Transitional fossil earwigs - a missing link in Dermaptera evolution

<p>Abstract</p> <p>Background</p> <p>The Dermaptera belongs to a group of winged insects of uncertain relationship within Polyneoptera, which has expanded anal region and adds numerous anal veins in the hind wing. Evolutional history and origin of Dermaptera have been in contention.</p> <p>Results</p> <p>In this paper, we report two new fossil earwigs in a new family of Bellodermatidae fam. nov. The fossils were collected from the Jiulongshan Formation (Middle Jurassic) in Inner Mongolia, northeast China. This new family, characterized by an unexpected combination of primitive and derived characters, is bridging the missing link between suborders of Archidermaptera and Eodermaptera. Phylogenetic analyses support the new family to be a new clade at the base of previously defined Eodermaptera and to be a stem group of (Eodermaptera+Neodermaptera).</p> <p>Conclusion</p> <p>Evolutional history and origin of Dermaptera have been in contention, with dramatically different viewpoints by contemporary authors. It is suggested that the oldest Dermaptera might possibly be traced back to the Late Triassic-Early Jurassic and they had divided into Archidermaptera and (Eodermaptera+Neodermaptera) in the Middle Jurassic.</p

A New Single-blade Based Hybrid CFD Method for Hovering and Forward-flight Rotor Computation

AbstractA hybrid Euler/full potential/Lagrangian wake method, based on single-blade simulation, for predicting unsteady aerodynamic flow around helicopter rotors in hover and forward flight has been developed. In this method, an Euler solver is used to model the near wake evolution and transonic flow phenomena in the vicinity of the blade, and a full potential equation (FPE) is used to model the isentropic potential flow region far away from the rotor, while the wake effects of other blades and the far wake are incorporated into the flow solution as an induced inflow distribution using a Lagrangian based wake analysis. To further reduce the execution time, the computational fluid dynamics (CFD) solution and rotor wake analysis (including induced velocity update) are conducted parallelly, and a load balancing strategy is employed to account for the information exchange between two solvers. By the developed method, several hover and forward-flight cases on Caradonna-Tung and Helishape 7A rotors are performed. Good agreements of the loadings on blade surface with available measured data demonstrate the validation of the method. Also, the CPU time required for different computation runs is compared in the paper, and the results show that the present hybrid method is superior to conventional CFD method in time cost, and will be more efficient with the number of blades increasing

CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles

This paper develops a decentralized reinforcement learning (RL) scheme for multi-intersection adaptive traffic signal control (TSC), called "CVLight", that leverages data collected from connected vehicles (CVs). The state and reward design facilitates coordination among agents and considers travel delays collected by CVs. A novel algorithm, Asymmetric Advantage Actor-critic (Asym-A2C), is proposed where both CV and non-CV information is used to train the critic network, while only CV information is used to execute optimal signal timing. Comprehensive experiments show the superiority of CVLight over state-of-the-art algorithms under a 2-by-2 synthetic road network with various traffic demand patterns and penetration rates. The learned policy is then visualized to further demonstrate the advantage of Asym-A2C. A pre-train technique is applied to improve the scalability of CVLight, which significantly shortens the training time and shows the advantage in performance under a 5-by-5 road network. A case study is performed on a 2-by-2 road network located in State College, Pennsylvania, USA, to further demonstrate the effectiveness of the proposed algorithm under real-world scenarios. Compared to other baseline models, the trained CVLight agent can efficiently control multiple intersections solely based on CV data and achieve the best performance, especially under low CV penetration rates.Comment: 29 pages, 14 figure

Magnetic Field Enhanced Superconductivity in Epitaxial Thin Film WTe2.

In conventional superconductors an external magnetic field generally suppresses superconductivity. This results from a simple thermodynamic competition of the superconducting and magnetic free energies. In this study, we report the unconventional features in the superconducting epitaxial thin film tungsten telluride (WTe2). Measuring the electrical transport properties of Molecular Beam Epitaxy (MBE) grown WTe2 thin films with a high precision rotation stage, we map the upper critical field Hc2 at different temperatures T. We observe the superconducting transition temperature T c is enhanced by in-plane magnetic fields. The upper critical field Hc2 is observed to establish an unconventional non-monotonic dependence on temperature. We suggest that this unconventional feature is due to the lifting of inversion symmetry, which leads to the enhancement of Hc2 in Ising superconductors

Seasonal Dynamics of a Temperate Tibetan Glacier Revealed by High-Resolution UAV Photogrammetry and In Situ Measurements

The seasonal dynamic changes of Tibetan glaciers have seen little prior investigation, despite the increase in geodetic studies of multi-year changes. This study compares seasonal glacier dynamics (&ldquo;cold&rdquo; and &ldquo;warm&rdquo; seasons) in the ablation zone of Parlung No. 4 Glacier, a temperate glacier in the monsoon-influenced southeastern Tibetan Plateau, by using repeat unpiloted aerial vehicle (UAV) surveys combined with Structure-from-Motion (SfM) photogrammetry and ground stake measurements. Our results showed that the surveyed ablation zone had a mean change of &minus;2.7 m of ice surface elevation during the period of September 2018 to October 2019 but is characterized by significant seasonal cyclic variations with ice surface elevation lifting (+2.0 m) in the cold season (September 2018 to June 2019) but lowering (&minus;4.7 m) in the warm season (June 2019 to October 2019). Over an annual timescale, surface lowering was greatly suppressed by the resupply of ice from the glacier&rsquo;s accumulation area&mdash;the annual emergence velocity compensates for about 55 of surface ablation in our study area. Cold season emergence velocities (3.0 &plusmn; 1.2 m) were ~5-times larger than those observed in the warm season (0.6 &plusmn; 1.0 m). Distinct spring precipitation patterns may contribute to these distinct seasonal signals. Such seasonal dynamic conditions are possibly critical for different glacier responses to climate change in this region of the Tibetan Plateau, and perhaps further afield

Aboveground Biomass Retrieval in Tropical and Boreal Forests Using L-Band Airborne Polarimetric Observations

Forests play a crucial part in regulating global climate change since their aboveground biomass (AGB) relates to the carbon cycle, and its changes affect the main carbon pools. At present, the most suitable available SAR data for wall-to-wall forest AGB estimation are exploiting an L-band polarimetric SAR. However, the saturation issues were reported for AGB estimation using L-band backscatter coefficients. Saturation varies depending on forest structure. Polarimetric information has the capability to identify different aspects of forest structure and therefore shows great potential for reducing saturation issues and improving estimation accuracy. In this study, 121 polarimetric decomposition observations, 10 polarimetric backscatter coefficients and their derived observations, and six texture features were extracted and applied for forest AGB estimation in a tropical forest and a boreal forest. A parametric feature optimization inversion model (Multiple linear stepwise regression, MSLR) and a nonparametric feature optimization inversion model (fast iterative procedure integrated into a K-nearest neighbor nonparameter algorithm, KNNFIFS) were used for polarimetric features optimization and forest AGB inversion. The results demonstrated the great potential of L-band polarimetric features for forest AGB estimation. KNNFIFS performed better both in tropical (R2 = 0.80, RMSE = 22.55 Mg/ha, rRMSE = 14.59%, MA%E = 12.21%) and boreal (R2 = 0.74, RMSE = 19.82 Mg/ha, rRMSE = 20.86%, MA%E = 20.19%) forests. Non-model-based polarimetric features performed better compared to features extracted by backscatter coefficients, model-based decompositions, and texture. Polarimetric observations also revealed site-dependent performances