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Modeling the climate system requires a formal representation of the characteristics of the system elements and the processes that change them. The Climate System Ontology (CSO) represents the semantics of the processes that continuously cause change at component and system levels. The CSO domain ontology logically represents various links that relate the nodes in this complex network. It models changes in the radiative balance caused by human activities and other forcings as solar energy flows through the system. CSO formally expresses various processes, including non-linear feedbacks and cycles, that change the compositional, structural, and behavioral characteristics of system components. By reusing the foundational logic of a set of top- and mid-level ontologies, we have modeled complex concepts such as hydrological cycle, forcing, greenhouse effect, feedback, and climate change in the ontology. This coherent, publicly available ontology can be queried to reveal the input and output of processes that directly impact the system elements and causal chains that bring change to the whole system. Our description of best practices in ontology development and explanation of the logics that underlie the extended upper-level ontologies help climate scientists to design interoperable domain and application ontologies, and share and reuse semantically rich climate data.

Background: Seasonal pollen is a common cause of allergic respiratory disease. In the United States, pollen monitoring occurs via manual counting, a method which is both labor-intensive and has a considerable time delay. In this paper, we report the field-testing results of a new, automated, real-time pollen imaging sensor in Atlanta, GA. Methods: We first compared the pollen concentrations measured by an automated real-time pollen sensor (APS-300, Pollen Sense LLC) collocated with a Rotorod M40 sampler in 2020 at an allergy clinic in northwest Atlanta. An internal consistency assessment was then conducted with two collocated APS-300 sensors in downtown Atlanta during the 2021 pollen season. We also investigated the spatial heterogeneity of pollen concentrations using the APS-300 measurements. Results: Overall, the daily pollen concentrations reported by the APS-300 and the Rotorod M40 sampler with manual counting were strongly correlated (r = 0.85) during the peak pollen season. The APS-300 reported fewer tree pollen taxa, resulting in a slight underestimation of total pollen counts. Both the APS-300 and Rotorod M40 reported Quercus (Oak) and Pinus (Pine) as dominant pollen taxa during the peak tree pollen season. Pollen concentrations reported by APS-300 in the summer and fall were less accurate. The daily total and speciated pollen concentrations reported by two collocated APS-300 sensors were highly correlated (r = 0.93–0.99). Pollen concentrations showed substantial spatial and temporal heterogeneity in terms of peak levels at three locations in Atlanta. Conclusions: The APS-300 sensor was able to provide internally consistent, real-time pollen concentrations that are strongly correlated with the current gold-standard measurements during the peak pollen season. When compared with manual counting approaches, the fully automated sensor has the significant advantage of being mobile with the ability to provide real-time pollen data. However, the sensor’s weed and grass pollen identification algorithms require further improvement.

In this paper, a new reconfigurable parallel mechanism by applying a spatial multi-loop over-constrained mechanism is proposed. The reconfigurable parallel mechanism has 4 degrees of freedom (DOF), and its different performance indices can be continuously changed. According to its inverse kinematic model, its workspace, dexterity, and stiffness are all analyzed. This paper also establishes the dynamics model, and a novel dynamic index is proposed to optimize the proposed mechanism. With these novel performance indices, both the kinematic and the dynamic performance for this mechanism are shown to be greatly improved.

Kinematic error model plays an important role in improving the positioning accuracy of robot manipulators by kinematic calibration. The identifiability of kinematic parameters in the error model directly affects the positioning accuracy of the mechanism. And the number of identifiable kinematic parameters determines how many parameters can be accurately identified by kinematic calibration, which is one of the theoretical basis of kinematic error modeling. For serial mechanisms, a consensus has been reached that the maximum number of identifiable kinematic parameters is 4R + 2P + 6, where R and P represent the numbers of revolute and prismatic joints, respectively. Due to complex topologies of parallel mechanisms, there is still no agreement on the formula of the maximum number of identifiable parameters. In this paper, a comprehensive numerical study on the number of identifiable kinematic parameters of parallel mechanisms is conducted. The number of identifiable parameters of 3802 kinds of limbs with different types or actuation arrangements are analyzed. It can be concluded that the maximum number of identifiable kinematic parameters is Σ i = 1 n 4Ri + 2Pi + 6 − Ci − 2(PP)i/3(PPP1)i/(2Ri + 2Pi)(PPP)i, where Ci represents the number of joints whose motion cannot be measured and n denotes the number of limbs in a parallel mechanism; (PP)i, (PPP1)i, and (PPP)i represent two consecutive unmeasurable P joints, three consecutive P joints in which two of them cannot be measured, and three unmeasurable P joints, respectively.

Nadir-viewing satellite observations of tropospheric ozone in the UV have been shown to have some sensitivity to boundary layer ozone pollution episodes, but so far they have not yet been compared to surface ozone observations collected by large-scale monitoring networks. Here we use 2013–2017 surface ozone data from China's new Ministry of Ecology and Environment (MEE) network of ∼ 1000 sites, together with vertical profiles from ozonesondes and aircraft, to quantify the ability of tropospheric ozone retrievals from the Ozone Monitoring Instrument (OMI) and to detect boundary layer ozone pollution in China. We focus on summer when ozone pollution in China is most severe and when OMI has the strongest sensitivity. After subtracting the Pacific background, we find that the 2013–2017 mean OMI ozone enhancements over eastern China have strong spatial correlation with the corresponding multiyear means in the surface afternoon observations (R=0.73), and that OMI can estimate these multiyear means in summer afternoon surface ozone with a precision of 8 ppb. The OMI data show significantly higher values on observed surface ozone episode days (>82 ppb) than on non-episode days. Day-to-day correlations with surface ozone are much weaker due to OMI noise and are stronger for sites in southern China (<34∘ N; R=0.3–0.6) than in northern China (R=0.1–0.3) because of weaker retrieval sensitivity and larger upper tropospheric variability in the north. Ozonesonde data show that much of the variability of OMI ozone over southern China in summer is driven by the boundary layer. Comparison of 2005–2009 and 2013–2017 OMI data indicates that mean summer afternoon surface ozone in southern China (including urban and rural regions) has increased by 3.5±3.0 ppb over the 8-year period and that the number of episode days per summer has increased by 2.2±0.4 (as diagnosed by an extreme value model), generally consistent with the few long-term surface records. Ozone increases have been particularly large in the Yangtze River Delta and in the Hubei, Guangxi and Hainan provinces.

Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (level 2) to a regular grid (level 3). When averaging level 2 data over a long period to a target level 3 grid that is significantly finer than the sizes of level 2 pixels, this process is referred to as. An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous methods. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g. OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g. GOSAT, IASI, and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOVs) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids finer than ∼ 16 km. The generalized 2-D super Gaussian function also enables smoothing of the level 3 results by decreasing the shape-determining exponents, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods., SCOPUS: ar.j, info:eu-repo/semantics/published

This paper presents a novel 4-DOF (Degree of freedom) reconfigurable parallel mechanism which owns the continuously reconfigurable ability. The reconfigurable parallel mechanism can change its configurations by driving a gear train system. Then, the structural parameters will be changed to form different configurations which have different performances. Some performance indices are analyzed, like dexterity, stiffness and workspace. Finally, a numerical example has been proposed to verify the kinematic analysis.

Osteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents. Although activator of HSP90 ATPase activity 1 (AHA1) is reported to be a potential oncogene, its role in osteosarcoma progression remains largely unclear. Since metabolism reprogramming is involved in tumorigenesis and cancer metastasis, the relationship between AHA1 and cancer metabolism is unknown. In this study, we found that AHA1 is significantly overexpressed in osteosarcoma and related to the prognosis of osteosarcoma patients. AHA1 promotes the growth and metastasis of osteosarcoma both in vitro and in vivo. Mechanistically, AHA1 upregulates the metabolic activity to meet cellular bioenergetic needs in osteosarcoma. Notably, we identified that isocitrate dehydrogenase 1 (IDH1) is a novel client protein of Hsp90-AHA1. Furthermore, the IDH1 protein level was positively correlated with AHA1 in osteosarcoma. And IDH1 overexpression could partially reverse the effect of AHA1 knockdown on cell growth and migration of osteosarcoma. Moreover, high IDH1 level was also associated with poor prognosis of osteosarcoma patients. This study demonstrates that AHA1 positively regulates IDH1 and metabolic activity to promote osteosarcoma growth and metastasis, which provides novel prognostic biomarkers and promising therapeutic targets for osteosarcoma patients.

Supplemental Material, Appendix for Analysis and control for a new reconfigurable parallel mechanism by Guanyu Huang, Dan Zhang, Hongyan Tang, Lingyu Kong and Sumian Song in International Journal of Advanced Robotic Systems

We validate the Ozone Monitoring Instrument (OMI) ozone profile (PROFOZ) product including ozone profiles between 0.22–261 hPa and Stratospheric Ozone Columns (SOCs) down to 100, 215, and 261 hPa from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against the latest Microwave Limb Sound (MLS) v4.2x data. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the data set into before and after the occurrence of serious RA, i.e., pre-RA (2004–2008) and post-RA (2009–2014). During the pre-RA period, OMI ozone profiles agree very well with MLS data. Tthe global mean biases (MBs) are within 3 % between 0.22–100 hPa and negative 3–9 % for lower layers, and the standard deviations (SDs) are 3.5–5 % from 1–40 hPa, 6–10 % for upper layers and 5–20% for lower layers, after applying OMI averaging kernels to MLS data. OMI shows latitude and solar zenith angle (SZA) dependent biases, but MBs and SDs are mostly within 10 % except for low/high altitudes of high latitudes/SZAs. During the post-RA period, OMI retrievals degrade slightly between 5–261 hPa with MBs and SDs typically larger by 2–5%, and degrade much more, with larger MBs by up to 8 % and SDs by up to 15% for pressure less than ~ 5 hPa, where the MBs are larger by 10–15 % south of 40 °N due to the blockage effect of RA and smaller by 15–20 % north of 40 °N due to the solar contamination effect of RA. The much worse comparison at high altitudes indicates the UV-1 channel of pixels that are not flagged as RA is still affected by the RA. During the pre-RA period, OMI SOCs show very good agreement with MLS data with global mean MBs within 0.6 % and SDs of 1.9 % for SOCs down to 215 and 261 hPa and of 2.30 % for SOC down to 100 hPa. Despite clearly worse ozone profile comparison during the post-RA period, OMI SOCs only slightly degrade, with SDs larger by 0.4–0.6 % mostly due to looser spatial coincidence criterion as a result of missing data from RA and MBs larger by 0.4–0.7 %. The retrieval comparison indicates significant bias trends, especially during the post-RA period. The spatiotemporal variation of the retrieval performance suggests the need to improve OMI’s radiometric calibration to maintain the long-term stability and spatial consistency of the PROFOZ product. The good comparison with SOC down to 261 hPa supports that MLS ozone at 261 hPa, recommended for further evaluation by the MLS team, is suitable for scientific use.

Kinematic error model plays an important role in the kinematic calibration of robot manipulators. In order to guarantee the best calibration result, the error model should meet the requirements of completeness, minimality and continuity. For the conventional serial robots, these issues have been intensively studied and a consensus has been reached during the past decades. However, for parallel manipulators, the problem of minimality, namely the determination of identifiable parameters mainly relies on numerical studies. There is a lack of effective methods to analyze the parameters’ identifiability in the error modeling process. Besides, there is still no agreement on the formula of the maximum number of identifiable parameters. In our previous work, a complete, minimal and continuous error modeling approach for parallel manipulators has been proposed based on product of exponential (POE) formula and the equation that the maximal number of identifiable parameters 4 r + 2 p + 6 was proved. However, the correctness and effectiveness of this method in kinematic calibration have not been verified on any parallel manipulator or robot. Therefore, in this paper a kinematic calibration is conducted on a prototype of a 3- P RRU parallel manipulator based on the proposed error model. The method of validating the completeness and minimality are respectively given by numerical simulations. Furthermore, to illustrate the advantage of the proposed error model, the kinematic parameters of the prototype are also calibrated based on the conventional error model which is derived by means of differentiating the limbs’ inverse kinematics. By comparing the calibration results, it can be concluded that the error model satisfying the three criteria can better improve the positioning accuracy. In addition, since the calibration method is described uniformly, it can be applied to most parallel manipulators by referring to the calibration procedure this paper presented.

Visual place recognition(VPR) is challenging because the places to be recognized are often affected by complex environmental changes. It is important for loop closure detection in SLAM. In recent years, a large range of approaches have been developed to address this challenge. Among these approaches, the use of biological heuristics and structural information have improved the VPR performance. In this paper, we attempt to improve the biological heuristic method MCN, and combine the available but often overlooked structural information intra-set similarity to propose our first algorithm SMCN. Furthermore, we propose a novel temporal filter that considers temporal continuity and combines it with SMCN to get our second algorithm SMCNTF. Evaluation of both algorithms on ten dataset combinations shows that, our best model SMCNTF has a maximum increase of 32% in average precision and at least a 100-fold increase in computing efficiency. Moreover, fewer parameters need to be tuned comparing with MCN. Generally, our algorithm is much better than MCN.

Highlights • ZOSL-1 was the first report that isolated directly from lung metastases of OS. • ZOSL-1 cells were highly metastatic, and the lung metastasis rate was 100% in vivo. • ZOSL-1 cells were high expression in GNAS, SCARB1 and CXCR4 genes., OS (Osteosarcoma) is the most common malignant tumor in adolescents, and lung metastasis limits its therapeutic outcome. The present study aimed to establish a highly metastatic human OS cell line directly from lung metastases and characterize its biological functions. In this study, epithelioid tumor cells with large nucleo-cytoplasmic ratio and abundant organelles were obtained by the tissue mass adherent and repeated digestion adherent method and named ZOSL-1 cells. ZOSL-1 cells had the potential to proliferate in vitro with a doubling time of 39.28 ± 3.04 h and migrate with or without a matrix. ZOSL-1 cells were tumorigenic in vivo, and had the ability to develop lung metastasis after intratibial injection. ZOSL-1 cells expressed the osteogenic-related genes osteocalcin and osteopontin. In addition, the expression of ZOSL-1 in Fas cell surface death receptor (FAS), CD44 molecule (CD44), GNAS complex locus (GNAS), scavenger receptor class B member 1 (SCARB1), C-X-C motif chemokine receptor 4 (CXCR4), cadherin 11 (CDH11), neurofibromin 2 (NF2) and ezrin (EZR) genes may be related to its transfer efficiency. Taken together, these results indicated the high metastatic capability and important biological functions of ZOSL-1 cells. ZOSL-1 establishment provided a relevant model for the study of osteosarcoma lung metastasis.

SUMMARYThis paper presents a novel reconfigurable parallel mechanism, which can serve as a machine tool. The proposed parallel mechanism can change its structure parameters by driving a bevel gear system fixed in the base platform. First, the forward and inverse kinematics of the proposed mechanism are investigated. Second, the reachable workspace and Jacobian matrix are conducted. Based on the Jacobian matrix, the stiffness model and dexterity of the end effector are developed in detail. Finally, a multi-objective optimization is performed by using the Genetic Algorithm, and the workspace and global performance indexes of stiffness as well as the dexterity are considered as the performance indices to improve the performance of the reconfigurable parallel mechanism. Finally, Pareto frontier figure and several tables are provided to illustrate the results of the optimization. The results showed the proposed method has improved the performance of the reconfigurable machine tool in terms of its stiffness and dexterity.

Stratosphere-to-troposphere transport (STT) and biomass burning (BB) are two important natural sources for tropospheric ozone that can result in elevated ozone and air-quality episode events. High-resolution observations of multiple related species are critical for complex ozone source attribution. In this article, we present an analysis of coinciding ground-based and airborne observations, including ozone lidar, ozonesonde, high spectral resolution lidar (HSRL), and multiple airborne in situ measurements, made on 28 and 29 June 2013 during the Southeast Nexus field campaign. The ozone lidar and HSRL reveal detailed ozone and aerosol structures as well as the temporal evolution associated with a cold front passage. The observations also captured two enhanced (+30 ppbv) ozone layers in the free troposphere (FT), which were determined from this study to be caused by a mixture of BB and stratospheric sources. The mechanism for this STT is tropopause folding associated with a cutoff upper level low-pressure system according to the analysis of its potential vorticity structure. The depth of the tropopause fold appears to be shallow for this case compared to events observed in other seasons; however, the impact on lower tropospheric ozone was clearly observed. This event suggests that strong STT may occur in the southeast United States during the summer and can potentially impact lower troposphere during these times. Statistical analysis of the airborne observations of trace gases suggests a coincident influence of BB transport in the FT impacting the vertical structure of ozone during this case study.

The NASA/Smithsonian Tropospheric Emissions: Monitoring of Pollution (TEMPO; tempo.si.edu) satellite instrument will measure atmospheric pollution and much more over Greater North America at high temporal resolution (hourly or better in daylight, with selected observations at 10 minute or better sampling) and high spatial resolution (10 km2 at the center of the field of regard). It will measure ozone (O3) profiles (including boundary layer O3), and columns of nitrogen dioxide (NO2), nitrous acid (HNO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), water vapor (H2O), bromine oxide (BrO), iodine oxide (IO), chlorine dioxide (OClO), as well as clouds and aerosols, foliage properties, and ultraviolet B (UVB) radiation. The instrument has been delivered and is awaiting spacecraft integration and launch in 2022. This talk describes a selection of TEMPO applications based on the TEMPO Green Paper living document (http://tempo.si.edu/publications.html). Applications to air quality and health will be summarized. Other applications presented include: biomass burning and O3 production; aerosol products including synergy with GOES infrared measurements; lightning NOx; soil NOx and fertilizer application; crop and forest damage from O3; chlorophyll and primary productivity; foliage studies; halogens in coastal and lake regions; ship tracks and drilling platform plumes; water vapor studies including atmospheric rivers, hurricanes, and corn sweat; volcanic emissions; air pollution and economic evolution; high-resolution pollution versus traffic patterns; tidal effects on estuarine circulation and outflow plumes; air quality response to power blackouts and other exceptional events.

We use data from the new China Ministry of Ecology and Environment (MEE) network to show that OMI satellite observations of tropospheric ozone can successfully map the distribution of surface ozone pollution in China and the frequency of high-ozone episodes. After subtracting the Pacific background, OMI ozone enhancements over China can quantify mean summer afternoon surface ozone with a precision of 10.7 ppb and a spatial correlation coefficient R=0.73. Day-to-day correlations between OMI and the MEE ozone data are statistically significant but limited by noise in the individual OMI retrievals. OMI shows significantly higher values on surface ozone episode days (>82 ppb). An extreme value model can successfully predict the probability of surface ozone episodes from the daily OMI data. The 2005–2017 OMI record shows a 0.67 ppb a−1 increase in mean summer afternoon ozone in eastern China and an increasing frequency of ozone pollution episodes particularly in the north.

We studied the impact of COVID-19 (coronavirus disease 2019) lockdown on the air quality over the Atlanta area using satellite and ground-based observations, meteorological reanalysis data and traffic information. Unlike other cities, we found the air quality has improved slightly over the Atlanta area during the 2020 COVID-19 lockdown period (March 14–April 30, 2020), compared to the analogous period of 2019 (March 14-April 30, 2019). Ground NO2 concentrations have decreased slightly 10.8% and 8.2% over the near-road (NR) and urban ambient (UA) stations, respectively. Tropospheric NO2 columns have reduced 13%-49% over the Atlanta area from space-borne observations of TROPOspheric Monitoring Instrument (TROPOMI). Ground ozone and PM2.5 have decreased 15.7% an ~5%, respectively. This slight air quality improvement is primarily caused by the reduced human activities, as COVID-19 lockdowns have reduced ~50% human activities, measured by traffic volume. Higher wind speed and precipitations also make the meteorological conditions favorable to this slight air quality improvement. We have not found a significant improvement in Atlanta amid the lockdown when human activities have reduced ~50%. Further studies are needed to understand the impacts of reduced human activities on atmospheric chemistry. We also found TROPOMI and ground measurements have disagreements on NO2 reductions, as collocated TROPOMI observations revealed ~23% and ~21% reductions of tropospheric NO2 columns over NR and UA stations, respectively. Several factors may explain this disagreement: First, tropospheric NO2 columns and ground NO2 concentrations are not necessarily the same, although they are highly correlated in the afternoon; Second, meteorological conditions may have different impacts on TROPMI and ground measurements. Third, TROPOMI may underestimate tropospheric NO2 due to uncertainties from air mass factors. Fourth, the uncertainties of chemiluminescence NO2 measurements used by ground stations. Consequently, studies using space-borne tropospheric NO2 column and ground NO2 measurements should take these factors into account.

This article proposes a new reconfigurable parallel mechanism using a spatial overconstrained platform. This proposed mechanism can be used as a machine tool. The mobility is analyzed by Screw Theory. The inverse kinematic model is established by applying the closed-loop equation. Next, the dynamic model of the presented mechanism is established by Lagrange formulation. To control the presented mechanism, some controllers have been used. Based on this dynamic model, the fuzzy-proportion integration differentiation (PID) controller is designed to track the trajectory of the end effector. For each limb, a sliding mode controller is applied to track the position and velocity of the slider. Finally, some simulations using ADAMS and MATLAB are proposed to verify the effectiveness and stability of these controllers.

Spatial joints (Hooke and spherical joint) generally limit parallel manipulator's motion range and performance, while higher performance will be achieved when applying more planar pairs (prismatic and revolute joint), which can promise small movement constraints and high stiffness. In this paper, a novel design method for three translational degrees of freedom parallel mechanisms containing prismatic joints and parallelogram units ((R-R)2 linkage) is proposed by mapping relationship with augmented matrices of upper triangular matrix. All these mechanisms can be assembled quickly with modular parts and most of which are partially decoupled. In this study, one of these proposed structures is taken as an example for developing a redundant manipulator. Based on vector-loop equations, its position model is derived and the singular configurations are investigated. Kinematic and static performance of this architecture are conducted, as well as the multi-objective optimization is implemented using Genetic Algorithm.

To measure large external forces exerted on a loading platform, a novel three-dimensional force sensor is developed in this paper. The proposed sensor was designed with a parallel mechanism with three degrees of freedom. Kinematic analysis of this sensor was performed. Due to its structural characteristics, the working principle of the sensor was analyzed using a Jacobian matrix. The sensitivity diversity index and measuring capability were both calculated. The analysis showed that the proposed sensor is more suitable for measuring large forces than existing strain sensors. In addition, compared with existing strain sensors, this sensor is more suitable for measuring forces along the x and y axes. By changing the stiffness coefficients of the springs, the proposed sensor has reconfigurability. This sensor can change its measuring capability to meet different requirements. Next, the mode shapes and natural frequencies of the proposed sensor were performed. Finally, based on these performance indices, the design variables were optimized using a Multi-Objective Genetic Algorithm.