, this critical altitude can reach an altitude of 4.0–5.0km between 20°S and 40°N where dust and biomass burning aerosols are ubiquitous. The critical altitude gradually decreases as approaching the two poles and drops to 0.5–1.5km in the polar regions. When the laser pulse energy is reduced to 100mJ, the critical altitude is generally decreased by ∼0.5km and can still reach an altitude of 3.5–4.5km in the dust and smoke aerosol enriched tropical and subtropical regions. A laser pulse energy of only a few millijoules can still achieve velocity measurements with an RMS error smaller than 1ms−1 and a good estimate fraction better than 90% in the lowest kilometers of the troposphere. [Copyright &y& Elsevier]

Abstract: In this paper, Message Passing Interface (MPI) based parallel computation and particle swarm optimization (PSO) algorithm are combined to form the parallel particle swarm optimization (PPSO) method for solving the dynamic optimal reactive power dispatch (DORPD) problem in power systems. In the proposed algorithm, the DORPD problem is divided into smaller ones, which can be carried out concurrently by multi-processors. This method is evaluated on a group of IEEE power systems test cases with time-varying loads in which the control of the generator terminal voltages, tap position of transformers and reactive power sources are involved to minimize the transmission power loss and the costs of adjusting the control devices. The simulation results demonstrate the accuracy of the PPSO algorithm and its capability of greatly reducing the runtimes of the DORPD programs. [Copyright &y& Elsevier]

The features detected in monolayer atmospheric columns sounded by the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) and classified as cloud or aerosol layers by the CALIOP version 4 (V4) cloud and aerosol discrimination (CAD) algorithm are reassessed using perfectly collocated brightness temperatures measured by the Imaging Infrared Radiometer (IIR) onboard the same satellite. Using the IIR's three wavelength measurements of layers that are confidently classified by the CALIOP CAD algorithm, we calculate two-dimensional (2-D) probability distribution functions (PDFs) of IIR brightness temperature differences (BTDs) for different cloud and aerosol types. We then compare these PDFs with 1-D radiative transfer simulations for ice and water clouds and dust and marine aerosols. Using these IIR 2-D BTD signature PDFs, we develop and deploy a new IIR-based CAD algorithm and compare the classifications obtained to the results reported by the CALIOP-only V4 CAD algorithm. IIR observations are shown to be able to identify clouds with a good accuracy. The IIR cloud identifications agree very well with layers classified as confident clouds by the V4 CAD algorithm (88 %). More importantly, simultaneous use of IIR information reduces the ambiguity in a notable fraction of "not confident" V4 cloud classifications. 28 % and 14 % of the ambiguous V4 cloud classifications are confirmed thanks to the IIR observations in the tropics and in the midlatitudes respectively. IIR observations are of relatively little help in deriving high confidence classifications for most aerosols, as the low altitudes and small optical depths of aerosol layers yield IIR signatures that are similar to those from clear skies. However, misclassifications of aerosol layers, such as dense dust or elevated smoke layers, by the V4 CAD algorithm can be corrected to cloud layer classification by including IIR information. 10 %, 16 %, and 6 % of the ambiguous V4 dust, polluted dust, and tropospheric elevated smoke respectively are found to be misclassified cloud layers by the IIR measurements. [ABSTRACT FROM AUTHOR]

Lidars are uniquely capable of collecting high-precision and high spatiotemporal resolution observations that have been used for atmospheric process studies from the ground, aircraft, and space for many years. The Aeolus mission, the first space-borne Doppler wind lidar, was developed by the European Space Agency (ESA) and launched in August 2018. Its novel Atmospheric LAser Doppler INstrument (ALADIN) observes profiles of the component of the wind vector and aerosol/cloud optical properties along the instrument's line-of-sight (LOS) direction on a global scale. A total of two airborne lidar systems have been developed at NASA Langley Research Center in recent years that collect measurements in support of several NASA Earth Science Division focus areas. The coherent Doppler Aerosol WiNd (DAWN) lidar measures vertical profiles of LOS velocity along selected azimuth angles that are combined to derive profiles of horizontal wind speed and direction. The High Altitude Lidar Observatory (HALO) measures high resolution profiles of atmospheric water vapor (WV) and aerosol and cloud optical properties. Because there are limitations in terms of spatial and vertical detail and measurement precision that can be accomplished from space, airborne remote sensing observations like those from DAWN and HALO are required to fill these observational gaps and to calibrate and validate space-borne measurements. Over a 2-week period in April 2019, during their Aeolus Cal/Val Test Flight campaign, NASA conducted five research flights over the eastern Pacific Ocean with the DC-8 aircraft. The purpose was to demonstrate the following: (1) DAWN and HALO measurement capabilities across a range of atmospheric conditions, (2) Aeolus Cal/Val flight strategies and comparisons of DAWN and HALO measurements with Aeolus, to gain an initial perspective of Aeolus performance, and (3) ways in which atmospheric dynamic processes can be resolved and better understood through simultaneous observations of wind, WV, and aerosol profile observations, coupled with numerical model and other remote sensing observations. This paper provides a brief description of the DAWN and HALO instruments, discusses the synergistic observations collected across a wide range of atmospheric conditions sampled during the DC-8 flights, and gives a brief summary of the validation of DAWN, HALO, and Aeolus observations and comparisons. [ABSTRACT FROM AUTHOR]

In this paper, we describe a new two-dimensional and multi-channel feature detection algorithm (2D-McDA) and demonstrate its application to lidar backscatter measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Unlike previous layer detection schemes, this context-sensitive feature finder algorithm is applied to a 2-D lidar "scene", i.e., to the image formed by many successive lidar profiles. Features are identified when an extended and contiguous 2-D region of enhanced backscatter signal rises significantly above the expected "clear air" value. Using an iterated 2-D feature detection algorithm dramatically improves the fine details of feature shapes and can accurately identify previously undetected layers (e.g., subvisible cirrus) that are very thin vertically but horizontally persistent. Because the algorithm looks for contiguous 2-D patterns using successively lower detection thresholds, it reports strongly scattering features separately from weakly scattering features, thus potentially offering improved discrimination of juxtaposed cloud and aerosol layers. Moreover, the 2-D detection algorithm uses the backscatter signals from all available channels: 532 nm parallel, 532 nm perpendicular and 1064 nm total. Since the backscatter from some aerosol or cloud particle types can be more pronounced in one channel than another, simultaneously assessing the signals from all channels greatly improves the layer detection. For example, ice particles in subvisible cirrus strongly depolarize the lidar signal and, consequently, are easier to detect in the 532 nm perpendicular channel. Use of the 1064 nm channel greatly improves the detection of dense smoke layers, because smoke extinction at 532 nm is much larger than at 1064 nm, and hence the range-dependent reduction in lidar signals due to attenuation occurs much faster at 532 nm than at 1064 nm. Moreover, the photomultiplier tubes used at 532 nm are known to generate artifacts in an extended area below highly reflective liquid clouds, introducing false detections that artificially lower the apparent cloud base altitude, i.e., the cloud base when the cloud is transparent or the level of complete attenuation of the lidar signal when it is opaque. By adding the information available in the 1064 nm channel, this new algorithm can better identify the true apparent cloud base altitudes of such clouds. [ABSTRACT FROM AUTHOR]

The adhesion of lunar material is strong and harmful to the functional surfaces of spacecraft systems. Using lunar regolith simulant instead of rare real one to understand the adhesive property of lunar regolith is an economical approach. In this work, we firstly introduced a theoretical model of the adhesive behavior between lunar regolith simulant micro-particle and different aerospace materials. In this model, total adhesion force is determined by Van der Waals force, electrostatic force and capillary force. Surface energy, surface potential, particle size and inertia force are the key factors affecting adhesion. Then we designed an experimental method using centrifugal technique and image processing to quantify and visualize the adhesion effect. By using a newly developed lunar regolith simulant BHLD1000 and four types of aerospace functional surfaces, we found the size of adhesive micro-particles are a few micrometers and the adhesion forces on them are a few nN on average. This experimental value is three orders of magnitude smaller than the theoretical one (several μN) due to particle irregularity and surface roughness. The adhesion model, the experimental approach and measurement results developed here are useful in understanding the real adhesion of lunar regolith on the moon as well as designing dust-proof tools for future lunar explorations. • An adhesion model between lunar regolith simulant and typical aerospace materials is proposed. • Lunar regolith simulant BHLD1000 with a wide particle size distribution is developed for experimental study. • High Speed centrifuge technology with specially-made accessories is used to help quantify the adhesion indirectly. • Optical microscopy with picture processing is discussed to embody the particles detaching manner statistically. • Adhesion force between BHLD1000 and typical aerospace materials is evaluated in laboratory environment. [ABSTRACT FROM AUTHOR]

A gravimetric method with in situ density measurement is used to determine the adsorption isotherms and kinetic characteristics of CO on Chinese dry coal plug at 293.29, 311.11, 332.79 and 352.57 K and pressures up to 19 MPa. The adsorption and desorption process is reversible, which shows that it is a process of physical adsorption for CO on coal. The excess adsorption increases with the increasing pressure at low pressures until the CO phase transitions pressure is reached. Above this pressure, the excess adsorption decreases with the increasing pressure. The adsorption behaviour is described using the CO density instead of pressure in four thermodynamic models such as modified Langmuir, Langmuir + k, DR and DR + k. It is found that the modified Langmuir + k and DR + k models are more suitable for liquid and supercritical CO adsorption, respectively. The adsorption kinetics data are also obtained during the measurement of adsorption isotherms. The experimental data are fluctuant at the initial time range due to the temperature variation in the adsorption cell after high-pressure CO is injected. The diffusivity is estimated using a modified unipore model. It is observed that the kinetic parameter C, accounting for the effect of gas diffusivity, increases with the increasing pressure at low pressures and has no obvious relations with pressure at high pressures. In this study, C value has no dependencies with temperature for CO, and the order of magnitude of the effective diffusivity is approximately 10 to 10 s. [ABSTRACT FROM AUTHOR]

Abstract: Insufficient suppression of the elastic-scattering signal in the rotational Raman (RR) detection channels can result in a retrieval error particularly when the temperature of a thick cloud is measured using an RR lidar. To solve this problem, a technique is presented to obtain relative transmission factors for the two RR channels' thereby correcting for the influence of residual elastic-signal on the temperature retrieval. The feasibility of this technique is demonstrated by applying the algorithm to the Hampton University (HU) lidar measurements. Intercomparisons of these temperature retrievals from both water-phase and cirrus clouds show good agreement with radiosonde measurements. [Copyright &y& Elsevier]

Abstract: Land surface hydrology is important to regional climate, ecosystem, agriculture, and even human activities. Changes in soil moisture can produce considerable impacts on socioeconomics. Analysis of assimilation model results, especially those from the Community Land Model, shows that soil moisture over Oklahoma region is continuously reduced from 1980 to 2009. The potential drying trend in the Oklahoma region is evaluated by observations taken during last three decades in this study. Satellite data from Global Precipitation Climatology Project exhibit a clear precipitation decrease in the Oklahoma region during the last decade or so compared with those of two or three decades ago. Accompanying with the precipitation variation, land surface net radiation and temperature over the region are found increases by satellite and/or in-situ measurements. These changes in regional climate conditions also likely result in reduction of regional evaporation and enhancement of sensible heat transport from land surface into the atmosphere as indicated in assimilated data. These observed and modeled evidences of the changes in regional water and energy cycles lead us to conclude that the soil moisture over the Oklahoma region was reduced during the last decade. This soil moisture drop could increase a risk in water shortage for agriculture in the Oklahoma state if the dry period continues. Further investigations on the drying in the Oklahoma State or even entire Southern Great Plains are needed to mitigate potential droughts, reductions in vegetation products, and other socioeconomic impacts. [Copyright &y& Elsevier]

Abstract: A new boundary integral equation formulation for solving plane elasticity problems involving orthotropic media is presented in this paper. Based on the real variable fundamental solutions of the considered problems, a limit theorem for the transformation from domain integral equations into boundary integral equations (BIEs) and a novel decomposition technique to the fundamental solutions, the regularized BIEs with indirect unknowns, which do not involve the direct calculation of CPV and HFP integrals, are established. The limiting process is done in global coordinates and no separate numerical treatment for strong and weak singular integrals was necessary. The current method does not need to transform the considered problems into isotropic ones as is normally done in the existing literature, so no inverse transform is required. The numerical implementation is carried out using both discontinuous quadratic elements and exact elements, which is developed to model its boundary with negligible error. The validity of the proposed scheme is demonstrated by three numerical examples. Excellent agreement between the numerical results and exact solutions was obtained even with using small amounts of element. [Copyright &y& Elsevier]

Abstract: Ice water content (IWC) is a standard product of cloud radar measurements. In this work, cloud radar cross-sections of various ice clouds are modeled to examine the relationship between the radar signal and the IWC. We report that using backscatter signal at cloud radar wavelength to retrieve IWC results in large uncertainties. Particle size distribution is the primary cause for the uncertainty in the retrieved IWC at radar wavelengths, though particle shape and orientation also play significant roles. Particularly in this study, we demonstrate that using both transmitted waves through the clouds (extinction) and backscattered waves from the clouds to retrieve the mean particle size and then using the mean particle size for IWC retrieval reduces the uncertainty. IWC retrieval can be improved with size distribution derived from dual wavelength cloud radar. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the vertical and horizontal extent of clouds and aerosols in the earth’s atmosphere is critical in assessing the planet’s radiation budget and for advancing human understanding of climate change issues. To retrieve this fundamental information from the elastic backscatter lidar data acquired during the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a selective, iterated boundary location (SIBYL) algorithm has been developed and deployed. SIBYL accomplishes its goals by integrating an adaptive context-sensitive profile scanner into an iterated multiresolution spatial averaging scheme. This paper provides an in-depth overview of the architecture and performance of the SIBYL algorithm. It begins with a brief review of the theory of target detection in noise-contaminated signals, and an enumeration of the practical constraints levied on the retrieval scheme by the design of the lidar hardware, the geometry of a space-based remote sensing platform, and the spatial variability of the measurement targets. Detailed descriptions are then provided for both the adaptive threshold algorithm used to detect features of interest within individual lidar profiles and the fully automated multiresolution averaging engine within which this profile scanner functions. The resulting fusion of profile scanner and averaging engine is specifically designed to optimize the trade-offs between the widely varying signal-to-noise ratio of the measurements and the disparate spatial resolutions of the detection targets. Throughout the paper, specific algorithm performance details are illustrated using examples drawn from the existing CALIPSO dataset. Overall performance is established by comparisons to existing layer height distributions obtained by other airborne and space-based lidars. [ABSTRACT FROM AUTHOR]

Background/Objectives: Cardiovascular diseases are the primary cause of global morbidity and mortality, with cardiovascular health (CVH) remaining well below the ideal level and showing minimal improvement in the U.S. population over recent years. Bisphenol A (BPA), a pervasive environmental contaminant, has emerged as a potential contributor to adverse cardiovascular outcomes. This cross-sectional study delves into the impact of BPA exposure on achieving optimal CVH, as assessed by the Life's Essential 8 metric, among U.S. adults. Methods: Analyzing data from 6635 participants in the National Health and Nutrition Examination Survey (NHANES) collected between 2005 and 2016, BPA exposure was quantified through urinary BPA levels, while optimal CVH was defined using the American Heart Association's Life's Essential 8 criteria, scoring between 80 and 100. Multivariable logistic regression and propensity score matching were employed to evaluate the association between BPA exposure and CVH. Results: This study reveals that individuals in the highest tertile of urinary BPA levels were 27% less likely to attain optimal CVH compared with those in the lowest tertile (OR, 0.73; 95% CI: 0.59-0.92). This negative association persisted across diverse demographics, including age, sex, and race, mirrored in the link between urinary BPA levels and health factor scores. Conclusions: The findings underscore the potential benefits of reducing BPA exposure in enhancing the prevalence of optimal CVH and mitigating the burden of cardiovascular disease. Given the widespread use of BPA, ongoing monitoring of BPA's impact on CVH is essential. Further studies are necessary to elucidate the long-term and causative connections between BPA and CVH. These insights contribute to understanding the complex interplay between environmental factors and CVH outcomes, informing targeted interventions to mitigate cardiovascular disease risk within the population.