Single top partner production in the Higgs to diphoton channel in the Littlest Higgs Model with TT-parity

The top partner as a hallmark of the Littlest Higgs model with $T$-parity (LHT model) has been extensively searched for during the Large Hadron Collider (LHC) Run-1. With the increasing mass limits on the top partner, the single production of the top partner will be dominant over the pair production. Under the constraints from the Higgs data, the electroweak precision observables and $R_b$, we find that the mass of $T$-even top partner ($T_+$) has to be heavier than 730 GeV. Then, we investigate the observability of the single $T$-even top partner production through the process $pp \to T_+ j$ with the sequent decay $T_+ \to th$ in the di-photon channel in the LHT model at the LHC. We find that the mass of $T_+$ can be excluded up to 800 GeV at $2\sigma$ level at 14 TeV LHC with the integrated luminosity ${\cal L}=3$ ab$^{-1}$

Constraining Top partner and Naturalness at the LHC and TLEP

We investigate indirect constraints on the top partner within the minimal fermionic top partner model. By performing a global fit of the latest Higgs data, $B_s \to \mu^+\mu^-$ measurements and the electroweak precision observables we find that the top partner with the mass up to 830 GeV is excluded at $2\sigma$ level. Our bound on the top partner mass is much stronger than the bounds obtained from the direct searches at the LHC. Under the current constraints the fine-tuning measure is less than 9% and the branching ratio of $T \to tZ$ is bounded between 14% and 25%. We also find that precise measurements of Higgs couplings at 240 GeV TLEP will constrain the top partner mass in multi-TeV region.Comment: 16 pages, references and discussions adde

Construction of a Recombinant Eukaryotic Expression Plasmid Containing Human Calcitonin Gene and Its Expression in NIH3T3 Cells

Aim. To construct a recombinant eukaryotic expression plasmid containing human calcitonin (hCT) gene and express the gene in murine fibroblast NIH3T3 cells. Materials and Methods. A murine Igκ-chain leader sequence and hCT gene were synthesized and cloned into pCDNA3.0 to form the pCDNA3.0-Igκ-hCT eukaryotic expression vector, which was transfected into NIH3T3 cells. The mRNA and protein expressions and secretion of hCT were detected. Primarily cultured osteoclasts were incubated with the supernatant of pCDNA3.0-Igk-hCT-transfected NIH3T3 cells, and their numbers were counted and morphology observed. Results. The expression and secretion of hCT were successfully detected in pCDNA3.0-Igk-hCT-transfected NIH3T3 cells. The number of osteoclasts was decreased and the cells became crumpled when they were incubated with the supernatant of pCDNA3.0-Igk-hCT-transfected NIH3T3 cells. Conclusion. A recombinant eukaryotic expression vector containing hCT gene was successfully constructed and expressed in NIH3T3 cells. The secreted recombinant hCT inhibited the growth and morphology of osteoclasts

A synthesizing land-cover classification method based on Google Earth Engine : a case study in Nzhelele and Levhuvu catchments, South Africa

CITATION: Zeng, H. et al. 2020. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa. Chinese Geographical Science. 30: 397–409. doi:10.1007/s11769-020-1119-yThe original publication is available at https://www.springer.com/journal/11769This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based on an integration of Landsat 8, Sentinel-1, and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Google Earth Engine (GEE) platform. Random forest classifier with 300 trees is employed as land-cover classification model. In order to overcome the defect of insufficient ground data, the stratified sampling method was used to generate the training and validation samples from the existing land-cover product. Likewise, in order to recognize different land-cover categories, the percentile and monthly median composites were employed to expand input metrics of random forest classifier. Results showed that the overall accuracy of the land-cover of Nzhelele and Levhuvu catchments, South Africa in 2017–2018 reached to 76.43%. Three important results can be drawn from our research. 1) The participation of Sentinel-1 data can slightly improve overall accuracy of land-cover while its contribution on land-cover classification varied with land types. 2) Under-fitting problem was observed in the training of non-dominant land-cover categories using the random sampling, the stratified sampling method is recommended to make sure the classification accuracy of non-dominant classes. 3) When related reflectance bands participated in the training process, individual Normalized Difference Vegetation index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) have little effect on final land-cover classification result.https://link.springer.com/article/10.1007/s11769-020-1119-yPublishers versio

Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.Instituto de Clima y AguaFil: Waldner, François. Université catholique de Louvain. Earth and Life Institute - Environment, Croix du Sud; BelgicaFil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Veron, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Zhang, Miao. Chinese Academy of Science. Institute of Remote Sensing and Digital Earth; ChinaFil: Wu, Bingfang. Chinese Academy of Science. Institute of Remote Sensing and Digital Earth; ChinaFil: Plotnikov, Dmitry. Russian Academy of Sciences. Space Research Institute. Terrestrial Ecosystems Monitoring Laboratory; RusiaFil: Bartalev, Sergey. Russian Academy of Sciences. Space Research Institute. Terrestrial Ecosystems Monitoring Laboratory; RusiaFil: Lavreniuk, Mykola. Space Research Institute NAS and SSA. Department of Space Information Technologies; UcraniaFil: Skakun, Sergii. Space Research Institute NAS and SSA. Department of Space Information Technologies; Ucrania. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Kussul, Nataliia. Space Research Institute NAS and SSA. Department of Space Information Technologies; UcraniaFil: Le Maire, Guerric. UMR Eco&Sols, CIRAD; Francia. Empresa Brasileira de Pesquisa Agropecuária. Meio Ambiante; BrasilFil: Dupuy, Stéphane. Centre de Coopération Internationale en Recherche Agronomique pour le Développement. Territoires, Environnement, Télédétection et Information Spatiale; FranciaFil: Jarvis, Ian. Agriculture and Agri-Food Canada. Science and Technology Branch. Agri-Climate, Geomatics and Earth Observation; CanadáFil: Defourny, Pierre. Université Catholique de Louvain. Earth and Life Institute - Environment, Croix du Sud; Belgic

Correction of UAV LiDAR-derived grassland canopy height based on scan angle

Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R2 = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25–65cm) and segmented (25–40cm, 40–50cm and 50–65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R2 from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing