Identification Methods of the Deformation Memory Effect in the Stress Region above Crack Initiation Threshold

AbstractDeformation memory effect (DME) is one of the rock memory effects. One important application of the DME is to determine the in situ stress state. Compared to the traditional in situ stress measurements, the methods based on the DME are commercial and permit large number of measurements. Application of DME needs enough reliable identification methods. However, the existing methods sometimes are indistinct and the amount is insufficient. combined with three traditional methods including tangential modulus method, deformation rate analysis (DRA), acoustic emission method, two new potential methods were explored in the stress region above crack initiation threshold. One is based on the fractal dimension, called FD method. Another one is to take advantage of the lateral strain in the DRA method and the FD method, instead of using the axial strain. Based on the contact bond model in PFC2D, numerical model for granite sample was developed and cyclic uniaxial compressions were performed on it. Both the existing methods and new methods were used to detect the DME. The results demonstrate that the FD method is effective and reliable, result by DRA method with lateral strain is better than that with the axial strain, the tangential modulus method is not so distinct as other methods

Species dependence of the impurity injection induced poloidal flow and magnetic island rotation in a tokamak

Recent experiments have demonstrated the species dependence of the impurity poloidal drift direction along with the magnetic island rotation in the poloidal plane. Our resistive MHD simulations have reproduced such a dependence of the impurity poloidal flow, which is found mainly determined by a local plasmoid formation due to the impurity injection. The synchronized magnetic island rotation is dominantly driven by the electromagnetic torque produced by the impurity radiation primarily through the modification to the axisymmetric components of current density

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Risk factors for surgical site infection of pilon fractures

OBJECTIVES: Pilon fracture is a complex injury that is often associated with severe soft tissue damage and high rates of surgical site infection. The goal of this study was to analyze and identify independent risk factors for surgical site infection among patients undergoing surgical fixation of a pilon fracture. METHODS: The medical records of all pilon fracture patients who underwent surgical fixation from January 2010 to October 2012 were reviewed to identify those who developed a surgical site infection. Then, we constructed univariate and multivariate logistic regressions to evaluate the independent associations of potential risk factors with surgical site infection in patients undergoing surgical fixation of a pilon fracture. RESULTS: A total of 519 patients were enrolled in the study from January 2010 to October 2012. A total of 12 of the 519 patients developed a surgical site infection, for an incidence of 2.3%. These patients were followed for 12 to 29 months, with an average follow-up period of 19.1 months. In the final regression model, open fracture, elevated postoperative glucose levels (≥125 mg/dL), and a surgery duration of more than 150 minutes were significant risk factors for surgical site infection following surgical fixation of a pilon fracture. CONCLUSIONS: Open fractures, elevated postoperative glucose levels (≥125 mg/dL), and a surgery duration of more than 150 minutes were related to an increased risk for surgical site infection following surgical fixation of a pilon fracture. Patients exhibiting the risk factors identified in this study should be counseled regarding the possible surgical site infection that may develop after surgical fixation

Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion

The force field describing the calculated interaction between atoms or molecules is the key to the accuracy of many molecular dynamics (MD) simulation results. Compared with traditional or semi-empirical force fields, machine learning force fields have the advantages of faster speed and higher precision. We have employed the method of atomic cluster expansion (ACE) combined with first-principles density functional theory (DFT) calculations for machine learning, and successfully obtained the force field of the binary Fe-Co alloy. Molecular dynamics simulations of Fe-Co alloy carried out using this ACE force field predicted the correct phase transition range of Fe-Co alloy.Comment: 17 pages, 6 figure

Information-sharing outage-probability analysis of vehicular networks

In vehicular networks, information dissemination/sharing among vehicles is of salient importance. Although diverse mechanisms have been proposed in the existing literature, the related information credibility issues have not been investigated. Against this background, in this paper, we propose a credible information-sharing mechanism capable of ensuring that the vehicles do share genuine road traffic information (RTI). We commence with the outage-probability analysis of information sharing in vehicular networks under both a general scenario and a specific highway scenario. Closed-form expressions are derived for both scenarios, given the specific channel settings. Based on the outage-probability expressions, we formulate the utility of RTI sharing and design an algorithm for promoting the sharing of genuine RTI. To verify our theoretical analysis and the proposed mechanism, we invoke a real-world dataset containing the locations of Beijing taxis to conduct our simulations. Explicitly, our simulation results show that the spatial distribution of the vehicles obeys a Poisson point process (PPP), and our proposed credible RTI sharing mechanism is capable of ensuring that all vehicles indeed do share genuine RTI with each other