A comparison of the effects of Methylprednisolone Acetate, Sodium Hyaluronate and Tenoxicam in the treatment of non-reducing disc displacement of the temporomandibular joint

This clinical study aimed to radiologically and clinically compare the effect of intra-articular injection of methylprednisolone, sodium hyaluronate or tenoxicam following arthrocentesis with that of arthrocentesis alone in patients with non-reducing disc displacement. A total of 44 patients radiographically diagnosed with non-reducing disc displacement of the temporomandibular joint (TMJ) were randomly divided into four treatment groups, as follows: Group 1, arthrocentesis alone; Group 2, arthrocentesis plus methylprednisolone acetate; Group 3, arthrocentesis plus sodium hyaluronate; Group 4, arthrocentesis plus tenoxicam. Maximum mouth opening (MMO), lateral movement, pain severity and tenderness of TMJ and muscles of mastication on palpation were measured before treatment and at 1 week and 1, 3 and 6 months after treatment. Disc position, presence or absence of disc reduction, level of effusion, joint movement and joint space were also evaluated using magnetic resonance imaging (MRI) before treatment and 6 months after treatment. No significant differences in treatment success were found among the four groups. MRI findings did not vary significantly among the groups, but pre- and post-operative MRI findings varied significantly within all four groups (p<0.001). According to the data from this study, it may be concluded that either arthrocentesis alone or arthrocentesis with methylprednisolone acetate or sodium hyaluronate or tenoxicam intra-articular injections are similarly effective and promising methods in the treatment of TMJ with non-reducing disc displacement

Sparsity Based Image Retrieval using relevance feedback

In this paper, a Content Based Image Retrieval (CBIR) algorithm employing relevance feedback is developed. After each round of user feedback Biased Discriminant Analysis (BDA) is utilized to find a transformation that best separates the positive samples from negative samples. The algorithm determines a sparse set of eigenvectors by L1 based optimization of the generalized eigenvalue problem arising in BDA for each feedback round. In this way, a transformation matrix is constructed using the sparse set of eigenvectors and a new feature space is formed by projecting the current features using the transformation matrix. Transformations developed using the sparse signal processing method provide better CBIR results and computational efficiency. Experimental results are presented. © 2012 IEEE

Flame detection method in video using covariance descriptors

Video fire detection system which uses a spatio-temporal covariance matrix of video data is proposed. This system divides the video into spatio-temporal blocks and computes covariance features extracted from these blocks to detect fire. Feature vectors taking advantage of both the spatial and the temporal characteristics of flame colored regions are classified using an SVM classifier which is trained and tested using video data containing flames and flame colored objects. Experimental results are presented. © 2011 IEEE

Spectroscopic investigation of nitrate-metal and metal-surfactant interactions in the solid AgNO3/C12EO10 and liquid-crystalline [M(H2O)n](NO3)2/C12EO 10 systems

Interactions of the nitrate ions in various metal nitrate salts with CnH2n-1(CH2CH2O)mOH (CnEOm)-type nonionic surfactants have been investigated both in the solid and in the liquid-crystalline (LC) systems. In the ternary system, the mixture of salt/water/CnEOm has a mesophase up to a certain concentration of salt, and the nitrate ions in this phase are usually in a free-ion form. However, upon the evaporation of the water phase, the nitrate ion interacts with the metal center and coordinates as either a bidentate or unidentate ligand. It is this interaction that makes the AgNO3 ternary system undergo a phase separation by releasing solid Ag(CnEOm)xNO3 complex crystals. In contrast, the salt/surfactant systems maintain their stable LC phases for months. Note also that the salt/surfactant systems consist of transition-metal aqua complexes in which the coordinated water molecules play a significant role in the self-assembly and organization of the nonionic surfactant molecules into an LC mesophase. Throughout this work, Fourier transform infrared spectroscopy has been extensively used to investigate the interactions of the nitrate ions with a metal center and the metal ions with the surfactant molecules. Polarized optical microscopy and X-ray diffraction techniques have been applied to investigate the nature of the crystalline and LC phases

Risk-Adaptive Learning of Seismic Response using Multi-Fidelity Analysis

Performance-based earthquake engineering often requires a large number of sophisticated nonlinear time-history analyses and is therefore demanding both with regard to computing resources and technical expertise. We develop a risk-adaptive statistical learning method based on multi-fidelity analysis that enables engineers to conservatively predict structural response using only low-fidelity analyses such as Pushover analyses. Using a structural model of a 35-story building in California and a training data set consisting of nonlinear time-history and pushover analyses for 160 ground motions, we accurately and conservatively predict maximum story drift ratio, top-story drift ratio, and normalized base shear under the effect of 40 ground motions not seen during the training

Energy efficient cosine similarity measures according to a convex cost function

We propose a new family of vector similarity measures. Each measure is associated with a convex cost function. Given two vectors, we determine the surface normals of the convex function at the vectors. The angle between the two surface normals is the similarity measure. Convex cost function can be the negative entropy function, total variation (TV) function and filtered variation function constructed from wavelets. The convex cost functions need not to be differentiable everywhere. In general, we need to compute the gradient of the cost function to compute the surface normals. If the gradient does not exist at a given vector, it is possible to use the sub-gradients and the normal producing the smallest angle between the two vectors is used to compute the similarity measure. The proposed measures are compared experimentally to other nonlinear similarity measures and the ordinary cosine similarity measure. The TV-based vector product is more energy efficient than the ordinary inner product because it does not require any multiplications. © 2016, Springer-Verlag London

Fire detection in video using LMS based active learning

In this paper, a video based algorithm for fire and flame detection is developed. In addition to ordinary motion and color clues, flame flicker is distinguished from motion of flame colored moving objects using Markov models. Irregular nature of flame boundaries is detected by performing temporal wavelet analysis using Hidden Markov Models as well. Color variations in fire is detected by computing the spatial wavelet transform of moving fire-colored regions. Boundary of flames are represented in wavelet domain and irregular nature of the boundaries of fire regions is also used as an indication of the flame flicker. Decisions from sub-algorithms are linearly combined using an adaptive active fusion method. The main detection algorithm is composed of four sub-algorithms (i) detection of fire colored moving objects, (ii) temporal, and (iii) spatial wavelet analysis for flicker detection and (iv) contour analysis of fire colored region boundaries. Each algorithm yields a continuous decision value as a real number in the range [-1, 1] at every image frame of a video sequence. Decision values from sub-algorithms are fused using an adaptive algorithm in which weights are updated using the least mean square (LMS) method in the training (learning) stage. © 2009 Springer Science+Business Media, LLC

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Deep Convolutional Generative Adversarial Networks Based Flame Detection in Video

Real-time flame detection is crucial in video based surveillance systems. We propose a vision-based method to detect flames using Deep Convolutional Generative Adversarial Neural Networks (DCGANs). Many existing supervised learning approaches using convolutional neural networks do not take temporal information into account and require substantial amount of labeled data. In order to have a robust representation of sequences with and without flame, we propose a two-stage training of a DCGAN exploiting spatio-temporal flame evolution. Our training framework includes the regular training of a DCGAN with real spatio-temporal images, namely, temporal slice images, and noise vectors, and training the discriminator separately using the temporal flame images without the generator. Experimental results show that the proposed method effectively detects flame in video with negligible false positive rates in real-time