Technical research on the emission performance of vehicles with different Technique route under real driving conditions

Based on a large number of test data obtained from real driving emission test of gasoline vehicles, the emission performance of vehicles with different technique route under real driving conditions were studied, the emission sensitivities and feasible schemes to meet the China 6 RDE standards for vehicles with different technologies were also evaluated. It is revealed that for the tested fleet covering different emission control technologies and under current proposed RDE limit, the passing rate can reach 72% at the initial implementation stage of China 6 standard, and increased to more than 85% after more than one year of China 6 standard implementation, the main failure cause were the over standard emission of PN; the RDE pollution control level of domestic brands is equivalent to that of the foreign brands, but there is a certain gap between WLTC pollution control level; adding GPF is a relatively safe technology to deal with PN emission both in on road RDE tests and laboratory WLTC tests, and vehicles with additional coated GPF can obtain relatively better NOx emission performance

Facile Synthesis of ZnO Nanoparticles on Nitrogen-Doped Carbon Nanotubes as High-Performance Anode Material for Lithium-Ion Batteries

ZnO/nitrogen-doped carbon nanotube (ZnO/NCNT) composite, prepared though a simple one-step sol-gel synthetic technique, has been explored for the first time as an anode material. The as-prepared ZnO/NCNT nanocomposite preserves a good dispersity and homogeneity of the ZnO nanoparticles (~6 nm) which deposited on the surface of NCNT. Transmission electron microscopy (TEM) reveals the formation of ZnO nanoparticles with an average size of 6 nm homogeneously deposited on the surface of NCNT. ZnO/NCNT composite, when evaluated as an anode for lithium-ion batteries (LIBs), exhibits remarkably enhanced cycling ability and rate capability compared with the ZnO/CNT counterpart. A relatively large reversible capacity of 1013 mAh_g-1 is manifested at the second cycle and a capacity of 664 mAh_g-1 is retained after 100 cycles. Furthermore, the ZnO/NCNT system displays a reversible capacity of 308 mAh_g-1 even at a high current density of 1600 mA_g-1. These electrochemical performance enhancements are ascribed to the reinforced accumulative effects of the well-dispersed ZnO nanoparticles and doping nitrogen atoms, which can not only suppress the volumetric expansion of ZnO nanoparticles during the cycling performance but also provide a highly conductive NCNT network for ZnO anode

Nitrogen-doped carbon nanotubes coated with zinc oxide nanoparticles as sulfur encapsulator for high-performance lithium/sulfur batteries

Nitrogen-doped carbon nanotubes coated with zinc oxide nanoparticles (ZnO@NCNT) were prepared via a sol–gel route as sulfur encapsulator for lithium/sulfur (Li/S) batteries. The electrochemical properties of the S/ZnO@NCNT composite cathode were evaluated in Li/S batteries. It delivered an initial capacity of 1032 mAh·g−1 at a charge/discharge rate of 0.2C and maintained a reversible capacity of 665 mAh·g−1 after 100 cycles. The coulombic efficiency of the cathode remains unchanged above 99%, showing stable cycling performance. X-ray photoelectron spectroscopy analysis confirmed the formation of S–Zn and S–O bonds in the composite. This indicates that an enhanced cycling and rate capability of the S/ZnO@NCNT composite could be ascribed to advantages of the ZnO@NCNT matrix. In the composite, the active ZnO-rich surfaces offer a high sulfur-bonding capability and the NCNT core acts as a conductive framework providing pathways for ion and electron transport. The as-prepared S/ZnO@NCNT composite is a promising cathode material for Li/S batteries

Facile Synthesis of SiO2@C Nanoparticles Anchored on MWNT as High-Performance Anode Materials for Li-ion Batteries

Carbon-coated silica nanoparticles anchored on multi-walled carbon nanotubes (SiO2@C/MWNT composite) were synthesized via a simple and facile sol-gel method followed by heat treatment. Scanning and transmission electron microscopy (SEM and TEM) studies confirmed densely anchoring the carbon-coated SiO2 nanoparticles onto a flexible MWNT conductive network, which facilitated fast electron and lithium-ion transport and improved structural stability of the composite. As prepared, ternary composite anode showed superior cyclability and rate capability compared to a carbon-coated silica counterpart without MWNT (SiO2@C). The SiO2@C/MWNT composite exhibited a high reversible discharge capacity of 744 mAh g−1 at the second discharge cycle conducted at a current density of 100 mA g−1 as well as an excellent rate capability, delivering a capacity of 475 mAh g−1 even at 1000 mA g−1. This enhanced electrochemical performance of SiO2@C/MWNT ternary composite anode was associated with its unique core-shell and networking structure and a strong mutual synergistic effect among the individual components

Remote Sensing / An object-based semantic classification method for high resolution remote sensing imagery using ontology

Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in remote sensing. GEOBIA has been claimed to represent a paradigm shift in remote sensing interpretation. Still, GEOBIAsimilar to other emerging paradigmslacks formal expressions and objective modelling structures and in particular semantic classification methods using ontologies. This study has put forward an object-based semantic classification method for high resolution satellite imagery using an ontology that aims to fully exploit the advantages of ontology to GEOBIA. A three-step workflow has been introduced: ontology modelling, initial classification based on a data-driven machine learning method, and semantic classification based on knowledge-driven semantic rules. The classification part is based on data-driven machine learning, segmentation, feature selection, sample collection and an initial classification. Then, image objects are re-classified based on the ontological model whereby the semantic relations are expressed in the formal languages OWL and SWRL. The results show that the method with ontologyas compared to the decision tree classification without using the ontologyyielded minor statistical improvements in terms of accuracy for this particular image. However, this framework enhances existing GEOBIA methodologies: ontologies express and organize the whole structure of GEOBIA and allow establishing relations, particularly spatially explicit relations between objects as well as multi-scale/hierarchical relations.(VLID)219563

Soybean inclusion reduces soil organic matter mineralization despite increasing its temperature sensitivity

Legume-based cropping increased the diversity of residues and rhizodeposition input into the soil, thus affecting soil organic matter (SOM) stabilization. Despite this, a comprehensive understanding of the mechanisms governing SOM mineralization and its temperature sensitivity across bulk soil and aggregate scales concerning legume inclusion remains incomplete. Here, a 6-year field experiment was conducted to investigate the effects of three cropping systems (i.e., winter wheat/summer maize, winter wheat/summer maize-soybean, and nature fallow) on SOM mineralization, its temperature sensitivity, and the main drivers in both topsoil (0–20 cm) and subsoil (20–40 cm). Soybean inclusion decreased the SOM mineralization by 17%–24%, while concurrently increasing the majority of soil biochemical properties, such as carbon (C) acquisition enzyme activities (5%–22%) and microbial biomass C (5%–9%), within the topsoil regardless of temperature. This is attributed to the increased substrate availability (e.g., dissolved organic C) facilitating microbial utilization, thus devoting less energy to mining nutrients under diversified cropping. In addition, SOM mineralization was lower within macroaggregates (∼12%), largely driven by substrate availability irrespective of aggregate sizes. In contrast, diversified cropping amplified the Q10 of SOM mineralization in mesoaggregates (+6%) and microaggregates (+5%) rather than in macroaggregates. This underscores the pivotal role of mesoaggregates and microaggregates in dominating the Q10 of SOM mineralization under soybean-based cropping. In conclusion, legume-based cropping diminishes soil organic matter mineralization despite increasing its temperature sensitivity, which proposes a potential strategy for C-neutral agriculture and climate warming mitigation

Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia

ObjectiveTo establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors.MethodsThe cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool.ResultsThe area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877–0.926), 0.901 (95% CI: 0.874–0.923), 0.902 (95% CI: 0.876–0.924), and 0.955 (95% CI: 0.935–0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5).ConclusionMachine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application