Miracle-dz/UniFAR-FTUs huggingface dataset:
RH-RT: A DATA ANALYTICS FRAMEWORK FOR REDUCING WAIT TIME AT EMERGENCY DEPARTMENTS AND CENTRES FOR URGENT CARE

Right Hospital – Right Time (RH-RT) is the conceptualization of the use of descriptive, predictive and prescriptive analytics with real-time data from Accident & Emergency (A&E)/Emergency Departments (ED) and centers for urgent care; its objective is to derive maximum value from wait time data by using data analytics techniques, and making them available to both patients and healthcare organizations. The paper presents an architecture for the implementation of RH-RT that is specific to the authors’ current work on a digital platform (NHSquicker) that makes available live waiting time from multiple centers of urgent care (e.g., A&E/ED, Minor Injury Units) in Devon and Cornwall. The focus of the paper is on the development of a Hybrid Systems Model (HSM) comprising of healthcare business intelligence, forecasting techniques and computer simulation. The contribution of the work is the conceptual RH-RT framework and its implementation architecture that relies on near real-time data from NHSquicker.
CHALLENGES OF DATA ACQUISITION FOR SIMULATION MODELS OF PRODUCTION SYSTEMS IN NEED OF STANDARDS

In this paper, we analyze the challenges in data acquisition for simulation models of production systems based on two cases from the robotics and aerospace industries. Unlike prior research, we focus not only on the challenges of data acquisition but also on how these challenges affect decisions in production systems. We examine this linkage using the concepts of strategic objectives, decision areas, and internal fit from operations management literature. Empirical findings show that for data acquisition to lead to improved production system performance it is necessary to develop standards. Standards should consider ownership of data by different functions within a manufacturing company, alignment of data to performance measurements, and the connection between data, information, and production decisions. Using these concepts, this paper proposes a set of guidelines that facilitate the standardization of data acquisition for simulation models in production systems. We conclude by discussing the managerial implications of our findings.
Using Modern Architectures of Recurrent Neural Networks for Technical Diagnosis of Complex Systems

The paper provides an overview of modern recurrent neural network architectures that can be used as a model (building tool) for diagnosis and predicting of infocommunication systems elements. The paper presents arguments in favor of the using of recurrent neural networks. Also, to determine the best architecture, were conducted a number of experiments, and on the basis of the results, conclusions were made about the future prospects of using modern architecture.
Foreground Segmentation in Video Sequences with a Dynamic Background

Segmentation of a moving foreground from video sequences, in the presence of a rapidly changing background, is a difficult problem. In this paper, a novel technique for an effective segmentation of the moving foreground from video sequences with a dynamic background is developed. The segmentation problem is treated as a problem of classifying the foreground and background pixels of a video frame using the color components of the pixels as multiple features of the images. The gray levels of the pixels and the hue and saturation level components in the HSV representation of the pixels of a frame are used to form a scalar-valued feature image. This feature image incorporating multiple features of the pixels is then used to devise a simple classification scheme in the framework of a support vector machine classifier. Unlike some other data classification approaches for foreground segmentation in which a priori knowledge of the shape and size of the moving foreground is essential, in the proposed method, training samples are obtained in an automatic manner. In order to assess the effectiveness of the proposed method, the new scheme is applied to a number of video sequences with a dynamic background and the results are compared with those obtained by using other existing methods. The subjective and objective results show the superiority of the proposed scheme in providing a segmented foreground binary mask that fits more closely with the corresponding ground truth mask than those obtained by the other methods do.
Optical Flow Estimation from Layered Nearest Neighbor Flow Fields

The goal of this paper is to study a strategy of preserving local weak motion regions in large displacement motions variational optical flow methods. We review the development of optical flow estimation for large displacement motions, and then point out to the limitation of the existing method in the local weak motion regions. Finally, we present a layered nearest neighbor flow fields estimation method to overcome this drawback. we perform pyramid layering on the input image sequences and use TreeCANN method to obtain the nearest neighbor field between each layer of images. Then, according to the dominant motion modes in the nearest neighbor field to performe motion segmentation refinement and to get the nearest neighbor flow field. Finally, the nearest neighbor flow field is integrated into the non-local total variational with $L^{1}$ norm (TV-$L^{1}$) flow fields estimation model to recover the flow field detail information of the local weak motion regions lost in the TV-$L^{1}$ model. The test results prove that our method can effectively recover the flow field in the local weak motion region and has good accuracy and robustness.
Preclinical Stages of Alzheimer's Disease Classification by a Rs-fMRI Study

A new method for classifying the preclinical stages of Alzheimer's disease (AD)and positioning the related brain areas is described in this paper in order to slow the progress of AD. The method is based on multi-voxel pattern analysis (MVPA). , which is used to classify normal control (NC)participants and patients and find the changes in different brain areas with AD progression. In the classification. , each voxel's blood oxygen level dependence (BOLD)signal during resting-state functional magnetic resonance imaging (rs-fMRI)was extracted as the basic features. To reduce the amount of features, principal component analysis (PCA)and two-class support vector machine (SVM)were applied to classify 62 NC participants and 162 different stages of patients, which included 47 subjective cognitive decline (SCD)patients, 60 amnestic mild cognitive impairment (aMCI)patients and 55 AD patients respectively. The accuracy of classification reached to 62.71% in SCD. , 70.67% in aMCI and 86.36% in AD (all of them were classified with NC participants). Based on the accuracy, we innovatively combined “weight vectors” in SVM with permutation test as discrimination patterns to further investigate the related brain areas. The discriminating areas, including middle cingulum (right), insula (left). , paracentral lobule (right)and middle temporal (left)are responsible for different cognitive functions and could provide a large application of AD biomarkers. Our method and results suggest the potential of real-time diagnosis and cognitive therapy because of no complex feature calculations.
Analysis of Variance of Opinion Scores for MPEG-4 Scalable and Advanced Video Coding

In this paper, we conduct an analysis of variance (ANOVA) on opinion scores for MPEG-4 scalable video coding (SVC) and advanced video coding (AVC) standards. This work resorts on a publicly available database providing opinion scores from subjective experiments for several scenarios such as different bit rates and resolutions. In particular, ANOVA is used for statistical hypothesis testing to compare two or more sets of opinion scores instead of being constrained to pairs of sets of opinion scores as would be the case for t-tests. As the ANOVA tests of the different scenarios are performed for mean opinion scores (MOS), box plots are also provided in order to assess the distribution of the opinion scores around the median. It is shown that the opinion scores given to the reference videos in SVC and AV C for different resolutions are statistically significantly similar regardless of the content. Further, for the opinion scores of the considered database, the ANOVA tests support the hypothesis that AVC generally outperforms SVC although the performance difference may be less pronounced for higher bit rates. This work also shows that additional insights on the results of subjective experiments can be obtained by extending the analysis of opinion scores beyond MOS to ANOVA tests and box plots.
Regularized Integrated Metric for Person Re-Identification

Integrated metric combining both difference and commonness of image pairs has shown to achieve superior performance over difference-only based metrics in similarity learning. The integrated metric can be learned quickly by computing the log-likelihood ratio between the probability distribution functions of similar and dissimilar image pairs. Under pair constrained Gaussian assumption, the learning involves the computation of inverse of covariance matrices using maximum likelihood criterion which may lead to a degraded solution if proper regularization is not done. In this paper, we study the influence of regularization in integrated metric learning. Person re-identification is chosen as the target application to demonstrate the effectiveness of the regularized metric. Moreover, comparison with recent methods on challenging benchmark datasets in the domain of person reidentification like VIPeR and PRID450S shows that our method achieves better or comparable re-identification rates than other methods.
Complex Parameters Evaluation of Wavelet Transformation

The article is devoted to increasing of analysis and diagnostics efficiency for various nature objects by means of developing methods and information technology for complex parameters evaluation of discrete wavelet transformation. The approaches are practically applied in medical (cardiology) and engineering (milling) industries in the following tasks such as waviness and roughness allocation of metal surface profile, detection of the basic electrocardiogram complexes.
Analysis of Traffic Signals on an SDN for Detection and Classification of a Man-in-the-Middle Attack

Software-defined networking (SDN) has the potential to revolutionize the management capabilities of a highly distributed communications environment. Yet, adoption of SDN is contingent on a thorough analysis of security implications. In this paper, we investigate a man-in-the-middle (MITM) attack that exploits the centralized topological view critical to SDN operations. In particular, we present a new scheme for detection and classification of the attack at the network layer. We apply wavelet analysis to detect anomalous conditions introduced by the MITM attack using traffic signals collected at network switch ports. We then identify unique characteristics of reported anomalies in the collected traffic signals to build a classification framework. Other cyber events, such as a distributed denial-of-service and network congestion, are presented to the detection scheme to validate its general applicability. Overall, we successfully demonstrate the capability to detect and classify the MITM attack in addition to other cyber events at the network layer, thereby contributing to the security of SDN.
Investigating the Impact of eLearning Using LMS on the Performance of Teaching and Learning in Higher Education

This study investigates the impact of eLearning using LMS on the performance of teaching and learning in higher education. It adopts a pre-test-post-test control group experimental design with the use of survey of the participants in the experiment. Statistical analysis is performed on the data collected from 137 undergraduate students in South African universities through pre-test and post-test surveys. The results reveal that eLearning using LMS is more effective than traditional instruction methods with respect to the improvement of the performance of teaching and learning in higher education. Such findings are useful for higher education institutions particularly in developing countries in their decision to adopt these digital technologies for improving teaching and learning performance.
Soft-Switching High Step-Up/Down Bidirectional DC–DC Converter

A nonisolated bidirectional dc–dc converter with high voltage gain, low voltage stress, low component count, and soft-switching features is presented in this paper. In this topology, coupled inductors and voltage multiplier cells are merged to achieve high step-up/down voltage gain. Also, due to using active clamp circuits, the voltage stress of power switches is relatively low. Thus, the low voltage switches with low on-resistance can be employed to reduce the conduction losses. Furthermore, zero-voltage switching is accomplished in both high step-up and high step-down modes for all power switches, and due to zero-current switching operation of all antiparallel diodes, the reverse recovery losses are reduced. In order to verify the theoretical analysis and the converter performance, a 200 W prototype circuit of the proposed converter is implemented in the laboratory.
Performance Analysis of Cryptographic Algorithms for Selecting Better Utilization on Resource Constraint Devices

In this paper, we provide a comprehensive performance evaluation of popular symmetric and asymmetric key encryption algorithms to selecting better utilization on resource constraint and mobile devices. We focus on AES, RC4, Blowfish, CAST, 3DES, Twofish as symmetric key encryption and DSA and ElGamal as an asymmetric encryption algorithms. The performance is compared using different parameters such as key size, data blocks, data types, and encryption/decryption speed. The experiments are performed to exploit the effectiveness of different cryptographic algorithms to use in real life applications where fast execution time and less memory usage requirement is of prime importance.
Mechatronic Design and Control of a 3D Printed Low Cost Robotic Upper Limb

Robots and robotic technologies are changing how society functions. Found throughout industry, education, and more recently consumers homes, robotic devices perform and assist with large varieties of tasks. Robotic limbs allow users to regain or enhance their abilities, notably when completing challenging tasks, ultimately leading to greater productivity with less stain on the user. However, robotic limbs tend to be very costly, a factor that limits the availability of current robotic limbs. In this paper, we have developed an inexpensive 3D printed, 4-degrees of freedom, robotic upper limb. An Arduino microcontroller, electromechanical actuators and additional electronics were integrated to and assembled in to the 3D printed chassis, forming a functional contained system. The assembled robotic upper limb, with potential for use as a supernumerary robotic limb or transhumeral prosthesis for both disabled and non disabled person to use, was then generally tested for appropriate function. Discussion of how this design can be improved leads to the conclusion that 3D printing and inexpensive components certainly pose potential for use in affordable robotic limbs, with the prototype limb being proclaimed a successful foundation.
JUST-IN-TIME PARALLEL SIMULATION

Due to the evolution of programming languages, interpreted languages have gained widespread use in scientific and research computing. Interpreted languages excel at being portable, easy to use, and fast in prototyping than their ahead-of-time (AOT) counterparts, including C, C++, and Fortran. While traditionally considered as slow to execute, advancements in Just-in-Time (JIT) compilation techniques have significantly improved the execution speed of interpreted languages and in some cases outperformed AOT languages. In this paper, we explore some challenges and design strategies in developing a high performance parallel discrete event simulation engine, called Simian, written with interpreted languages with JIT capabilities, including Python, Lua, and Javascript. Our results show that Simian with JIT performs similarly to AOT simulators, such as MiniSSF and ROSS. We expect that with features like good performance, user-friendliness, and portability, the just-in-time parallel simulation will become a common choice for modeling and simulation in the near future.
Semantic Segmentation in Architectural Floor Plans for Detecting Walls and Doors

When architectural floor plans are rasterized, they lose valuable information, limiting human post-processing. To recover lost information, a process known as vectorization should be applied. Several studies have been concerned with vectorization. Even though previous methods do not perform well on images with various graphical styles and a large amount of noise, an approach based on deep learning has achieved exceptional results. The aim of this study is to segment walls and doors from architectural floor plan images using deep neural networks and construct a dataset with ground truth for wall segmentation.
The Impact of an Experiment Design Software Scaffold on Students' Conceptual Understanding and Inquiry Skills

The aim of this study was to examine the impact of the use of a software scaffold, developed to guide students during experimental design procedure, on students' conceptual understanding and inquiry skills. Moreover, it was investigated if there was a positive effect on students' learning products, which were created in the computer-supported inquiry learning environment used for the purpose of this study. The study involved two experimental groups and one control group. In the control group participants did not use the scaffold for the design of their experiments, while in the two experimental groups the scaffold was configured in a way that contradicts the degree of structuring and problematizing needed for a student to complete the task at hand. In the first configuration the scaffold offered more structure by providing the variables at hand, while in the second configuration students received less guidance in terms of the variables at hand. The sample of the study comprised of 63 ninth graders and data were collected through the use of pre- and post-tests. The results showed that the students in the three groups improved equally their conceptual understanding, however, differences between groups emerged for inquiry skills and learning products, in favor of the second experimental group.
Early Posterior Negativity as Facial Emotion Recognition Index in Children With Attention Deficit Hyperactivity Disorder

Introduction: Studies indicate that children with Attention Deficit Hyperactivity Disorder (ADHD) have deficits in social and emotional functions. It can be hypothesized that these children have some deficits in early stages of facial emotion discrimination. Based on this hypothesis, the present study investigated neural correlates of early visual processing during emotional face recognition in this group compared with typically developing children using the Event-Related Potentials (ERPs). Methods: Nineteen boys between the ages of 7 and 11 years diagnosed with ADHD (Combined type) based on DSM-IV-TR classification were compared with 19 typically developing children matched on age and gender. The participants performed an emotional face recognition task while their brain activities were recorded using the event-related potentials procedure. Results: A significant reduction in the Early Posterior Negativity (EPN) for happy and angry faces has been revealed in ADHD children compared to normal ones (P<0.05). Conclusion: The present study supports the notion that individuals with ADHD have some impairments in early stage of emotion processing which can leading to their misinterpretation of emotion in faces.
Capsular Type, Sequence Type and Microbial Resistance Factors Impact on DNase Activity of Streptococcus agalactiae Strains from Human and Bovine Origin

Extracellular deoxyribonucleases (DNases) contribute to the spread of pathogenic bacteria through the evasion from host innate immunity. Our main objective was to evaluate the production of extracellular DNases by human and bovine Streptococcus agalactiae clinical strains and perform a correlation of genetic lineages and DNase activity with capsular type, genetic determinants, clinical origin (colonization and infection), and host (human or bovine). DNase activity was evaluated by qualitative and quantitative assays for a collection of 406 human (n = 285) and bovine (n = 121) strains. All (121/121) bovine were isolated from mastitis and revealed to be DNase (+), indicating a putative pathogenic role in this clinical scenario. From the human S. agalactiae strains, 86% (245/285) showed DNase activity, among which all strains belonging to capsular types, namely, Ia, Ib, III-2, and IV. All CC17 strains (n = 58) and 56/96 (58.3%) of the CC19 displayed DNase activity. DNase (–) strains belonged to the CC19 group. However, the subcharacterization of CC19 S. agalactiae strains through multiple-locus variable number tandem repeat analysis (MLVA), antibiotic resistance, mobile elements, and surface proteins did not provide any distinction among DNase producers and non-producers. The production of DNases by all human CC17 strains, about two-fifths of human CC19, and all bovine strains, suggest an important contribution of DNases to hypervirulence.
Inhibition by O-desmethyltramadol of glutamatergic excitatory transmission in adult rat spinal substantia gelatinosa neurons

To reveal cellular mechanisms for antinociception produced by clinically used tramadol, we investigated the effect of its metabolite O-desmethyltramadol (M1) on glutamatergic excitatory transmission in spinal dorsal horn lamina II (substantia gelatinosa; SG) neurons. The whole-cell patch-clamp technique was applied at a holding potential of −70 mV to SG neurons of an adult rat spinal cord slice with an attached dorsal root. Under the condition where a postsynaptic action of M1 was inhibited, M1 superfused for 2 min reduced the frequency of spontaneous excitatory postsynaptic current in a manner sensitive to a μ-opioid receptor antagonist CTAP; its amplitude and also a response of SG neurons to bath-applied AMPA were hardly affected. The presynaptic effect of M1 was different from that of noradrenaline or serotonin which was examined in the same neuron. M1 also reduced by almost the same extent the peak amplitudes of monosynaptic primary-afferent Aδ-fiber and C-fiber excitatory postsynaptic currents evoked by stimulating the dorsal root. These actions of M1 persisted for >10 min after its washout. These results indicate that M1 inhibits the quantal release of L-glutamate from nerve terminals by activating μ-opioid but not noradrenaline and serotonin receptors; this inhibition is comparable in extent between monosynaptic primary-afferent Aδ-fiber and C-fiber transmissions. Considering that the SG plays a pivotal role in regulating nociceptive transmission, the present findings could contribute to at least a part of the inhibitory action of tramadol on nociceptive transmission together with its hyperpolarizing effect as reported previously.