Fingerprinting Sediment Transport in River-Dominated Margins Using Combined Mineral Magnetic and Radionuclide Methods

Both magnetic properties and radionuclides are widely used to trace sediment transport in aquatic environments; however, these methods have not been used in combination. In this study, the East China Sea (ECS), a typical river-dominated margin, was chosen to demonstrate the advantages of combining these two methods to track sediment movements on a seasonal to annual timescale. The ratios between saturation isothermal remnant magnetization and anhysteretic remnant magnetization (χARM/SIRM) and 7Be/210Pbex activity ratios as well as mass balance of 7Be provide information on the seasonal transport of sediment from the Changjiang Estuary to the neighboring shelf. Both 210Pb budget and SIRM distribution in the inner shelf of the ECS show that a small fraction (at most 14% of annual Changjiang sediment discharge) of particles could be transported offshore. Most of 7Be activities in inner shelf sediments of the ECS were below detection limit due to relatively lower residence times and dilution by the older sediment. The observation that radionuclide activities exhibit a better correlation with χARM/SIRM ratios than with grain size suggests that iron oxides are the primary carriers of 7Be, 210Pb, and 234Th. The absorption of radionuclides onto magnetic minerals further reinforces the reliability of this combined approach in tracing sediment transport. Our study indicates that radionuclides, with different half-lives, can be utilized for quantifying sediment dynamics, whereas magnetic properties can yield more detailed information on sediment transport directions. The combined analysis of magnetic parameters and radionuclides offers a better understanding of sediment transport in river-dominated areas

The Development of LLMs for Embodied Navigation

In recent years, the rapid advancement of Large Language Models (LLMs) such as the Generative Pre-trained Transformer (GPT) has attracted increasing attention due to their potential in a variety of practical applications. The application of LLMs with Embodied Intelligence has emerged as a significant area of focus. Among the myriad applications of LLMs, navigation tasks are particularly noteworthy because they demand a deep understanding of the environment and quick, accurate decision-making. LLMs can augment embodied intelligence systems with sophisticated environmental perception and decision-making support, leveraging their robust language and image-processing capabilities. This article offers an exhaustive summary of the symbiosis between LLMs and embodied intelligence with a focus on navigation. It reviews state-of-the-art models, research methodologies, and assesses the advantages and disadvantages of existing embodied navigation models and datasets. Finally, the article elucidates the role of LLMs in embodied intelligence, based on current research, and forecasts future directions in the field. A comprehensive list of studies in this survey is available at https://github.com/Rongtao-Xu/Awesome-LLM-E

Educational big data : predictions, applications and challenges

Educational big data is becoming a strategic educational asset, exceptionally significant in advancing educational reform. The term educational big data stems from the rapidly growing educational data development, including students' inherent attributes, learning behavior, and psychological state. Educational big data has many applications that can be used for educational administration, teaching innovation, and research management. The representative examples of such applications are student academic performance prediction, employment recommendation, and financial support for low-income students. Different empirical studies have shown that it is possible to predict student performance in the courses during the next term. Predictive research for the higher education stage has become an attractive area of study since it allowed us to predict student behavior. In this survey, we will review predictive research, its applications, and its challenges. We first introduce the significance and background of educational big data. Second, we review the students' academic performance prediction research, such as factors influencing students' academic performance, predicting models, evaluating indices. Third, we introduce the applications of educational big data such as prediction, recommendation, and evaluation. Finally, we investigate challenging research issues in this area. This discussion aims to provide a comprehensive overview of educational big data. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record*

Readiness of as-built horizontal curved roads for LiDAR-based automated vehicles: a virtual simulation analysis

Emerging automated vehicle (AV) technology is being deployed on as-built roadways due to its promising safety improvements. However, realistic problems concerning whether and how perception sensor-based AVs can safely adapt to the existing roadway infrastructures remain to be well addressed due to a lack of consideration of the sensor's angular resolution and detection threshold. In this study, we aim to assess whether LiDAR-based AVs (LAVs) could safely adapt to as-built horizontal curved roads from the perspective of available sight distances (ASDs) through virtual simulations. In specific, i) numerous driving scenarios featuring the design speed (Vd: 40 ∼ 100 km/h), circular curve radius (R: limited minimum radius ∼ common minimum radius), LAV (with LiDAR technical parameters, e.g., number of channels, Nc: 32, 64, 128), and the front target vehicle were simulated in PreScan/MATLAB/Simulink co-simulation platform; ii) an ASD extraction algorithm was proposed considering the point threshold for detection (NT); iii) effects of Vd, R, Nc, and NT on the ASD were analyzed and polynomial models were adopted to capture relationships between the ASD, Vd, R at different Nc and NT; iv) the minimum speed against as-built sight obstructions along the roadside and the maximum speed against inadequate sight distance were proposed by comparing the ASD with the required stopping sight distance of human-driven vehicles and LAVs (level 3 ∼ 5), respectively; and v) speed limits (VL) against inadequate sight distances for level 3 ∼ 5 LAVs were proposed. The results indicate that: i) a larger R or Vd, fewer Nc, or a higher NT would cause a shorter ASD in general; ii) attention should be paid to the occlusion imposed by as-built roadside infrastructures even with more Nc or/and a lower NT, particularly to curved roads with more rigorous geometric design controls (e.g., small Vd); and iii) level 3 LAVs struggle to adapt to as-built horizontal curved roads, and level 4 or 5 LAVs cannot assure adequate ASDs on high-type curved roads (e.g., large Vd). These findings shall help road administrators make decisions on speed limits for LAVs on as-built curved roads.This study was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province [grant number: KYCX18_0152]; the National Natural Science Foundation of China [grant number: 51878163]; Jiangsu Transportation Science and Technology Project [grant number: 2020Y19-1(1)]; China Scholarship Council [202006090095]; and the National Demonstration Center for Experimental Road and Traffic Engineering Education (Southeast University)

Emodin attenuates high lipid-induced liver metastasis through the AKT and ERK pathways in vitro in breast cancer cells and in a mouse xenograft model

Emodin, a natural anthraquinone derivative, can inhibit lipid synthesis and breast cancer cell proliferation. We previously found that emodin decreased breast cancer liver metastasis via epithelial-to-mesenchymal transition (EMT) inhibition. However, the mechanism through which emodin affects breast cancer liver metastasis in high-fat diet-induced obese and hyperlipidemic mice has not been elucidated. Bioinformatics analysis was used to reveal the potential targets and pathways of emodin. The mouse model of liver metastasis was established by injecting breast cancer cells into the left ventricle in high-fat diet-induced obese mice. The effect of emodin on inhibiting liver metastasis of breast cancer was evaluated by animal experiments. The mechanisms through which emodin inhibits liver metastasis of breast cancer were studied by cell and molecular biological methods. Emodin reduced lipid synthesis by inhibiting the expression of triglyceride (TG) synthesis-related genes, such as fatty acid synthase (Fasn), glycerol-3-phosphate acyltransferase 1 (Gpat1), and stearoyl-CoA desaturase (Scd1), and ultimately reduced liver metastasis in breast cancer. In addition, emodin inhibited breast cancer cell proliferation and invasion through the serine/threonine kinase (AKT) signaling and extracellular-regulated protein kinase (ERK) pathways by interacting with CSNK2A1, ESR1, ESR2, PIM1 and PTP4A3. Our results indicate that emodin may have therapeutic potential in the prevention or treatment of breast cancer liver metastasis

Curcumin Inhibits Mitochondrial Injury and Apoptosis from the Early Stage in EAE Mice

The exact pathophysiological change concerning mitochondrial injury and oligodendrocyte apoptosis in MS and EAE model is still unknown. Whether curcumin is able to inhibit mitochondrial injury and suppress the apoptosis in the early stages of MS/EAE is still unclear. We first explored mitochondrial injury and apoptosis at different time points p.i. in C57 BL/6 EAE mice. We then explored the effects of curcumin on mitochondria and apoptosis. Results showed that mitochondrial injury can be observed 3 days p.i. Apoptosis in the spinal cord occurred 3 days p.i. and the apoptotic cells were shown to be oligodendrocytes and neuronal cells. Curcumin significantly reduced the number of apoptotic cells and inhibited the upregulation of cyt-c, caspase-9, and caspase-3 at 7 days p.i. in the EAE mice. These observations demonstrate that mitochondrial injury and oligodendrocyte/neuronal apoptosis occur in the early stages of EAE. Curcumin can inhibit apoptosis in EAE mice which maybe act through protection of mitochondrial injury and inhibition of the intrinsic apoptotic pathway

Repulsive Guidance Molecule-a and Central Nervous System Diseases

Repulsive guidance molecule-a (RGMa) is a member of glycosylphosphatidylinositol- (GPI-) anchored protein family, which has axon guidance function and is widely involved in the development and pathological processes of the central nervous system (CNS). On the one hand, the binding of RGMa and its receptor Neogenin can regulate axonal guidance, differentiation of neural stem cells into neurons, and the survival of these cells; on the other hand, RGMa can inhibit functional recovery of CNS by inhibiting axonal growth. A number of studies have shown that RGMa may be involved in the pathogenesis of CNS diseases, such as multiple sclerosis, neuromyelitis optica spectrum diseases, cerebral infarction, spinal cord injury, Parkinson’s disease, and epilepsy. Targeting RGMa can enhance the functional recovery of CNS, so it may become a promising target for the treatment of CNS diseases. This article will comprehensively review the research progression of RGMa in various CNS diseases up to date

Current Status and Controversies in the Diagnosis and Treatment of Gastric Neuroendocrine Neoplasms

With the increasing morbidity of gastric neuroendocrine neoplasms (G-NENs), the understanding of clinicians about G-NENs is also deepening. However, there are still many controversies regarding the diagnosis and treatment of G-NENs. Therefore, this article reviews the current status of diagnosis and treatment of G-NENs through a literature review, and discusses the existing controversies, with the aim of providing reference for clinicians in standardized diagnosis and treatment

A BP neural network-based micro particle parameters calibration and an energy criterion for the application of strength reduction method in MatDEM to evaluate 3D slope stability

To enhance the applicability of discrete element method in 3D slope stability analysis, a BP neural network-based micro parameter calibration method and an energy criterion are proposed by taking MatDEM as an example. Firstly, the relationship between the micro particle parameters and the shear strengths of particle aggregate are represented by using the BP neural network. And then the micro particle parameters are obtained for the given shear strengths by using a correction calibration. Next, the energy conversions are investigated for the stable and instable slope models in MatDEM. From a view of practical application, the abrupt in variation tendency and magnitude of the kinetic energy is selected for indicating the emergence of the limit equilibrium state of a slope. Finally, the effectiveness of the proposed improvements is testified by taking Baijiabao landslide as an example. Results verify that the calibration method established in this study is applicable to provide the micro particle parameters when the shear strength is constantly reduced, and the factor of safety determined by the kinetic energy criterion reflects the landslide stability at the global level