Current trends and developments in progressive collapse research on reinforced concrete flat plate structures

Progressive collapse of structures caused by extreme or accidental loads may lead to significant loss of life and property. Considerable research efforts have been made to date to mitigate the probability of progressive collapse and its consequences. This study summarises the fundamentals of progressive collapse in relation to the existing theoretical concepts and understanding. Specifically the existing theories pertinent to progressive collapse of building structures, in particular reinforced concrete (RC) flat plates, are examined from the following four key aspects: (1) definition of progressive collapse from deformation and/or strength perspectives with respect to the failure criteria of structural members and the entire structural system; (2) failure mechanisms of load-bearing systems undergoing progressive collapse with respect to the structural ultimate capacity, which has not been considered in the design process; (3) research methodologies for investigating collapse mechanisms, with emphases on experimental and numerical approaches; and (4) collapse-resistant design principles as covered in several international design standards in which a number of robustness requirements have been recognised. Based on the schematic review of the current trends and developments, gaps and limitations in progressive collapse research are identified and a new research direction is established to advance the progressive collapse study of RC flat plate structures

PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation

The Segment Anything Model (SAM) has exhibited outstanding performance in various image segmentation tasks. Despite being trained with over a billion masks, SAM faces challenges in mask prediction quality in numerous scenarios, especially in real-world contexts. In this paper, we introduce a novel prompt-driven adapter into SAM, namely Prompt Adapter Segment Anything Model (PA-SAM), aiming to enhance the segmentation mask quality of the original SAM. By exclusively training the prompt adapter, PA-SAM extracts detailed information from images and optimizes the mask decoder feature at both sparse and dense prompt levels, improving the segmentation performance of SAM to produce high-quality masks. Experimental results demonstrate that our PA-SAM outperforms other SAM-based methods in high-quality, zero-shot, and open-set segmentation. We're making the source code and models available at https://github.com/xzz2/pa-sam.Comment: Code is available at https://github.com/xzz2/pa-sa

Classification of Traditional Chinese Medicine Syndromes in Patients with Chronic Hepatitis B by SELDI-Based ProteinChip Analysis

Traditional Chinese medicine (TCM) syndrome, also called ZHENG, is the basis concept of TCM theory. It plays an important role in TCM practice. There are excess and deficiency syndromes in TCM syndrome. They are the common syndromes in chronic hepatitis B (CHB) patients. Here we aim to explore serum protein profiles and potential biomarkers for classification of TCM syndromes in CHB patients. 24 healthy controls and two cohorts of CHB patients of excess syndrome (n = 25) or deficiency syndrome (n = 19) were involved in this study. Protein profiles were obtained by surface-enhanced laser desorption ionization time-flight mass spectrometry (SELDI-TOF/MS) and multiple analyses were performed. Based on SELDI ProteinChip data, healthy controls and CHB patients or excess and deficiency syndromes in CHB patients were obviously differentiated by orthogonal partial least square (OPLS) analysis. Two significant serum proteins (m/z 4187 and m/z 5032) for classifying excess and deficiency syndromes were found. Moreover, the area under the receiver operating characteristic (ROC) curve was 0.887 for classifying excess and nonexcess syndrome, and 0.700 for classifying deficiency and nondeficiency syndrome, respectively. Therefore, the present study provided the possibility of TCM syndrome classification in CHB patients using a universally acceptable scientific approach

Enhanced oral bioavailability of cyclosporine A by liposomes containing a bile salt

The main purpose of this study was to evaluate liposomes containing a bile salt, sodium deoxycholate (SDC), as oral drug delivery systems to enhance the oral bioavailability of the poorly water-soluble and poorly permeable drug, cyclosporine A (CyA). Liposomes composed of soybean phosphatidylcholine (SPC) and SDC were prepared by a thin-film dispersion method followed by homogenization. Several properties of the liposomes including particle size, polydispersity index, and entrapment efficiency were characterized. The in vitro release of CyA from these liposomes was less than 5% at 12 hours as measured by a dynamic dialysis method. The pharmacokinetic results in rats showed improved absorption of CyA in SPC/SDC liposomes, compared with CyA-loaded conventional SPC/cholesterol (Chol) liposomes and microemulsion-based Sandimmune Neoral®. The relative oral bioavailability of CyA-loaded SPC/SDC and SPC/Chol liposomes was 120.3% and 98.6%, respectively, with Sandimmun Neoral as the reference. The enhanced bioavailability of CyA was probably due to facilitated absorption by the liposomes containing SDC rather than improved release rate

SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation

Depth estimation from images serves as the fundamental step of 3D perception for autonomous driving and is an economical alternative to expensive depth sensors like LiDAR. The temporal photometric constraints enables self-supervised depth estimation without labels, further facilitating its application. However, most existing methods predict the depth solely based on each monocular image and ignore the correlations among multiple surrounding cameras, which are typically available for modern self-driving vehicles. In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. We apply cross-view self-attention to efficiently enable the global interactions between multi-camera feature maps. Different from self-supervised monocular depth estimation, we are able to predict real-world scales given multi-camera extrinsic matrices. To achieve this goal, we adopt the two-frame structure-from-motion to extract scale-aware pseudo depths to pretrain the models. Further, instead of predicting the ego-motion of each individual camera, we estimate a universal ego-motion of the vehicle and transfer it to each view to achieve multi-view ego-motion consistency. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets DDAD and nuScenes.Comment: Accepted to CoRL 2022. Project page: https://surrounddepth.ivg-research.xyz Code: https://github.com/weiyithu/SurroundDept

Rethinking the Redlines Against AI Existential Risks

The ongoing evolution of advanced AI systems will have profound, enduring, and significant impacts on human existence that must not be overlooked. These impacts range from empowering humanity to achieve unprecedented transcendence to potentially causing catastrophic threats to our existence. To proactively and preventively mitigate these potential threats, it is crucial to establish clear redlines to prevent AI-induced existential risks by constraining and regulating advanced AI and their related AI actors. This paper explores different concepts of AI existential risk, connects the enactment of AI red lines to broader efforts addressing AI's impacts, constructs a theoretical framework for analyzing the direct impacts of AI existential risk, and upon that proposes a set of exemplary AI red lines. By contemplating AI existential risks and formulating these red lines, we aim to foster a deeper and systematic understanding of the potential dangers associated with advanced AI and the importance of proactive risk management. We hope this work will contribute to the strengthening and refinement of a comprehensive AI redline system for preventing humanity from AI existential risks

Molecular Mechanisms of Same TCM Syndrome for Different Diseases and Different TCM Syndrome for Same Disease in Chronic Hepatitis B and Liver Cirrhosis

Traditional Chinese medicine (TCM) treatment is based on the traditional diagnose method to distinguish the TCM syndrome, not the disease. So there is a phenomenon in the relationship between TCM syndrome and disease, called Same TCM Syndrome for Different Diseases and Different TCM Syndrome for Same Disease. In this study, we demonstrated the molecular mechanisms of this phenomenon using the microarray samples of liver-gallbladder dampness-heat syndrome (LGDHS) and liver depression and spleen deficiency syndrome (LDSDS) in the chronic hepatitis B (CHB) and liver cirrhosis (LC). The results showed that the difference between CHB and LC was gene expression level and the difference between LGDHS and LDSDS was gene coexpression in the G-protein-coupled receptor protein-signaling pathway. Therein genes GPER, PTHR1, GPR173, and SSTR1 were coexpressed in LDSDS, but not in LGDHS. Either CHB or LC was divided into the alternative LGDHS and LDSDS by the gene correlation, which reveals the molecular feature of Different TCM Syndrome for Same Disease. The alternatives LGDHS and LDSDS were divided into either CHB or LC by the gene expression level, which reveals the molecular feature of Same TCM Syndrome for Different Diseases