Prediction of Ground Settlement Induced by Slurry Shield Tunnelling in Granular Soils

Underground structures play an important role in achieving the requirements of rapid urban development such as tunnels, parking garages, facilities, etc. To achieve what is needed, new transportation methods have been proposed to solve traffic congestion problems by using of high-speed railway and subway tunnels. One of the issues in urban spaces due to tunnel excavation is considerable surface settlements that also induce problems for surface structures. There are a variety of published relationships concerned with field measurements and theoretical approaches to evaluating the amount of the maximum surface settlement value due to tunneling. This paper studies the ground surface settlement caused by the Greater Cairo Metro – Line 3 - Phase-1. This project was constructed by a slurry shield Tunnel Boring Machine (TBM). Therefore, this work consists of two parts. The first part presents the details of the project and monitoring results field and laboratory geotechnical investigations in order to determine the soil properties. The second part is to the comparison between the field measurements and theoretical approaches for surface settlement due to tunneling construction. At the end of the works, the results show that the more convenient methods which approach the field measurements, and the major transverse settlement occurs within the area about 2.6 times the diameter of the tunnel excavation. Doi: 10.28991/cej-2020-03091617 Full Text: PD

A Mathematical Model for Ballast Tamping Decision Making in Railway Tracks

Ballast tamping is considered as an important maintenance process for railway infrastructures and has a large influence on the capacity of any railway networks. But optimizing the plan of that process is a complex problem with a high cost. This paper discusses optimizing tamping operations on ballasted tracks to improve the track geometry and reduce the total maintenance cost. A mathematical model for this problem in the literature is improved here by including the restriction on the resources (tools, workers and budget) in the model and including constant/variable values for track possession cost and available resources. The optimal solutions obtained for all instances are found by using the global optimization. Besides, a numerical study is presented to test and evaluate the model performance. The results show that the proposed model can be adopted by the infrastructure manager (IM) to make suitable tamping scheduling decisions under normal or private conditions; however, the private conditions lead to an increase of the final cost compared to that of the normal ones. Doi: 10.28991/cej-2020-03091601 Full Text: PD

SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision-language alignment, we unfreeze the large language model (LLM) during pre-training, and introduce a weight mix strategy between LLMs trained by real-world and synthetic data. By directly integrating the weights from two domains, the mixed LLM can efficiently incorporate diverse semantics with favorable robustness. Then, to enable multi-purpose capabilities, we mix a variety of tasks for joint visual instruction tuning, and design task-specific instructions to avoid inter-task conflict. In addition to the basic visual question answering, we include more challenging tasks such as region-level understanding, caption grounding, document layout detection, and human pose estimation, contributing to mutual enhancement over different scenarios. Additionally, we propose to extract comprehensive visual embeddings from various network architectures, pre-training paradigms, and information granularity, providing language models with more robust image representations. Based on our proposed joint mixing, SPHINX exhibits superior multi-modal understanding capabilities on a wide range of applications. On top of this, we further propose an efficient strategy aiming to better capture fine-grained appearances of high-resolution images. With a mixing of different scales and high-resolution sub-images, SPHINX attains exceptional visual parsing and reasoning performance on existing evaluation benchmarks. We hope our work may cast a light on the exploration of joint mixing in future MLLM research. Code is released at https://github.com/Alpha-VLLM/LLaMA2-Accessory.Comment: Work in progress. Code and demos are released at https://github.com/Alpha-VLLM/LLaMA2-Accessor

Antitumor Effects of Berberine on Gliomas via Inactivation of Caspase-1-Mediated IL-1β and IL-18 Release

Gliomas arise in the glial cells of the brain or spine and are the most prevalent and devastating type of brain tumors. Studies of tumor immunology have established the importance of the tumor micro-environment as a driver of oncogenesis. Inflammatory mediators such as IL-1β and IL-18 released by monocytes regulate transcriptional networks that are required for malignant cell growth. Berberine is a natural botanical alkaloid that is widely found in the Berberis species. Although it has been widely used as an anti-diarrheal treatment in North America for several decades, our study is the first to investigate berberine as an anti-tumor agent in glioma cells. In this study, we demonstrate that berberine significantly inhibits inflammatory cytokine Caspase-1 activation via ERK1/2 signaling and subsequent production of IL-1β and IL-18 by glioma cells. Moreover, we found that berberine treatment led to decreased motility and subsequently cell death in U251 and U87 cells. In addition, our study is the first to indicate that berberine can reverse the process of epithelial-mesenchymal transition, a marker of tumor invasion. Taken together, our work supports berberine as a putative anti-tumor agent targeting glioma cells

A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise

The surge of interest towards Multi-modal Large Language Models (MLLMs), e.g., GPT-4V(ision) from OpenAI, has marked a significant trend in both academia and industry. They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks. Very recently, Google released Gemini, its newest and most capable MLLM built from the ground up for multi-modality. In light of the superior reasoning capabilities, can Gemini challenge GPT-4V's leading position in multi-modal learning? In this paper, we present a preliminary exploration of Gemini Pro's visual understanding proficiency, which comprehensively covers four domains: fundamental perception, advanced cognition, challenging vision tasks, and various expert capacities. We compare Gemini Pro with the state-of-the-art GPT-4V to evaluate its upper limits, along with the latest open-sourced MLLM, Sphinx, which reveals the gap between manual efforts and black-box systems. The qualitative samples indicate that, while GPT-4V and Gemini showcase different answering styles and preferences, they can exhibit comparable visual reasoning capabilities, and Sphinx still trails behind them concerning domain generalizability. Specifically, GPT-4V tends to elaborate detailed explanations and intermediate steps, and Gemini prefers to output a direct and concise answer. The quantitative evaluation on the popular MME benchmark also demonstrates the potential of Gemini to be a strong challenger to GPT-4V. Our early investigation of Gemini also observes some common issues of MLLMs, indicating that there still remains a considerable distance towards artificial general intelligence. Our project for tracking the progress of MLLM is released at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.Comment: Total 120 pages. See our project at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model

New mechanism to cross the phantom divide

Recently, type Ia supernovae data appear to support a dark energy whose equation of state $w$ crosses -1, which is a much more amazing problem than the acceleration of the universe. We show that it is possible for the equation of state to cross the phantom divide by a scalar field in the gravity with an additional inverse power-law term of Ricci scalar in the Lagrangian. The necessary and sufficient condition for a universe in which the dark energy can cross the phantom divide is obtained. Some analytical solutions with $w<-1$ or $w>-1$ are obtained. A minimal coupled scalar with different potentials, including quadratic, cubic, quantic, exponential and logarithmic potentials are investigated via numerical methods, respectively. All these potentials lead to the crossing behavior. We show that it is a robust result which is hardly dependent on the concrete form of the potential of the scalar.Comment: 11 pages, 5 figs, v3: several references added, to match the published versio

The Effect of Raw Soybean on Oxidative Status of Digestive Organs in Mice

The present study was undertaken to specify the effect of raw soybean on oxidative status of digestive organs in mice. For this purpose, thirty male (C57BL/6J) mice were randomly divided into three groups and fed on different diets as follows: Group 1 was fed on control diet, Group 2 was fed on raw soybean diet and Group 3 was fed on raw soybean diet supplemented with 30 mg/kg cysteamine. After two weeks of feeding, duodenum, liver and pancreas samples were collected to measure oxidative and antioxidative parameters. The results show that ingestion of raw soybean markedly increased contents of superoxide anion and malondialdehyde (MDA) and activity of inducible nitric oxide synthase (iNOS), decreased activity of superoxide dismutase (SOD), T-AOC and content of reduced glutathione (GSH) in digestive organs of mice (P < 0.05). In the group fed with raw soybean diet supplemented with cysteamine, oxidative stress was mitigated. However, oxidative parameter levels were still higher than those of control diet-fed group. The present study indicates that ingestion of raw soybean could result in an imbalance between oxidant and antioxidant, and thus induce oxidative stress in digestive organs of mice