Experimental study on secondary bearing mechanism of weakly cemented broken rock mass

In order to study the secondary bearing mechanism of weakly cemented broken surrounding rock, the surface of granite, limestone and mudstone broken rock samples were poured by cement slurry, and the weakly cemented rock mass was formed by static pressure infiltration method, and then an uniaxial loading test was carried out. The results show that the weakly cemented broken rock mass has a certain bearing capacity, but the bearing capacity is low, and the dispersion is high. The secondary bearing capacity of weakly cemented rock mass is affected by factors such as broken rock strength, rock particle size and rock gradation. The larger the rock particle size and strength are, the higher the secondary bearing capacity of the weakly cemented rock mass is. The average bearing capacity of the mudstone weak cementation specimen is 18.77 kN, and the residual bearing capacity is 1.46 kN, and a dispersion coefficient is 0.34. The average bearing capacity of granite is 343.65 kN, and the residual carrying capacity is 25.81 kN, and a dispersion coefficient is 0.11. The average bearing capacity of limestone is 367.22 kN, and the residual carrying capacity is 22.78 kN, and a dispersion coefficient is 0.3. After a certain grading, the average residual secondary bearing capacity of the weakly cemented rock mass is obviously improved, and the dispersion coefficient of peak bearing capacity is reduced. The grading scheme 1 has an average peak carrying capacity of 330.06 kN, a residual carrying capacity of 34.56 kN, and a dispersion coefficient is 0.07. The averaging scheme 2 has an average peak carrying capacity of 297.8 kN, a residual carrying capacity of 29.86 kN, and a dispersion coefficient is 0.14. The cementation regeneration mechanism of the broken rock mass mainly includes the cement-bonding effect of the cement slurry inside and on the broken rock mass. Under the loaded condition, the internal load-bearing network of the broken rock mass is the main mechanism for the secondary load of the broken rock mass, and the stability of the force-chain network is affected by the constraint. After the loss of the confinement, the force chain network fails, and the residual secondary bearing mechanism of the weakly cemented broken rock mass is transformed into the friction between the broken rock masses in the residual core rock pillar

Analysis of the anatomic eligibility for transcarotid artery revascularization in Chinese patients who underwent carotid endarterectomy and transfemoral carotid artery stenting

ObjectiveTranscarotid artery revascularization (TCAR) is thought to be a promising technique and instrument for treating carotid stenosis with favorable outcomes. Since there remain several differences in anatomic characteristics among races, this study was conducted to investigate the anatomic eligibility of TCAR in Chinese patients who underwent carotid revascularization.MethodsA retrospective review of patients with carotid stenosis from 2019 to 2021 was conducted. The anatomic eligibility of TCAR was based on the instruction of the ENROUTE Transcarotid Neuroprotection System. The carotid artery characteristics and configuration of the circle of Willis (CoW) were evaluated by CT angiography. The demographic and clinical characteristics and procedure-related complications were recorded. Logistic regression was used to analyze the independent factors for TCAR eligibility.ResultsOf 289 consecutive patients [222 for carotid endarterectomy (CEA) and 67 for transfemoral carotid artery stenting (TF-CAS)] identified, a total of 215 patients (74.4%) met TCAR anatomic eligibility. Specifically, 83.7% had mild common carotid artery (CCA) puncture site plaque, 95.2% had 4–9 mm internal carotid artery diameters, 95.8% had >6 mm CCA diameter, and 98.3% had >5 cm clavicle to carotid bifurcation distance. Those who were female (OR, 5.967; 95% CI: 2.545–13.987; P < 0.001), were of an older age (OR, 1.226; 95% CI: 1.157–1.299; P < 0.001), and higher body mass index (OR, 1.462; 95% CI: 1.260–1.697; P < 0.001) were prone to be associated with TCAR ineligibility. In addition, 71 patients with TCAR eligibility (33.0%) were found to combine with incomplete CoW. A high risk for CEA was found in 29 patients (17.3%) with TCAR eligibility, and a high risk for TF-CAS was noted in nine patients (19.1%) with TCAR eligibility. Overall, cranial nerve injury (CNI) was found in 22 patients after CEA, while 19 of them (11.3%) met TCAR eligibility.ConclusionA significant proportion of Chinese patients meet the anatomic criteria of TCAR, making TCAR a feasible treatment option in China. Anatomic and some demographic factors play key roles in TCAR eligibility. Further analysis indicates a potential reduction of procedure-related complications in patients with high-risk carotid stenosis under the TCAR procedure

Uncovering Download Fraud Activities in Mobile App Markets

Download fraud is a prevalent threat in mobile App markets, where fraudsters manipulate the number of downloads of Apps via various cheating approaches. Purchased fake downloads can mislead recommendation and search algorithms and further lead to bad user experience in App markets. In this paper, we investigate download fraud problem based on a company's App Market, which is one of the most popular Android App markets. We release a honeypot App on the App Market and purchase fake downloads from fraudster agents to track fraud activities in the wild. Based on our interaction with the fraudsters, we categorize download fraud activities into three types according to their intentions: boosting front end downloads, optimizing App search ranking, and enhancing user acquisition&retention rate. For the download fraud aimed at optimizing App search ranking, we select, evaluate, and validate several features in identifying fake downloads based on billions of download data. To get a comprehensive understanding of download fraud, we further gather stances of App marketers, fraudster agencies, and market operators on download fraud. The followed analysis and suggestions shed light on the ways to mitigate download fraud in App markets and other social platforms. To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.Comment: Published as a conference paper in IEEE/ACM ASONAM 201

A novel intelligent adaptive control of laser-based ground thermal test

AbstractLaser heating technology is a type of potential and attractive space heat flux simulation technology, which is characterized by high heating rate, controlled spatial intensity distribution and rapid response. However, the controlled plant is nonlinear, time-varying and uncertainty when implementing the laser-based heat flux simulation. In this paper, a novel intelligent adaptive controller based on proportion–integration–differentiation (PID) type fuzzy logic is proposed to improve the performance of laser-based ground thermal test. The temperature range of thermal cycles is more than 200K in many instances. In order to improve the adaptability of controller, output scaling factors are real time adjusted while the thermal test is underway. The initial values of scaling factors are optimized using a stochastic hybrid particle swarm optimization (H-PSO) algorithm. A validating system has been established in the laboratory. The performance of the proposed controller is evaluated through extensive experiments under different operating conditions (reference and load disturbance). The results show that the proposed adaptive controller performs remarkably better compared to the conventional PID (PID) controller and the conventional PID type fuzzy (F-PID) controller considering performance indicators of overshoot, settling time and steady state error for laser-based ground thermal test. It is a reliable tool for effective temperature control of laser-based ground thermal test

Multi-scale approaches for high-speed imaging and analysis of large neural populations

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to “zoom out” by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution

Differential Expression of MicroRNA-19b Promotes Proliferation of Cancer Stem Cells by Regulating the TSC1/mTOR Signaling Pathway in Multiple Myeloma

Background/Aims: MiR-19b has been reported to be involved in several malignancies, but its role in multiple myeloma (MM) is still unknown. The objective of this study was to explore the biological mechanism of miR-19b in the progression of MM. Methods: First, we performed real-time polymerase chain reaction (PCR) and Western blot to study the expression of miR-19b, tuberous sclerosis 1 (TSC1), and caspase-3 in different groups. MTT assay was performed to explore the effect of miR-19b on survival and apoptosis of cancer stem cells (CSCs). Computation analysis and luciferase assay were utilized to confirm the interaction between miR-19b and TSC1. Results: A total of 38 participants comprising 20 subjects with MM and 18 healthy subjects as normal controls were enrolled in our study. Real-time PCR showed dramatic upregulation of miR-19b, but TSC1 was evidently suppressed in the MM group. MiR-19b overexpression substantially promoted clonogenicity and cell viability, and further inhibited apoptosis of CSCs in vitro. Furthermore, miR-19b overexpression downregulated the expression of caspase-3, which induced apoptosis. Using in silico analysis, we identified that TSC1 might be a direct downstream target of miR-19b, and this was further confirmed by luciferase assay showing that miR-19b apparently reduced the luciferase activity of wild-type TSC1 3´-UTR, but not that of mutant TSC1 3´-UTR. There was also evident decrease in TSC1 mRNA and protein in CSCs following introduction of miR-19b. Interestingly, reintroduction of TSC1 abolished the miR-19b-induced proliferation promotion and apoptosis inhibition in CSCs. Conclusion: These findings collectively suggest that miR-19b promotes cell survival and suppresses apoptosis of MM CSCs via targeting TSC1 directly, indicating that miR-19b may serve as a potential and novel therapeutic target of MM based on miRNA expression

Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques

The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications. While previous studies have commonly focused on feature learning at a single learning scale, investigation on integrating multi-scale information is lacking, which may hinder the potential for mutual reinforcement among these features. This paper aims to bridge this gap by proposing a method that effectively exploits multi-scale information to enhance the performance of medical foundation models. The proposed method simultaneously exploits features at the local, instance, modality and global aspects, facilitating comprehensive representation learning within the models. We evaluate the effectiveness of the proposed method on six open-source datasets across different clinical tasks, demonstrating its ability to enhance the performance of medical foundation models

Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM

Dense SLAM based on monocular cameras does indeed have immense application value in the field of AR/VR, especially when it is performed on a mobile device. In this paper, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone. Specifically, we present a specifically optimized multi-basis depth completion network, called BBC-Net, tailored to the characteristics of traditional sparse SLAM systems. BBC-Net can predict multiple balanced bases and a confidence map from a monocular image with sparse points generated by off-the-shelf keypoint-based SLAM systems. The final depth is a linear combination of predicted depth bases that can be optimized by tuning the corresponding weights. To seamlessly incorporate the weights into traditional SLAM optimization and ensure efficiency and robustness, we design a set of depth weight factors, which makes our network a versatile plug-in module, facilitating easy integration into various existing sparse SLAM systems and significantly enhancing global depth consistency through bundle adjustment. To verify the portability of our method, we integrate BBC-Net into two representative SLAM systems. The experimental results on various datasets show that the proposed method achieves better performance in monocular dense mapping than the state-of-the-art methods. We provide an online demo running on a mobile phone, which verifies the efficiency and mapping quality of the proposed method in real-world scenarios