Miracle-dz/UniFAR-FTUs huggingface dataset:
FRACTIONS: A PIECE OF CAKE? AN EXPLORATION OF STUDENT TEACHERS' UNDERSTANDING, ATTITUDES AND BELIEFS IN RELATION TO FRACTIONS. Thesis submitted for the degree of Doctor of Education at the University of Leicester

Fractions: A Piece of Cake? An exploration of student teachers’ understanding, attitudes and beliefs in relation to fractions. The title of this study shows the aims on which the research questions were based. These included the areas in which the student teachers felt confident as well as those they perceived to be more difficult. This study adopted a phenomenographic approach in order to provide further insight into each student teacher’s subject knowledge. The purpose of this study was to discover the individual and distinct ways in which student teachers understood fractions. The study was undertaken in two universities with a group of thirteen undergraduate and postgraduate Initial Teacher Education students. The creation of a comfortable, supportive working atmosphere and the use of selfselected small groups, enabled a range of rich and honestly reflective data to be collected. Observations were made of groups working on two collaborative tasks involving the sequencing of fractions by magnitude and finding, followed by diagnostic interviews. Each interview was structured by the student’s individual selections from a range of questions where they indicated which they felt were most and least accessible. A constructivist perspective was adopted where the students had the opportunity to reconstruct their own understanding of fractions through the explanation and discussion of their existing ideas. A range of successful strategies was demonstrated, especially the use of mathematical anchors as a means of comparison and the use of residual or gap thinking to consider differences in magnitude. Improper fractions and reunitising were difficulties cited by many in the group. A certain level of anxiety and lack of flexibility in their chosen approaches was evident with the common assumption that there was a particular method which should be adopted; this was usually based on their “secondary school” experiences. CONTENTS Chapter
Fast Fading Characterization for Body Area Networks in Circular Metallic Indoor Environments

With the increasing development of 5G and Body Area Network based systems being implemented in unusual environments, propagation inside metallic structures is a key aspect to characterize propagation effects inside ships and other similar environments, mostly composed of metallic walls. In this paper, indoor propagation inside circular metallic structures is addressed and fast fading statistical distributions parameters are obtained from simulation, being assessed with measurements at 2.45 GHz in a passenger ferry discotheque with an 8 m diameter circular shape. It is observed that, in this kind of environments, second order reflections are particularly relevant due to the walls’ high reflective nature. Globally, it is concluded that the Rayleigh distribution can be used to characterize fast fading effects with no significant loss of accuracy compared to the Rice one, since a low value of the Rice parameter is observed, being below 3.1 dB, even under Line-of-Sight conditions. Moreover, it is observed that, from the fast fading viewpoint, the best transmitter position is at the circle center.
Monitoring and Alarm System Timing Analysis for an Interconnecting Grid

Power system oscillation is a common problem in interconnected power grids. Progress has been made in measurement-based oscillation analysis due to the increased utilization of wide-area measurement system (WAMS). However, robust solution set approaches, such as oscillation classification at the “edge” and dynamic re-tuning of power system stabilizers (PSS), are often not exercised; rather generation shedding has become a common approach vector. This interim approach has risen in prevalence while the actual engineering to address root causes has been sidelined; this paper delineates the issue and posits a prospective solution for the current timing problem.
Cloud Shape Classification System Based on Multi-Channel CNN and Improved FDM

This paper presents a new meteorological photo classification system based on the Multi-channel Convolutional Neural Network (CNN) and improved Frame Difference Method (FDM). This system can work in an embedded system with limited computational resources and categorize cloud observation photos captured by ground cameras. We propose the improved FDM extractor to detect and extract cloud-like objects from large photos into small images. Then, these small images are sent to a Multi-channel CNN image classifier. We construct the classifier and train it on the photo-set that we established. After combining the extractor and classifier to form the classification system, the images can be classified into three different types of clouds, namely, cumulus, cirrus and stratus, based on their meteorological features. The testing phase uses 200 actual photos of real scenes as the experimental data. The results show that the classification accuracy can reach 94%, which indicates that the system has a competitive classification ability. Moreover, the time cost and computational resource consumption for image recognition are greatly reduced. By using this system, meteorologists can lighten their workload of processing meteorological data.
SVM classification of EEG signal to analyze the effect of OM Mantra meditation on the brain

Meditation can significantly contribute to improving physical and mental health in modern stressful life. "OM" mantra is very easy to practice for meditation . This study is undertaken to classify the EEG band to observe abrupt changes in band as an effect of Om mantra meditation. Twenty-three naive meditators were experimented to chant OM mantra for 30 min and EEG signal recorded before and after meditation. The stationary wavelet transform is used to exact five bands from the EEG. The different statistical features were calculated. SVM classifier with Radial Basis Kernel is employed to classify the band. Results show the significant changes in the delta band which represent the brain in deep sleep. Thus OM meditation gives the experience of deep sleep. Thus study can be helpful to give new direction towards the meditation.
A New Maximum Weight Submatrix Solver Tool for Identifying Mutated Driver Pathways in Cancer using Python

Cancer may be a title given to all kind of infections caused by hereditary changes, such as substantial changes in DNA. Cancers are caused by mutations-which occur during cell division-that may possibly be inherited, provoked by environmental determinants, or result from some random DNA replication errors which simply maybe considered as bad luck. Recently in bioinformatics, one of the most heated topics is to search about the separation between driver mutations that lead to cancer and passenger mutations, which are well noted to be neutral and do not have any part in this disease development. Indeed, to solve this problem, several approaches and techniques were used in the literature. The main aim of this work is to show a new computation tool called “The Maximum Weight Submatrix Solver (MWS Solver)” using Python language. In fact, we programmed three metaheuristics called: Genetic Algorithm, Evolutionary Programming and Random Optimization Algorithm, as an approximate approach in order to maximize the weight and sample all the possible driver pathways that can be found. A comparison of these approximate methods with other previous exact and random methods has also been taken into consideration. The experimental tests demonstrate that the proposed algorithms implemented via MWS Solver may become a useful open source and free complementary instrument for finding cancer pathways.
Empirical Results for Human-Induced Shadowing Events for Indoor 60 GHz Wireless Links

Satisfying the exponential data growth is one of the main challenges for the design of future wireless networks. Given the abundant spectrum availability at millimeter-wave bands, exploiting these frequencies is a natural choice. WiGig (also known as 60 GHz WiFi) allows multi-gigabit transmissions in short range wireless links. The maximum data transfer rates occur in line of sight (LOS) connections. Given the small operating wavelength, obstructing the LOS causes significant power losses, severely impacting the attainable throughput of each user. This article presents empirical results to parameterize typical small-scale fades caused by pedestrians when blocking the LOS, in areas where a WiGig access point serves indoor users. Our results show that fades can be as large as 21 dB, in average. Furthermore, burst of fades are common, lasting up to 5200 ms in 90% of the observed cases.
Strictly Proper Control Design for the Stabilization of 2×2 Linear Hyperbolic ODE-PDE-ODE Systems

In this paper, we consider the problem of L2-exponential stabilization of a coupled ODE-hyperbolic PDE-ODE system, where actuation is available through one ODE. Based on the backstepping technique, the system is mapped into an equivalent delay form which allows for the construction of a strictly proper controller realized as a full-state feedback. The result extends previous control designs, lifting some restrictions on the structure of the ODEs under consideration and guaranteeing a non-zero delay margin for the closed-loop system.
Optimal Power-Area Polar Decoder Design based on Iterative Decomposition Technique

Polar codes have a very good error correcting capacity compared to turbo and LDPC codes of similar length. They are the first to attain the channel capacity. The aim of the proposed work is to design an Area and Power optimal 2b SC polar decoder exhibiting reduced area and power without degrading the latency by iterative decomposition technique. The targeted performances in decoder architecture are achieved by novel reformulation of F-node, G-node and P-nodes.
On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery

X-ray imagery security screening is essential to maintaining transport security against a varying profile of prohibited items. Particular interest lies in the automatic detection and classification of prohibited items such as firearms and firearm components within complex and cluttered X-ray security imagery. We address this problem by exploring various end-to-end object detection Convolutional Neural Network (CNN) architectures. We evaluate several leading variants spanning the Faster R-CNN, Mask R-CNN, and RetinaNet architectures. Overall, we achieve maximal detection performance using a Faster R-CNN architecture with a ResNet101classification network, obtaining 0.91 and 0.88 of mean Average Precision (mAP) for a two-class problem from varying X-ray imaging dataset. Our results offer very low false positive (FP) complimented by a high accuracy (A) $(\mathrm{FP}=0.00\%,\ \mathrm{A}=99.96\%)$. This result illustrates the applicability and superiority of such integrated region based detection models within this X-ray security imagery context.
Performance Analysis of Ammonia Sensors based on Nanostructured PANI-DBSA Synthesised by Template Free Direct Doping Route using Surfactant and Indirect Doping Route

Prolonged exposure of ammonia results in many potential health hazards for humans as it interacts immediately upon contact with available moisture in the eyes, skin, oral cavity, respiratory tract and particularly mucous surfaces, thereby, causes cellular destruction and tissue necrosis. In this paper, we report a detailed comparison between ammonia gas sensing, spectral, structural, and morphological properties of the dodecyl benzene sulphonic acid doped polyaniline (PANI-DBSA) nanostructures synthesized using a template free direct route using surfactant dopants as structure directing agents and using indirect route. Two different samples of PANI-DBSA nanostructures have been prepared via template free direct route (Nanostructured DBSA doped PANI has been prepared by emulsion polymerization of aniline in the presence of DBSA) and indirect doping route (Nanostructured DBSA doped PANI has been prepared via chemical oxidative polymerization of aniline monomer). Different characterizations revealed that the spectral, structural, morphological, and ammonia gas sensing properties of the synthesized PANI-DBSA nanostructures are highly dependent on the synthesis route. Thin film of nanostructured PANI-DBSA prepared using template free direct route exhibited better ammonia gas sensing response (7.97) as compared to thin film of nanostructured PANI-DBSA prepared using indirect doping (3.24) due to availability of large number of grain boundaries and higher doping levels.
Element-Wise Adaptive Thresholds for Learned Iterative Shrinkage Thresholding Algorithms

In this paper, we propose element-wise adaptive threshold methods for learned iterative shrinkage thresholding algorithms. The threshold for each element is adapted in such a way that it is set to be smaller when the previously recovered estimate or the current one-step gradient descent at that element has a larger value. This adaptive threshold gives a lower misdetection probability of the true support, which speedups the convergence to the optimal solution. We show that the proposed element-wise threshold adaption method has better convergence rate than the existing non-adaptive threshold methods. Numerical results show that the proposed neural network has the best recovery performance among the tested algorithms. In addition, it is robust to the sparsity mismatch, which is very desirable in the case of unknown signal sparsity.
Ultrasonographic assessment of the shoulder in patients with carpal tunnel syndrome

Objectives: To detect the causes of shoulder pain with shoulder ultrasonography in patients with carpal tunnel syndrome (CTS)and compare the findings of the patients with CTS to healthy controls.Patients and Methods: Patients with CTS and healthy controls were evaluated clinically, electrophysiologically and ultrasonographically by three masked researchers.Electrophysiological assessment consisted median and ulnar nerve conduction studies (NCS). In ultrasonographic assessment, crosssectionalareas of the median nerve were measured at hamatum hook, psiform bone, radio-ulnar joint levels. Shoulder joint, axial and longitudinal biceps tendon, subscapularis tendon, supraspinatus tendon, infraspinatus tendon, rotator cuff, acromioclavicular joint were evaluated by a standardized protocol.Results: The study was completed with 30 patients with CTS and 30 healthy subjects. The presence of shoulder pain in the last week was 53.3% in the CTS group and 30% in the control group but there was no statistically significant difference between the groups (p=0.16; p>0.05). Subdeltoid bursitis was significantly more common in CTS group when compared to the control group (p=0.03) in ultrasonographic assessment, and there was no significant difference between the two groups for other parameters.Conclusion: Shoulder pain and subdeltoid bursitis were common in patients with CTS however, shoulder pain was not directly related to shoulder problems alone. Central sensitization may play a role for shoulder pain rather than shoulder problems.Keywords: Ultrasonography, Carpal tunnel syndrome, Shoulderpain, Central sensitization
On LP Formulations of Optimal Control Problems with Time Averaging and Time Discounting Criteria in Non-Ergodic Case

This paper is devoted to the study of infinite horizon optimal control problems with time discounting and time averaging criteria. These problems are related to certain infinite-dimensional linear programming (IDLP) problems, and this relation is used to obtain properties of the optimal value functions. We focus on the general non-ergodic case, where the optimal value functions may depend on the initial condition of the system. We also obtain IDLP-based sufficient and necessary optimality conditions for optimal control problems.
Lattice piecewise affine representations on convex projection regions

In this paper, continuous piecewise affine (PWA) functions are realized using the lattice PWA representations on convex projection regions. Detailed proof for the realization is given. Compared with previous methods that develop lattice PWA representations based on base regions, the computational burden can be largely decreased for the current representation. Based on the result, continuous PWA functions can be more efficiently studied and used in modelling as well as approximation of nonlinear systems.
Robust Superimposed Training Optimization for UAV Assisted Communication Systems

In this paper, we propose a superimposed training based two-phase robust channel estimation scheme for the unmanned aerial vehicle (UAV) assisted cellular communication system, in which various unitarily-invariant channel statistics errors are considered. Specifically, in the first phase, mobile station (MS) estimates the UAV-MS channel via the UAV training sequence, of which the robust design can be solved based on convex-concave theory. While in the second phase, the superimposed training scheme is considered at the ground base station (GBS) to improve spectrum efficiency. Then the robust GBS training sequence, the information signal power and the UAV amplifying factor are jointly optimized for the partially cascaded GBS-UAV-MS channel estimation subject to GBS and UAV transmit power constraints as well as the required information signal strength at the MS. To tackle this NP-hard problem, the optimal structures of involved variables are firstly derived, based on which the robust superimposed training design is simplified and proved to be quasi-convex in the UAV amplifying factor. Particularly, for Spectral norm and Nuclear norm bounded errors, the optimal training sequence can be obtained via convex-concave theory and Golden section searchWhile for Frobenius norm bounded error, a tractable upper-bounding scheme is proposed for the robust superimposed training design. Furthermore, we extend our work into the more general probabilistic path loss scenario of UAV-ground channels, and analyze the impacts of the probabilistic path loss and Rician $K$ -factor on channel estimation performance. Numerical results illustrate the excellent performance of the proposed superimposed training based two-phase channel estimation scheme.
Optimal Power Control for Cognitive LEO Constellation With Terrestrial Networks

Power control plays a significant role in cognitive networks, which promotes the spectrum sharing between heterogeneous systems. This letter presents the mathematical model for cognitive low earth orbit (LEO) satellite constellation with terrestrial networks, which takes the dynamic characteristics of the LEO satellite into consideration. Two optimal power control schemes are proposed from the long-term and short-term perspectives, which aims to maximize the delay-limited capacity and minimize the outage probability, respectively. Solutions to the optimization problems are analyzed, and numerical results evaluate the performance of the schemes.
Maximum Hands-Off Distributed Bearing-Based Formation Control

This paper investigates a distributed bearing-constrained formation control of continuous-time multi-agent systems based on sampled bearing information. The problem is considered in arbitrary dimensional spaces. Our proposed method penalizes the control effort by L0 control cost, and hence the obtained distributed control is enhanced to take exactly zero value. Such a control is called maximum hands-off control. The proposed method tracks a distributed control for an associated discrete-time multi-agent system. Hence, we also newly characterize a bearing-constrained formation for discrete-time one. The analysis relies on the recently developed bearing rigidity theory. With the results, we show the feasibility, closed form, and stability of the proposed control.
PLL Position and Speed Observer With Integrated Current Observer for Sensorless PMSM Drives

In this article, a phase-locked loop (PLL) rotor position and speed observer, including an integrated decoupled stator current observer, is proposed for wide-speed-range sensorless control of permanent magnet synchronous motor (PMSM) drives. This new PLL scheme consists of two interconnected observers: 1) a second-order PLL position observer using the direct current error as correction term and 2) a third-order extended Luenberger speed observer, based on the mechanical model of the drive, using the quadrature current error as the correction term. The estimated speed is just an unrefined reference for the position integrator, and it is further corrected by the PLL. The current observer is a subsystem integrated as a PLL phase detector. Key relations between the current errors and the position and speed errors are proven, and a practical two-step observer design method is presented. A method for real-time identification of the stator resistance at low speeds and of the permanent magnet flux at high speeds is proposed, using the current errors in the current reference frame. Experimental results for a 0.5-kW surface-mounted PMSM drive confirm the effectiveness, excellent dynamic performance, and robustness of the new PLL observer. Stable motor and generator operation at 15 r/min (0.01 p.u.) with ramp and step speed reversals, high-speed operation, and stator resistance adaptation is shown.
A Review on Neural network based Abstractive Text Summarization models

Text summarization is the process to create short and concise summaries from the text document with the aim of provide most important or salient content in a condensed form from the source document. As the information is overloaded on the web, text summarization becomes necessary. Extractive and abstractive are two broad approaches of text summarization. Extractive method assembles summaries from the document, which directly take a sentence or phrase from the original document while abstractive method may generate new words to make a summary. This paper gives the comparison of various neural network based abstractive text summarization models and also discuss the types of summarization based on categories and different approaches of abstractive text summarization. At last, comparison of these models are also given.
Model Predictive Control of the Blood Glucose Concentration for Critically Ill Patients in Intensive Care Units

In this paper we present a linear model predictive control (MPC) algorithm for control of the blood glucose concentration of critically ill patients in an intensive care unit (ICU). We present a version of the algorithm that is suitable for automatic control. The algorithm can administer insulin and glucose. The insulin and glucose are administered using a parenteral (intravenous) route. We use a hysteresis logic to switch between insulin and glucose administration to prevent simultaneous administration of insulin and glucose. We test our algorithm in silico using three different simulation models to demonstrate the closed-loop performance of the algorithm. The MPC algorithm is based on linear models in the form of parsimonious second-order transfer functions describing the effect of intravenously injected insulin and glucose on the blood glucose concentration and a first-order disturbance transfer function containing an integrator to ensure offset-free control. The numerical results show that even in the ideal case without any measurement delay, tight glycemic control (80-110 mg/dL) cannot be achieved. Moreover, parenteral glucose is a requirement to avoid hypoglycemia and ensure extended tight glycemic control (60-140 mg/dL).