Interaction effect in two-dimensional Dirac fermions

Based on the Dirac equations in the two-dimensional $\pi-$ flux model, we study the interaction effects both in nontrivial gapped and gapless Dirac equations with numerical exact diagonalization method. In the presence of the nearest and next nearest neighbor interactions: for nontrivial gapped Dirac equation, the topological phase is robust and persists in a finite region of the phase diagram; while for gapless Dirac equation, charge-density-wave and stripe phases are identified and the phase diagram in $(V_1, V_2)$ plane is obtained. When the next-next-nearest neighbor interaction is further included to gapless Dirac equation, the topological phase expected in the mean-field theory is absent. Our results are related to the possibility of dynamically generating topological phase from the electronic correlations.Comment: 7 pages, 8 figures. More discussins are added; accepted for publication in Physical Review

Deepened winter snow cover enhances net ecosystem exchange and stabilizes plant community composition and productivity in a temperate grassland

Global warming has greatly altered winter snowfall patterns, and there is a trend towards increasing winter snow in semi-arid regions in China. Winter snowfall is an important source of water during early spring in these water-limited ecosystems, and it can also affect nutrient supply. However, we know little about how changes in winter snowfall will affect ecosystem productivity and plant community structure during the growing season. Here, we conducted a 5-year winter snow manipulation experiment in a temperate grassland in Inner Mongolia. We measured ecosystem carbon flux from 2014 to 2018 and plant biomass and species composition from 2015 to 2018. We found that soil moisture increased under deepened winter snow in early growing season, particularly in deeper soil layers. Deepened snow increased the net ecosystem exchange of CO 2 (NEE) and reduced intra- and inter-annual variation in NEE. Deepened snow did not affect aboveground plant biomass (AGB) but significantly increased root biomass. This suggested that the enhanced NEE was allocated to the belowground, which improved water acquisition and thus contributed to greater stability in NEE in deep-snow plots. Interestingly, the AGB of grasses in the control plots declined over time, resulting in a shift towards a forb-dominated system. Similar declines in grass AGB were also observed at three other locations in the region over the same time frame and are attributed to 4 years of below-average precipitation during the growing season. By contrast, grass AGB was stabilized under deepened winter snow and plant community composition remained unchanged. Hence, our study demonstrates that increased winter snowfall may stabilize arid grassland systems by reducing resource competition, promoting coexistence between plant functional groups, which ultimately mitigates the impacts of chronic drought during the growing season

Theoretical Analysis of Plate-Type Thermoelectric Generator for Fluid Waste Heat Recovery Using Thermal Resistance and Numerical Models

In current research, there are excessive assumptions and simplifications in the mathematical models developed for thermoelectric generators. In this study, a comprehensive mathematical model was developed based on a plate-type thermoelectric generator divided into multiple thermoelectric units. The model takes into account temperature-dependent thermoelectric material parameters and fluid flow. The model was validated, and a maximum error of 6.4% was determined. Moreover, the model was compared and analyzed with a numerical model, with a maximum discrepancy of 7.2%. The model revealed the factors and their degree of influence on the performance of the thermoelectric generator unit. In addition, differences in temperature distribution, output power, and conversion efficiency between multiple thermoelectric units were clearly studied. This study can guide modeling and some optimization measures to improve the overall performance of thermoelectric generators

Predominant control of moisture on soil organic carbon mineralization across a broad range of arid and semiarid ecosystems on the Mongolia plateau

Soil moisture and temperature are known to be the two environmental constraints regulating mineralization of soil organic carbon (SOC). However, it remains unclear to what extent the moisture, temperature, and other abiotic and biotic factors affect the mineralization of SOC across broad geographic regions. Here, we examined the effects of multiple abiotic and biotic factors on SOC mineralization across 12 widespread arid and semiarid ecosystems on the Mongolia plateau, by using an integrative approach combining short-term laboratory incubations (28-day), field survey, and structure equation modeling (SEM). Our results showed that soil moisture had a predominant control on SOC mineralization across all sites. The average CO2 emissions over all sites increased by 23 % from 30 to 60 % water filled pore space (WFPS) and by 176 % from 60 to 90 % WFPS. Under conditions of 25 A degrees C and 60 % WFPS, the cumulative CO2-C emissions in the topsoil (0-20 cm) diminished in the following order: meadow steppe (227 mg kg(-1)) \u3e typical steppe (216 mg kg(-1)) \u3e desert (99 mg kg(-1)) \u3e desert steppe (72 mg kg(-1)). The temperature sensitivity of SOC mineralization (Q(10)), the proportional change in carbon mineralization rate given a 10 A degrees C temperature gradient, was highest under conditions of low temperature and high moisture, but it was lowest under high temperature and low moisture. The SEM analyses demonstrate that the mineralization potential of SOC seems to be directly regulated by microbe activity and substrate availability. Climatic factors (e.g. mean annual precipitation, mean annual temperature), above- and belowground biomass, and soil pH, which regulate SOC and microbial biomass carbon content, also indirectly influence the SOC mineralization. Our results indicate that global climate change, particularly the increase in the frequency of extreme storms and droughts, will substantially affect SOC mineralization and ecosystem carbon cycle in arid and semiarid regions

Research on Classification Method of Building Function Oriented to Urban Building Stock Management

With the development of human society, the urban population and the urban building stock have been continuously increasing. Environmental issues such as greenhouse gases emissions, air pollution, and construction waste have gradually emerged. Due to the lack of an urban functional area database, it is very time-consuming to manually identify building functional areas. As a result, most of the current research on urban building functions are estimated at a large regional scale or only detailed calculations of individual buildings. The building functions classification method needs to be further improved. Based on the traditional methods, this paper proposes a building function classification method with higher recognition accuracy and is less time-consuming. The method is then applied to a certain area of Chaoyang District, Beijing, for validation and verification. The results show that the urban building function classification method in this paper has a recognition rate of 96.18%, an overall classification accuracy of 94.37%, and a kappa coefficient of 0.9089. The classification results are in good agreement with the virtual interpretation. In addition, automatic classification of building functions is implemented using ArcPy in ArcGIS, which significantly improves the classification efficiency

Synthesis of Honeycomb-Like Co3O4 Nanosheets with Excellent Supercapacitive Performance by Morphological Controlling Derived from the Alkaline Source Ratio

Honeycomb-like Co3O4 nanosheets with high specific surface area were successfully synthesized on porous nickel foam by the facile hydrothermal method followed by an annealing treatment (300 °C), which were used as high-performance supercapacitor electrodes. The effects of the mole ratio of hexamethylenetetramine (HMT) and Co(NO3)2 (1:1, 2:1, 3:1, 4:1, 5:1 and 6:1) as the reactants on the morphological evolution and electrochemical performance of the electrodes were investigated in detail. X-ray diffractometry (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) were applied to characterize the structure and morphology of the products. The electrochemical performance was measured by cyclic voltammetry (CV) and galvanostatic charge/discharge. The mole ratio of HMT and Co(NO3)2 produced a significant effect on the morphological evolution of Co3O4. The morphological evolution of Co3O4 with the increase in the mole ratio was followed: the nanosheets accompanied with a large number of spherical nanoparticles → the formation of some strip-like particles due to the agglomeration of spherical nanoparticles → the formation of new nanosheets resulting from the growth of strip-like particles → the formation of coarse flower-like particles owing to the connection among the nanosheets → the nanosheets gradually covered with flower-like particles. Accompanied with the change, the specific surface area was increased firstly, and then decreased. A maximum was obtained at a HMT and Co(NO3)2 mole ratio of 4:1. The evolution in morphology of Co3O4 was responsible for the change in electrochemical performance of the electrode. The specific capacitance value of the electrode prepared at a HMT and Co(NO3)2 mole ratio of 4:1 was highest (743.00 F·g−1 at 1 A·g−1 in the galvanostatic charge/discharge test). The similar result was also observed in the CV test with a scanning rate of 5 mV·s−1. Moreover, the electrode also demonstrated an excellent cyclic performance, in which about 97% of the initial specific capacitance remained at 1 A·g−1 for 500 cycles in the galvanostatic charge/discharge test. This excellent electrochemical performance was ascribed to high specific surface area of Co3O4 nanosheets that provide added channels and space for the ions transportation

An Observation Capability Metadata Model for EO Sensor Discovery in Sensor Web Enablement Environments

Accurate and fine-grained discovery by diverse Earth observation (EO) sensors ensures a comprehensive response to collaborative observation-required emergency tasks. This discovery remains a challenge in an EO sensor web environment. In this study, we propose an EO sensor observation capability metadata model that reuses and extends the existing sensor observation-related metadata standards to enable the accurate and fine-grained discovery of EO sensors. The proposed model is composed of five sub-modules, namely, ObservationBreadth, ObservationDepth, ObservationFrequency, ObservationQuality and ObservationData. The model is applied to different types of EO sensors and is formalized by the Open Geospatial Consortium Sensor Model Language 1.0. The GeosensorQuery prototype retrieves the qualified EO sensors based on the provided geo-event. An actual application to flood emergency observation in the Yangtze River Basin in China is conducted, and the results indicate that sensor inquiry can accurately achieve fine-grained discovery of qualified EO sensors and obtain enriched observation capability information. In summary, the proposed model enables an efficient encoding system that ensures minimum unification to represent the observation capabilities of EO sensors. The model functions as a foundation for the efficient discovery of EO sensors. In addition, the definition and development of this proposed EO sensor observation capability metadata model is a helpful step in extending the Sensor Model Language (SensorML) 2.0 Profile for the description of the observation capabilities of EO sensors