Influence of normal stress on the shear strength of the structural plane considering the size effect

The shear strength of a structural plane is a critical parameter in the analysis of engineering rock stability. Significant differences exist due to the various normal stresses in the structural plane. Therefore, evaluating the rock deformation to effectively determine the influence of normal stresses at different scales on the shear strength of structural planes is of great significance. This study discusses the effects of normal stress and structural plane size on shear strength through numerical simulations and regression analysis. The results showed that the shear strength of the structural plane increases linearly with increasing normal stress. The shear strength of the structural plane decreases with increasing size, and the corresponding curve is exponential. The characteristic size and shear strength increase linearly with increasing normal stress. This paper presents the concrete form of these relationships, which can be used to calculate and predict the shear strength, which has significance in guiding engineering

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report

CompenHR: Efficient Full Compensation for High-resolution Projector

Full projector compensation is a practical task of projector-camera systems. It aims to find a projector input image, named compensation image, such that when projected it cancels the geometric and photometric distortions due to the physical environment and hardware. State-of-the-art methods use deep learning to address this problem and show promising performance for low-resolution setups. However, directly applying deep learning to high-resolution setups is impractical due to the long training time and high memory cost. To address this issue, this paper proposes a practical full compensation solution. Firstly, we design an attention-based grid refinement network to improve geometric correction quality. Secondly, we integrate a novel sampling scheme into an end-to-end compensation network to alleviate computation and introduce attention blocks to preserve key features. Finally, we construct a benchmark dataset for high-resolution projector full compensation. In experiments, our method demonstrates clear advantages in both efficiency and quality

The Relationship Between Urban Community Collaborative Governance and Building Resilience Cities in Zhengzhou City, Henan Province, China

Purpose: This study explores the impact of urban community collaborative governance on building resilience cities.   Theoretical framework: The Synergistic Governance Theory (SGT) was applied in this study.   Design/Methodology/Approach: The population of this study is the community of Zhengzhou City, Henan Province, China. The unit of analysis is individuals living in the community of Zhengzhou City. Through random sampling method, 384 community residents were selected to participate in the research. This study used a questionnaire survey method to obtain primary data for analysis.   Findings: The results of the study show that (1) there is a significant positive correlation between government regulation, corporate capabilities, social organization involvement and building resilience cities. (2) Strengthening government regulation, corporate capabilities, and social organization involvement can effectively strengthen urban community collaborative governance, which is beneficial to building resilience cities. Thus, it is favorable to building Resilience Cities.   Research, Practical & Social implications: This research will be useful in creating a new model of urban community governance that will enhance the ability of cities to cope with disasters and achieve the goal of building resilient cities. A multifaceted and collaborative urban community governance model will be developed by strengthening the collaboration of the three groups - government, corporations, and social organizations，in order to ensure that the collaborative urban community governance model promotes the city's ability to cope with disasters, thereby enhancing the city's resilience. Enhancing urban resilience can fundamentally improve residents' ability to cope with the potential risks that persist in cities, thereby resolving social conflicts and satisfying people's pursuit of a better life.   Originality/Value: This study presents an innovative form of urban community management model that provides valuable insights on the impact of collaborative urban community governance models on resilient city buildings

Role of the Ang2-Tie2 Axis in Vascular Damage Driven by High Glucose or Nucleoside Diphosphate Kinase B Deficiency

Ablation of nucleoside diphosphate kinase B (NDPK-B) in mice causes a breakdown of the neurovascular unit in the retina, mimicking diabetic retinopathy. The NDPK-B deficiency-induced vascular damage is mediated by excessive angiopoietin 2 (Ang2). Herein, the potential involvement of its receptor, Tie2, was investigated. NDPK-B-deficient mouse retinas showed an upregulation of Tie2, specifically in the deep capillary layer. A similar upregulation of Tie2 was observed in cultured endothelial cells (ECs) from different origins upon NDPK-B depletion, whereas high glucose (HG) treatment did not alter Tie2 expression. Immunofluorescence staining and subcellular fractionation showed that the majority of Tie2 upregulation occurred at the plasma membrane. Similar to HG, however, NDPK-B depletion reduced Tie2 tyrosine phosphorylation. Compared to HG, a stronger increase of Ang2 was observed in NDPK-B depleted ECs. Treatment of ECs with soluble Tie2 or siRNA-mediated Tie2 knockdown attenuated NDPK-B depletion- but not HG-induced Ang2 upregulation. Like NDPK-B depletion, overexpression of recombinant Ang2 in ECs enhanced Ang2 secretion and concomitantly promoted the upregulation of Tie2. Thus, we identified a new mechanism showing that after reaching a threshold level of secretion, Ang2 sustains its own expression and secretion by a Tie2-dependent positive feedback loop

DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain

With the wide application of face recognition systems, there is rising concern that original face images could be exposed to malicious intents and consequently cause personal privacy breaches. This paper presents DuetFace, a novel privacy-preserving face recognition method that employs collaborative inference in the frequency domain. Starting from a counterintuitive discovery that face recognition can achieve surprisingly good performance with only visually indistinguishable high-frequency channels, this method designs a credible split of frequency channels by their cruciality for visualization and operates the server-side model on non-crucial channels. However, the model degrades in its attention to facial features due to the missing visual information. To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask. The mask is further refined by deriving and overlaying a facial region of interest (ROI). Extensive experiments on multiple datasets validate the effectiveness of the proposed method in protecting face images from undesired visual inspection, reconstruction, and identification while maintaining high task availability and performance. Results show that the proposed method achieves a comparable recognition accuracy and computation cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods. The source code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/duetface.Comment: Accepted to ACM Multimedia 202

Privacy-Preserving Face Recognition Using Random Frequency Components

The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.Comment: ICCV 202

What Are the New Challenges of the Current Cancer Biomarkers?

Biomarkers are emerging research filed in the past decade. Even though numerous biomarkers were reported, the efficiency of cancer therapy remains low. So the question emerges as to how much can we trust the current biomarkers on cancer therapy? In this upcoming chapter, we would like to illustrate the outcomes of classical cancer therapies on advanced pancreatic cancer disclosed successful, neutral and failed in clinical trials. The analysis will be carried on the perspective interdisciplinary on the biomarkers including anatomy, physiology, pharmacology, biochemistry, history path and development of pharmacy. Particular in-depth insight may open a window for new researches and lighting the therapies