Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovating Pedagogy 2014

Executive summary This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This third report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below, starting with two updates to last year’s report, followed by eight new entries, in an approximate order of immediacy and timescale to widespread implementation.
Machine Learning for Wind Turbine Fault Prediction through the Combination of Datasets from Same Type Turbines

Abstract. Early fault detection in wind turbines is key to reduce both costs and uncertainty in the generation of energy and operation of these structures. The isolation of many wind farms, especially those offshore, makes scheduled maintenance very costly and on many occasions inefficient. In addition, the downtime of these structures is typically long and a predictive solution is much needed to 1) help prepare for the maintenance procedure beforehand, for instance to avoid delays when waiting for the required resources and components for maintenance to be available and, 2) avoid the possibility of more destructive system failures. Predicting failures in such complex systems requires modeling of multiple components in isolation and as a whole. Physics-based and data-based models are used for this purpose, which have been proven useful in this regard. Specifically, Machine Learning algorithms are proven to be a valuable resource in a wide range of problems in this industry, however a solution capable of accurately predicting the range of faults of a particular type of wind turbine is still a challenge. In this paper, we will introduce the capabilities of machine learning for wind turbine fault prediction, as well as a technique to predict different types of faults. We will compare the performance of two well established machine learning algorithms (namely K-Nearest Neighbour and Random Forest classifiers) on real wind turbine data which have produced great levels of prediction accuracy. We also propose data augmentation methods to help enhance the training of ML models when wind turbine data is scarce by merging data from turbines of the same type.
Optimizing a PIFA Using a Genetic Algorithms Approach

An optimized planar inverted F antenna (PIFA), used in mobile communications, is proposed in this paper. With the aid of the Genetic Algorithms (GA) optimization technique, a PIFA with a wide impedance bandwidth and a sufficient gain for use in mobile communications is presented. A comparison with results emerging from an application of the Finite Difference Time Domain (FDTD) method is demonstrated.
Audiometry and doping control in competitive deaf sport

In the past forty years both the participating countries and the participants at the Summer Deaflympics Games have increased by 2.5-fold. Furthermore, there are approximately fifty European or World sports events organized for the hearing impaired. The aim of the paper was to discuss the procedures and requirements related to doping control in deaf people’s competitive sport in the context of the rising number of sports events and the participating athletes.For the sake of the fair play spirit of the dynamically developing deaf sports, The International Committee of Sports for the Deaf (ICSD) introduced rules governing the participation of hard-of-hearing athletes in sports events. Healthy individuals with a hearing loss of at least 55 dB are allowed. Thus, audiometric examination constitutes the first doping control criterion. Since 2004, ICSD has been cooperating with the World Anti-Doping Agency. The second criterion is, therefore, blood and urine sample control for prohibited substances. The 23rd Summer Deaflympics in 2017 involved 2858 athletes. Overall, 842 (29.5%) participants were randomly subjected to an audiological test; 9 competitors from different countries were disqualified because of non-compliance with the ICSD standards. A total of 300 randomly selected athletes underwent doping control for prohibited substances; 1 was disqualified. The world literature lacks scientific reports on deaf sports, including doping control.
Functional maps based dense 3D human body correspondence from single view point clouds

We propose a new method based on Functional Maps to estimate the dense point-pair relationship between single-view 3D human point clouds and template point clouds. At present, most of the relevant work is based on triangular information to estimate the correspondence, and our innovation is to process directly on the point clouds. Because the single-perspective point clouds don’t have the full human body information, these methods cannot effectively find out the correspondence. Firstly, the template is used to complete the missing human body information to obtain the full human structure, so that the Laplace-Beltrami operator (LBO) can be calculated effectively. Then, features are extracted based on the deep learning method, and geometric information was converted to spatial information. Finally, the linear function mapping is calculated to characterize the dense point-to-point correspondence.
Elucidating the auxetic behavior of cementitious cellular Elucidating the auxetic behavior of cementitious cellular composites using finite element analysis and interpretable composites using finite element analysis and interpretable machine learning machine learning

FEA)-based framework with ML to predict the Poisson’s ratios. In particular, the FEA-based approach is used to generate a dataset containing 850 combinations of different mesoscale architectural void features. The dataset is leveraged to develop an ML-based prediction tool using a feed-forward multilayer perceptron-based neural network (NN) approach which shows excellent prediction efﬁcacy. To shed light on the relative inﬂuence of the design parameters on the auxetic behavior of the ACCCs, Shapley additive explanations (SHAP) is employed, which establishes the volume fraction of voids as the most inﬂuential parameter in inducing auxetic behavior. Overall, this paper develops an efﬁcient approach to evaluate geometry-dependent auxetic behaviors for cementitious materials which can be used as a starting point toward the design and development of auxetic behavior in cemen-titious composites
Design of new hybrid-ring, directional coupler using lambda /8 or lambda /6 sections

The authors propose a design method for 1.25 lambda -ring and 7 lambda /6-ring 3-dB directional couplers using lambda /8 and lambda /6 sections, respectively. In addition, the frequency characteristics of the couplings together with the isolations, the return losses, and the phase differences between the output ports are calculated. The method was experimentally verified for a microstrip network, proving its validity for a microwave component with the basic lambda /8 and lambda /6 sections proposed. >
Short-Term Traffic Flow Prediction with Regime Switching Models

Accurate short-term prediction of traffic parameters is a critical component for many intelligent transportation system applications. Traffic flow is subject to abrupt disturbances because of various unexpected events (e.g., accidents, weather-induced disruption) that may change the underlying dynamics and the stability of the data generation process. Short-term prediction models that do not account for these changes produce biased and less accurate predictions. This paper proposes a new adaptive approach to short-term prediction that explicitly accounts for occasional regime changes by using statistical change-point detection algorithms. In this context, the expectation maximization and the CUSUM (cumulative sum) algorithms are implemented to detect shifts in the mean level of the process in real time. Autoregressive integrated moving average models are used for developing the forecasting models while the process mean is monitored by the two detection algorithms. The intercept of the forecasting models is updated on the basis of the detected shifts in the mean level to adapt to any potential new regimes. The proposed approach is tested on real-world loop data sets. The results show significant improvements in prediction accuracy compared with traditional autoregressive integrated moving average models with fixed parameters.
Image compressed sensing reconstruction algorithm based on attention mechanism

Very deep convolutional neural networks (CNNs) have shown great power in image compressed sensing (CS) reconstruction and achieved significant improvements against traditional methods. Among these CNN-based methods, the number of convolutional feature maps is critical to the performance of the network. However, existing algorithms only perform average weighting processing on feature maps, and do not make full use of image feature differences to adaptively assign feature weights. To address this issue, we propose an attention mechanism network for image compression sensing reconstruction (AM-CSNet). AM-CSNet uses multiple attention modules (AM) to adaptively learn feature weights in the channel and spatial dimensions, which makes the model more lightweight and efficient. To maximize the performance of AM-CSNet, we use Residual Feature Aggregation Group (RFAG) to fully retain the features on different residual branches. Extensive CS experiments demonstrate that the proposed AM-CSNet is superior to many other state-of-the-art methods, such as TIP-CSNet and SCSNet.
Phenology and mechanisms of the early upwelling formation in the southern coast of Senegal

Mechanisms of the interannual variability of upwelling onset dates on the Petite-Cote of Senegal are investigated using daily NEMO model data outputs and NOAA ocean surface temperature observations (OISST). We first determined the phenology (onset dates, end dates, duration and occupied area of the upwelling) over the Petite-Côte located in south of the Senegalese coast. Our results show that upwelling in the Petite-Côte starts on average on 03-December (with a standard deviation of 13 days) and ends on average on 13-June (with a standard deviation of 11 days). Upwelling lasts, on average, 6 months in the region. The maximum upwelling intensity is noted on 15-May and the maximum occupied area exhibits a February-May plateau. We then performed a composite analysis based on the earliness of the upwelling setup dates to understand the mechanisms involved. Our results show that for the earliest upwelling years, we note a strengthening of the vertical velocities 3 weeks before the onset of the upwelling but we note especially an abnormal intensification of the coastal jet. The latter seems to be the initiator of the early onset of the upwelling in the region. These reinforcements are associated with a significant decrease in surface temperature (SST) and an intensification of northerly trade winds. of upwelling for all atmospheric and oceanic variables to better diagnose the behavior of the parameters for early upwelling years. Finally, for the composite analysis of the different variables, we apply a lanczos filter to the data at intra-seasonal timescale to remove frequencies greater than 90 days.
Polymeric Integrated Electro-Optic Modulators

Polymeric optical waveguides for integrated optical devices are of current interest for their potential application in very high bandwidth opto-electronic systems. We report our progress in defining and characterising channel waveguides in polymers having large electro-optic coefficients. The waveguide patterns are formed as etched channels in a polymer cladding layer. Further layers deposited onto these channels form 2-dimensional waveguides with monomode characteristics at a wavelength of 1.3 microns. The mode size is controllable and may be optimised for fibre mode coupling to minimise the coupling loss. Electro-optic phase and amplitude modulation is demonstrated for slab and channel waveguides. We report values for the off-resonance electro-optic coefficient in poled polymer films somewhat higher than the largest value in lithium niobate. The implications of such device structures for applications in high performance multi-GHz amplitude modulators are discussed. In particular, the large values of n3r and the near equality of the optical and microwave refractive indices indicates that these polymers will have significant advantage over traditional inorganic materials for wide bandwidth integrated optical modulators.
Who Registers? Village Networks, Household Dynamics, and Voter Registration in Rural Uganda

Who registers to vote? Although extensive research has examined the question of who votes, our understanding of the determinants of political participation will be limited until we know who is missing from the voter register. Studying voter registration in lower-income settings is particularly challenging due to data constraints. We link the official voter register with a complete social network census of 16 villages to analyze the correlates of voter registration in rural Uganda, examining the role of individual-level attributes and social ties. We find evidence that social ties are important for explaining registration status within and across households. Village leaders—and through them, household heads—play an important role in explaining the registration status of others in the village, suggesting a diffuse process of social influence. Socioeconomic factors such as income and education do not explain registration in this setting. Together these findings suggest an alternate theory of participation is required.
Does population ageing impact inflation?

Modern society is characterised by clear and distinct demographic processes, such as the constant decline in the number of children born and the ageing population in developed countries, resulting from complex biological, economic, social, political, and other factors. Demographic changes observed through population ageing have an impact on the economy and inflation. Therefore, the aim of this paper is to examine the impact of population ageing on inflation in Economic and Monetary Union (EMU) countries. The paper? s authors used data from 1970 to 2016. The ARDL approach was used to test the long- and short-term relationship between population ageing and inflation. The results showed a positive relationship between population ageing and inflation in the long term and a negative relationship in the short term. The ageing population decreases inflation in the short term and increases inflation in the long term.
O-methylated N-glycans Distinguish Mosses from Vascular Plants

In the animal kingdom, a stunning variety of N-glycan structures has emerged with phylogenetic specificities of various kinds. In the plant kingdom, however, N-glycosylation appears as strictly conservative and uniform. From mosses to all kinds of gymno- and angiosperms, land plants mainly express structures with the common pentasaccharide core substituted with xylose, core α1,3-fucose, maybe terminal GlcNAc residues and Lewis A determinants. In contrast, green algae biosynthesize unique and unusual N-glycan structures with uncommon monosaccharides, a plethora of different structures and various kinds of O-methylation. Mosses, a group of plants that are separated by at least 400 million years of evolution from vascular plants, were hitherto seen as harbouring an N-glycosylation machinery identical to that of vascular plants. To challenge this view, we have analysed the N-glycomes of several moss species using MALDI-TOF/TOF, PGC-MS/MS and GC-MS. While all species contained the plant-typical heptasaccharide with no, one or two terminal GlcNAc residues (MMXF, MGnXF and GnGnXF, respectively), many species exhibited MS signals with 14.02 Da increments as characteristic for O-methylation. Throughout all analysed moss N-glycans the level of methylation differed strongly even in the same family. In some species, methylated glycans dominated, while others had no methylation at all. GC-MS revealed the main glycan from Funaria hygrometrica to contain 2,6-O-methylated terminal mannose. Some mosses additionally presented very large, likewise methylated complex-type N-glycans. This first finding of methylation of N-glycans in land plants mirrors the presumable phylogenetic relation of mosses to green algae, where O-methylation of mannose and many other monosaccharides is a common trait.
Design and Modeling of High Performance Permanent Magnet Synchronous Machines

The electrification of transportation, and especially aerospace transportation, increases the demand for high performance electrical machines. Those machines often need to be fault-tolerant, cheap, highly efficient, light and small, and interface well with the inverter. In addition, the development time and cost are usually limited, with little room for later changes. Meeting those expectations requires an accurate and flexible design and optimization approach. This thesis will present such an approach, as well as discuss various modeling methods needed to speed up the optimization and finalize the details of the design. The design process of a brushless permanent magnet synchronous machine (PMSM) for a starter/generator application serves as an example throughout the thesis. A multi-objective optimization method is discussed first, demonstrating the insights that can be gained with such a method. Next, the rotor eddy current losses cannot be neglected in high speed machines, but their computation is numerically complex. Therefore, an alternate and faster method is also presented. Another potential loss source in high speed machines are winding AC losses. A computationally efficient method to compute unequal current sharing among parallel strands is proposed and tested. Next, the consequences, detection and mitigation of inter-turn faults are extensively discussed. Short circuit current densities over 400 A/mm² are experimentally demonstrated, highlighting the need for a good fault detection scheme. One potential detection scheme is introduced and tested, and found to be capable of detecting faults across 0.4% of the winding. With the models and knowledge obtained at this point, the actual starter/generator is designed. Particular to this design is a temporary starting torque four times larger than the nominal generator torque and the need to be able to withstand terminal short circuits. Key to meeting those specifications is a carefully chosen per-unit inductance, as this strongly affects machine and inverter size. Experiments on a prototype demonstrate satisfactory machine performance in all operating conditions. Finally, after having confirmed the used models, a design study into the specific power density limits of PMSMs is performed and the results are presented in detail.
Delamination control in electronic packaging using the energy method

Interfacial delamination, due to the presence of dissimilar material systems, is one of the primary concerns in electronic package design. The mismatch in the coefficient of thermal expansion between different layers in packages can generate high interfacial stresses when subjected to thermal loading during fabrication and assembly. Many of the failure criteria were developped to solve the problems with a precrack. However, in real electronic packages, the size and location of the cracks or/and delamination cannot be predicted. It is not easy to use the traditional fracture criteria to deal with complicated 3-D delamination problems. The delamination of copper IeadframeEpoxy Molding Compound (EMC) was selected in the study. The allowable stresses of the interface were evaluated hy the Button Shear Test (BST). In this paper, the critical load acting on the upper part nf the button shear sample was measured at a certain shear height and a finite element model was used to evaluate the interfacial stresses for different material system. An energy-based method is proposed by deriving the energy to initiate each of the tensile and shear modes of failure across the interfaces of the button shear test samples for the chosen interfacial material system. In order to benchmark the delamination failure criterion, two electronic packages, SOT #1 and SOT #2 were studied to investigate delamination in the soldering reflow process. Four kinds of interfaces in two packages were investigated under solder reflow respectively. The button shear test was done to obtain the critical force for each material system, and the allowable strain energy density U, of each material system was evaluated. By comparing the calculated interfacial energy density to Uc of each material system, delamination at each interface can then he predicted. The predicted results were consistent with those fiom CSAM.
A Customized Ontology Shape for Web Data Congregation

As a sculpt for associate clarification and exemplification, ontology's are lengthily used to represent user profile in modified web in sequence parishioners. Equally, when in place of user profiles, several mold include used only associate from either a universal associate pedestal or user confined information. In this paper, a modified ontology mold is designed for associate design and estimate over user profile. These molds learn ontological user profiles from equally a world associate stand and user curbed event repositories. The ontology shape is evaluated by compare it adjacent to measure models in web information parishioners. The penalty illustrate that this ontology mold is affluent.
Voltage gradient suppression in application of voltage-fed PWM inverter to AC motor drives-resonant DC link inverter approach

In this paper, a hazardous phenomenon which occurs when the switching device of an inverter switches very fast or when a cable feeding a motor is very long is described and a suppression method for the voltage transition gradient of the voltage source inverter is presented. By using the resonant circuit in the DC link of the inverter, the inverter output voltage gradient at the switching moment can be suppressed. Compared to the hard switching inverter, the suppression of the voltage rise gradient and the performance of the proposed inverter in the control of the motor are verified through the experiment.