Expressions of Cyr61 and WISP-3 in Non-small Cell Lung Cancer and Its Clinical Significance

Background and objective Cysteine-rich protein 61 (Cyr61) plays a role as a tumor suppressor in non-small cell lung cancer (NSCLC). Cyr61 and WISP-3 have a very significant sequence homology, belonging to the same CCN gene family. The aim of this study is to investigate the expressions of Cyr61 and WISP-3 in NSCLC, and explore the relationship between their expressions and tumor's clinicopathological characteristics. Methods The expressions of Cyr61 and WISP-3 were detected in 54 cases with primary NSCLC and their corresponding normal lung tissues in control group by immunohistochemical staining (SP), and the clinical data were analyzed. Results Down-regulation of Cyr61 and up-regulation of WISP-3 were both found in lung cancer tissue compared with the corresponding normal lung tissue (both P < 0.001); The expression of Cyr61 was negatively correlated with the expression of WISP-3 (r=-0.395, P=0.003); Cyr61 expression levels was closely correlated with tumor grade, tumor type, clinical stage, family history, smoking and metastasis (P < 0.05). Also, WISP-3 was closely correlated with tumor grade, clinical stage and age (P < 0.05). Conclusion The expressions of Cyr61 and/or WISP-3 may be important biological markers in reflecting the progression, biological behaviors, metastatic potential and prognosis of NSCLC

Computer-Aided Analysis of Flow in Water Pipe Networks after a Seismic Event

This paper proposes a framework for a reliability-based flow analysis for a water pipe network after an earthquake. For the first part of the framework, we propose to use a modeling procedure for multiple leaks and breaks in the water pipe segments of a network that has been damaged by an earthquake. For the second part, we propose an efficient system-level probabilistic flow analysis process that integrates the matrix-based system reliability (MSR) formulation and the branch-and-bound method. This process probabilistically predicts flow quantities by considering system-level damage scenarios consisting of combinations of leaks and breaks in network pipes and significantly reduces the computational cost by sequentially prioritizing the system states according to their likelihoods and by using the branch-and-bound method to select their partial sets. The proposed framework is illustrated and demonstrated by examining two example water pipe networks that have been subjected to a seismic event. These two examples consist of 11 and 20 pipe segments, respectively, and are computationally modeled considering their available topological, material, and mechanical properties. Considering different earthquake scenarios and the resulting multiple leaks and breaks in the water pipe segments, the water flows in the segments are estimated in a computationally efficient manner.ope

Mass Inertia Effect Based Vibration Control Systems for Civil Engineering Structure and Infrastructure

This chapter introduces some recent research works carried out in the Blast Resistance and Protective Engineering laboratory of Harbin Institute of Technology (HIT-BRPE) during the past few years. The EMD control system is shown to be effective and feasible for vibration control of civil engineering structures subjected to, such as earthquake, excitations. The DDVC based AMD control system is suitable for low frequency vibration and motion control. The innovative passive TRID system is applicable for rotation and swing motion control, whereas linear TMD system is shown to be invalid for structural swinging motion. All of the control systems mentioned in this chapter, whatever active or passive or hybrid, have a common characteristic, which is to utilize the mass inertia effect either to provide counter force support for functioning of actuator, e.g. AMD subsystem, or to provide gyrus or rotary inertia for anti-swinging motion of suspended structure. Traditionally, these systems have been called Active Mass Damper/Driver (AMD) or Tuned Mass Damper (TMD), herein we want to emphasize the mass inertia effect and its functions. The basic is to be a necessary component of a control system, and more important is its way of working in the subsystem

A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems

The recurrent neural network has been greatly developed for effectively solving time-varying problems corresponding to complex environments. However, limited by the way of centralized processing, the model performance is greatly affected by factors like the silos problems of the models and data in reality. Therefore, the emergence of distributed artificial intelligence such as federated learning (FL) makes it possible for the dynamic aggregation among models. However, the integration process of FL is still server-dependent, which may cause a great risk to the overall model. Also, it only allows collaboration between homogeneous models, and does not have a good solution for the interaction between heterogeneous models. Therefore, we propose a Distributed Computation Model (DCM) based on the consortium blockchain network to improve the credibility of the overall model and effective coordination among heterogeneous models. In addition, a Distributed Hierarchical Integration (DHI) algorithm is also designed for the global solution process. Within a group, permissioned nodes collect the local models' results from different permissionless nodes and then sends the aggregated results back to all the permissionless nodes to regularize the processing of the local models. After the iteration is completed, the secondary integration of the local results will be performed between permission nodes to obtain the global results. In the experiments, we verify the efficiency of DCM, where the results show that the proposed model outperforms many state-of-the-art models based on a federated learning framework

A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs

Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs

Experimental investigation on semi-active control of base isolation system using magnetorheological dampers for concrete frame structure

The traditional passive base isolation is the most widely used method in the engineering practice for structural control, however, it has the shortcoming that the optimal control frequency band is significantly limited and narrow. For the seismic isolation system designed specifically for large earthquakes, the structural acceleration response may be enlarged under small earthquakes. If the design requirements under small earthquakes are satisfied, the deformation in the isolation layer may become too large to be accepted. Occasionally, it may be destroyed under large earthquakes. In the isolation control system combined with rubber bearing and magnetorheological (MR) damper, the MR damper can provide instantaneous variable damping force to effectively control the structural response at different input magnitudes. In this paper, the control effect of semi-active control and quasi-passive control for the isolation control system is verified by the shaking table test. In regard to semi-active control, the linear quadratic regulator (LQR) classical linear optimal control algorithm by continuous control and switch control strategies are used to control the structural vibration response. Numerical simulation analysis and shaking table test results indicate that isolation control system can effectively overcome the shortcoming due to narrow optimum control band of the passive isolation system, and thus to provide optimal control for different seismic excitations in a wider frequency range. It shows that, even under super large earthquakes, the structure still exhibits the ability to maintain overall stability performance

Pore-scale investigation of the effects of wetting phase re-imbibition on gas capillary trapping in porous media

Capillary trapping of the non-wetting phase in porous media is vital for long-term CO2 sequestration and underground gas storage. While injection strategies have received extensive research attention, the pore-scale mechanisms controlling residual gas stability during wetting phase re-imbibition under varying injection directions coupled with buoyancy remain unclear. This study used high-resolution micro-focus X-ray computed microtomography imaging and quantitative analysis to investigate gas trapping in a hydrophilic glass bead pack across multiple capillary numbers. Both downward (gravityaligned) and upward (gravity-opposing) re-imbibition were tested. The results demonstrate that downward injection promotes bubble fragmentation and stabilization, sustaining higher residual saturation, increased populations of small bubbles, and greater specific surface area even under elevated capillary numbers. Upward injection, in which buoyancy aligns with flow, enhances bubble coalescence and mobilization, lowering residual saturation and trapping efficiency. These pore-scale trends highlight the critical interplay of capillary, viscous, and buoyancy forces in shaping gas trapping behavior. The findings of this study provide valuable experimental insights for optimizing injection direction and flow rate, in order to improve long-term CO2 storage security and underground gas storage operations.Document Type: Original articleCited as: Zhang, C., Zhang, K., Yoshida, S., Li, Z., Zhao, W., Suekane, T. Pore-scale investigation of the effects of wetting phase re-imbibition on gas capillary trapping in porous media. Capillarity, 2025, 17(1): 16-26. https://doi.org/10.46690/capi.2025.10.0

Image Super-resolution with An Enhanced Group Convolutional Neural Network

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN

Heterogeneous window transformer for image denoising

Deep networks can usually depend on extracting more structural information to improve denoising results. However, they may ignore correlation between pixels from an image to pursue better denoising performance. Window transformer can use long- and short-distance modeling to interact pixels to address mentioned problem. To make a tradeoff between distance modeling and denoising time, we propose a heterogeneous window transformer (HWformer) for image denoising. HWformer first designs heterogeneous global windows to capture global context information for improving denoising effects. To build a bridge between long and short-distance modeling, global windows are horizontally and vertically shifted to facilitate diversified information without increasing denoising time. To prevent the information loss phenomenon of independent patches, sparse idea is guided a feed-forward network to extract local information of neighboring patches. The proposed HWformer only takes 30% of popular Restormer in terms of denoising time