Efficacy of autologous bone marrow buffy coat grafting combined with core decompression in patients with avascular necrosis of femoral head: a prospective, double-blinded, randomized, controlled study

Introduction Avascular necrosis of femoral head (ANFH) is a progressive disease that often leads to hip joint dysfunction and even disability in young patients. Although the standard treatment, which is core decompression, has the advantage of minimal invasion, the efficacy is variable. Recent studies have shown that implantation of bone marrow containing osteogenic precursors into necrotic lesion of ANFH may be promising for the treatment of ANFH. Methods A prospective, double-blinded, randomized controlled trial was conducted to examine the effect of bone-marrow buffy coat (BBC) grafting combined with core decompression for the treatment of ANFH. Forty-five patients (53 hips) with Ficat stage I to III ANFH were recruited. The hips were allocated to the control group (core decompression + autologous bone graft) or treatment group (core decompression + autologous bone graft with BBC). Both patients and assessors were blinded to the treatment options. The clinical symptoms and disease progression were assessed as the primary and secondary outcomes. Results At the final follow-up (24 months), there was a significant relief in pain (P \u3c0.05) and clinical joint symptoms as measured by the Lequesne index (P \u3c0.05) and Western Ontario and McMaster Universities Arthritis Index (P \u3c0.05) in the treatment group. In addition, 33.3% of the hips in the control group have deteriorated to the next stage after 24 months post-procedure, whereas only 8% in the treatment group had further deterioration (P \u3c0.05). More importantly, the non-progression rates for stage I/II hips were 100% in the treatment group and 66.7% in the control group. Conclusion Implantation of the autologous BBC grafting combined with core decompression is effective to prevent further progression for the early stages of ANFH. Trial registration ClinicalTrials.gov identifier NCT01613612. Registered 13 December 2011

GazeMotive: A Gaze-Based Motivation-Aware E-Learning Tool for Students with Learning Difficulties

Part 8: Demonstrations and InstallationsInternational audienceWe developed a gaze-based motivation-aware e-learning tool, a Windows desktop learning application, for students with learning difficulties that aims at motivation-enhanced learning by dynamically assessing and responding to students’ motivational states based on the motivation model that we developed previously using rigorous methodologies including domain knowledge and empirical studies with participants with learning difficulties. The learning application uses an eye tracker to monitor a user’s eye movements during the user’s learning process, assesses the user’s motivational states using the prediction models we developed before to output personalised feedback from a pedagogical agent in the system based on both the eye gaze features and user’s self-input data for enhancing users’ motivation and engagement in real-time. Our e-learning tool is an example of applying user modelling and personalisation to an e-learning environment targeting at users’ learning motivation, producing great insight on how eye tracking can assist with students’ learning motivation and engagement in e-learning environments

Compressive Sensing Based Grant-Free Communication

Grant-free communication, where each user can transmit data without following the strict access grant process, is a promising technique to reduce latency and support massive users. In this thesis, compressive sensing (CS), which exploits signal sparsity to recover data from a small sample, is investigated for user activity detection (UAD), channel estimation, and signal detection in grant-free communication, in order to extract information from the signals received by base station (BS). First, CS aided UAD is investigated by utilizing the property of quasi-time-invariant channel tap delays as the prior information for the burst users in internet of things (IoT). Two UAD algorithms are proposed, which are referred to as gradient based and time-invariant channel tap delays assisted CS (g-TIDCS) and mean value based and TIDCS (m-TIDCS), respectively. In particular, g-TIDCS and m-TIDCS do not require any prior knowledge of the number of active users like the existing approaches and therefore are more practical. Second, periodic communication as one of the salient features of IoT is considered. Two schemes, namely periodic block orthogonal matching pursuit (PBOMP) and periodic block sparse Bayesian learning (PBSBL), are proposed to exploit the non-continuous temporal correlation of the received signal for joint UAD, channel estimation, and signal detection. The theoretical analysis and simulation results show that the PBOMP and PBSBL outperform the existing schemes in terms of the success rate of UAD, bit error rate (BER), and accuracy in period estimation and channel estimation. Third, UAD and channel estimation for grant-free communication in the presence of massive users that are actively connected to the BS is studied. An iteratively UAD and signal detection approach for the burst users is proposed, where the interference of the connected users on the burst users is reduced by applying a preconditioning matrix to the received signals at the BS. The proposed approach is capable of providing significant performance gains over the existing algorithms in terms of the success of UAD and BER. Last but not least, since the physical layer security becomes a critical issue for grant-free communication, the channel reciprocity in time-division duplex systems is utilized to design environment-aware (EA) pilots derived from transmission channels to prevent eavesdroppers from acquiring users’ channel information. The proposed EA-pilots based approach possesses a high level of security by scrambling the eavesdropper’s normalized mean square error performance of channel estimation

Experimental Study on Forecasting Mathematical Model of Drying Shrinkage of Recycled Aggregate Concrete

On the basis of basic law in AASHTO2007 model, the forecasting mathematical model of drying shrinkage of recycled aggregate concrete (RAC) is established by regression analysis and experimental study. The research results show that (1) with the replacement rate of RCA increases, the drying shrinkage value of RAC increases; this trend is even more obvious in the early drying time. (2) The addition of fly ash can inhibit the drying shrinkage of RAC, but the effect is not very obvious. Specifically, the addition of fly ash will increase the shrinkage to some extent when the mixing amount is 20%. (3) The addition of expansive agent can obviously inhibit the shrinkage of RAC; the inhibition affection is better than that of fly ash. (4) The forecasting mathematical models of drying shrinkage of RAC established in this paper have high accuracy and rationality according to experiment validation and error analysis

Pore Structure and Influence of Recycled Aggregate Concrete on Drying Shrinkage

Pore structure plays an important role in the drying shrinkage of recycled aggregate concrete (RAC). High-precision mercury intrusion and water evaporation were utilized to study the pore structure of RAC, which has a different replacement rate of recycled concrete aggregate (RCA), and to analyze its influence on drying shrinkage. Finally, a fractal-dimension calculation model was established based on the principles of mercury intrusion and fractal-geometry theory. Calculations were performed to study the pore-structure fractal dimension of RAC. Results show the following. (1) With the increase in RCA content, the drying shrinkage values increase gradually. (2) Pores with the greatest impact on concrete shrinkage are those whose sizes ranging from 2.5 nm to 50 nm and from 50 nm to 100 nm. In the above two ranges, the proportions of RAC are greater than those of RC0 (natural aggregate concrete, NAC), which is the main reason the shrinkage values of RAC are greater than those of NAC. (3) The pore structure of RAC has good fractal feature, and the addition of RCA increases the complexity of the pore surface of concrete

Multicluster-Coordination Industrial Internet of Things: The Era of Nonorthogonal Transmission

The imminent industrial Internet of Things (IIoT) aims to provide massive device connectivity and support ever-increasing data demands, putting today's production environment on the edge of a new era of innovations and changes. In a multicluster IIoT, devices may suffer severe intercluster interference due to the intensive frequency reuse among adjacent access points (APs), thus deteriorating their quality of service (QoS). To address this issue, conventional multicluster coordination in the IIoT provides orthogonal code-, frequency-, time- or spatial-domain multiple access for interference management, but this results in a waste of resources, especially in the context of the explosively increased number of devices

Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection

Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real-world applications. Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods. This is motivated by the hypothesis that nodes in the graph tend to exhibit consistent behaviors with their neighborhoods. However, such consistency can be disrupted by graph anomalies in multiple ways. Most existing methods directly employ GNNs to learn representations, disregarding the negative impact of graph anomalies on GNNs, resulting in sub-optimal node representations and anomaly detection performance. While a few recent approaches have redesigned GNNs for graph anomaly detection under semi-supervised label guidance, how to address the adverse effects of graph anomalies on GNNs in unsupervised scenarios and learn effective representations for anomaly detection are still under-explored. To bridge this gap, in this paper, we propose a simple yet effective framework for Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection (G3AD). Specifically, G3AD introduces two auxiliary networks along with correlation constraints to guard the GNNs from inconsistent information encoding. Furthermore, G3AD introduces an adaptive caching module to guard the GNNs from solely reconstructing the observed data that contains anomalies. Extensive experiments demonstrate that our proposed G3AD can outperform seventeen state-of-the-art methods on both synthetic and real-world datasets.Comment: 14 pages, 9 figure

Theory and practice for assessing structural integrity and dynamical integrity of high-speed trains

Purpose – The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented. Design/methodology/approach – First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented. Findings – Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments. Originality/value – The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains

Dynamic characteristics analysis of a circular vibrating screen

Aiming at the structural reliability and operation stability of the vibrating screen, this study takes the 2460 double-layer circular vibrating screen as the research object, applies Creo to build a three-dimensional model of the vibrating screen, and uses ANSYS Workbench to analyze the dynamic characteristics of the vibrating screen. Through modal analysis, the natural frequencies and vibration modes of the vibrating screen are obtained. Through harmonic response analysis, the displacement and stress distribution of the vibrating screen at its working frequency are achieved. The analysis results show that the vibrating operates stably without transverse swing, the stress distribution of the screen body is uniform, can bear large dynamic loads, and has good structural strength and stiffness. This research provides a reference for the design of the domestic circular vibrating screen

Distractor-aware Event-based Tracking

Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture light intensity changes, event camera has an inherent advantage to capture moving objects in challenging scenarios including objects under low light, high dynamic range, or fast moving objects. Thus event camera are natural for visual object tracking. However, the current event-based trackers derived from RGB trackers simply modify the input images to event frames and still follow conventional tracking pipeline that mainly focus on object texture for target distinction. As a result, the trackers may not be robust dealing with challenging scenarios such as moving cameras and cluttered foreground. In this paper, we propose a distractor-aware event-based tracker that introduces transformer modules into Siamese network architecture (named DANet). Specifically, our model is mainly composed of a motion-aware network and a target-aware network, which simultaneously exploits both motion cues and object contours from event data, so as to discover motion objects and identify the target object by removing dynamic distractors. Our DANet can be trained in an end-to-end manner without any post-processing and can run at over 80 FPS on a single V100. We conduct comprehensive experiments on two large event tracking datasets to validate the proposed model. We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency