Miracle-dz/UniFAR-FTUs huggingface dataset:
THE INFLUENCE OF SOURCE TERMS ON STABILITY AND CONSERVATION IN 1 D HYPERBOLIC SYSTEM OF EQUATIONS : APLICATION TO FIXED AND MOVABLE BEDS

The non conservative terms that appear in systems of conservation laws poses an extra difficulty when managing practical cases. The influence of this terms in the stability region for finite volume schemes is analyzed under the hypothesis of a well balanced scheme, based in the C-Property. It is demonstrated that in each particular situation the stability region is amplified or reduced, and how in some special cases depends on the initial conditions. The time step requirements for stability are defined at the cell edges, related with the traditional Courant-Friedrichs-Lewy (CFL) condition. It is applied to the shallow water equations, to the shallow water equations with solute transport and to sediment transport problems.
A comprehensive model of hidden curriculum management in medical education

Introduction: Hidden curriculum plays a main role in professional learning, formation of professional identity, socialization, moral development and learning values, attitudes, beliefs, and knowledge in learners, so it needs to be managed. Although the majority of the theorists believe in the existence of a hidden curriculum and its greater effect and sustainability compared to the formal curriculum; none has proposed a comprehensive model or approach for its management. This study aimed to design a hidden curriculum management model in medical education. Methods: In this study, the authors used the theory or model construction methodology to synthesize a hidden curriculum management model in medical education. According to Walker and Avant; this methodology includes the following three steps for synthesizing the model: specifying focal concepts, reviewing the literature, and organizing concepts into an integrated and efficient representation. Results: The results of the study showed that numerous factors affected the hidden curriculum including environmental factors (professional, organizational), human factors (teachers, peers and staff), and formal curriculum and learner’s influenceability filter which bear important messages for learners, staff and teachers. To manage the hidden curriculum, in addition to the above factors, it is necessary to manage knowledge and the learners' learning in an educational institution. Conclusion: This study revealed that to achieve the desired performance in students, the formal curriculum reform is not sufficient. Moreover, other factors such as environmental factors, human factors, learner’s influenceability filter, and knowledge management should also be taken into account. The hidden curriculum management model can be used for training and educating the staff and students with the desired performance in any educational institution.
Protective Effects of Nutria Bile against Thioacetamide-Induced Liver Injury in Mice

Several eradication programs have been developed and executed to curb alien invasive species that tend to damage the ecological environments they colonize; however, only few studies have evaluated the utilization of carcasses of these species after eradication. Nutria (Myocastor coypus) is an invasive rodent species targeted by eradication programs in many countries. We noted that nutria produce large amounts of ursodeoxycholic acid (UDCA) in their bile. UDCA is a unique component responsible for the anti-inflammatory and hepatoprotective effects exerted by bear bile. Therefore, we sought to examine the medicinal utility of nutria carcasses by investigating the hepatoprotective effect of their bile in mice. C57BL/6 mice were injected with thioacetamide (TAA), which induced liver damage by increasing Kupffer cell infiltration. Administration of nutria bile reduced hepatic inflammation, improved hepatic function, and increased the levels of senescence marker protein 30 (an indicator of hepatocyte viability). Our results show that nutria bile exerts protective effects against TAA-induced liver injury in mice, suggesting that nutria carcasses may be used for the treatment of liver injuries.
How Acute and Chronic Alcohol Consumption Affects Brain Networks : Insights into Multimodal Neuroimaging

Background: Multimodal imaging combining 2 or more techniques is becoming increasingly important because no single imaging approach has the capacity to elucidate all clinically relevant characteristics of a network. Methods: This review highlights recent advances in multimodal neuroimaging (i.e., combined use and interpretation of data collected through magnetic resonance imaging [MRI], functional MRI, diffusion tensor imaging, positron emission tomography, magnetoencephalography, MR perfusion, and MR spectroscopy methods) that leads to a more comprehensive understanding of how acute and chronic alcohol consumption affect neural networks underlying cognition, emotion, reward processing, and drinking behavior. Results: Several innovative investigators have started utilizing multiple imaging approaches within the same individual to better understand how alcohol influences brain systems, both during intoxication and after years of chronic heavy use. Conclusions: Their findings can help identify mechanism-based therapeutic and pharmacological treatment options, and they may increase the efficacy and cost effectiveness of such treatments by predicting those at greatest risk for relapse.
Implementation of Diabetic Retinopathy Detection System for Enhance Digital Fundus Images

Diabetic retinopathy is the most common diabetic eye disease and a leading cause of blindness. It is caused by changes in the blood vessels of the retina. This paper, basically work on a computer based approach for the detection of diabetic retinopathy using enhances digital fundus images. There are many features present in retina but to examine it carefully and properly and to extract the feature properly which is one of the primitive step to detect signs of diabetic retinopathy and which is used to identify main cause of blindness that could be prevented with the help of this automatic detection process. The automatic detection process reduces examination time, and increase accuracy. In this paper provide key technique that helps to diagnose Diabetic Retinopathy in retinal fundus images. In this paper a new algorithm to detect the blood vessels effectively has been proposed. The initial enhancement of the image is carried out using pre-processing stage, followed by Curvelet Transforms that are applied to the equalized image. This enhanced image is used for the extraction of the blood vessels. The estimation of exudates is obtained from blood vessels and optic disc extracted image. The results shows the enhanced retinal images of blood vessels have a better PSNR and area shows the
Chapter 20 Scalable Multi-camera Tracking in a Metropolis

The majority of work in person re-identification is focused primarily on the matching process at an algorithmic level, from identifying reliable features to formulating effective classifiers and distance metrics in order to improve matching scores on established ‘closed-world’ benchmark datasets of limited scope and size. Very little work has explored the pragmatic and ultimately challenging question of how to engineer working systems that best leverage the strengths and tolerate the weaknesses of the current state of the art in re-identification techniques, and which are capable of scaling to ‘open-world’ operational requirements in a large urban environment. In this work, we present the design rationale, implementational considerations and quantitative evaluation of a retrospective forensic tool known as Multi-Camera Tracking (MCT). The MCT system was developed for re-identifying and back-tracking individuals within huge quantities of open-world CCTV video data sourced from a large distributed multi-camera network encompassing different public transport hubs in a metropolis. There are three key characteristics of MCT, associativity, capacity and accessibility, that underpin its scalability to spatially large, temporally diverse, highly crowded and topologically complex urban environments with transport links.We discuss amultitude of functional features that in combination address these characteristics. We consider computer vision techniques and machine learning algorithms, including relative feature ranking for inter-camera matching, Y. Raja Vision Semantics Ltd, London, UK e-mail: yraja@visionsemantics.com S. Gong (B) Queen Mary University of London, London, UK e-mail: sgg@eecs.qmul. ac.uk S. Gong et al. (eds.), Person Re-Identification, 413 Advances in Computer Vision and Pattern Recognition, DOI: 10.1007/978-1-4471-6296-4_20, © Springer-Verlag London 2014 414 Y. Raja and S. Gong global (crowd-level) and local (person-specific) space–time profiling, attribute reranking and machine-guided data mining using a ‘man-in-the-loop’ interactive paradigm. We also discuss implementational considerations designed to facilitate linear scalability to an aribitrary number of cameras by employing a distributed computing architecture. We conduct quantitative trials to illustrate the potential of theMCT system and its performance characteristics in coping with very large-scale open-world multi-camera data covering crowded transport hubs in a metropolis.
SBT in Action: The Simple Scala Build Tool

Simple Build Tool: sbt. The idea of a high quality build tool for Scala is indeed simple, but sbt itself is richly-featured and powerful. Documentation for sbt is sparse, and the blogs, tutorials, and other bits scattered across the web demand a comprehensive, accurate picture of how to build Scala projects effectively and efficiently. This book will help transform ugly, complex builds into a streamlined, automated, and repeatable process. SBT in Action is first and foremost a tutorial about effectively building Scala projects. It introduces the sbt tool with a simple project that establishes the fundamentals of running commands and tasks. Next, it shows how to use the peripheral libraries in sbt to make common tasks simpler. This includes an I/O library to deal with files and how to parse user input in a meaningful fashion. Finally, it covers how to deploy software effectively. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
PROVIDING SECURITY TO PUBLISH / SUBSCRIBE SYSTEM USING STRONG NETWORK CRYPTOGRAPHIC TECHNIQUES AND TWO TIER ENCRYPTION

There are different types of distributed systems existing in this world which needs the massive amount of information to be transferred between them and along with it comes the need of secure communication between different parties. Publish subscribe system is the communication archetype for information broadcasting in huge distributed system. Selling a product and merchandising it, isn't a cup of tea for each manufactures, thus all manufactures are probing for various third party product promoters like promotion agencies, dealers or third party brokers. For a business to explore, brokers are playing a very crucial role. However loyalty of the brokers cannot be assured. So to handle this problem many broker less systems are been proposed for publish-subscribe system in distributed pattern. However most of these systems have not completely succeeded in achieving privacy of the data over the transactions. So proposed system forwards an idea of preserving the confidentiality of information by key generation for each publish/ subscribe transactions and the keys are randomly generated based on the event details and subscriber data using reverse circle cipher[10] and two tier encryption.
Digital WPI Interactive Qualifying Projects ( All Years ) Interactive Qualifying Projects April 2019 Exploring AI Moral Reasoning

Artificial intelligence is being deployed in increasingly autonomous systems where it will have to make moral decisions. However, the rapid growth in artificial intelligence is outpacing the research in building explainable systems. In this paper, a number of problems around one facet of explainable artificial intelligence, training data, is explored. Possible solutions to these problems are presented. Additionally, the human decision-making process in unavoidable accident scenarios is explored through qualitative analysis of survey results.
Effects of tetramethylpyrazine combined with bone marrow mesenchymal stem cells transplantation on recovery of neural function in rats with spinal cord injury

Objective: This study aims to investigate and analyze the effects of combined use of tetramethylpyrazine and bone marrow mesenchymal stem cells (BMSCs) transplantation on neurological recovery in rats with spinal cord injury (SCI). Methods: Male SD rats (aged from 3 to 4 weeks) were selected. BMSCs were isolated and cultured in vitro, and 3rd generation BMSCs were collected for follow-up experiments. Adult Wistar male rats were divided into the control group (sham-operated rats without spinal cord injury), the model group (spinal cord injured rats without BMSCs or drug treatment), the BMSCs group (spinal cord injury rats with BMSCs treatment) and the combination group (spinal cord injury rats with BMSCs and drug treatments). The changes of hindlimb motor function in each group were compared between 1 week and 2 weeks after surgery. At 2 weeks after surgery, the levels of neuroelectrophysiological sensory evoked potential (SEP) and motor evoked potential (MEP) were compared in each group. In addition, immunohistochemistry was used to analyze the changes of glial fibrillary acidic protein (GFAP) and nerve growth factor (NGF) in the spinal cord of rats in each group. Results: Compared with the control group, the motor function of the other groups was significantly reduced (all P<0.05). The latency of neural electrophysiological sensory evoked potentials and motor evoked potentials was significantly increased, and the amplitude was significantly reduced (all P<0.05). The positive rates of GFAP and NGF were decreased significantly (all P<0.05). Compared with the model group, the BMSCs and the combination groups could significantly restore the motor function of rats, and could significantly reduce the latency period of rats for electrophysiological sensory evoked potentials and motor evoked potentials. Moreover, it could improve the amplitude of rat’s neural electrophysiology sensory evoked potentials and motor evoked potentials (all P<0.05). The combined effects of BMSCs and ligustrazine hydrochloride were significantly better than that of BMSCs only (all P<0.05). The effects of BMSCs on recovery of spinal cord motor function in injured rats were more significant. Compared with the model group, the positive expression rates of GFAP and NGF in the BMSCs group and the combination group were significantly higher (all P<0.05). Compared with BMSCs group, the positive expression of GFAP and NGF of rates in the combination group was significantly higher (all P<0.05). Conclusion: Tetramethylpyrazine combined with bone marrow mesenchymal stem cells transplantation could significantly restore the neurological function of rats with spinal cord injury.
Interactive Sensor Visualization for Smart Manufacturing System

This paper presents a case study of augmenting an electronic screen with a projected display to help visualizing sensor data in a smart manufacturing set up. The visualization module displays both spatial and temporal data and by holding a semi-transparent screen in front of an electronic screen, a user can augment spatial information with temporal trend or vice versa. The system also supports interaction through an interactive laser pointer and is integrated to an early warning system to alert any deviation in sensor readings in the contexts of environment and people tracking.
MEDICAL IMAGE FUSION USING STATIONARY WAVELET TRANSFORM WITH DIFFERENT WAVELET FAMILIES

The Medical image fusion restrain the complementary and significant information from multiple source images that used for identify the diseases and better treatment. Image fusion has become vital part of medical diagnosis. This paper presents a comparative study of wavelet families along with its performance analysis. Magnetic Resonance Imaging (MRI) is used to fuse which form a contemporary image so as to improve the complementary and redundant information for diagnosis function. The proposed method of Stationary Wavelet Transform (SWT) with Fusion using Principle Component Analysis (PCA)are employed along with its analysis both Qualitative and Quantitative Analysis methods. Quantitative Analysis of experimental results are evaluated by way of performance metrics like peak signal to noise ratio (PSNR), Entropy (E), Standard deviation(SD)and Image Quality Assessment(Q). Assessment of different wavelet family techniques concludes the better approach for its upcoming research.
A Clustering Protocol for Wireless Sensor Networks Based on Chaos-PSO Optimization

In order to reduce the energy consumption of nodes and prolong the lifetime of WSN,a clustering protocol based on Chaos-PSO is proposed.The protocol improves the cluster-head selection mechanism,it considers nodes ' residual energy,distance to sink node and range of clusters.It optimizes cluster-head selection by Chaos-PSO.Then the other nodes decide whether they join the cluster or do not join it by comparing the signal strength of advertisement message with a predefined threshold,and the range of cluster is restricted.The simulation results show that,compared with LEACH,the new protocol can efficiently save energy and prolong the lifetime of WSN.
Labor Statistics Office of Prices and Living Conditions Uncertainty and Trade Elasticities

Incomplete information makes trade more elastic. When firms face uncertainty about demand, selection into exporting occurs based on productivity alone. In contrast, with complete information firms can condition export decisions on both productivity and demand. The difference in the information available to firms alters the value of trade at the extensive margin. We show that the identification of trade elasticities with incomplete information requires quantity data, while trade elasticities in complete information economies require sales data. Using Brazilian export data, we quantify trade elasticities in models with and without uncertainty, and find that the elasticities are larger under uncertainty. This gap increases when demand is more uncertain.
Ice Skating Motion Using Feedforward Control System by Model of Humanoid Robot

The research of humanoid is widely discussed whether by simulations or real machines. To bipedal walking, inverted pendulum has been used frequently for making a stable controller that enables researchers to realize stable gait through well-known control strategy. In our research, a model of humanoid robot including slipping, bumping, surface-contacting and point-contacting of foot is discussed, and its dynamical equation is derived by Newton-Euler method. Our research purpose aims to create a control system by utilize slip and stop slip using the model. In this paper, the ice skating, which is a kind of typical utilize slip motion will be discussed. And a new feedforward control method was prepared for realize this motion. Also some discussion from the viewpoint of step cycle, slipping speed and slipping distance during the ice skating motion will be introduced.
EPROCUREMENT INNOVATIONS IN ORGANIZATIONS

Nowadays, companies are facing several competitive pressures, which push them to seek for new and better ways to conduct their internal processes. One of this ways is throughout processes innovations that allow the company to establish and maintain competitive advantages; one example of this new trend is the emergence of eProcurement, an electronic system aimed to automate purchasing processes in organizations, which, if it is properly implemented, will derive in processes cost reduction and efficiency improvement on those same procedures. This study is focused on identifying the propeller factors that make companies to engage in this kind of ventures; as well as studying which are the outcomes generated inside the organizations once the electronic system has been implemented. Purpose The purpose of this thesis is to identify the elements that trigger automation of procurement processes in organizations; as well as describing the implications derived from automation during and after such implementation. The study was conducted through interviews with managers and final user from two Mexican companies which are currently using Oracle as their eProcurement tool. Conclusions The study found that depending on how well organizations prepare themselves and their staff for the upcoming change, different results may be obtained in terms of benefits and acceptability from final users. In order to avoid potential pitfalls, firms must bear in mind that, both operational and social awareness are key factors for a successful implementation. If a company takes into consideration their internal operational needs and staff opinions to base their decision of engaging on eProcurement, it will be more likely to obtain benefits related to cost reduction, improved efficiency and final user satisfaction. Title: eProcurement innovations in organizations: Reasons for Implementation and outcomes
REAL-TIME FACE RECOGNITION FOR ATTENDANCE MONITORING SYSTEM

Authentication is a momentous issue in controlling system in computer based globally. Human face recognition has been widely used to be an important branch of biometrics verifications in many applications such as door control, video monitoring system, networks security and also human computer interactions. This paper presented an automated attendance monitoring system with face recognition in a real-time background world for with a database of student’s information by using Personal Component Analysis (PCA) algorithm. This task is very difficult as the real-time background subtraction in an image is still a challenge. Addition to that, managing a database with multiple of student information’s is also an extra challenge to this system. The proposed biometric system is a real time attendance system based on the human face recognition with simple and fast algorithms and most important is gaining a high accuracy rate.
Demonstrating the Cost of Collecting In-Network Measurements for High-Speed VNFs

Recent advances in the state-of-the-art of software packet processing along with the incarnation of SDN and NFV in networking brings the utility of software switches in production to a high level. Accompanied with the wide deployment of the latter, comes the practical and urgent need of monitoring networks that are composed of software forwarders/switches. On the one hand, this may provide new types of very finegrain operational data that can be collected, thus bringing the opportunity for network managers to get a deeper understanding of the underlying network state and performance. On the other hand, this massive data availability comes at a cost: software measurements can highly affect the measured values, thus biasing the collected data. The intensity of this bias becomes stronger when measurements are taken close to the data path. We believe that this trade-off should be explored more in detail, since the availability of fine-grained data offers new opportunities to apply machine learning techniques to infer changes in the network state, to forecast the evolution of some performance metrics or to automatically respond to event triggers without the human intervention. While our long-run objective1 is a full framework for performing automated test on software routing platforms, in this demonstration we focus on two key points that are prerequisite for our approach: (i) we showcase the impact of collecting the desired data within a Virtual Network Function and (ii) we setup a simple environment for data visualization on the same physical device.