Application of large underground seasonal thermal energy storage in district heating system: A model-based energy performance assessment of a pilot system in Chifeng, China

Seasonal thermal energy storage (STES) technology is a proven solution to resolve the seasonal discrepancy between heating energy generation from renewables and building heating demands. This research focuses on the performance assessment of district heating (DH) systems powered by low-grade energy sources with large-scale, high temperature underground STES technology. A pilot DH system, located in Chifeng, China that integrates a 0.5 million m3 borehole thermal energy storage system, an on-site solar thermal plant and excess heat from a copper plant is presented. The research in this paper adopts a model-based approach using Modelica to analyze the energy performance of the STES for two district heating system configurations. Several performance indicators such as the extraction heat, the injection heat and the storage coefficient are selected to assess the STES system performance. Results show that a lower STES discharge temperature leads to a better energy performance. A sensitivity analysis of the site properties illustrates that the thermal conductivity of soil is the most influential parameter on the STES system performance. The long-term performance of the STES is also discussed and a shorter stabilization time between one and two years could be achieved by discharging the STES at a lower temperature.This research is part of the seasonal storage for solar and industrial waste heat utilization for urban district heating project funded by the Joint Scientific Thematic Research Programme (JSTP)–Smart Energy in Smart Cities. We gratefully acknowledge the financial support from the Netherlands Organisation for Scientific Research (NWO). We would also like to thank our research partners from Tsinghua University working on the project of the International S&T Cooperation Programof China (ISTCP) (project No. 2015DFG62410). Without their efforts, we would not have been able to obtain the technical data to conduct the case study

Potential immune evasion of the severe acute respiratory syndrome coronavirus 2 Omicron variants

Coronavirus disease 2019 (COVID-19), which is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a global pandemic. The Omicron variant (B.1.1.529) was first discovered in November 2021 in specimens collected from Botswana, South Africa. Omicron has become the dominant variant worldwide, and several sublineages or subvariants have been identified recently. Compared to those of other mutants, the Omicron variant has the most highly expressed amino acid mutations, with almost 60 mutations throughout the genome, most of which are in the spike (S) protein, especially in the receptor-binding domain (RBD). These mutations increase the binding affinity of Omicron variants for the ACE2 receptor, and Omicron variants may also lead to immune escape. Despite causing milder symptoms, epidemiological evidence suggests that Omicron variants have exceptionally higher transmissibility, higher rates of reinfection and greater spread than the prototype strain as well as other preceding variants. Additionally, overwhelming amounts of data suggest that the levels of specific neutralization antibodies against Omicron variants decrease in most vaccinated populations, although CD4+ and CD8+ T-cell responses are maintained. Therefore, the mechanisms underlying Omicron variant evasion are still unclear. In this review, we surveyed the current epidemic status and potential immune escape mechanisms of Omicron variants. Especially, we focused on the potential roles of viral epitope mutations, antigenic drift, hybrid immunity, and “original antigenic sin” in mediating immune evasion. These insights might supply more valuable concise information for us to understand the spreading of Omicron variants

Association between short-term exposure to atmospheric NO2 and coagulation indexes of young individuals of different weights and modification effect of temperature

BackgroundNitrogen dioxide (NO2) is one of the main air pollutants, and though China's NO2 pollution has been improving year by year, it maintains at a high level, threatening the health of the population. ObjectiveTo investigate the effect of short-term exposure to atmospheric NO2 on the coagulation indexes in obese and normal-weight young individuals and potential modification effect of temperature. MethodsBased on a parallel control panel study design, this study recruited 53 normal-weight and 44 obese young individuals. Three prospective follow-ups were conducted. Air pollution data were obtained from the fixed monitoring station closest to the participant's residences, and personal air pollution exposure was simulated based on time-activity log and infiltration factor for the week before every follow-up. Temperature was collected from China Meteorological Data Service Center. Venous blood samples were taken to measure platelet (PLT) count, mean platelet volume (MPV), soluble CD40 ligand (sCD40L), soluble P-selectin (sP-selectin), platelet aggregation rate (PAgT), and plasminogen activator inhibitor type-1 (PAI-1) during every follow-up. A linear mixed-effect model was used to assess the association between short-term atmospheric NO2 exposure and the coagulation indexes of weight grouped young individuals, and a stratified analysis was used to explore potential modification effect of temperature. ResultsThe median [interquartile range (IQR)] of personal atmospheric NO2 exposure concentrations was 21.47 (8.01) µg·m−3. Short-term exposure to atmospheric NO2 was significantly associated the increase of sCD40L and PAgT in the obese individuals, while the most significant association appeared at 5 d lag, and for each IQR increase in the average sliding exposure concentration of atmospheric NO2 with a 5 d lag, sCD40L increased by 27.4% (95%CI: 4.2%, 56.6%) and PAgT increased by 37.5% (95%CI: 12.2%, 68.6%); short-term exposure to atmospheric NO2 was significantly associated with the decrease of PLT and PAgT in the normal-weight individuals, while the most significant association appeared at 5 d lag or 7 d lag, and for each IQR increase in the average sliding exposure concentration of atmospheric NO2 with a 5 d lag, PLT decreased by 11.8% (95%CI: −17.8%, −5.3%), and for each IQR increase in the average sliding exposure concentration of atmospheric NO2 with a 7 d lag, PAgT decreased by 16.8% (95%CI: −30.6%, −0.4%). We didn't find statistically significant association of short-term exposure to atmospheric NO2 with PLT in the obese individuals or sCD40L in the normal-weight individuals, nor statistically significant association between short-term exposure to atmospheric NO2 and PAI-1, MPV, and sP-selectin in different weight grouped individuals. The stratified analysis found that short-term exposure to atmospheric NO2 was significantly associated with PAgT in the normal-weight individuals, or with PLT, sCD40L, and sP-selectin in the obese individuals only at high temperature. ConclusionsShort-term exposure to atmospheric NO2 has adverse effects on the coagulation indexes of different weight grouped young individuals, and the obese individuals are more sensitive to it than the normal-weight individuals. High temperature can enhance the adverse health effect of short-term exposure to atmospheric NO2

Preparation and enhanced properties of Fe3O4 nanoparticles reinforced polyimide nanocomposites

Polyimide (PI) nanocomposite reinforced with Fe3O4 nanoparticles (NPs) at various NPs loadings levels of 5.0, 10.0, 15.0, and 20.0 wt% were prepared. The chemical interactions of the Fe3O4 NPs/PI nanocomposites were characterized using Fourier Transform Infrared (FT-IR) spectroscopy. X-ray Diffraction (XRD) results revealed that the addition of NPs had a significant effect on the crystallization of PI. Scanning electron microscope (SEM) and the atomic force microscope (AFM) were used to characterize the dispersion and surface morphology of the Fe3O4 NPs and the PI nanocomposites. The obtained optical band gap of the nanocomposites characterized using Ultraviolet-Visible Diffuse Reflectance Spectroscopy (UV-Vis DRS) was decreased with increasing the Fe3O4 loading. Differential scanning calorimetry (DSC) results showed a continuous increase of Tg with increasing the Fe3O4 NPs loading. Some differences were observed in the onset decomposition temperature between the pure PI and nanocomposites since the NPs and the PI matrix were physically entangled together to form the nanocomposites. The contact angle of pure PI was larger than that of Fe3O4/PI nanocomposites films, and increased with increasing the loading of Fe3O4. The degree of swelling was increased with increasing the Fe3O4 loading and the swelling time. The dielectric properties of the nanocomposite were strongly related to the Fe3O4 loading levels. The Fe3O4/PI magnetic property also had been improved with increasing the loading of the magnetic nanoparticles

In search of selective and potent Golgi alpha -mannosidase II inhibitors as potential anticancer agents: Synthesis of 3-substituted-swainsonine analogs and preparation of immobilized swainsonine analogs on solid support.

The major goal of this thesis research is to design and synthesize analogs of swainsonine that may be more selective and potent inhibitors for Golgi alpha-mannosidase II, but devoid of inhibitory activities toward lysosomal alpha-mannosidase. Alpha-3-benzyloxymethyl substitutions on swainsonine were tolerated well by mannosidases, while the respective beta-substitution rendered the analogs inactive. The first synthesis of a pseudodisaccharide with swainsonine at the glycone portion was accomplished. Although the pseudodisaccharide mimics the Man(alpha-1,6)Man disaccharide portion of the natural substrate, it showed low biological activities toward mannosidases. Polyethylene glycol linked swainsonine analogs were designed to bind both the active site and the putative GlcNAc binding site of Golgi alpha-mannosidase II. However, they were only weak inhibitors of mannosidases. Also, C3- and C6-derivatized swainsonine analogs were synthesized and used to prepare affinity columns for the purification of alpha-mannosidases. The C3-alpha-swainsonine affinity analogs were found to be potent alpha-mannosidase inhibitors. None of the synthesized C3-substituted swainsonine analogs showed significant selectivity in favor of Golgi alpha-mannosidase II.Ph.D.Organic chemistryPharmacy sciencesPure SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/123849/2/3106069.pd

Identification of Short-Rotation Eucalyptus Plantation at Large Scale Using Multi-Satellite Imageries and Cloud Computing Platform

A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high spatial resolution (30 m) at large scales, with the use of open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI), as well as a free cloud computing platform, Google Earth Engine (GEE). The method is composed of three main steps. First, a time series of Enhanced Vegetation Index (EVI) is constructed from Landsat data for each pixel, and a statistical hypothesis testing is followed to determine whether the pixel belongs to a tree plantation or not based on the idea that tree crops should be harvested in a specific period. Then, a broadleaf/needleleaf classification is applied to distinguish eucalyptus from coniferous trees such as pine and fir using the red-edge bands of Sentinel-2 data. Refinements based on superpixel are performed at last to remove the salt-and-pepper effects resulted from per-pixel detection. The proposed method allows gaps in the time series that are very common in tropical and subtropical regions by employing time series segmentation and statistical hypothesis testing, and could capture forest disturbances such as conversion of natural forest or agricultural lands to eucalyptus plantations emerged in recent years by using a short observing time. The experiment in Guangxi province of China demonstrated that the method had an overall accuracy of 87.97%, with producer’s accuracy of 63.85% and user’s accuracy of 66.89% for eucalyptus plantations

Statistical Assessments of InSAR Tropospheric Corrections: Applicability and Limitations of Weather Model Products and Spatiotemporal Filtering

Tropospheric correction is a crucial step for interferometric synthetic aperture radar (InSAR) monitoring of small deformation magnitude. However, most of the corrections are implemented without a rigorous evaluation of their influences on InSAR measurements. In this paper, we present three statistical metrics to evaluate the correction performance. Firstly, we propose a time series decomposition method to estimate the tropospheric noise and mitigate the bias caused by ground displacement. On this basis, we calculate the root-mean-square values of tropospheric noise to assess the general performance of tropospheric corrections. Then, we propose the use of semi-variograms with model-fitted range and sill to investigate the reduction of distance-dependent signals, and Spearman’s rank correlation between phase and elevation to evaluate the mitigation of topography-correlated signals in hilly areas. The applicability and limitations were assessed on the weather model-derived corrections, a representative spatiotemporal filtering method, and the integration of the two mainstream methods. Furthermore, we notice that the persistent scatter InSAR processing resulted in two components, the primary and secondary images’ contribution to the tropospheric and orbit errors. To the best of our knowledge, this paper for the first time analyzes the respective roles of the two components in the InSAR tropospheric corrections

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination

Cocconeis crisscrossis sp. nov., a new monoraphid diatom (Bacillariophyta) from southern China

A novel monoraphid diatom species, Cocconeis crisscrossis You, Yu, Kociolek & Wang, sp. nov. is examined and described from the Qingyi River and Maolan Nature Reserve of southern China. The morphological description is based on light microscopy and scanning electron microscopy observations and the new species is compared with similar taxa in this genus. The characteristics unique to Cocconeis crisscrossis sp. nov. include its central area extending irregularly to both sides, it having closed valvocopulae with heavily silicified fimbriate margins and poles of the valvocopulae have ‘sword-shaped’ siliceous extensions. These features differentiate this new species from others in the genus. This new species was found in alkaline waterbodies, including streams, waterfall and ponds. It was usually found as an epiphyte on the stones; however, it was present on other substrates such as mosses

Exploring the Individualized Effect of Climatic Drivers on MODIS Net Primary Productivity through an Explainable Machine Learning Framework

Along with the development of remote sensing technology, the spatial–temporal variability of vegetation productivity has been well observed. However, the drivers controlling the variation in vegetation under various climate gradients remain poorly understood. Identifying and quantifying the independent effects of driving factors on a natural process is challenging. In this study, we adopted a potent machine learning (ML) model and an ML interpretation technique with high fidelity to disentangle the effects of climatic variables on the long-term averaged net primary productivity (NPP) across the Amazon rainforests. Specifically, the eXtreme Gradient Boosting (XGBoost) model was employed to model the Moderate-resolution Imaging Spectroradiometer (MODIS) NPP data, and the Shapley addictive explanation (SHAP) method was introduced to account for nonlinear relationships between variables identified by the model. Results showed that the dominant driver of NPP across the Amazon forests varied in different regions, with temperature dominating the most considerable portion of the ecoregion with a high importance score. In addition, light augmentation, increased CO2 concentration, and decreased precipitation positively contributed to Amazonia NPP. The wind speed for most vegetated areas was under the optimum, which benefits NPP, while sustained high wind speed would bring substantial NPP loss. We also found a non-monotonic response of Amazonia NPP to VPD and attributed this relationship to the moisture load in Amazon forests. Our application of the explainable machine learning framework to identify the underlying physical mechanism behind NPP could be a reference for identifying relationships between components in natural processes