Renaming Detection

Finding changed identifiers is important for understanding the difference between two versions of a program and for detecting and resolving conflicts while merging variants of a program together. Standard practice for differencing and merging relies on line based techniques that do not recognize renamed identifiers. The design and implementation of a tool to automatically detect renamed identifiers between two versions of a program is presented. The system uses an abstract representation of language constructs to enable language awareness without introducing language dependence. Modules for Java and Scheme have been written. The detector works with multiple file pairs, taking into account renamings that span several files. A case study is presented that demonstrates proof of concept. The detector is part of a suite of intelligent differencing and merging programs that exploit the static semantics of programming languages.     

Direct Segmentation of Algebraic Models for Reverse Engineering

In Reverse Engineering a physical object is digitally reconstructed from a set of boundary points. In the segmentation phase these points are grouped into subsets to facilitate consecutive steps as surface fitting. In this paper we present a segmentation method with subsequent classification of simple algebraic surfaces. Our method is direct in the sense that it operates directly on the point set in contrast to other approaches that are based on a triangulation of the data set. The segmentation process involves a fast algorithm for k-nearest neighbors search and an estimation of first and second order surface properties. The first order segmentation, that is based on normal vectors, provides an initial subdivision of the surface and detects sharp edges as well as flat or highly curved areas. One of the main features of our method is to proceed by alternating the steps of segmentation and normal vector estimation. The second order segmentation subdivides the surface according to principal curvatures and provides a sufficient foundation for the classification of simple algebraic surfaces. If the boundary of the original object contains such surfaces the segmentation is optimized based on the result of a surface fitting procedure.     

Benchmarking Least Squares Support Vector Machine Classifiers

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.     

Analysis of Test Specimens for Cohesive Near-Bond Failure of Fiber-Reinforced Polymer-Plated Concrete

The failure mechanisms of reinforced concrete (RC) members change due to the application of externally bonded fiber-reinforced polymer reinforcement. Although an extensive literature is available describing the failure mechanisms of poststrengthened flexural systems, brittle failure modes caused by bond failure, such as midspan debonding and end peeling, need to be further investigated in order to identify and quantify the fracture processes that result in bond failure. Simplified experimental tests have been designed to idealize the bond between the laminate and the RC member. However, it is unclear how the simplified test results can be related to the actual flexural debonding failures. This paper investigates and compares two bond failure tests: a simplified test (or simple shear test) and a recently proposed shear/normal test. After discussing the characteristics of both tests and how they relate to the midspan debonding and end peeling failures, the shear/normal test is studied in more detail using a nonlinear finite-element fracture mechanics program. The program accounts for cohesive localized and distributed concrete crack damage and is capable of describing the geometrical discontinuities that induce different brittle failure mechanisms. The numerical results compare well with available experimental data and help explain the crack formation and propagation pattern up to specimen failure. Parametric studies are presented to elucidate the influence of different material parameters on the failure mechanisms.     

Design of an active-passive device for human ankle movement during functional magnetic resonance imaging analysis

Functional magnetic resonance imaging analysis has made major strides in recent years, both because of the development of new scanners and owing to magnetic resonance compatible systems that make it possible to stimulate parts of the human body during analysis. The significant gains in our knowledge of the brain that can thus be achieved justify efforts to construct machines with control circuits suitable for this purpose. This paper presents a magnetic resonance compatible mechatronic device with electropneumatic control that can be used to move one or both feet during functional magnetic resonance imaging analysis of the cerebral motor zones. The system is innovative and original. The results obtained at the end of the investigation were good, and demonstrated that the design is feasible.     

An operating system independent WORM archival system

We describe the organization of a general purpose data archival system for Write-Once, Read-Many (WORM) optical disks. The system has been designed for large-scale and long-term data storage and retrieval. The archival system is independent of the operating system, flat, self-consistent, does not use any write cache on magnetic disk, and allows the exploitation of auxiliary information on magnetic disk, which can be rebuilt immediately in case of a crash, to speed up file retrieval. A library in C language, called pODLIB, has been implemented as a portable interface to the archival system.     

Sustained Increase in Serum Glial Fibrillary Acidic Protein after First ST-Elevation Myocardial Infarction

Acute ischemic cardiac injury predisposes one to cognitive impairment, dementia, and depression. Pathophysiologically, recent positron emission tomography data suggest astroglial activation after experimental myocardial infarction (MI). We analyzed peripheral surrogate markers of glial (and neuronal) damage serially within 12 months after the first ST-elevation MI (STEMI). Serum levels of glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) were quantified using ultra-sensitive molecular immunoassays. Sufficient biomaterial was available from 45 STEMI patients (aged 28 to 78 years, median 56 years, 11% female). The median (quartiles) of GFAP was 63.8 (47.0, 89.9) pg/mL and of NfL 10.6 (7.2, 14.8) pg/mL at study entry 0–4 days after STEMI. GFAP after STEMI increased in the first 3 months, with a median change of +7.8 (0.4, 19.4) pg/mL (p = 0.007). It remained elevated without further relevant increases after 6 months (+11.7 (0.6, 23.5) pg/mL; p = 0.015), and 12 months (+10.3 (1.5, 22.7) pg/mL; p = 0.010) compared to the baseline. Larger relative infarction size was associated with a higher increase in GFAP (ρ = 0.41; p = 0.009). In contrast, NfL remained unaltered in the course of one year. Our findings support the idea of central nervous system involvement after MI, with GFAP as a potential peripheral biomarker of chronic glial damage as one pathophysiologic pathway.     

Noise peaks influence communication in the operating room. An observational study

Noise peaks are powerful distractors. This study focuses on the impact of noise peaks on surgical teams’ communication during 109 long abdominal surgeries. We related measured noise peaks during 5-min intervals to the amount of observed communication during the same interval. Results show that noise peaks are associated with less case-relevant communication; this effect is moderated by the level of surgical experience; case-relevant communications decrease under high noise peak conditions among junior, but not among senior surgeons. However, case-irrelevant communication did not decrease under high noise level conditions, rather there was a trend to more case-irrelevant communication under high noise peaks. The results support the hypothesis that noise peaks impair communication because they draw on attentional resources rather than impairing understanding of communication. As case-relevant communication is important for surgical performance, exposure to high noise peaks in the OR should be minimised especially for less experienced surgeons.

Practitioner Summary: This study investigated whether noise during surgeries influenced the communication within surgical teams. During abdominal surgeries, noise levels were measured and communication was observed. Results showed that high noise peaks reduced the frequency of patient-related communication, but did not reduce patient-irrelevant communication. Noise may negatively affect team coordination in surgeries.     

Online Adaptive Approximate Optimal Tracking Control with Simplified Dual Approximation Structure for Continuous-time Unknown Nonlinear Systems

This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics. The requirement of the complete knowledge of system dynamics is avoided by employing an adaptive identifier in conjunction with a novel adaptive law, such that the estimated identifier weights converge to a small neighborhood of their ideal values. An adaptive steady-state controller is developed to maintain the desired tracking performance at the steady-state, and an adaptive optimal controller is designed to stabilize the tracking error dynamics in an optimal manner. For this purpose, a critic neural network (NN) is utilized to approximate the optimal value function of the Hamilton-Jacobi-Bellman (HJB) equation, which is used in the construction of the optimal controller. The learning of two NNs, i.e., the identifier NN and the critic NN, is continuous and simultaneous by means of a novel adaptive law design methodology based on the parameter estimation error. Stability of the whole system consisting of the identifier NN, the critic NN and the optimal tracking control is guaranteed using Lyapunov theory; convergence to a near-optimal control law is proved. Simulation results exemplify the effectiveness of the proposed method.