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Study region: a typical arid mountain region of northwestern China. Study focus: Soil water content (SWC) is the key factor regulating patchy vegetation patterns in arid/semiarid areas. However, accurately determining the regional SWC status remains a challenge due to the time and labor-intensive nature of manual sampling methods. Furthermore, a thorough understanding of the influence of different sampling frequencies (SFs) on SWC spatio-temporal dynamics in arid mountain forests is lacking. New hydrological insights for the region: SFs had a distinct effect on mean SWC, and temporal stability characteristics under lower (15–45 days, LSFs) and higher SFs (within 7 days, HSFs). SF influenced mean SWC for 0–20 cm under HSFs only but had a significant influence for 0–20 and 40–60 cm under LSFs. SF did not influence Spearman’s rank correlation coefficient (rs) for the 0–20 cm layer, but had a significant effect on the standard deviation of mean relative difference (SDRD) under HSFs; however, SF had a significant effect on rs for the deep layer (80–100 cm), but did not influence SDRD under LSFs. Although the number of representative locations (RLs) was significantly higher under HSFs than LSFs, no RLs were found at 100–120 cm. The mean SWC for all soil depths except 40–100 cm under HSFs was predicted accurately for each SF. This indicated that HSFs were not conducive to the identification of deep soil RLs, and had a significant impact on the prediction accuracy of SWC for deep layers. LSFs were not conducive to the identification of surface soil RLs but they can accurately estimate mean SWC, and prediction accuracy improved when SF was reduced. These results have important implications for optimizing water sampling schemes and promoting sustainable ecological development in water-deficient regions.

Abstract Objective This study aimed to analyze the factors influencing glycemic control in patients with type 2 diabetes mellitus (T2DM). Methods Baseline data, encompassing basic information, lifestyle habits, and treatment of 305 T2DM patients from March 2021 to January 2023, were collected and analyzed using SPSS 26.0 software. Results Univariate and multivariate logistic regression analyses identified insulin therapy (OR = 2.233; 95%Cl = 1.013–4.520; P = 0.026) and regular clinic visits (OR = 0.567; 95%Cl = 0.330–0.973; P = 0.040) as independent factors influencing glycemic control. No observed interactions between the two variables were noted. Conclusion History of insulin therapy and regular clinic visits were significantly and independently associated with glycated hemoglobin control in T2DM patients. Tailored interventions based on individual circumstances are recommended to optimize glycemic control.

The poppet valves two-stroke (PV2S) aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio, uniform torque output, and flexible valve timings. However, its high-altitude gas exchange performance remains unexplored, presenting new opportunities for optimization through artificial intelligence (AI) technology. This study uses validated 1D + 3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines. The valve timings of the PV2S engine exhibit considerable flexibility, thus the Latin hypercube design of experiments (DoE) methodology is employed to fit a response surface model. A genetic algorithm (GA) is applied to iteratively optimize valve timings for varying altitudes. The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance. The optimal valve overlap angle emerged as 93 °CA at sea level and 82 °CA at 4000 m altitude. The effects of operating parameters, including engine speed, load, and exhaust back pressure, on the gas exchange process at varying altitudes are further investigated. The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes. This effect is especially pronounced at elevated altitudes. The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency. This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines, serving as a valuable reference for further optimization efforts.

Along with the battery charge/discharge processes, the migration of the Li ions inevitably leads to an inhomogeneous distribution of the battery internal electrochemical and thermal characteristics, deteriorating the battery capacity and safety. In this work, the internal characteristics of a 20 Ah pouch LiFePO4 lithium-ion battery have been investigated based on a 3D electrochemical-thermal coupled model. It is found that the reduction of the particle size for either the cathode or anode would increase the battery capacity. Moreover, with the modulation of the thickness of cathode (Lpos) and convective heat transfer coefficient (h), the homogeneity of solid lithium concentration and temperature has been optimized. A relatively smaller Lpos and an h higher than the critical value should be adopted. From the aspect of thermal distribution, as the discharge rate increases, the irreversible reaction heat always dominates, while the proportion of ohmic heat among the total heat increases, and the high temperature region always locates on the inner sides of the tabs. The methodology proposed in this work could be applied to pouch batteries with more repeated cell units and larger sizes.

Understanding activity levels during fasting is important for promoting healthy fasting practices. While most existing studies focus on step counts to objectively assess the impact of fasting on activity levels and behavioral changes, the results have been mixed. Despite evidence showing that individuals spend a significant amount of time sitting while fasting, there has been no objective measurement of body movement or activity levels during sitting and fasting. This research employs a video-based, unobtrusive human body movement measurement system to monitor upper body movements during fasting and non-fasting periods over several days. Key movement features, such as inactivity, movement speed, and movement scale, were automatically extracted from the video monitoring data using a computer vision pipeline. These features were then statistically compared using t tests between fasting and non-fasting periods, analyzed by hour of the day and across different days. The results of the monitoring of five participants during typical daily sitting office work and fasting for 3–5 days indicate no consistent pattern of upper body movement changes due to fasting among the participants.

Background: Alzheimer's disease (AD) and Parkinson's disease (PD) are the leading causes of death among the elderly. Recent research has demonstrated that mitochondrial dysfunction, which is hallmark of neurodegenerative diseases, is a contributor to the development of these diseases. Methods and materials: Methylmalonic acid (MMA), AD, PD, inflammatory markers and covariates were extracted from the National Health and Nutrition Examination Survey (NHANES). The classification of the inflammatory markers was done through quartile conversion. A restricted cubic spike function was performed to study their dose-response relationship. MMA subgroups from published studies were used to explore the correlation between different subgroups and cause-specific mortality. Multivariable weighted Cox regression was carried out to investigate MMA and cause-specific mortality in patients with AD and PD. Weighted survival analysis was used to study the survival differences among MMA subgroups. Results: A non-linear correlation was observed between MMA and AD-specific death and PD-specific mortality. The presence of MMA Q4 was linked to increased death rates among AD patients (HR = 6.39, 95%CI: 1.19–35.24, P = 0.03) after controlling for potential confounders in a multivariable weighted Cox regression model. In PD patients, the MMA Q4 (Q4: HR: 5.51, 95 % CI: 1.26–24, P = 0.02) was also related to increased mortality. The results of survival analysis indicated that the poorer prognoses were observed in AD and PD patients with MMA Q4. Conclusion: The higher level of mitochondria-derived circulating MMA was associated with a higher mortality rate in AD and PD patients. MMA has the potential to be a valuable indicator for evaluating AD and PD patients' prognosis in the clinic.

BackgroundAs a rare neurodegenerative disease, sporadic Creutzfeldt-Jakob disease (sCJD) is poorly understood in the elderly populace. This study aims to enunciate the multidimensional features of sCJD in this group.MethodsA case of probable sCJD was reported in a 90-year-old Chinese man with initial dizziness. Then, available English literature of the elderly sCJD cases (aged 80 years and over) was reviewed and analyzed. Patients (15 cases) were subdivided and compared geographically.ResultsIn the elderly sCJD cohort, the onset age was 84.9 ± 4.5 years and the median disease duration was 6.8 months, with respiratory infection/failure as the commonest death cause. Various clinical symptoms were identified, with cognitive disorder (86.7%) as the commonest typical symptom and speech impairment (66.7%) as the most atypical one. Restricted hyperintensities were reported in 60.0% cases on DWI, periodic sharp wave complexes in 73.3% cases on electroencephalogram, and cerebral hypoperfusion/hypometabolism in 26.7% cases on molecular imaging. The sensitive cerebrospinal fluid biomarkers were total tau (83.3%), 14-3-3 protein (75.0%), and PrP RT-QuIC (75.0%). Neuropathological profiles in the cerebral cortex revealed vacuolar spongiosis, neuronal loss, gliosis, and aging-related markers, with synaptic deposit as the commonest PrP pattern (60.0%). The polymorphic PRNP analysis at codon 129 was M/M (90.9%), with MM1 and MM2C as the primary molecular phenotypes. Latency to first clinic visit, hyperintense signals on DWI, and disease duration were significantly different between the patient subgroups.ConclusionThe characteristics of sCJD are multidimensional in the elderly, deepening our understanding of the disease and facilitating an earlier recognition and better care for this group.

Increasing aviation activities have heightened concerns about particulate emissions from aircraft engines. Current ICAO standards mainly focus on large civil turbofan engines, leaving a gap in the research on PN emissions from small aircraft engines. This study examined the PN emission characteristics of micro-turbojet engines, including the morphology, PN, and size distribution under different load conditions, compared with a micro-piston engine. The results showed that the nvPM from micro-turbojet engines was larger and more complex than typical aviation soot, likely due to reduced combustion efficiency and incomplete fuel combustion. Micro-turbojet engines exhibited fewer fluctuations in their emission index number (EIn) and emission index mass (EIm) at lower speeds. The geometric mean diameter (GMD) of particles was inversely correlated with thrust, while the geometric standard deviation (GSD) slightly increased with thrust. Quantitative comparisons indicated that PN emissions from micro-turbojet engines were higher, with EIn values ranging from 2.0 to 3.3 × 1016/kg fuel compared with 1.2 to 1.5 × 1016/kg fuel for micro-piston engines. EIm values for micro-turbojet engines ranged from 8 to 40 mg/kg fuel, while micro-piston engines had slightly higher values due to better carbonization. These findings validated the measurement methodology used to accurately assess PN emissions under low-thrust conditions in micro-turbojet engines. These results provide crucial insights and support for the future monitoring and regulation of PN emissions across all thrust conditions in small aircraft engines.

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers’ life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

ObjectivesTo realize the online identification of hydrogen evolution characteristics and the quantitative design of the hydrogen control system for the lead-acid battery rooms of ships, a hydrogen concentration identification method based on the lumped model is proposed which comprehensively considers key parameters such as the hydrogen evolution rate of the battery and the ventilation & purification air volume and leakage air volume of the battery room.MethodsAccording to the environmental characteristics of the battery room, a mathematical model of the hydrogen concentration characteristics and hydrogen evolution rate prediction of the battery is established, and a corresponding calculation program is developed to analyze the hydrogen concentration variation characteristics of the battery room under different hydrogen evolution rates and ventilation & purification air volumes.ResultsThe various characteristics of the hydrogen concentration in the battery room are affected by the hydrogen evolution rate of the battery and the ventilation & purification air volume and leakage air volume of the battery room. When the hydrogen evolution rate is low (such as with a new battery), the leakage air volume will significantly affect the concentration rise rate. The daily operation frequency of the hydrogen purification system can reflect the actual hydrogen evolution rate of the battery and guide battery life evaluation.ConclusionsThe hydrogen evolution rate of the battery is correlated with several parameters. The proposed prediction model can accurately predict the hydrogen evolution rate of the battery and guide system design and operation strategy optimization. The related ideas and methods can also be extended to other cabin components similar to the hydrogen control system.

15Cr14Co12Mo5Ni2WA steel has huge application prospects in manufacturing aeroengine gears owing to its high strength and toughness. However, just because of its high strength, 15Cr14Co12Mo5Ni2WA steel is difficult to integrally deform at room temperature, which greatly restricts its industrial application. Cold rotary forging (CRF) is a local incremental metal forming process, and the small deformation force makes CRF ideal for deforming 15Cr14Co12Mo5Ni2WA steel. Thus, this paper is aimed to study the microstructure and mechanical property evolution mechanisms of 15Cr14Co12Mo5Ni2WA aviation gear steel during CRF process. The results show that grain size decreases from 26.2 μm to 16.9 μm, and the lattice orientation in each grain gradually becomes uniform during CRF process, which is mainly attributed to the combined effects of (Cr, Mo)23C6 carbide migration, dislocation movement and lattice rotation. Owing to the complex normal-external-transversal multi-directional metal flow, as-received ξ-fiber (//ND) firstly transforms to single dominant Brass texture in CRF-20% condition, and then transforms to θ-fiber (//ED) and Rotated-Cu texture at last deformation stage. Tensile/yield strength and Vickers hardness increase from 1049.3 MPa/1015.1 MPa and 316.6 HV in as-received condition to 1180.1 MPa/1146.7 MPa and 376.3 HV in CRF-50% condition, which is mainly attributed to the gradually increasing grain boundary strengthening, dislocation strengthening and secondary phase strengthening during CRF process.

Carbon emission intensity is an important index reflecting an entity’s low-carbon competitiveness. This paper presents an extended logarithmic mean divisia index (LMDI) model to dissect carbon intensity within China’s civil aviation from 1998 to 2019, revealing a significant reduction in CO2 emissions per air transport revenue. It attributes this decrease to technological advancements, optimized fleet structures, and improved operational efficiencies, highlighting the impact of larger, more efficient aircraft and enhanced load factors. The study also explores economic factors influencing carbon efficiency, suggesting a comprehensive approach encompassing technological innovation and strategic operational improvements for sustainable aviation development.

Local path planning, as an essential technology to ensure intelligent ships’ safe navigation, has attracted the attention of many scholars worldwide. In most existing studies, the impact of COLREGS has received limited consideration, and there is insufficient exploration of the method in complex waters with multiple interfering ships and static obstacles. Therefore, in this paper, a generation method for a time–space overlapping equivalent static obstacle line for ships in multi-ship encounter scenarios where both dynamic and static obstacles coexist is proposed. By dynamically inferring ships’ encounter situations and considering the requirements of COLREGS, the influence of interfering ships and static obstacles on the navigation of the target ship at different times in the near future is represented as static obstacle lines. These lines are then incorporated into the scene that the target ship encountered at the path planning moment. Subsequently, the existing path planning methods were extensively utilized to obtain the local path. Compared with many common path planning methods in random scenarios, the effectiveness and reliability of the method proposed are verified. It has been demonstrated by experimental results that the proposed method can offer a theoretical basis and technical support for the autonomous navigation of unmanned ships.

Traditional cloud-centric approaches to medical data sharing pose risks related to real-time performance, security, and stability. Medical and healthcare data encounter challenges like data silos, privacy breaches, and transmission latency. In response to these challenges, this paper introduces a blockchain-based framework for trustworthy medical data sharing in edge computing environments. Leveraging healthcare consortium edge blockchains, this framework enables fine-grained access control to medical data. Specifically, it addresses the real-time, multi-attribute authorization challenge in CP-ABE through a Distributed Attribute Authorization strategy (DAA) based on blockchain. Furthermore, it tackles the key security issues in CP-ABE through a Distributed Key Generation protocol (DKG) based on blockchain. To address computational resource constraints in CP-ABE, we enhance a Distributed Modular Exponentiation Outsourcing algorithm (DME) and elevate its verifiable probability to “1”. Theoretical analysis establishes the IND-CPA security of this framework in the Random Oracle Model. Experimental results demonstrate the effectiveness of our solution for resource-constrained end-user devices in edge computing environments.

Soil carbon emissions from subalpine ecosystems have been demonstrated to be influenced by freeze–thaw cycles (FTCs). Under climate change, moisture and number of FTCs have been altered significantly in subalpine regions. Thus, we selected a typical subalpine forest in Northwest China and conducted an incubation study to explore the effects of various soil moisture (SM) levels and numbers of FTCs on soil CO2 and CH4 fluxes during nine FTCs. Our results revealed that the soil CO2 emissions and CH4 uptakes had significant responses to changes in SM (FCO2 = 2327.32, p

BackgroundThe oxidative balance score (OBS) can be used to represent the overall burden of oxidative stress in an individual. This study aimed to explore the association between the risk of stroke and OBS.Methods and materialsThe National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018 was used to extract a series of variables for participants who took the stroke questionnaire. The construction of OBS relied on diet and lifestyle components, which included 16 nutrients and 4 lifestyle factors. Weighted multivariable-adjusted logistic regression was performed to investigate the association between stroke risk and OBS. A stratified analysis was also conducted. The dose-response relationship between stroke risk and OBS was elucidated by performing a restricted cubic spline function.ResultsA total of 20,680 participants were included for analysis, 768 of whom suffered from stroke. Based on weighted multivariable logistic regression analysis, we discovered that the stroke prevalence decreased by 2% for each OBS unit added [OR: 0.98 (0.97–1.00), P

Abstract Stage III non‐small cell lung cancer (NSCLC) encompasses a group of diseases with high heterogeneity. Such patients should actively receive comprehensive treatments. It is imperative for all stage III NSCLC patients to receive consultation with a multiple disciplinary team, which allows the development of a proposal for clinical diagnosis and treatment. In this consensus, stage III NSCLC is divided into two types (operable and inoperable) according to different clinical conditions. Resectable NSCLC is further subdivided into two conditions (with or without driver genes). For each clinical scenario, this consensus emphasizes that the foundation of any medical decisions regarding the optimal diagnostic or therapy procedure is scientific evidence from clinical research. Finally, based on the level of evidence and strength of recommendations, this consensus provides recommendations for the management of stage III NSCLC from six perspectives. The objective of this consensus is to help clinicians choose the best treatment and promote the standardization of stage III NSCLC diagnosis and treatment in China.

IntroductionColorectal adenoma can develop into colorectal cancer. Determining the risk of tumorigenesis in colorectal adenoma would be critical for avoiding the development of colorectal cancer; however, genomic features that could help predict the risk of tumorigenesis remain uncertain.MethodsIn this work, DNA and RNA parallel capture sequencing data covering 519 genes from colorectal adenoma and colorectal cancer samples were collected. The somatic mutation profiles were obtained from DNA sequencing data, and the expression profiles were obtained from RNA sequencing data.ResultsDespite some similarities between the adenoma samples and the cancer samples, different mutation frequencies, co-occurrences, and mutually exclusive patterns were detected in the mutation profiles of patients with colorectal adenoma and colorectal cancer. Differentially expressed genes were also detected between the two patient groups using RNA sequencing. Finally, two random forest classification models were built, one based on mutation profiles and one based on expression profiles. The models distinguished adenoma and cancer samples with accuracy levels of 81.48% and 100.00%, respectively, showing the potential of the 519-gene panel for monitoring adenoma patients in clinical practice.ConclusionThis study revealed molecular characteristics and correlations between colorectal adenoma and colorectal cancer, and it demonstrated that the 519-gene panel may be used for early monitoring of the progression of colorectal adenoma to cancer.

Global route planning has garnered global scholarly attention as a crucial technology for ensuring the safe navigation of intelligent ships. The comprehensive influence of time-varying factors such as water depth, prohibited areas, navigational tracks, and traffic separation scheme (TSS) on ship navigation in coastal global route planning has not been fully considered in existing research, and the study of route planning method from the perspective of practical application is still needed. In this paper, a global route planning method based on human-like thinking for coastal sailing scenarios is proposed. Based on the historical route’s information, and taking into full consideration those time-varying factors, an abnormal waypoint detection and correction method is proposed to make the planned route conform to relevant regulations of coastal navigation and the common practices of seafarers as much as possible, and better meet the coastal navigation needs of unmanned ships. Taking the global route planning of “ZHIFEI”, China’s first autonomous navigation container ship, as an example, the validity and reliability of the proposed method are verified. Experimental findings demonstrate the efficacy of the proposed method in global route planning for coastal navigation ships. The method offers a solid theoretical foundation and technical guidance for global route planning research of unmanned ship.

Aviation soot constitutes a significant threat to human well-being, underscoring the critical importance of accurate measurements. The condensation particle counter (CPC) is the primary instrument for quantifying aviation soot, with detection efficiency being a crucial parameter. The properties of small particles and the symmetry of their growth pathways are closely related to the detection efficiency of the CPC. In laboratory environments, sodium chloride is conventionally utilized to calibrate the CPC’s detection efficiency. However, aviation soot exhibits distinctive morphological characteristics compared to the calibration particles, leading to detection efficiencies obtained from calibration particles that may not be applicable to aviation soot. To address this issue, a quantitative study was performed to explore the detection efficiency deviations between aviation soot and calibration particles. The experiment initially utilized a differential mobility analyzer to size select the two types of polydisperse particles into monodisperse particles. Subsequently, measurements of the separated particles were performed using the TSI Corporation’s aerosol electrometer and a rigorously validated CPC (BH-CPC). These allowed for determining the detection efficiency deviation in the BH-CPC for the two types of particles at different particle sizes. Furthermore, the influence of the operating temperature of the BH-CPC on this detection efficiency deviation was investigated. The experimental results indicate a significant detection efficiency deviation between aviation soot and sodium chloride. In the range of 10–40 nm, the absolute detection efficiency deviation can reach a maximum of 0.15, and the relative deviation can reach a maximum of 0.75. And this detection efficiency deviation can be reduced by establishing a relevant relationship between the detection efficiency of the operating temperature and the calibration temperature. Compared to the saturated segment calibration temperature of 50 °C, the aviation soot detection efficiency is closer to the sodium chloride detection efficiency at the calibration temperature of 50 °C when the saturated segment operates at a temperature of 45 °C. These studies provide crucial theoretical guidance for enhancing the precision of aviation soot emission detection and establish a foundation for future research in monitoring and controlling soot emissions within the aviation sector.

As one of the key technologies restricting the development of intelligent ships, autonomous collision avoidance has attracted the attention of many scholars all over the world. Existing research on collision-avoidance behavior focuses more on collision risk assessment and local path-planning methods for studies on the human-like sequential logic of the whole collision-avoidance process, as well as the decision-making process of various stages. Further in-depth thinking is needed urgently. Based on this, a construction method of a human-like sequential decision chain for the autonomous collision avoidance of unmanned ships is proposed through the construction of a collision-avoidance rule base and strategy set, efficient data access based on the Knowledge Graph concept, global collision risk assessment considering sequential decision process, and the construction of a complete collision-avoidance logic process to simulate the decision-making process of humans in complex multi-ship encounters in open waters. For multi-ship encounter scenarios, considering the sequential decision-making process of collision avoidance, a method was proposed to divide the collision risk of the target ship into direct collision risk and potential collision risk. The validity and reliability of the constructed sequential decision chain are verified by simulation experimental results. The results show that the method is effective for collision avoidance (especially multi-ship collision avoidance) in open waters and can provide a theoretical basis and technical support with good interpretability for the decision-making process of an unmanned ship’s autonomous collision avoidance.

Northwest China has experienced dramatic changes in agricultural land area in recent years. The effects of these changes on carbon storage are unknown, and this ambiguity hinders land development policies related to carbon emissions. In this study, we evaluated the effects of annual cropland changes (expansion and abandonment) during 2000 to 2020 on carbon storage in Northwest China by using land use data, carbon density data, and statistical yearbooks using the Intergovernmental Panel on Climate Change (IPCC) method. The results indicated that the area of cropland increased by 1.47 × 106 ha from 2000 to 2020, in that the area of cropland expansion and abandonment are 3.58 × 106 and −2.11 × 106 ha, respectively. Cropland expansion was mainly from other land and grassland, and the conversion of cropland to grassland made up the largest proportion of cropland abandonment, followed by built-up land. The cropland changes resulted in a total carbon sequestration of 4.05 Tg (0.20 Tg C year−1), including a 17.66 Tg decrease and 21.71 Tg increase in carbon storage due to, respectively, cropland expansion and cropland abandonment, in which the conversion of forest to cropland (−8.60 Tg) and cropland to forest (11.16 Tg) were the main causes of the increase and decrease in carbon storage. Specifically, regional carbon storage due to cropland changes exhibited an increasing variation characteristic during 2000 to 2007, a gradually decreasing variation characteristics during 2007 to 2014, and fluctuated stabilization since then (during 2014 to 2020). In addition, the highest carbon emission was found in Xinjiang (−3.68 Tg), followed by Ningxia (−0.21 Tg) province, while Shanxi (3.44 Tg), Gansu (3.17 Tg) and Qinhai (1.33 Tg) had carbon accumulation. Overall, cropland changes acted as a carbon sink in Northwest China from 2000 to 2020. We suggest that the development of high-carbon-density lands or the conversion of low-carbon-density lands are critical to increasing future carbon sequestration due to cropland change.

The deep-learning-based approach has drawn significant attention in glacier extraction due to its advantages over traditional techniques. In this study, to verify the feasibility and effectiveness of LandsNet architecture for glacier extraction, we applied a modified LandsNet (M-LandsNet) to extract the glacier outlines in the Three-Rivers Headwater Region. The band ratio method, U-Net, U-Net++, GlacierNet, SaU-Net, U-Net+cSE, and LandsNet, and two scenes were used for comparison. Analysis of the two scenes indicated that the M-LandsNet had the best performance and generalization ability among the eight methods. Weather conditions had the greatest negative impact on the eight methods, followed by geographic environment and geographic location. We further extracted the glacier outlines in the Three-Rivers Headwater Region in 1986−2021 in a total of 12 periods using the M-LandsNet and through manual adjustments. The glacier area in the Three-Rivers Headwater Region has decreased by 416.40 ± 102.71 km2 (16.53 ± 4.08%) in 1986−2021. The reduction rate (16.13 ± 5.63 km2 a−1) in 2003−2021 was almost twice that (7.42 ± 5.97 km2 a−1) in 1986−2003. The reduction rate of the glacier area varied among different periods and areas. Comparison with previous results indicated that the obtained glacier outline dataset in this study is reliable, and can effectively reflect the glacier area and spatio-temporal glacier changes in the Three-Rivers Headwater Region. A long time-series dataset of glacier outlines in the Three-Rivers Headwater Region in 1986−2021 is available at https://doi.org/10.5281/zenodo.5512064. This study can provide data support for the estimation of regional water resources storage.

Abstract As a crucial biophysical property, red blood cell (RBC) deformability is pathologically altered in numerous disease states, and biochemical and structural changes occur over time in stored samples of otherwise normal RBCs. However, there is still a gap in applying it further to point-of-care blood devices due to the large external equipment (high-resolution microscope and microfluidic pump), associated operational difficulties, and professional analysis. Herein, we revolutionarily propose a smart optofluidic system to provide a differential diagnosis for blood testing via precise cell biophysics property recognition both mechanically and morphologically. Deformation of the RBC population is caused by pressing the hydrogel via an integrated mechanical transfer device. The biophysical properties of the cell population are obtained by the designed smartphone algorithm. Artificial intelligence-based modeling of cell biophysics properties related to blood diseases and quality was developed for online testing. We currently achieve 100% diagnostic accuracy for five typical clinical blood diseases (90 megaloblastic anemia, 78 myelofibrosis, 84 iron deficiency anemia, 48 thrombotic thrombocytopenic purpura, and 48 thalassemias) via real-world prospective implementation; furthermore, personalized blood quality (for transfusion in cardiac surgery) monitoring is achieved with an accuracy of 96.9%. This work suggests a potential basis for next-generation blood smart health care devices.

It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning in time. In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. Dlib library was used to extract the coordinate values of face feature points, collect the characteristic parameters of driver's eyes and mouth, and calculate the Euler Angle parameters of head posture. A fatigue identification model was constructed by using multiple characteristic parameters. (3) Genetic Algorithm (GA) was used to find the optimal smooth factor of Generalized Regression Neural Network (GRNN) and construct GA-GRNN fatigue driving identification model. Compared with K-Nearest Neighbor (KNN), Random Forest (RF), and GRNN fatigue driving identification algorithms. GA-GRNN has the best generalization ability and high stability, with an accuracy of 93.3%. This study provides theoretical and technical support for the application of driver fatigue identification.

The establishment of large-scale forest plantations in the arid and semi-arid area of the Qilian Mountains in China has effectively protected water and soil resources and enhanced carbon sequestration capacity of forest ecosystems. However, the effects of different management practices in these plantations on soil water holding capacity (SWHC) and soil water availability (SWA) are uncertain in this fragile ecosystem. Here, we investigated the effects of no thinning (NT), light thinning (LT, 20% thinning intensity), and heavy thinning (HT, 40% thinning intensity) on SWHC and SWA in different soil depths of a forest plantation, and compared them to those in a natural Picea crassifolia forest (NF). Our results revealed that at low soil water suction stage, SWHC in the plantations (LT, HT, and NT) was greater in the topsoil layer (0-40 cm) than that in the NF site, while SWHC in the subsoil layer (40-80 cm) in NF was significantly greater than that in the thinning stands. At medium and high-water suction stage, SWHC in LT and NF stands was greater than that in HT and NT. Soil water characteristic curves fitted by VG model showed that the relative change in soil water content in LT topsoil layer was the smallest and SWHC was greatest. Changes in soil physicochemical properties included higher bulk density and lower total porosity, which reduced the number of macropores in the soil and affected SWHC. The bulk density, total porosity, silt content, and field capacity were the main factors jointly affecting SWA. High planting density was the main reason for the low SWA and SWHC in NT, but this can be alleviated by stand thinning. Overall, 20% thinning intensity (light intensity thinning) may be an effective forest management practice to optimize SWHC and SWA in P. crassifolia plantations to alleviate soil water deficits.

Abstract Lacking systematic evaluations in soil quality and microbial community recovery after different amendments addition limits optimization of amendments combination in coal mine soils. We performed a short‐term incubation experiment with a varying temperature over 12 weeks to assess the effects of three amendments (biochar: C; nitrogen fertilizer at three levels: N‐N1~N3; microbial agent at two levels: M‐M1~M2) based on C/N ratio (regulated by biochar and N level: 35:1, 25:1, 12.5:1) on mine soil properties and microbial community in the Qilian Mountains, China. Over the incubation period, soil pH and MBC/MBN were significantly lower than unamended treatment in N addition and C + M + N treatments, respectively. Soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK), microbial biomass carbon (MBC), and nitrogen (MBN) contents increased significantly in all amended treatments (p 1%) of the bacterial phyla Bacteroidetes and Firmicutes, and decreased the relative abundance of fungal phyla Chytridiomycota (p

Chicken infectious anemia virus (CIAV) can be transmitted through contaminated live poultry vaccine. However, the pathogenicity of contaminated CIAV strains is rarely reported. Previously, the chickens showed the typical symptoms of anemia after using the attenuated live fowl pox virus (FPV) vaccine. Therefore, exogenous CIAV contamination was suspected. We detected anti-CIAV antibodies in SPF chicks vaccinated with the FPV vaccine. CIAV contamination was confirmed in the FPV vaccine, and the CIAV strain was named JS2020-FPV. This study aims to rescue JS2020-FPV by reverse genetic assays and investigate its pathogenicity. Firstly, double-copies infectious clone of JS2020-FPV was constructed. For the pathogenicity study, infectious clone of JS2020-FPV was used to inoculate 1-day-old SPF chicks. The typical symptoms of anemia were observed in the JS2020-PFV group 14 days post inoculation. The hematocrit and body weight of chicks in the JS2020-PFV group were significantly lower than those in the mock group. Notably, the thymus development index and antibody levels of NDV were lower in chicks in the JS2020-PFV group than those in the mock group. Different degrees of apoptosis of MSB1 and DF-1 were observed after inoculated with the JS2020-FPV VP3 recombinant fusion protein expressed by E. coli system, indicating that VP3 induced apoptosis in the transformed cells. Overall, the pathogenicity of the CIAV detected in the contaminated vaccine was confirmed by inoculating SPF chicks with the double-copies infectious DNA clone in this study. Our findings indicate that the dangers of vaccine contamination cannot be ignored.

In the context of rapid social and economic development, it is important to clarify the evolution process of production–living–ecological space (PLES) and its eco-environmental effects on the sustainable utilization of land resources on industrial bases. Based on existing research, it is difficult to accurately measure the differences in eco-environment quality caused by conversions between land-use types in different years. This paper puts forward an improved area-weighted calculation method of the eco-environmental quality index (EQI), combining the land-use transfer matrix and ecological contribution rate to analyze eco-environmental effects on typical industrial bases. The results show that PLES transitions in Gangcheng District from 2009 to 2020 have been mainly from agricultural production and grassland ecology to forest ecology space, from urban living to industrial production space, and from grassland ecology to agricultural production space. This improved method can capture the environmental changes caused by conversions between land-use types in different years. The EQI of agricultural production space increased from 0.373 to 0.388, while the EQI of forest ecology space decreased from 0.810 to 0.739, and the comprehensive EQI increased from 0.441 to 0.470, showing that the eco-environment quality of the study area was improved overall. The conversions of agricultural production and grassland ecology space to forest ecology space were the main factors in improving eco-environment quality, and the conversions of grassland and forest ecology to agricultural production and of agricultural production to industrial production space were the main reasons for the deterioration of eco-environment quality. This paper may contribute to the scientific planning of land-use direction, support the coordination of regional economic and ecological development, and provide case references for similar regions.

Live attenuated vaccines have been extensively used to prevent infectious disease in poultry flocks. Freedom from exogenous virus is a high priority for any veterinary vaccines. Recently, attenuated Newcastle disease virus (NDV) vaccines were detected to be contaminated with chicken infectious anemia virus (CIAV) in a routine screening for exogenous viruses. To investigate the possible source of the contamination, we conducted virological tests on a specific-pathogen-free (SPF) layer breeder flock that provide the raw materials for vaccines in this manufacturer. Firstly, CIAV antibodies in serum and egg yolks samples of the SPF laying hens were detected by ELISA assays. The results showed that CIAV antibodies in serum and egg yolks were 62% positive and 57% positive, respectively. Then, DNA was extracted from the NDV vaccines and SPF chicken embryonated eggs, and detected by molecular virology assays. The results showed that three assays for pathogens in embryonated eggs had similar positive rates (35.8%). And the sequences of CIAV from SPF embryos and NDV vaccines consisted of 2,298 nucleotides (nt) with 100% homology. The new full-length genome of CIAV was designated SDSPF2020 (Genbank accession number: MW660821). Data showed SDSPF2020 had the sequence similarities of 95.8–99.6% with reference strains, and shared the highest homology with the Chinese strain HLJ15125. These results strongly suggested that exogenous CIAV contamination is most likely caused by wild virus infection in SPF flocks and vertical transmission to chicken embryos. Collectively, this study illustrated that vertical transmission of CIAV from a SPF layer breeder flock to embryos was a non-neglible way for exogenous virus contamination in vaccine production.

Traffic lights detection and recognition (TLDR) is one of the necessary abilities of multi-type intelligent mobile platforms such as drones. Although previous TLDR methods have strong robustness in their recognition results, the feasibility of deployment of these methods is limited by their large model size and high requirements of computing power. In this paper, a novel lightweight TLDR method is proposed to improve its feasibility to be deployed on mobile platforms. The proposed method is a two-stage approach. In the detection stage, a novel lightweight YOLOv5s model is constructed to locate and extract the region of interest (ROI). In the recognition stage, the HSV color space is employed along with an extended twin support vector machines (TWSVMs) model to achieve the recognition of multi-type traffic lights including the arrow shapes. The dataset, collected in naturalistic driving experiments with an instrument vehicle, is utilized to train, verify, and evaluate the proposed method. The results suggest that compared with the previous YOLOv5s-based TLDR methods, the model size of the proposed lightweight TLDR method is reduced by 73.3%, and the computing power consumption of it is reduced by 79.21%. Meanwhile, the satisfied reasoning speed and recognition robustness are also achieved. The feasibility of the proposed method to be deployed on mobile platforms is verified with the Nvidia Jetson NANO platform.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. To distinguish the stage of the disease, AD classification technology challenge has been proposed in Pattern Recognition and Computer Vision 2021 (PRCV 2021) which provides the gray volume and average cortical thickness data extracted in multiple atlases from magnetic resonance imaging (MRI). Traditional methods either train with convolutional neural network (CNN) by MRI data to adapt the spatial features of images or train with recurrent neural network (RNN) by temporal features to predict the next stage. However, the morphological features from the challenge have been extracted into discrete values. We present a multi-atlases multi-layer perceptron (MAMLP) approach to deal with the relationship between morphological features and the stage of the disease. The model consists of multiple multi-layer perceptron (MLP) modules, and morphological features extracted from different atlases will be classified by different MLP modules. The final vote of all classification results obtains the predicted disease stage. Firstly, to preserve the diversity of brain features, the most representative atlases are chosen from groups of similar atlases, and one atlas is selected in each group. Secondly, each atlas is fed into one MLP to fetch the score of the classification. Thirdly, to obtain more stable results, scores from different atlases are combined to vote the result of the classification. Based on this approach, we rank 10th among 373 teams in the challenge. The results of the experiment indicate as follows: (1) Group selection of atlas reduces the number of features required without reducing the accuracy of the model; (2) The MLP architecture achieves better performance than CNN and RNN networks in morphological features; and (3) Compared with other networks, the combination of multiple MLP networks has faster convergence of about 40% and makes the classification more stable.

Chicken infectious anemia virus (CIAV) can be transmitted by contaminated live vaccines, and causes huge economic losses. This study evaluated the contamination status of CIAV in 24 batches of vaccines by recombinase-aided amplification assay (RAA), fluorescence quantitative PCR and dot blot assay, and then found a contaminated attenuated vaccine. The whole genome of the CIAV contaminant was then sequenced and named JS2020-PFV (Genbank accession number: MW234428, 2296bp). It showed 94.5 to 99.9% identities with reference strains and shared the closest evolution relationship with AB1K strain which was isolated from a chicken farm in Turkey. All of these suggested that the use of CIAV contaminated live vaccine may be one of the reason for its epidemic in poultry.

ABSTRACT: Avian leukosis virus (ALV) induces multiple tumors in chicken and is still prevalent in a lot of local flocks in China. In this study, we analyzed the ALV infection status in an Anyi tile-like gray chicken flock by DF1-cells isolation, virus identification, and genome sequencing. Results showed a 29% (29/100) ALV positive rate in this flock. Homology analysis based on env genes illustrated that all these stains belong to subgroup J (92–100% identities) and can be further divided into 5 batches, suggesting a higher diversity of ALV-J within the same flock. The whole-genome analysis of representative stains from each batch confirmed the close relationship between these isolated strains with previously reported strains from different regions (Guangxi, Shandong, and Heilongjiang), revealing the enrichment of different strains in Anyi tile-like grey chickens. This study provides the epidemiological data of ALV-J in a special chicken flock and a reference for the further eradication of ALV in China.

Abstract Patterns and elevational controls on the response of soil organic matter (SOM) decomposition to temperature in alpine forest soils are critical to efforts to quantify the regional carbon cycle–climate feedback, but are not well known. Here, we report rates of SOM decomposition (RS) and temperature sensitivity (Q10) determined in a short‐term laboratory incubation with gradual warming from 5 to 29°C of soils from different elevations in the Qilian Mountains, China (2600, 2800, 3000, and 3200 m). The results showed the RS significantly increased with increasing elevation (p 20°C), most likely in response to the content of labile C (greater at the start of incubation and declining over time). Q10 of SOM decomposition increased significantly with increasing elevation and decreasing incubation temperature (p

Abstract Target‐site mutations and detoxification gene overexpression are two major mechanisms conferring insecticide resistance. Molecular assays applied to detect these resistance genetic markers are time‐consuming and with high false‐positive rates. RNA‐Seq data contains information on the variations within expressed genomic regions and expression of detoxification genes. However, there is no corresponding method to detect resistance markers at present. Here, we collected 66 reported resistance mutations of four insecticide targets (AChE, VGSC, RyR, and nAChR) from 82 insect species. Next, we obtained 403 sequences of the four target genes and 12,665 sequences of three kinds of detoxification genes including P450s, GSTs, and CCEs. Then, we developed a Perl program, FastD, to detect target‐site mutations and overexpressed detoxification genes from RNA‐Seq data and constructed a web server for FastD (http://www.insect-genome.com/fastd). The estimation of FastD on simulated RNA‐Seq data showed high sensitivity and specificity. We applied FastD to detect resistant markers in 15 populations of six insects, Plutella xylostella, Aphis gossypii, Anopheles arabiensis, Musca domestica, Leptinotarsa decemlineata and Apis mellifera. Results showed that 11 RyR mutations in P. xylostella, one nAChR mutation in A. gossypii, one VGSC mutation in A. arabiensis and five VGSC mutations in M. domestica were found to be with frequency difference >40% between resistant and susceptible populations including previously confirmed mutations G4946E in RyR, R81T in nAChR and L1014F in VGSC. And 49 detoxification genes were found to be overexpressed in resistant populations compared with susceptible populations including previously confirmed detoxification genes CYP6BG1, CYP6CY22, CYP6CY13, CYP6P3, CYP6M2, CYP6P4 and CYP4G16. The candidate target‐site mutations and detoxification genes were worth further validation. Resistance estimates according to confirmed markers were consistent with population phenotypes, confirming the reliability of this program in predicting population resistance at omics‐level.

Soil-rock mixtures (S-RMs) composed of rock slope colluvium and landslide deposits are common in superficial hazards of bank slopes. The physical and mechanical properties of S-RMs are closely related to environmental conditions, especially reservoir water level fluctuation. Therefore, immersion-air dry (I-AD) circulations tests were carried out to simulate the phenomenon in this study. The macroscopic, mesoscopic, and microcosmic studies of S-RMs are carried out after experiencing cycles (N) of 0, 1, 5, and 10, respectively. Conventional triaxial tests, particle analysis tests, and scanning electron microscopy were performed to study the physical and mechanical properties of S-RMs. According to test results, the maximum deviator stress of S-RMs samples with confining pressures of 100, 200, 300, and 400 kPa after ten cycles is reduced by 41.13, 37.11, 32.17, and 30.20%. With the increase in N there is a significant trend towards reducing the strength of S-RMs, and the strength deterioration after the first I-AD circulation reached 15.73%. After I-AD circulations, the content of fine particles in S-RMs decreases, the grain-size characteristic and non-uniformity coefficient increase significantly, and the gradation of S-RMs shows discontinuity. Moreover, the repeated changes of water pressure have an irreversible impact on the microstructure of S-RMs, which will cause an increase in pore size and a higher pore disorder. In addition, it is found from the established strength parameter evolution equation that when N approaches infinity, S-RMs decomposes the weathering into cohesionless silt with an angle of repose of 0.90322φ0. The study results in this paper clarify the deterioration characteristics of wading S-RMs, and might be used in the bank slopes stability analysis.

ABSTRACT Plant secondary metabolites (PSMs) can affect the structures and functions of soil microbiomes. However, the core bacteria associated with PSMs, and their corresponding functions have not been explored extensively. In this study, soil physicochemical properties, tea saponin (TS) contents, microbial community compositions, and microbial community functions of different-age Camellia oleifera plantation soils from representative regions were analyzed. We evaluated the effects of plantation age increase on PSM accumulation, and the subsequent consequences on the structures and functions of soil microbiomes. Plantation ages increase positively correlated with accumulated TS contents, negative effects on soil physicochemical properties, and soil microbiome structures and functions. Clearly, the core functions of soil microbiomes transitioned to those associated with PSM metabolisms, while microbial pathways involved in cellulose degradation were inhibited. Our study systematically explored the influences of PSMs on soil microbiomes via the investigation of key bacterial populations and their functional pathways. With the increase in planting years, increased TS content simplified soil microbiome diversity, inhibited the degradation of organic matter, and enriched the genes related to the degradation of TS. These findings significantly advance our understanding on PSMs-microbiome interactions and could provide fundamental and important data for sustainable management of Camellia plantations. IMPORTANCE Plant secondary metabolites (PSMs) contained in plant litter will be released into soil with the decomposition process, which will affect the diversity and function of soil microbiomes. The response of soil microbiomes to PSMs in terms of diversity and function can provide an important theoretical basis for plantations to put forward rational soil ecological management measures. The effects of planting years on PSM content, soil physicochemical properties, microbial diversity, and function, as well as the interaction between each index in Camellia oleifera plantation soil are still unclear. We found that, with planting years increased, the accumulation of tea saponin (TS) led to drastic changes in the diversity and function of soil microbiomes, which hindered the decomposition of organic matter and enriched many genes related to PSM degradation. We first found that soil bacteria, represented by Acinetobacter, were significantly associated with TS degradation. Our results provide important data for proposing rational soil management measures for pure forest plantations.

Road hypnosis is a state which is easy to appear frequently in monotonous scenes and has a great influence on traffic safety. The effective detection for road hypnosis can improve the intelligent vehicle. In this paper, the simulated experiment and vehicle experiment are designed and carried out to obtain the physiological characteristics data of road hypnosis. A road hypnosis recognition model based on physiological characteristics is proposed. Higher-order spectra are used to preprocess the electrocardiogram (ECG) and electromyography (EMG) data, which can be further fused by principal component analysis (PCA). The Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbor (KNN) models are constructed to identify road hypnosis. The proposed model has good identification performance on road hypnosis. It provides more alternative methods and technical support for real-time and accurate identification of road hypnosis. It is of great significance to improve the intelligence and active safety of intelligent vehicles.

Turbulent convective heat-transfer characteristics in a concentric annular channel with both walls heated are theoretically modeled and numerically computed in this article. Generalized algebraic predictive models and equations for heating over a single wall are first reviewed by summarizing the well-known methods in the literature. These methods are then scrutinized according to the most recent investigations such that new viewpoints and corrections are introduced accordingly. Moreover, the application of superposition in temperature is used in the current work instead of the Nusselt number as seen in the literature. The numerical integration method is applied to the generalized equations to obtain the solutions, which are found to be in decent agreement with the direct numerical simulation (DNS) data in the literature. The results in this work also indicate that the wall heat flux density ratio and the annular radius ratio are two key factors that have a great influence on the heat-transfer characteristics of the case with both walls heated.

Nanoencapsulated phase change materials (NePCMs) are promising thermal energy storage (TES) and heat transfer materials that show great potential in battery thermal management systems (BTMSs). In this work, nanocapsules with a paraffin core and silica shell were prepared using an optimized sol-gel method. The samples were characterized by different methods regarding chemical composition, thermal properties, etc. Then, the nanocapsules were used as the coolant by mixing with insulation oil in the immersion cooling of a simulative battery. The sample doped with Ag on the shell with a core-to-shell ratio of 1:1 showed the best performance. Compared to the sample without doping material, the thermal conductivity increased by 49%, while the supercooling degree was reduced by 35.6%. The average temperature of the simulative battery cooled by nanocapsule slurries decreased by up to 3.95 °C compared to the test performed with pure insulation oil as the coolant. These novel nanocapsules show great potential in the immersion cooling of a battery.

Road traffic safety can be influenced by road hypnosis. Accurate detection of the driver’s road hypnosis is a very important function urgently required in the driver assistance system. Road hypnosis recurs frequently in a certain period, and it tends to occur in a typical monotonous scene such as a tunnel or a highway. Taking the scene of a tunnel or a highway as a typical example, road hypnosis was studied through simulated driving experiments and vehicle driving experiments. A road hypnosis recognition model based on principal component analysis (PCA) and a long short-term memory network (LSTM) was proposed, where PCA was used to extract various parameters collected by the eye tracker, and the LSTM model was constructed to identify road hypnosis. The accuracy rates of 93.27% and 97.01% in simulated driving experiments and vehicle driving experiments were obtained. The proposed method was compared with k-nearest neighbor (KNN) and random forest (RF). The results showed that the proposed PCA-LSTM model had better performance. This paper provides a novel and convenient method to realize the driver’s road hypnosis detection function of the intelligent driver assistance system in practical applications.

The codling moth, Cydia pomonella, is one of the major pests of pome fruit (apples and pears) and walnuts. Here, the authors sequence and analyze its genome, providing insights on olfactory and detoxification processes that may underlie its worldwide expansion.

Driving propensity is the driver’s attitude towards the actual traffic situation and the corresponding decision-making or behavior during the driving process. It is of great significance to improve the accuracy of safety early warning and reduce traffic accidents. In this paper, a real-time identification system of driving propensity based on AutoNavi navigation data is proposed. The main work includes: (1) A dynamic data acquisition method of AutoNavi navigation is proposed to obtain the time, speed and acceleration of the driver during the navigation process. (2) The dynamic data collection method of AutoNavi navigation is analyzed and verified through the dynamic data obtained in the real vehicle experiment. The principal component analysis method is used to process the experimental data to extract the driving propensity characteristics variables. (3) The fruit fly optimization algorithm combined with GRNN (generalized neural network) and the feature variable set are used to build a FOA-GRNN-based model. The results show that the overall accuracy of the model can reach 94.17%. (4) A driving propensity identification system is constructed. The system has been verified through real vehicle test experiments. This paper provides a novel and convenient method for building personalized intelligent driver assistance systems in practical applications.

A conserved open reading frame, dps, is described in Pestalotiopsis microspora, sharing a remarkable similarity with fungal diterpene synthases whose function is less studied. Loss-of-function approach manifested that dps was necessary for the growth and the development of the fungus. A deletion strain, dpsΔ, showed a fundamental retardation in growth, which could deliberately be restored by the addition of exogenous sterols to the media. Gas chromatography–mass spectrometry analysis confirmed the loss of the ability to produce certain sterols. Thus, the tolerance and the resistance of dpsΔ to several stress conditions were impaired. Secondary metabolites, such as the polyketide derivative dibenzodioxocinones, were significantly diminished. At the molecular level, the deletion of dps even affected the expression of genes in the mevalonate pathway. This report adds knowledge about fungal diterpene synthases in Pestalitiopsis microspora.

Carbon Capture, Utilization and Storage, also referred to as Carbon Capture, Utilization and Sequestration (CCUS), is one of the novel climate mitigation technologies by which CO2 emissions are captured from sources, such as fossil power generation and industrial processes, and further either reused or stored with more attention being paid on the utilization of captured CO2. In the whole CCUS process, the dominant migration pathway of CO2 after being injected underground becomes very important information to judge the possible storage status as well as one of the essential references for evaluating possible environmental affects. Interferometric Synthetic Aperture Radar (InSAR) technology, with its advantages of extensive coverage in surface deformation monitoring and all-weather traceability of the injection processes, has become one of the promising technologies frequently adopted in worldwide CCUS projects. In this study, taking the CCUS sequestration area in Shizhuang Town, Shanxi Province, China, as an example, unmanned aerial vehicle (UAV) photography measurement technology with a 3D surface model at a resolution of 5.3 cm was applied to extract the high-resolution digital elevation model (DEM) of the study site in coordination with InSAR technology to more clearly display the results of surface deformation monitoring of the CO2 injection area. A 2 km surface heaving dynamic processes before and after injection from June 2020 to July 2021 was obtained, and a CO2 migration pathway northeastward was observed, which was rather consistent with the monitoring results by logging and micro-seismic studies. Additionally, an integrated monitoring scheme, which will be the trend of monitoring in the future, is proposed in the discussion.

The application of vehicle-to-everything (V2X) technology has resulted in the traffic environment being different from how it was in the past. In the V2X environment, the information perception ability of the driver–vehicle unit is greatly enhanced. With V2X technology, the driver–vehicle unit can obtain a massive amount of traffic information and is able to form a connection and interaction relationship between multiple vehicles and themselves. In the traditional car-following models, only the dual-vehicle interaction relationship between the object vehicle and its preceding vehicle was considered, making these models unable to be employed to describe the car-following behavior in the V2X environment. As one of the core components of traffic flow theory, research on car-following behavior needs to be further developed. First, the development process of the traditional car-following models is briefly reviewed. Second, previous research on the impacts of V2X technology, car-following models in the V2X environment, and the applications of these models, such as the calibration of the model parameters, the analysis of traffic flow characteristics, and the methods that are used to estimate a vehicle’s energy consumption and emissions, are comprehensively reviewed. Finally, the achievements and shortcomings of these studies along with trends that require further exploration are discussed. The results that were determined here can provide a reference for the further development of traffic flow theory, personalized advanced driving assistance systems, and anthropopathic autonomous-driving vehicles.

Abstract Changes in climate extremes pose far-reaching consequences to ecological processes and hydrologic cycles in alpine ecosystems of the arid mountain regions. Therefore, regional assessments in various climates and mountain regions are needed for understanding the uncertainties of the change trends for extreme climate events. The objective of this study was to assess the spatial distribution and temporal trends of extreme precipitation and temperature events responses to global warming on the arid mountain regions of China. Results found that temperature extremes exhibited a significant warming trend, consistent with global warming. Warming trend in autumn and winter were greater than in spring and summer. Besides, precipitation extremes also exhibited statistically increase trend, such as number of days with heavy precipitation and rain day precipitation, etc. The distribution of the number of rainy days was showed a significant increasing trend in many sites, indicating that the increase of rain day precipitation mainly contributed by the increase of single precipitation event duration and moderate-rain days. The greater increasing trend of extreme climate events mainly existed in higher altitudes. This results lend an evidence to earlier predictions that the climate in northwestern China is changing from cold-dry to warm-wet.

ABSTRACT Oral infection of C57BL/6J mice with Toxoplasma gondii results in a marked bacterial dysbiosis and the development of severe pathology in the distal small intestine that is dependent on CD4+ T cells and interferon gamma (IFN-γ). This dysbiosis and bacterial translocation contribute to the development of ileal pathology, but the factors that support the bloom of bacterial pathobionts are unclear. The use of microbial community profiling and shotgun metagenomics revealed that Toxoplasma infection induces a dysbiosis dominated by Enterobacteriaceae and an increased potential for nitrate respiration. In vivo experiments using bacterial metabolic mutants revealed that during this infection, host-derived nitrate supports the expansion of Enterobacteriaceae in the ileum via nitrate respiration. Additional experiments with infected mice indicate that the IFN-γ/STAT1/iNOS axis, while essential for parasite control, also supplies a pool of nitrate that serves as a source for anaerobic respiration and supports overgrowth of Enterobacteriaceae. Together, these data reveal a trade-off in intestinal immunity after oral infection of C57BL/6J mice with T. gondii, in which inducible nitric oxide synthase (iNOS) is required for parasite control, while this host enzyme is responsible for specific modification of the composition of the microbiome that contributes to pathology. IMPORTANCE Toxoplasma gondii is a protozoan parasite and a leading cause of foodborne illness. Infection is initiated when the parasite invades the intestinal epithelium, and in many host species, this leads to intense inflammation and a dramatic disruption of the normal microbial ecosystem that resides in the healthy gut (the so-called microbiome). One characteristic change in the microbiome during infection with Toxoplasma—as well as numerous other pathogens—is the overgrowth of Escherichia coli or similar bacteria and a breakdown of commensal containment leading to seeding of peripheral organs with gut bacteria and subsequent sepsis. Our findings provide one clear explanation for how this process is regulated, thereby improving our understanding of the relationship between parasite infection, inflammation, and disease. Furthermore, our results could serve as the basis for the development of novel therapeutics to reduce the potential for harmful bacteria to bloom in the gut during infection.

Continuous measurement of dissolved oxygen (DO) is essential for water quality monitoring and biomedical applications. Here, a phosphorescence quenching-based intelligent dissolved oxygen sensor on an optofluidic platform for continuous measurement of dissolved oxygen is presented. A high sensitivity dissolved oxygen-sensing membrane was prepared by coating the phosphorescence indicator of platinum(II) meso-tetrakis(pentafluorophenyl)porphyrin (PtTFPP) on the surface of the microfluidic channels composed of polydimethylsiloxane (PDMS) microstructure arrays. Then, oxygen could be determined by its quenching effect on the phosphorescence, according to Stern–Volmer model. The intelligent sensor abandons complicated optical or electrical design and uses a photomultiplier (PMT) counter in cooperation with a mobile phone application program to measure phosphorescence intensity, so as to realize continuous, intelligent and real-time dissolved oxygen analysis. Owing to the combination of the microfluidic-based highly sensitive oxygen sensing membrane with a reliable phosphorescent intensity detection module, the intelligent sensor achieves a low limit of detection (LOD) of 0.01 mg/L, a high sensitivity of 16.9 and a short response time (22 s). Different natural water samples were successfully analyzed using the intelligent sensor, and results demonstrated that the sensor features a high accuracy. The sensor combines the oxygen sensing mechanism with optofluidics and electronics, providing a miniaturized and intelligent detection platform for practical oxygen analysis in different application fields.