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The magnetic nitrogen-doped carbon nanotube is prepared by one-pot using waste plastics as feedstock in the existence of the urea and Fe(NO3)3 for Cr(Ⅵ) removal form wastewater. The characterization analysis indicates that the nitrogen is successfully doped on the magnetic nitrogen-doped carbon nanotube with specific surface area of the 158.71 m2/g and saturation magnetization of the 36.47 emu g−1. The existence of the nitrogen group and Fe3C contributes to Cr(Ⅵ) removal by complexation and reduction with adsorption capacity of the 27.47 mg g−1. The adsorption data is fitting well with the Pseudo-second order model, demonstrating chemisorption of Cr(Ⅵ). The Cr(Ⅵ) removal mechanism analysis indicates that Fe0, Fe2+, H*, oxygen-containing group and nitrogen-containing group are crucial to Cr(Ⅵ) removal. The key mechanism of the enhanced Cr (VI) removal is the reductive capacity by dissolved Fe2+, which accounts for 23.6 % of the overall Cr(VI) removal. The magnetic nitrogen-doped carbon nanotube is prepared from waste plastic for Cr (VI) removal from wastewater.

The Cd2+ adsorption performance of biochar derived from soybean straw pyrolysis at 400–600 °C is investigated. Langmuir model can accurately describe the Cd2+ adsorption data with adsorption amount of 47.30 mg/g with pH=5. Cd2+ adsorption kinetic process could be analyzed using Pseudo-second-order. Biochar has large Cd2+ wastewater treatment volume, based on column adsorption experiment. The π-electrons play an important role in Cd2+ adsorption, based on adsorption mechanism analysis. Biochar has good stability after three cycles. Biochar also has good application potential used in lithium ion battery anode with specific capacity of 132 mA g−1. Besides, pyrolysis gas is recycled in the preparation process of biochar with heating value of the 12.10 MJ/Nm3. These results indicate that soybean straw waste is converted into sustainable adsorbent for Cd2+ wastewater treatment.

ABSTRACT Cats (Felidae) have become an integral part of many households. However, our understanding of the full spectrum of pathogens affecting cats (referred to as the infectome) is limited, mainly due to the inadequacy of commonly used diagnostic tools in capturing the complete diversity of potential pathogens and the prevalence of pathogen co-infections. In this study, we employed a meta-transcriptomic approach to simultaneously characterize the infectome contributing to different disease syndromes and to investigate spatial, demographic, and ecological factors influencing pathogen diversity and community composition in a cohort of 27 hospitalized cats and seven stray cats. We identified 15 species of pathogens, with Candidatus Rickettsia tarasevichiae and Tritrichomonas foetus representing potential spillover risks. Importantly, although most cases of ascites hyperplasia were explained by coinfection with multiple pathogens, we identified the potential novel clinical outcomes of M. aubagnense infection among cats. We demonstrated that the increase in infectome diversity can be explained by a variety of predictors including age growth, temperature increase, and a higher proportion of females, with age growth presenting the strongest effect. Fine-scale analysis indicated that a higher diversity of infectomes were harbored in young cats rather than adult ones. Our results demonstrated that most feline diseases are better explained by the presence of virus–bacteria or virus–virus coinfection. This study serves as a timely endorsement for clinical diagnosis by vets to consider the cause of a disease based on a panel of cryptical co-infecting pathogens rather than on individual infectious agents.IMPORTANCEFrequent studies reported the risks of cats as an intermediate host of zoonotic pathogens (e.g., SARS-CoV-2). Cats have a physically close interaction with their owners through activities like petting, kissing, and being licked on the cheek and hands. However, there are still limited studies that systematically investigate the infectome structure of cats. In this study, we employed a meta-transcriptomics approach to characterize 15 species of pathogens in cats, with Candidatus Rickettsia tarasevichiae first characterizing infection in diseased cats. Most feline diseases were better explained by the presence of virus–bacteria or virus–virus coinfection. The increase in infectome diversity could be influenced by a variety of predictors including age growth, temperature increase, and a higher proportion of females. A higher diversity of pathogens was harbored in young cats rather than adults. Importantly, we showed the value of linking the modern influx of meta-transcriptomics with comparative ecology and demography and of utilizing it to affirm that ecological and demographic variations impact the total infectome.

The diversified clean and high-value utilization of coal plays an important role in promoting the low-carbon sustainable development of China's coal industry, and the materialization of coal is one of the important ways to enhance the level of its clean and efficient utilization. In this study, lignite-based hard carbons (LHC) were prepared by high-temperature carbonization (1000−1600 ℃) method using Huating lignite as precursor, taking full advantage of lignite's aromatic ring structure, well-developed primary pores and abundant surface active groups. The formation mechanism and evolution behavior of graphite-like microcrystals and defect structures including amorphous carbon, nanopores, and surface functional groups in LHC was explored, while the influence of carbonization temperature on the microstructure of LHC was further revealed. The electrochemical performance of different LHC as anode for sodium ion batteries (SIBs) was also tested by galvanostatic charge/discharge, galvanostatic intermittent titration and cyclic voltammetry, the influence mechanism of microstructure on the electrochemical sodium storage performance of anode materials was explored, and the electrochemical sodium storage mechanism of LHC was eventually elucidated. The results show that graphite-like microcrystalline structure and defective structures such as amorphous carbon, nanopores, and oxygen/nitrogen-containing functional groups of LHC can be effectively regulated by adjusting the carbonization temperature. When the carbonization temperature was 1400 ℃, the LHC-1400 was rich in graphite-like microcrystals with reasonable layer spacing (0.371 nm), and also had appropriate amorphous carbon and nanopores with a specific surface area of 4.92 m2/g, and oxygen/nitrogen-containing functional groups including C—O, C=O, O—C=O, pyridinic-N, pyrrolic-N, graphitic-N. The LHC-1400 as anode for SIBs can deliver a high reversible capacity of 275 mAh/g, and has a reversible capacity of 111 mAh/g at a current density of 0.2 A/g with a capacity retention rate 96% after 200 cycles, which exhibits good rate performance and cycle stability. The excellent sodium storage performance of LHC is closely related to the functions and roles of their different microstructures. The graphite-like microcrystals with reasonable layer spacing in LHC can provide a transport channel for the rapid intercalation and de-intercalation of Na+ to provide capacity with intercalated sodium storage; the defect structures such as amorphous carbon, open nanopores, and oxygen/nitrogen-containing in LHC can provide sufficient active sites for Na+ storage to contribute capacity by adsorption, and a small amount of closed pores in LHC can provide sufficient space for the storage of Na+ and supply sodium storage capacity by pore filling. The “adsorption-insertion-filling” sodium storage methods in LHC are synergistic with each other to realize its efficient electrochemical energy storage.

ABSTRACT Through a long-term micro-area positioning experiment (2006-2017), the precise fertilization locations of spring maize (Zea mays L.) in Jilin, China, were investigated using an embedded cement bucket pot. The effects of different fertilization placements and additional application of organic manure on maize yield, biomass, grain nutrient uptake, and soil fertility were examined. Six treatments were designed: base fertilizer + topdressing shallow application (BF1TD1); base fertilizer deep + topdressing shallow application (BF2TD1); base fertilizer + topdressing deep application (BF1TD2); base fertilizer deep + topdressing deep application (BF2TD2); base fertilizer + topdressing + organic manure shallow application (BF1TD1 + OS); and base fertilizer + topdressing shallow application + organic manure deep application (BF1TD1 + OD). After 12 years of continuous experimentation, the findings revealed that BF2TD2, BF1TD1 + OS, and BF1TD1 + OD exhibited 10.59 %, 25.17 %, and 29.34 % higher average yields than BF1TD1, respectively. Deep topdressing was more beneficial in increasing maize yield and nutrient accumulation, and additionally, increasing the use of organic fertilizer enhanced plant biomass and nutrient uptake. Over the 12 years considered, the soil organic matter in the 0-20 cm and 20-40 cm layers increased by 45.96 % and 80.61 %, respectively, and the soil pH increased from 0.38 to 0.48. In general, the deep application of organic manure was more beneficial for soil retention as it can increase soil fertility in the 0-40 cm layer. Considering the high yield and nutrient absorption and utilization of maize, technical advancement for soil fertilizer in whole-field tillage layers was crucial.

Abstract The unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system leverages the high maneuverability of UAVs to provide efficient computing services to terminals. A dynamic deployment algorithm based on double deep Q-networks (DDQN) is suggested to address issues with energy limitation and obstacle avoidance when providing edge services to terminals by UAV. First, the energy consumption of the UAV and the fairness of the terminal’s geographic location are jointly optimized in the case of multiple obstacles and multiple terminals on the ground. And the UAV can avoid obstacles. Furthermore, a double deep Q-network was introduced to address the slow convergence and risk of falling into local optima during the optimization problem training process. Also included in the learning process was a pseudo count exploration strategy. Finally, the improved DDQN algorithm achieves faster convergence and a higher average system reward, according to experimental results. Regarding the fairness of geographic locations of terminals, the improved DDQN algorithm outperforms Q-learning, DQN, and DDQN algorithms by 50%, 20%, and 15.38%, respectively, and the stability of the improved algorithm is also validated.

The sludge is pretreated to prepare the sludge bicohar by pyrolysis, which is divided into high-carbon sludge biochar and high-ash sludge biochar using flotation. The high-carbon sludge biochar and high-ash sludge biochar are used to prepare the sludge activated carbon, MgAl-layered double hydroxides (MgAl-LDO) and sludge ceramsite for Pb2+, methyl orange (MO) and ciprofloxacin (CIP) removal from wastewater, respectively. The sludge activated carbon is prepared from high-carbon sludge biochar using ZnCl2 as chemical agent in the additive of pine sawdust for MO and CIP removal with adsorption amount of the 754.05 mg/g and 635.62 mg/g, respectively. Mg-Al LDO is prepared from the leaching solution of the high-ash sludge bicohar, which can quickly remove Pb2+ from wastewater with adsorption amount of 147.89 mg/g. Mg-Al LDO has good reusability after three cycles. The MO/CIP and Pb2+ adsorption mechanism are analyzed and investigated. The sludge ceramsite prepared from the leaching residue of high-ash sludge bicohar is also used for Pb2+ removal. The above results indicate that sludge is converted into three kinds of the adsorbents for pollution removal from wastewater.

Abstract The insulation paper is a crucial factor for evaluating the insulation status of transformers. The traditional evaluation methods for insulation paper are dissolved gas component content analysis (CO and CO2) and furfural (2‐FAL) content analysis in oil. The detection principle of the former leads to its low accuracy, while the field application effect of the latter is not ideal due to the easy oxidation of furfural and low content. Methanol, result of its good stability and high production compared with other marker products (CO, CO2, 2‐FAL) has been investigated as a novel marker for aging evaluation of transformer insulating paper in recent years. The results of the investigation indicated that there is a good correlation between the content of methanol in oil and the degree of polymerization under laboratory conditions, which reflected the insulation aging state of the transformer insulating paper more accurately. This paper summarizes the current research status of methanol formation mechanism, detection methods and aging evaluation in insulating oil, and points out the key problems to be solved and development prospects, hoping to provide relevant reference for aging evaluation of oil immersed power transformer.

Feature adaptation(FA) and result alignment(RA) are critical issues in one-stage object detection, since FA amends feature misalignment problem by adapting sampling points to semantically significant locations, and RA corrects result misalignment problem by estimating the localization quality. For FA, the aligned and consistent prediction of sampling points is important. Previous studies directly inherit cascaded “proposal-refine” philosophy from multi-stage detectors and predict sampling points by approximating their minimal external rectangle to ground-truth bounding boxes. This manner generates poorly aligned and consistent sampling points and induces irrelevant features aggregated. Moreover, their sampling points for classification are conventionally generated through the localization branch, without feeding the corresponding features of the classification branch. For RA, previous studies have verified the superiority of utilizing the predicted edge distribution to estimate localization quality; however, their directly regressed distribution is incompatible with the cascaded regression framework. To solve these problems, we firstly propose a focused feature adaptation method by softening the supervision of the proposal points. This method can predict sampling points focused above the assigned objects with excellent alignment and consistency. Subsequently, inner-branch and cross-branch merging were investigated to promote feature sharing from the classification branch. Finally, cascaded distribution-guided result alignment is advanced and verified to predict accurate localization quality. After integrating our proposed adaptation, merging, and alignment, we created AMA-Det with an enhanced shared head, which impressively reaches 43.9 mAP with ResNet50 as the backbone. AMA-Det also achieves a 54.5 mAP by multi-scale testing on MSCOCO test-dev and outperforms all existing CNN-based one-stage counterparts.

Methanol was considered as a new chemical marker for estimating the aging state of transformer insulation paper, whereas oil-replacement could influent greatly the accuracy of the assessment results. In this paper, the 130 °C thermal aging experiment was carried out, and the oil-paper samples were divided into four groups for investigating the effect of the oil-replacement on assessment deviation of aging states of insulation paper. The results indicated that the present aging assessment model was not suitable for oil-replacement based on methanol in oil and the aging assessment errors of degree of polymerization (DP) had a decreasing tendency with increasing the aging time after oil-replacement. Furthermore, the oil-replacement correction factor was proposed as power-series expansion based on the experiment results, and a modified model was proposed by integrating the basic Methanol-DP model and the correction factor. Eventually, the verification experiment was prepared to test the accuracy of modified model and the results indicated that the relative assessment error of DP was within 4.8%. The accuracy of aging assessment are improved by oil-replacement correction factor.

The natural flake graphite (GO) with an initial fixed carbon content of 6.23% is purified using flotation combined with alkali-melting acid leaching to obtain the high purity graphite (PG3) for energy storage. The graphite concentrate (PG1) with fixed carbon content of 85.62% is obtained by the selective enrichment of GO particles based on the multi-stage flotation. The alkali-melting acid leaching purification method is used to further purify the PG1 to obtain the PG3 with the fixed carbon content of 99.93%. The fixed carbon content is increased by 93.7% through flotation combined with alkali solution and acid leaching. The purification process of GO is systematically evaluated and analyzed by the several characterization techniques. The impurities removal mechanisms are also analyzed, which indicates that silicate and quartz in GO react with sodium hydroxide to form soluble substances, resulted in impurities removal. Oxide in GO and hydroxide formed in the alkali-melting process are subsequently removed in acid leaching process. The obtained PG3 could be used in lithium ion battery with a higher reversible capacity of 387 mAh g−1 compared to the 345 mAh g−1 of commercial layered graphite, which has promising potential application in energy storage. The above analysis indicates that the low purity GO is converted into high purify PG3 for energy storage, realizing high quality utilization of GO.

Materialization of coal is one of effective and clean pathways for its utilization. The microstructures of coal-based carbon materials have an important influence on their functional applications. Herein, the microstructural evolution of anthracite in the temperature range of 1000–2800 °C was systematically investigated to provide a guidance for the microstructural regulation of coal-based carbon materials. The results indicate that the microstructure of anthracite undergoes an important change during carbonization-graphitization process. As the temperature increases, aromatic layers in anthracite gradually transform into disordered graphite microcrystals and further grow into ordered graphite microcrystals, and then ordered graphite microcrystals are laterally linked to form pseudo-graphite phase and eventually transformed into highly ordered graphite-like sheets. In particular, 2000–2200 °C is a critical temperature region for the qualitative change of ordered graphite crystallites to pseudo-graphite phase, in which the relevant structural parameters including stacking height, crystallite lateral size and graphitization degree show a rapid increase. Moreover, both aromaticity and graphitization degree have a linear positive correlation with carbonization-graphitization temperature in a specific temperature range. Besides, after initial carbonization, some defect structures in anthracite such as aliphatic carbon and oxygen-containing functional groups are released in the form of gaseous low-molecular volatiles along with an increased pore structure, and the intermediates derived from minerals could facilitate the conversion of sp3 amorphous carbon to sp2 graphitic carbon. This work provides a valuable reference for the rational design of microstructure of coal-based carbon materials.

The valuable pyrolysis products are prepared by pyrolysis of aspen wood using microwave heating at different pyrolysis temperature. High pyrolysis temperature restrains the production of the biochar, and contributes to the generation of the bio-gas. However, pyrolysis temperature has little influenced on the yield of bio-oil. Bio-oil mainly has the phenols, hydrocarbons, ketones, aldehydes and furans compound. The phenols compound of bio-oil generated from 500 ℃ is 54%. Pyrolysis temperature influences the composition and heating value of bio-gas, and the heating value of bio-gas produced at 700 °C is 13.41 MJ/Nm3. Biochar could be used to adsorb Ag+ from wastewater, which generates value-added A/char after adsorption. Ag+ adsorption and reduction process are systematically evaluated and analyzed. The generated Ag/char is employed as the catalyst for rhodamine B degradation. Besides, Ag/char has promising application potential in energy storage.

Poplar waste is employed to prepare the high quality adsorbent (PWA) using one-pot method for methylene blue (MB) removal from wastewater. The H2 rich fuel gas is recycled during preparation process. The adsorption isotherms and adsorption kinetics are used to describe the MB adsorption process. The MB adsorption capacity of the PWA is 254.21 mg/g based on the Langmuir model calculation. Pseudo-second-order model describe well with the adsorption kinetic process. The MB column adsorption process of the PWA is analyzed by the Thomas and Yoon-Nelson, which indicates that the adsorption capacity is 54 mg/g and saturation time is 540 min. PWA has excellent reusability, which still maintains 75.59% MB adsorption amount after five cycles. The MB adsorption mechanism of PWA includes chemical functional groups, electrostatic interaction and pore filling. Density functional theory calculation indicates that hydroxyl groups play an important role in MB adsorption process. MB adsorption performance of PWA is investigated in the actual water with excellent MB removal. This result demonstrates that PWA could be acted as the recoverability and efficient adsorbent for MB removal from wastewater.

Wood waste is employed as the feedstock for production of high-quality pyrolysis products such as bio-oil, bio-gas and biochar by microwave pyrolysis using ZnCl2 and Fe(NO3)3 as additive at 400–800 °C. Pyrolysis temperature influences the yield of the pyrolysis products, which indicates that the yield of bio-gas increases and the yield of biochar decreases as pyrolysis temperature increases. The yield of the bio-oil is little influenced by pyrolysis temperature, which is restrained in the existence of ZnCl2 and Fe(NO3)3. ZnCl2 increases the furfural content in bio-oil. While Fe(NO3)3 promotes H2 production during pyrolysis process. The maximum heating value of bio-gas produced at 800 °C is 12.26 MJ/Nm3. The added ZnCl2 and Fe(NO3)3 increase the yield of the biochar, which also promote the formation of pore structure of biochar. The ZnCl2 and Fe(NO3)3 are converted into ZnO and Fe3O4 onto biochar after pyrolysis, which could be used as the absorbent and photocatalyst for methyl orange removal.

Because of the high complementarity between global navigation satellite systems (GNSSs) and visual-inertial odometry (VIO), integrated GNSS-VIO navigation technology has been the subject of increased attention in recent years. In this paper, we propose an embedded high-precision multi-sensor fusion suite that includes a multi-frequency and multi-constellation GNSS module, a consumption-grade inertial measurement unit (IMU), and a grayscale camera. The suite uses an NVIDIA Jetson Xavier NX as the host and develops a Field Programmable Gate Array-based controller for hardware time synchronization between heterogeneous sensors. A multi-state constraint Kalman filter is used to generate the tightly-coupled estimation from the camera and the IMU. As a result, the GNSS output is loosely coupled to facilitate the acquisition of the global drift-free estimation. Results from the calibration reveal that the time synchronization accuracy of the suite is better than 30 µs (standard deviation [STD]) and that the projection error of camera-IMU is less than 0.1 pixels (STD); these results highlight the advantage of this hardware time synchronization mechanism. Results from the vehicle-mounted tests reveal reductions in the three-dimensional (3D) positioning error from 8.455 m to 5.751 m (root mean square) on experimental urban roads, which significantly improves the accuracy and continuity of GNSS individual positioning. In underground sites where the satellite signal is completely unavailable, the 3D position error drift of the suite is only 1.58 ‰, which also shows excellent performance.

Conventional spectrophotometric methods were unable to accurately detect urea concentrations below 100 μg/L. A modified spectrophotometric method was developed to determine the trace urea in the ultrapure water (UPW) production process of the semiconductor manufacturing industry. This method was optimized based on the dosage of chemical agents, length of the optical path, and mode of the water bath. Metal ions were added to promote the stability of the chromogenic system. A calibration graph was observed with ideal linearity in the range of 0.8–100 μg/L. The detection and quantification limits of urea were 0.24 and 0.80 μg/L, respectively. The distribution of urea in raw water for the UPW production process was observed and the urea in tap water was 10–20 μg/L. The urea of municipal reclaimed water was 24–40 μg/L, which was twice that of industrial reclaimed water at 10–18 μg/L. The total removal rate of urea by the UPW production process was 50–70%. Reverse osmosis membranes played a critical role in the removal of urea (over 30%). The urea in the final UPW produced from tap water was approximately 4.1 μg/L, which creates a potential risk of excessive total organic carbon. HIGHLIGHTS A spectrophotometric method for the determination of trace urea during ultrapure production process was first established.; Stability of the absorbance in the color reaction was increased by doping metal ions.; The urea concentration of municipal reclaimed water was twice that of industrial reclaimed water.; The urea removal efficiencies in each unit of the ultrapure water production process were explored.;

Camellia oleifera shell is used as the feedstock to prepare the valuable products by pyrolysis using microwave heating at 400-800 °C. The yield of pyrolysis product is influenced by pyrolysis temperature, which indicates that high pyrolysis temperature promotes to generate bio-gas and restrains the production of biochar. However, pyrolysis temperature little influences the yield of bio-oil. The main compound of bio-oil is phenols, hydrocarbons, ketones, aldehydes and furans, respectively. While, bio-oil produced at 600 °C has as high as 78 % of phenols, which has potential application in chemical industries. The pyrolysis temperature has significantly influenced the composition and heating value of bio-gas. The maximum heating value of bio-gas is 12.44 MJ/Nm3, which is achieved at 600 °C. The physiochemical properties of biochar are also influenced by pyrolysis temperature. Biochar could be used as an adsorbent to adsorb Ag+ from aqueous solution, which is formed the value-added ABiochar composite by reduction. The adsorption and reduction process of Ag+ are investigated. While, ABiochar composite can be used as the catalyst for methylene blue degradation. ABiochar composite can be also used in the lithium ion battery cathode material for energy storage.

This work seeks to characterize the fidelity needed in a gasoline surrogate with the intent to replicate the complex autoignition behavior exhibited within advanced combustion engines, and specifically Homogeneous Charge Compression Ignition (HCCI). A low-temperature gasoline combustion (LGTC) engine operating in HCCI mode and a rapid compression machine (RCM) are utilized to experimentally quantify fuel reactivity, through autoignition and preliminary heat release characteristics. Fuels considered include a research grade E10 U.S. gasoline (RD5-87), three multi-component surrogates (PACE-1, PACE-8, PACE-20), and a binary surrogate (PRF88.4). Each fuel was studied at lean/HCCI-like conditions covering a wide range of temperatures and pressures that are representative of naturally aspirated to high boost engine operation. Detailed chemical kinetic modeling is also undertaken using a recently updated gasoline surrogate kinetic model to simulate the RCM experiments and to provide chemical insight into surrogate-to-surrogate differences.The LGTC engine experiments demonstrate nearly identical reactivity between PACE-20 and RD5-87 across studied conditions, while faster phasing is seen for both PACE-1 and PACE-8 due to their stronger intermediate- and low-temperature heat release (ITHR/LTHR) at naturally aspirated and boosted conditions, respectively. The RCM experiments reveal typical low-temperature, negative temperature coefficient (NTC) and intermediate-temperature autoignition behaviors at all pressure conditions for RD5-87, which are qualitatively reproduced by all surrogates. Quantitative discrepancies in both autoignition and preliminary heat release are observed for all surrogates, while their ability to replicate RD5-87 autoignition behavior follows the order of PACE-20 > PACE-1 > PACE-8 > PRF88.4. Excellent mapping is obtained between the LGTC engine and the RCM, where the engine pressure-time trajectories can be characterized by the regimes represented by the RCM autoignition isopleths. The kinetic model performs commendably when simulating both autoignition and preliminary heat release of PACE-20, while typically overpredicting ignition delay times for PACE-1, PACE-8 and PRF88.4 at high-pressure and low-temperature/NTC conditions. Sensitivity and rate of production (ROP) analyses highlight surrogate-to-surrogate differences in the governing chemical kinetics where n-pentane initiates rapid OH branching at a faster rate and an earlier timing for PACE-20 than iso-pentane does for PACE-1 and PACE-8, making it computationally more reactive than the other surrogates. The current study highlights the need to include non-standardized properties, such as the lean/HCCI-like autoignition characteristics, in addition to ASTM properties (e.g., RON, MON) as metrics of fuel reactivity and targets to be matched when formulating high-fidelity surrogates that fully capture gasoline advanced combustion behavior such as HCCI-like autoignition.

Due to its advantages of low latency, low power consumption, and high flexibility, FPGA-based acceleration technology has been more and more widely studied and applied in the field of computer vision in recent years. An FPGA-based feature extraction and tracking accelerator for real-time visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) is proposed, which can realize the complete acceleration processing capability of the image front-end. For the first time, we implement a hardware solution that combines features from accelerated segment test (FAST) feature points with Gunnar Farneback (GF) dense optical flow to achieve better feature tracking performance and provide more flexible technical route selection. In order to solve the scale invariance and rotation invariance lacking problems of FAST features, an efficient pyramid module with a five-layer thumbnail structure was designed and implemented. The accelerator was implemented on a modern Xilinx Zynq FPGA. The evaluation results showed that the accelerator could achieve stable tracking of features of violently shaking images and were consistent with the results from MATLAB code running on PCs. Compared to PC CPUs, which require seconds of processing time, the processing latency was greatly reduced to the order of milliseconds, making GF dense optical flow an efficient and practical technical solution on the edge side.

Abstract Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating treatment responses. Converging evidence has now linked aberrant brain functional network organization in MDD to the dysregulation of neurotransmitter signaling and neuroplasticity, providing insights into the neurobiological mechanisms of the disease and antidepressant efficacy. Widespread connectome dysfunctions in MDD patients include multiple, large-scale brain networks as well as local disturbances in brain circuits associated with negative and positive valence systems and cognitive functions. Although the clinical utility of the brain functional connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments, and clinical outcomes of MDD.

Unsaturated hydrocarbons are major components of transportation fuels, combustion intermediates, and unburnt exhaust emissions. Conversely, NOx species are minor species present in the residual and exhaust gases of gasoline-fueled engines and gas turbines. Their co-existence in transportation engines is quite common, particularly with exhaust gas recirculation, which can greatly influence engine combustion characteristics. Therefore, this paper presents a review on the combustion chemistry of unsaturated hydrocarbons and NOx mixtures, with a focus on their chemical kinetic interactions. First, a comprehensive overview of fundamental combustion experiments is provided, covering mixtures of C2–C5 unsaturated/oxygenated species (namely alkenes, alkynes, dienes, alcohols, ethers, ketones, and furans) and three major NOx species (namely NO, NO2, and N2O), as well as reactors including jet-stirred reactors, flow reactors, burners, shock tubes, and rapid compression machines. Then, two widely adopted nitrogen chemistry models are evaluated in conjunction with a core chemistry model (i.e., NUIGMech1.1) via detailed chemical kinetic modeling, and the model similarities and differences across broad temperature ranges are highlighted. Thereafter, the unique interconversions between the three major NOx species are presented. In particular, the controversy regarding the pathways governing NO and NO2 conversion is discussed. Following this, the key direct interaction reactions between unsaturated species and NOx species are overviewed. Finally, the distinguishing features of the combustion chemistry for unsaturated hydrocarbon and NOx mixtures are summarized, and recommendations for future research on this topic are highlighted.

Alzheimer's disease has become more prevalent in our lives as the population ages. Accurate diagnosis and positive intervention can effectively delay the progress of early-stage Alzheimer’s disease. Accurate diagnosis of Alzheimer’s disease requires the combination of information from multiple regions of interest (ROIs), because the use of one single ROI may lose the connection and impact among multiple brain regions. In this paper, we firstly proposed a three-input convolutional neural network (CNN) to comprehensively utilize the information from three ROIs, hippocampus, other gray matter (without hippocampus) and white matter. In addition, as the neural network deepens, important feature information of original image will be partially lost. Therefore, we proposed a multi-output 3D CNN, which increases the connection and output of middle layers, shortens the distance between input and output, enhances feature propagation and reduces the loss of feature information. The results showed that the accuracy rate, precision rate, sensitivity, specificity and F1-score of the test set diagnosis obtained by multi-output 3D CNN model were 90.5%, 91.0%, 90.4%, 95.2% and 90.5%, respectively. The diagnostic performance was better than that of the single-output 3D CNN model.

For rural communities in poor countries to develop, farmers need money to invest in their farms. However, with limited assets of their own and poor conditions for obtaining loans, the farmers’ operations suffer. This study explores how farmers’ chances of obtaining a bank loan are related to the social capital that they receive from their cooperative membership. The data originated from 743 farmers in Fujian province, China, and was analyzed with the help of the instrumental variable probit (IV-probit) regression model. The results show that (a) cooperative members have a higher chance of obtaining a bank loan compared to non-members; (b) cooperative membership positively influences the chances of obtaining a bank loan for farmers with no acquaintances in banks and government or off-farm work; and (c) among farmers with higher financial knowledge, cooperative members are more likely to receive a bank loan than non-members are. Therefore, the conclusion provides empirical evidence for the financial function of cooperatives to farmers. The findings are especially relevant for cooperatives in developing countries, and they call for farmers and cooperatives to establish cooperative financial institutions. Moreover, the research conclusions point out the direction for further improving the financial effect of cooperatives.

In order to improve the traction performance of a wheel of a micro-tiller on the soil surface of a paddy field, we extracted the surface curve of a cow’s hoof and used the cow’s hoof as a bionic prototype to design a bionic paddy wheel. In order to verify the passability of the bionic paddy wheel in paddy soil, a wheel-soil test bench was built in the experimental field with a moisture content of 36%. The test results show that under the condition of the same load on the wheel, the changing laws of torque, drawbar pull, and slip ratio of the bionic paddy wheel and the conventional vaned wheel are similar. The torque and drawbar pull of the bionic paddy wheel are higher than those of the conventional vaned wheel at the same slip ratio. The maximum torque and hook traction provided by the bionic paddy wheel and the conventional vaned wheel both increase with the increase of the load on the wheel. Under the same load on the wheel, the bionic paddy wheel is at least 22% higher than the conventional vaned wheel. Compared with the conventional vaned wheel, the bionic paddy wheel can provide a higher driving force and hook traction, which can improve the working efficiency of the vehicle in the paddy field.

A classification method was developed to differentiate cognitive normal controls (CN), mild cognitive impairment (MCI) patients and Alzheimer's disease (AD) patients from the magnetic resonance (MR) image data. Three-dimensional (3D) local pattern transformation was used in the proposed method to obtain texture features, which were then fused with the conventional image features for the classification purposes. Region of interests (i.e., bilateral hippocampus, gray matter and white matter) were selected from the MR images of 46 CN, 61 MCI patients and 25 AD patients, from which the 3D local pattern transformation texture features and conventional image features were extracted, fused and used for classification with the support vector machine. It was demonstrated that the accuracy, sensitivity, specificity and area under the curve (AUC) were 88.73%, 78.00%, 95.7% and 0.886 5, respectively, for the fused texture feature/conventional image features in bilateral hippocampus of CN controls and AD patients. In comparison, the fused features in the gray matter gave an accuracy, sensitivity, specificity and AUC of 85.92%, 80.00%, 86.6% and 0.854 3, respectively. It is concluded that the texture features extracted from 3D local pattern transform in hippocampus could be used in conjunction with the conventional image features for better classification of the course of Alzheimer's disease.

DNA methylation is one of the most extensive epigenetic modifications. DNA 4mC modification plays a key role in regulating chromatin structure and gene expression. In this study, we proposed a generic 4mC computational predictor, namely, 4mCPred-MTL using multi-task learning coupled with Transformer to predict 4mC sites in multiple species. In this predictor, we utilize a multi-task learning framework, in which each task is to train species-specific data based on Transformer. Extensive experimental results show that our multi-task predictive model can significantly improve the performance of the model based on single task and outperform existing methods on benchmarking comparison. Moreover, we found that our model can sufficiently capture better characteristics of 4mC sites as compared to existing commonly used feature descriptors, demonstrating the strong feature learning ability of our model. Therefore, based on the above results, it can be expected that our 4mCPred-MTL can be a useful tool for research communities of interest.

With the integration of large-scale renewable energy into the power grids, cross-regional power trade can play a major role in promoting renewable energy consumption, as it can effectively achieve the optimal allocation of interconnected power grid resources and ensure the safe and economic operation of the power grid. An optimization model on a mid/long-term scale is established, considering the relationship between the renewable energy absorption interval and the regulation of resources in the system. The model is based on the load block curve and the renewable energy power model, considering the maintenance constraints of conventional units, the operation constraints of conventional units and renewable energy units, cross-regional power trade constraints and system operation constraints. By analyzing the results of the adapted IEEE RELIABILITY TEST SYSTEM (IEEE-RTS), the validity of the model and method proposed in this paper is proven. The results show that the coordinated optimization of conventional energy and renewable energy in the system can be achieved, and the complementarity of power supply and load can be promoted.

We study the long-distance asymptotic behavior of various correlation functions for the one-dimensional (1D) attractive Hubbard model in a partially polarized phase through the Bethe ansatz and conformal field theory approaches. We particularly find the oscillating behavior of these correlation functions with spatial power-law decay, of which the pair (spin) correlation function oscillates with a frequency ΔkF (2ΔkF). Here ΔkF=π(n↑−n↓) is the mismatch in the Fermi surfaces of spin-up and spin-down particles. Consequently, the pair correlation function in momentum space has peaks at the mismatch k=ΔkF, which has been observed in recent numerical work on this model. These singular peaks in momentum space together with the spatial oscillation suggest an analog of the Fulde–Ferrell–Larkin–Ovchinnikov (FFLO) state in the 1D Hubbard model. The parameter β representing the lattice effect becomes prominent in critical exponents which determine the power-law decay of all correlation functions. We point out that the backscattering of unpaired fermions and bound pairs within their own Fermi points gives a microscopic origin of the FFLO pairing in 1D.

Abstract Background The ability of Shigella to invade, colonize, and eventually kill host cells is influenced by many virulence factors. However, there is no analysis of related genes in Jiangsu Province of China so far. Shigella flexneri was collected from 13 cities of Jiangsu Province through the provincial Centers for Disease Control (CDC) for analysis of distribution of major virulence genes (ipaH, ipaBCD, ial, virF, virB, sigA, set1A, sepA, sat, pic, set1B and sen) detected by PCR technology. Results A total of 545 isolates received were confirmed as S. flexneri which belongs to 11 serotypes of S. flexneri, among which serotype 2a was the most predominant (n = 223, 40.9%). All isolates were positive for ipaH gene, followed by sat (94.1%), sigA (78.9%), set1B (78.0%), pic (77.6%), set1A (74.5%), virF (64.8%), sepA (63.5%), sen (56.9%), ipaBCD (50.5%), ial (47.0%) and virB (47.0%). The presence of virulence genes in different serotypes was distinct. The existence of virulence genes of serotype 1b was generally lower than other serotype-the positive rate for virulence genes was between 0.0 and 14.1% except for ipaH and sat. In addition, virulence genes also fluctuated in different regions and at different times in Jiangsu province. The result of analysis on the relationship between virulence genes of S. flexneri showed that the existence of virulence genes of Shigella could be well represented by multiplex PCR combination ipaH + ial + set1A, which had a high clinical value. Conclusions The present study was designed to explore the prevalence of 12 S. flexneri-associated virulence genes. The data showed high diversity of virulence genes with regard to periods, regions and serotypes in Jiangsu Province of China.