Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Vehicle Queue and Dissipation Detection Algorithm Based on Spatial-Temporal Markov Random Field

Based on video processing technology, this paper proposes a spatial-temporal Markov random field method. This method can track the position changes of vehicles queue tail and head at intersections in real time, which accurately describes the formation and dissipation of the queue. When the tracking algorithm is used, first we decided on an allocation of an initial label. Then through the spatial-temporal Markov random field, we refined and optimized the label’s allocation. In the refining process, the algorithm should consider the piece’s relation between time and space, and the Markov model can be used, and then a label can be assigned to a piece. Experimental results show the accuracy rate is more than 92% by real-time tracking of tail and head positions under varying weather conditions and levels of illumination.
Assistance algorithm of nursing for amiodarone intravenous infusion

This study aimed at identifying scientific publication on phlebitis caused by amiodarone and proposes a nursing care algorithm for interventions in intravenous amiodarone administration grounded in the Infusion Nursing Society and the Center for Disease Control and Prevention. It is a descriptive study mediated by integrative review in MedLine, LILACS, IBECS, BDENF, Cochrane Library and Scielo bases, published from 2006 to 2013. The sample consisted of nine articles. The evidence pointed the incidence of phlebitis due to the infusion of amiodarone and the need to control this event. The algorithm proposed shows the materials to be used and the procedure of drug administration in order to minimize injury. Besides subsidizing the development of future studies, this algorithm also promotes the incorporation of the best recommendation for the interventionist clinical practice.
A Unified Framework for the Evaluation of Complex Networks

Graph metrics are important tools for the comparisons between networks. However, different graph metrics may lead to different evaluation results for a certain network. In this paper, we generate a unified framework for the multi-metrics and study the collective evaluation method for complex networks with multi-metrics. Finally, the experimental analysis verifies the correctness and practical values of the proposed method.
Towards optimisation of the management of resources in the CloudSim simulator

In cloud computing, the availability and the performance of the services are two significant aspects to be dealt with. These two aspects can deteriorate to see even stopping the services of cloud computing, if they are not taken into account. Users see that cloud computing delivers elastic computing services to users based on their needs. This paper aims at improving operation of cloud service of the cloud computing environment. Cloud services must be available in every situation possible and improving performance in this environment. To meet this aim, we propose, in this paper, two approaches which aim at returning a better availability of datacenters without deteriorated the performances for the answers of the users. The first uses the principle of the messages of availability and the second uses the principle of reservation in advance.
Joint Source-Channel Coding in Dictionary Methods of Lossless Data Compression

—Limitations on memory and resources of communications systems require powerful data compression methods. De-compression of compressed data stream is very sensitive to errors which arise during transmission over noisy channels, therefore error correction coding is also required. One of the solutions to this problem is the application of joint source and channel coding. This paper contains a description of methods of joint source-channel coding based on the popular data compression algorithms LZ’77 and LZSS. These methods are capable of introducing some error resiliency into compressed stream of data without degradation of the compression ratio. We analyze joint source and channel coding algorithms based on these compression methods and present their novel extensions. We also present some simulation results showing usefulness and achievable quality of the analyzed algorithms.
A novel iris patterns matching algorithm of weighted polar frequency correlation

Iris recognition is recognized as one of the most accurate techniques for biometric authentication. In this paper, we present a novel correlation method - Weighted Polar Frequency Correlation(WPFC) - to match and evaluate two iris images, actually it can also be used for evaluating the similarity of any two images. The WPFC method is a novel matching and evaluating method for iris image matching, which is complete different from the conventional methods. For instance, the classical John Daugman’s method of iris recognition uses 2D Gabor wavelets to extract features of iris image into a compact bit stream, and then matching two bit streams with hamming distance. Our new method is based on the correlation in the polar coordinate system in frequency domain with regulated weights. The new method is motivated by the observation that the pattern of iris that contains far more information for recognition is fine structure at high frequency other than the gross shapes of iris images. Therefore, we transform iris images into frequency domain and set different weights to frequencies. Then calculate the correlation of two iris images in frequency domain. We evaluate the iris images by summing the discrete correlation values with regulated weights, comparing the value with preset threshold to tell whether these two iris images are captured from the same person or not. Experiments are carried out on both CASIA database and self-obtained images. The results show that our method is functional and reliable. Our method provides a new prospect for iris recognition system.
Nature Inspired Computational Optimisation Methods for System Dynamics

This thesis aims to provide the state of the art “nature-inspired computing” (NIC) approaches and investigate their applications in system dynamics (SD) models. Specifically, we will focus on particle swarm optimisation (PSO), genetic algorithms (GAs) and cooperative coevolutionary approaches (CCEAs), and their application in the beer distribution game (BDG). Three pieces of work have been conducted in this thesis. Firstly, we focus on improving the performance of PSO. PSO is an intelligent random search algorithm, and the key to its success is to effectively balance between the exploration and the exploitation of the solution space. This thesis presents a new dynamic topology called “gradually increasing directed neighbourhoods (GIDN)”. Each particle begins with a small number of connections and there are many small isolated swarms that improve the exploration ability. At each iteration, we gradually add a number of new connections between particles which improves the ability of exploitation. A series of experiments show that the PSO with GIDN performs much better than a number of the state of the art algorithms. Secondly, we are concerned with the applications of the PSO approaches to the BDG. The BDG offers a complex simulation environment involving multidimensional constrained parameters. In order to obtain the optimal strategies for the BDG, we proposed the use of our PSO with GIDN. A number of the state of the art PSO algorithms and GAs have also been used as benchmarks. Two scenarios for the BDG are examined. In one scenario, all sectors use the same inventory management strategies while, in the other one, each sector has different strategies. The optimal strategies and their performance for both scenarios are examined. Furthermore, our PSO’s performance is also investigated. Finally, we investigate the applications of CCEAs to the BDG. CCEAs are a flexible multipopulation based framework. Each population can evolve independently and have their own goals. We have developed a coevolutionary framework for evolving strategies across this fourtier BDG. Our results identify the effects of two different management strategies on the supply chain performance. The first is where sectors are individually oriented, and the second is where sectors cooperate to achieve a common goal and are group oriented. We design two fitness approaches to reflect both management strategies. We have used this framework to identify the impact of these two strategies on the supply chain performance. Furthermore, two different demand patterns: a step input and uniformly distributed demand are also examined. A series of managerial insights have been derived from our extensive simulations.
Nasopalatine Duct Cyst: A Case Report

Nasopalatine duct cysts are the most common of developmental non-odontogenic cysts in the oral cavity and represent 10% of maxillary cysts. The cyst arises from epithelial remnants from the nasopalatine duct. These cysts are usually asymptomatic, but the most commonly reported clinical symptom is swelling in the anterior part of the palate, just behind the palatal papilla. This swelling is not always on midline. The recurrence of this is rare (2%). In this report, a 21-year-old man from Ahvaz, with the chief complaints of numbness, anterior teeth mobility, intraoral swelling and facial asymmetry, is reported. On clinical examination, a non tender swelling on the midline of hard palate and buccal vestibule, covered by normal mucosa was seen. The crown of central incisors was displaced medially. In panoramic, maxillary occlusal cross sectional, anterior maxillary periapical and computed tomography views, well-defined unilacular radiolucency with corticated margins was seen in the anterior region of maxilla. Biopsy of the lesion was planned and final pathologic diagnosis was “nasopalatine duct cyst”. Therefore, nasopalatine duct cyst, is one of the differential diagnoses of unilacular radiolucencies in the anterior region of maxilla. Histopathologic examinations help us to reach the correct diagnosis and disburden us from unnecessary treatments.
Fast wavelet packet transform-based algorithm for numerical solution of image restoration problems in a parallel environment

We explore the filtering properties of wavelets functions in order to develop accurate and efficient numerical algorithms for Image Restoration problems. We propose a parallel implementation for MIMD distributed memory environments. The key insight of our approach is the use of distributed versions of Level 3 Basic Linear Algebra Subprograms as computational building blocks and the use of Basic Linear Algebra Communication Subprograms as communication building blocks for advanced architecture computers. The use of these low-level mathematical software libraries guarantees the development of efficient, portable and scalable high-level algorithms and hides many details of the parallelism from the user's point of view. Numerical experiments on a simulated image restoration applications are shown. The parallel software has been tested on a 12 nodes IBM SP2 available at the Center for Research on Parallel Computing and Supercomputers in Naples (Italy).
Right Sided Congenital Diaphragmatic Hernia, an Operative Challenge

Congenital diaphragmatic hernia is one of the most challenging malformations associated with high mortality. Right side diaphragmatic hernia is rare (20%) as compared to left side (80%) with overall incidence of 1 in 5000 live births. Risk factors associated with poor outcome include low birth weight, prematurity, associated structural anomalies, and chromosomal defects. This is a case of isolated CDH diagnosed post natally, respiratory distress started immediately after birth, with herniation of Liver in to right hemi thorax, wide diaphragmatic defect, severe pulmonary hypertension and no structural defects. The successful Post natal management of this case with high frequency oscillatory ventilation and diligent operative technique, and institution of muscle paralysis (atracurium infusion) in the postoperative period has yielded optimum result in this case. The inpatient stay was for 25 days.
The TACOMA learning architecture for reflective growing of neural networks

One of the important problems to be solved for neural network applications is to find a suitable network structure solving the given task. To reduce the engineering efforts for the architecture design a data driven algorithm is desirable which constructs a network structure during the learning process. There are different approaches for structure adaptation with evolutionary algorithms, growth algorithms and others. To solve large problems successfully it is necessary to divide the problem into subproblems and to solve them separately by experts. This is a fundamental principle of nature. To implement this principle in artificial neural networks there are different approaches, but these algorithms yield fixed network structures. The authors propose a learning architecture for growing complex artificial neural networks which tries to include both sides of the coin, structure adaptation and task decomposition. The growing process is controlled by self-observation or reflexion. The algorithm generates a feedforward network bottom up by cyclically inserting cascaded hidden layers. Inputs of a hidden layer unit are locally restricted with respect to the input space by using a new kind of activation function, combining the local characteristics of radial basis function units with sigmoid units. Contrary to the cascade-correlation learning architecture the authors introduce different correlation measures to train the network units featuring different goals. The task decomposition between subnetworks is done by maximizing the anticorrelation between the hidden layer units output and a connection routing algorithm which only connects cooperative units of different layers. These features resemble the TACOMA (TAsk decomposition, COrrelation Measures and local Attention neurons) learning architecture. Self-observation is done by transforming the errors and the network structure to the input space. So it is possible to infer from errors to structure and reverse.
Clinical, Biological, and Biochemical Effects of Pyrazofurin

Extensive conversion of Pyrazofurin to the 5 -PO, and higher phosphorylated derivatives occurs in naturally sensitive (Walker 256 carcinosarcoma) and resistant (L5178Y leukemia) tumors. A similar degree of conversion to phosphorylated derivatives was seen in acute myelogenous leukemic cells from patients. The insensitivity of the L5178Y leukemia may result from its greater capacity to utilize uridine in the presence of Pyrazofurin. Further more, L5178Y cells in culture can survive uridine depriva tion for much longer periods than can the Walker 256 tumor. Although intracellular concentrations of both uri dine triphosphate and cytidine triphosphate are depleted in culture, only a transient decrease is seen in the concen tration of cytidine triphosphate in L5178Y ascites cells from mice. The reductions in pyrimidine nucleotide pools may be responsible for the synergistic growth-inhibitory effects observed when Pyrazofurin is combined with 5fluorodeoxyuridine or 1-AŸ-D-arabinofuranosylcytosine. In patients, blockade of the metabolism of [carboxy/-14C]orotate was greater than 99% 15 min and 24 hr after a single i.v. dose of Pyrazofurin (200 mg/sq m). In 17 patients given weekly i.v. therapy, no complete remissions or major regressions of tumor masses were seen. Antitumor effect was observed in two of three patients with acute myelogenous leukemia and in one patient each with erythroleukemia, mycosis fungoides, and psoriasis. The limiting toxicity was oral mucositis; depression or erythropoiesis was apparent in all patients treated for more than 4 weeks.
Motion Control for a Planetary Exploration Rover with Six Steerable Wheels

IARES is a highly flexible planetary exploration demonstration rover developed mainly for autonomous navigation and locomotion studies. It has 19 degrees of freedom, including six active, steerable wheels. The rover has software for autonomous navigation, including stereo camera perception, path planning and motion control. It is complemented by a visual simulator that can substitute the real rover for practical purposes. The goal of this MSc thesis, carried out at CNES in Toulouse, has been to make the most of the locomotion capabilities of this rover. Given the hardware platform and and the software environment, the partly developed motion control system had to be studied. It was decided to improve parts of the existing motion control system by fixing smaller problems and doing adjustments to the behaviour. Extensions in form of so-called crab-like movements were also studied and developed. This includes using the steering capabilities of all six wheels of the rover in order to obtain a translational movement. The extensions were implemented into the existing control algorithm. The studies were conducted on a Solaris platform. Code was developed in C. The new performance was tested in the simulator and the results were analysed. The six-wheel steering extensions provided more flexibility to the motion control, by allowing position and heading deviations to be corrected independently. The rover managed to correct its position deviations more quickly, and to increase its locomotion speed.
Forecasting: Methods and Applications

Presents a wide range of forecasting methods useful for undergraduate or graduate students majoring in business management, economics, or engineering. Develops skills for selecting the proper methodology. Integrates forecasting with the planning and decision-making activities within an organization. Methods of forecasting include: decomposition, regression analysis, and econometrics. Stresses the strengths and weaknesses of the individual methods in various types of organizational areas. Numerous examples are included.
Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis

We describe the implementation, experience and preliminary results obtained with a 3-D computerized brain atlas for topographical and functional analysis of brain sub-regions. A volume-of-interest (VOI) atlas was produced by manual contouring on 64 adjacent 2 mm-thick MRI slices to yield 60 brain structures in each hemisphere which could be adjusted, originally by global affine transformation or local interactive adjustments, to match individual MRI datasets. We have now added a non-linear deformation (warp) capability (Bookstein, 1989) into the procedure for fitting the atlas to the brain data. Specific target points are identified in both atlas and MRI spaces which define a continuous 3-D warp transformation that maps the atlas on to the individual brain image. The procedure was used to fit MRI brain image volumes from 16 young normal volunteers. Regional volume and positional variability were determined, the latter in such a way as to assess the extent to which previous linear models of brain anatomical variability fail to account for the true variation among normal individuals. Using a linear model for atlas deformation yielded 3-D fits of the MRI data which, when pooled across subjects and brain regions, left a residual mis-match of 6 - 7 mm as compared to the non-linear model. The results indicate a substantial component of morphometric variability is not accounted for by linear scaling. This has profound implications for applications which employ stereotactic coordinate systems which map individual brains into a common reference frame: quantitative neuroradiology, stereotactic neurosurgery and cognitive mapping of normal brain function with PET. In the latter case, the combination of a non-linear deformation algorithm would allow for accurate measurement of individual anatomic variations and the inclusion of such variations in inter-subject averaging methodologies used for cognitive mapping with PET.
An Approach of Image Retrieval for Natural Scenes based on Local Features and Topology

According to the survey, nearly 80 percent information obtaining by humans are from the vision, it is a very critical way of human beings in knowing the objective world [1]. In the information age, image information as one of the most common multimedia information which provide a more intuitive, vivid, rich description has play more important role in acquiring knowledge. Digital cameras, scanners and other image equipments make digital images at an alarming rate to be created, while the large-capacity storage technology, database systems and Internet techAbstract. Image retrieval problem is a hotspot research filed in recent decades since it has a wide range of applications. Topology studies which used for image retrieval has rare research. This paper has characterized the departure from the image, fully tap the characteristics of the image itself, using image feature characteristics of local feature points to achieve the image content with regional dimensions. Marking the completion of the image content, the use of two images may be compared with similar characteristic sequence identification, signature sequence with the greatest similarity, which is considered to be similar. The approach which including topology theory can overcome the natural image retrieval at the local characteristics of defects, which can protect the global sequences, has a good characterization. Methods for the analysis of natural images operation, experimental results show that the combination of local feature topology method can overcome the mere use of local characteristics identified defects, to achieve the purpose of image retrieval.
Global modeling of microwave applications by combining the FDTD method and a general semiconductor device and circuit simulator

This paper presents the coupling of two commercially available simulation codes: DESSIS-ISE, a multidimensional semiconductor device and circuit simulator, and EMLAB-ISE, an electromagnetic-field solver based on the finite-difference time-domain (FDTD) method. Full-wave electromagnetics and nonlinear devices are simulated in a coupled self-consistent way using the lumped-element approach. The active region of the device is represented as a lumped element within the FDTD grid, while the packaging and waveguiding structures are modeled in their physical dimensions. For the nonlinear device, multidimensional semiconductor device simulation, as well as standard SPICE models, may be applied. Several examples show the capability of comprehensive analysis of microwave applications and the versatility in the simulation of the active elements. The coupling formalism is explained in detail, including time-step adjustment and biasing of active devices.
Optical Coherence Tomography in X-Linked Juvenile Retinoschisis

X-linked juvenile retinoschisis clinical presentation varies greatly due to the high phenotypic variability of the affected gene RS1. The use of optical coherence tomography (OCT) has been useful in describing a wide variety of findings in affected patients. In this article we describe the clinical presentation and use of optical coherence tomography in X-linked juvenile retinoschisis.