Study on the Influence of Road Geometry on Vehicle Lateral Instability

According to the accident analysis of vehicles in the curve, the skidding, rollover, and lateral drift of vehicles are determined as means to evaluate the lateral stability of vehicles. -e utility truck of rear-wheel drive (RWD) is researched, which is high accident rate. Human-vehicle-road simulation models are established by CarSim. -rough the orthogonal experiment method, the effects of different road geometries, speed, and interaction factors between road geometries on vehicle lateral stability are studied. In this paper, skidding risk of the vehicle is characterized by the Side-way Force Coefficient (SFC). Rollover risk of the vehicle is characterized by lateral acceleration and the load transfer ratio. Lateral drift risk of the vehicle is characterized by the sideslip angle of wheels. -e results of orthogonal analysis reveal that the maximum tire-road friction coefficient and speed are highly significant in skidding of the vehicle. -e effects of the combination of horizontal alignment and superelevation on vehicle skidding are important. -e effects of horizontal alignment and speed on vehicle rollover risk are highly significant. -e effects of superelevation on vehicle rollover risk are significant. -e effects of the interaction of horizontal alignment and superelevation are also important on vehicles’ rollover risk. -e speed and the maximum tire-road friction coefficient have highly significant effect on the vehicle’s lateral drift. -e superelevation has a significant effect on the vehicle’s lateral drift. -e effects of the interaction of horizontal alignment and superelevation and longitudinal slope are also important on the lateral drift of the vehicle

The Influence of Road Geometry on Vehicle Rollover and Skidding

This paper analyzes the influence of single and combined unfavorable road geometry on rollover and skidding risks of D-class mid-sized sport utility vehicles (SUVs) with front-wheel drive for roads with design speeds at 80 km/h. A closed-loop simulation model of human-vehicle-road interactions is established to examine the systematic influence of road geometry on vehicle rollover and skidding. The effects of different road geometry on rollover and skidding on SUVs are studied for pavement surface with good and poor friction when vehicles are in the action of steady state cornering. The rollover and skidding risks of the most unfavorable road segments are assessed. The critical wheel is defined by the threshold of skidding during curve negotiation. The results found that SUVs are not easy to rollover on the most unfavorable roads, regardless of good or poor friction of pavement surface. The safety margin of rollover is greater than that of skidding. The safety margin of skidding is minimal on poor friction roads. Therefore, for the sake of driving safety, it is not recommended to design the roads with these unfavorable road geometry combinations

Exploring the causal relationship between glutamine metabolism and leukemia risk: a Mendelian randomization and LC-MS/MS analysis

ObjectiveThis investigation sought to delineate the causal nexus between plasma glutamine concentrations and leukemia susceptibility utilizing bidirectional Mendelian Randomization (MR) analysis and to elucidate the metabolic ramifications of asparaginase therapy on glutamine dynamics in leukemia patients.MethodsA bidirectional two-sample MR framework was implemented, leveraging genetic variants as instrumental variables from extensive genome-wide association studies (GWAS) tailored to populations of European descent. Glutamine quantification was executed through a rigorously validated Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) protocol. Comparative analyses of glutamine levels were conducted across leukemia patients versus healthy controls, pre- and post-asparaginase administration. Statistical evaluations employed inverse variance weighted (IVW) models, MR-Egger regression, and sensitivity tests addressing pleiotropy and heterogeneity.ResultsThe MR findings underscored a significant inverse association between glutamine levels and leukemia risk (IVW p = 0.03558833), positing lower glutamine levels as a contributory factor to heightened leukemia susceptibility. Conversely, the analysis disclosed no substantive causal impact of leukemia on glutamine modulation (IVW p = 0.9694758). Notably, post-asparaginase treatment, a marked decrement in plasma glutamine concentrations was observed in patients (p = 0.0068), underlining the profound metabolic influence of the therapeutic regimen.ConclusionThis study corroborates the hypothesized inverse relationship between plasma glutamine levels and leukemia risk, enhancing our understanding of glutamine’s role in leukemia pathophysiology. The pronounced reduction in glutamine levels following asparaginase intervention highlights the critical need for meticulous metabolic monitoring to refine therapeutic efficacy and optimize patient management in clinical oncology. These insights pave the way for more tailored and efficacious treatment modalities in the realm of personalized medicine

Effective Drusen Localization for Early AMD Screening using Sparse Multiple Instance Learning

Age-related Macular Degeneration (AMD) is one of the leading causes of blindness. Automatic screening of AMD has attracted much research effort in recent years because it brings benefits to both patients and ophthalmologists. Drusen is an important clinical indicator for AMD in its early stage. Accurately detecting and localizing drusen are important for AMD detection and grading. In this paper, we propose an effective approach to localize drusen in fundus images. This approach trains a drusen classifier from a weakly labeled dataset, i.e., only the existence of drusen is known but not the exact locations or boundaries, by employing Multiple Instance Learning (MIL). Specifically, considering the sparsity of drusen in fundus images, we employ sparse Multiple Instance Learning to obtain better performance compared with classical MIL. Experiments on 350 fundus images with 96 having AMD demonstrates that on the task of AMD detection, multiple instance learning, both classical and sparse versions, achieve comparable performance compared with fully supervised SVM. On the task of drusen localization, sparse MIL outperforms MIL significantly

Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China

Spartina alterniflora (S. alterniflora) is one of the most harmful invasive plants in China. Google Earth (GE), as a free software, hosts high-resolution imagery for many areas of the world. To explore the use of GE imagery for monitoring S. alterniflora invasion and developing an understanding of the invasion process of S. alterniflora in the Zhangjiang Estuary, the object-oriented method and visual interpretation were applied to GE, SPOT-5, and Gaofen-1 (GF-1) images. In addition, landscape metrics of S. alterniflora patches adjacent to mangrove forests were calculated and mangrove gaps were recorded by checking whether S. alterniflora exists. The results showed that from 2003–2015, the areal extent of S. alterniflora in the Zhangjiang Estuary increased from 57.94 ha to 116.11 ha, which was mainly converted from mudflats and moved seaward significantly. Analyses of the S. alterniflora expansion patterns in the six subzones indicated that the expansion trends varied with different environmental circumstances and human activities. Land reclamation, mangrove replantation, and mudflat aquaculture caused significant losses of S. alterniflora. The number of invaded gaps increased and S. alterniflora patches adjacent to mangrove forests became much larger and more aggregated during 2003–2015 (the class area increased from 12.13 ha to 49.76 ha and the aggregation index increased from 91.15 to 94.65). We thus concluded that S. alterniflora invasion in the Zhangjiang Estuary had seriously increased and that measures should be taken considering the characteristics shown in different subzones. This study provides an example of applying GE imagery to monitor invasive plants and illustrates that this approach can aid in the development of governmental policies employed to control S. alterniflora invasion. View Full-Tex

NCAPD3 is a prognostic biomarker and is correlated with immune infiltrates in glioma

Non-SMC Condensin II Complex Subunit D3 (NCAPD3) has been linked with the genesis and progression of multiple human cancers. Nevertheless, the scientific value and molecular process of NCAPD3 in glioma remain unclear. We explored the level of NCAPD3 expression in pan-cancer by multiple online databases. And we focused on the level and prognostic value of NCAPD3 expression in glioma by immuno-histochemistry (IHC) and survival analysis. Meanwhile, we verified the relationship between NCAPD3, biological function and immune infiltration in glioma by Linkedomics and SangerBox databases. The expression of NCAPD3 was increased in a variety of cancers, including glioma. Its high expression was strongly related to WHO grade (P=0.002) and programmed cell death ligand 1 (PD-L1) expression of glioma (P=0.001). Patients with a high level of NCAPD3 expression had a lower overall survival (OS) in glioma than patients with a low level of NCAPD3 expression. Multivariate statistical analyses showed NCAPD3 expression (P=0.040), WHO grade P<0.001), 1p/19q codeletion (P<0.001), recurrence (P<0.001), age (P=0.023), and chemotherapy status (P=0.001) were meaningful independent prognostic factors in patients with glioma. Furthermore, bioinformatics analysis proved that NCAPD3 has been linked to immune infiltration in glioma. High level of NCAPD3 expression may serve as a promising prognostic biomarker and correlate with dendritic cell infiltration, representing a potential immunotherapy target in gliom

Dynamic Development of Fecal Microbiome During the Progression of Diabetes Mellitus in Zucker Diabetic Fatty Rats

Background: Although substantial efforts have been made to link the gut microbiota to type 2 diabetes, dynamic changes in the fecal microbiome under the pathological conditions of diabetes have not been investigated.Methods: Four male Zucker diabetic fatty (ZDF) rats received Purina 5008 chow [protein = 23.6%, Nitrogen-Free Extract (by difference) = 50.3%, fiber (crude) = 3.3%, ash = 6.1%, fat (ether extract) = 6.7%, and fat (acid hydrolysis) = 8.1%] for 8 weeks. A total of 32 stool samples were collected from weeks 8 to 15 in four rats. To decipher the microbial populations in these samples, we used a 16S rRNA gene sequencing approach.Results: Microbiome analysis showed that the changes in the fecal microbiome were associated with age and disease progression. In all the stages from 8 to 15 weeks, phyla Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria primarily dominated the fecal microbiome of the rats. Although Lactobacillus and Turicibacter were the predominant genera in 8- to 10-week-old rats, Bifidobacterium, Lactobacillus, Ruminococcus, and Allobaculum were the most abundant genera in 15-week-old rats. Of interest, compared to the earlier weeks, relatively greater diversity (at the genus level) was observed at 10 weeks of age. Although the microbiome of 12-week-old rats had the highest diversity, the diversity in 13–15-week-old rats was reduced. Spearman’s correlation analysis showed that F/B was negatively correlated with age. Random blood glucose was negatively correlated with Lactobacillus and Turicibacter but positively correlated with Ruminococcus and Allobaculum and Simpson’s diversity index.Conclusion: We demonstrated the time-dependent alterations of the abundance and diversity of the fecal microbiome during the progression of diabetes in ZDF rats. At the genus level, dynamic changes were observed. We believe that this work will enhance our understanding of fecal microbiome development in ZDF rats and help to further analyze the role of the microbiome in metabolic diseases. Furthermore, our work may also provide an effective strategy for the clinical treatment of diabetes through microbial intervention

Spatial Expansion and Soil Organic Carbon Storage Changes of Croplands in the Sanjiang Plain, China

Soil is the largest pool of terrestrial organic carbon in the biosphere and interacts strongly with the atmosphere, climate and land cover. Remote sensing (RS) and geographic information systems (GIS) were used to study the spatio-temporal dynamics of croplands and soil organic carbon density (SOCD) in the Sanjiang Plain, to estimate soil organic carbon (SOC) storage. Results show that croplands increased with 10,600.68 km2 from 1992 to 2012 in the Sanjiang Plain. Area of 13,959.43 km2 of dry farmlands were converted into paddy fields. Cropland SOC storage is estimated to be 1.29 ± 0.27 Pg C (1 Pg = 103 Tg = 1015 g) in 2012. Although the mean value of SOCD for croplands decreased from 1992 to 2012, the SOC storage of croplands in the top 1 m in the Sanjiang Plain increased by 70 Tg C (1220 to 1290). This is attributed to the area increases of cropland. The SOCD of paddy fields was higher and decreased more slowly than that of dry farmlands from 1992 to 2012. Conversion between dry farmlands and paddy fields and the agricultural reclamation from natural land-use types significantly affect the spatio-temporal patterns of cropland SOCD in the Sanjiang Plain. Regions with higher and lower SOCD values move northeast and westward, respectively, which is almost consistent with the movement direction of centroids for paddy fields and dry farmlands in the study area. Therefore, these results were verified. SOC storages in dry farmlands decreased by 17.5 Tg·year−1 from 1992 to 2012, whilst paddy fields increased by 21.0 Tg·C·year−1

GaAs quantum dots under quasi-uniaxial stress: experiment and theory

The optical properties of excitons confined in initially-unstrained GaAs/AlGaAs quantum dots are studied as a function of a variable quasi-uniaxial stress. To allow the validation of state-of-the-art computational tools for describing the optical properties of nanostructures, we determine the quantum dot morphology and the in-plane components of externally induced strain tensor at the quantum dot positions. Based on these experimentally determined parameters, we calculate the strain-dependent excitonic emission energy, degree of linear polarization, and fine-structure splitting using a combination of eight-band ${\bf k}\cdot{\bf p}$ formalism with multiparticle corrections using the configuration interaction method. The presented experimental observations are quantitatively well reproduced by our calculations