Miracle-dz/UniFAR-FTUs huggingface dataset:
Norwegian and Swedish post-compulsory school students’ perspectives on the purpose of school mathematics: An exploratory study

Many countries’ educational aspirations are reflected in government-produced national curricula that specify, with varying degrees of prescription, what should be taught, when it should be taught, and even how it should be taught. These systemic ambitions are typically located in culturally constructed images of the ideal citizen dependent on, for example, whether cultures privilege the individual or the collective, or whether knowledge, particularly mathematics, is construed as practical or abstract. Despite such differences, little research has examined students’ awareness of the systemic expectations that underpin their experiences of school mathematics. In this paper, drawing on data from 35 group interviews involving 92 students from post-compulsory schools, we present an exploratory study of Norwegian and Swedish students’ perspectives on the purpose of school mathematics. Constant comparison analyses yielded six themes, or purposes, concerning the role of mathematics in support of everyday shopping, future employment, the learning of other subjects, logical thinking and problem-solving, the development of an esthetic appreciation, and the management of personal finances. However, despite extensive emphases in both the Norwegian and the Swedish curricula on the role of mathematical knowledge in the creation of just and equitable societies, no students discussed such societal goals. Some implications are discussed.
Enhanced Multiview Fuzzy Clustering Using Double Visible-Hidden View Cooperation and Network LASSO Constraint

Multiview clustering is an important topic in multiview learning, where the cooperation of different views is used to improve clustering performance. Although multiview clustering has made considerable progress, most existing methods only utilize the information of the original visible views, or only consider some hidden space information shared by different views. Two of the challenges are: 1) insufficient exploitation of cooperative learning between visible and hidden information despite some preliminary attempts, and 2) inadequate consideration of topological information for improving multiview clustering. To meet the challenges, we propose the cooperation enhanced multiview fuzzy clustering method (CE-MVFC) in this article. First, we characterize multiview data with two hidden views, which are obtained by adaptive multiview non-negative matrix factorization (NMF) and fuzzy partition information of each sample in different clusters. Then, we integrated the hidden views and the original visible views to realize visible-hidden cooperation learning. Furthermore, we establish a similarity matrix for each visible view and the hidden view obtained through NMF to describe the data topology in these views. Based on the spatial topological relationship of the samples and the representation of hidden view obtained by fuzzy partition, the network least absolute shrinkage and selection operator is constructed to constrain multiview learning. Finally, we develop the multiview clustering method by exploiting the visible-hidden information cooperation and the spatial topological information constraints. Experiments on benchmark multiview datasets are conducted to demonstrate the highly competitive performance of the proposed CE-MVFC against the state-of-the-art methods.
An External Selection Mechanism for Differential Evolution Algorithm

The procedures of differential evolution algorithm can be summarized as population initialization, mutation, crossover, and selection. However, successful solutions generated by each iteration have not been fully utilized to our best knowledge. In this study, an external selection mechanism (ESM) is presented to improve differential evolution (DE) algorithm performance. The proposed method stores successful solutions of each iteration into an archive. When the individual is in a state of stagnation, the parents for mutation operation are selected from the archive to restore the algorithm's search. Most significant of all, a crowding entropy diversity measurement in fitness landscape is proposed, cooperated with fitness rank, to preserve the diversity and superiority of the archive. The ESM can be integrated into existing algorithms to improve the algorithm's ability to escape the situation of stagnation. CEC2017 benchmark functions are used for verification of the proposed mechanism's performance. Experimental results show that the ESM is universal, which can improve the accuracy of DE and its variant algorithms simultaneously.
Analytical and Experimental Vibration Analysis of Variable Update Rate Waveform Generation

Mark, Joshua. M.S.Egr. , Department of Mechanical and Materials Engineering, Wright State University, 2011. Analytical and Experimental Vibration Analysis of Variable Update Rate Waveform Generation. Typical vibration analysis of turbine engine components incorporates the use of a function generator to produce a signal that is routed to an excitation source as the forcing function of the test specimen. These waveforms are constructed by varying the amplitude of the signal over time with a fixed update rate (time increment between samples). This research investigates generating chirp waveforms by storing only one sinusoid of points and using an external timing signal to repetitively send out these stored data points. This results in varying the amplitude of the signal over time as well as the update rate. The update rate varies linearly as the frequency varies throughout the chirp. The number of samples stored is fixed as only one sinusoid is stored in memory. This results in a degraded waveform with step changes in the voltages of the analog output signal. This research incorporates a high speed analog output device from National Instruments used to generate the waveform built from user inputs for sweep range, time, and desired samples/cycle. For this research, both single and multiple degree of freedom systems were used to analytically predict the response of the system to the degraded waveforms. A series of experimental tests was conducted using a cantilevered beam to validate the analytical predictions. The response of the test article was captured using a scanning laser vibrometer from which the frequency response function (FRF) was calculated, and in turn, the natural frequencies, mode shapes, and damping characteristics were determined. The differences in the responses of the test article were quantified to determine the effect of the degraded waveform and the minimum number of samples/cycle in a waveform necessary to generate a signal sufficient for accurate modal analysis. A simulated bladed disk was modeled in state space to quantify the accuracy of modal analysis implementing the variable update rate waveforms with traveling wave excitation. iii
Online Pressure Map Reconstruction in a Multitouch Soft Optical Waveguide Skin

In this article, we present a reconstruction method for mapping a wide range of pressure stimuli in a multitouch soft optical waveguide skin (MSOWS). Distributed sensing information is typically obtained from wired soft sensing units. This approach hardly adapts to different shapes and has limited durability against stresses and shocks. In our MSOWS, the spatial tactile transduction relies on a continuum sensitive area of graded-stiffness polydimethylsiloxane, with a virtual grid free from electronics and connections. The sensing range of each cell is up to 234 kPa, and the spatial resolution is 5 mm. We use a time-delay neural network to reduce the hysteresis of the soft material to less than 1%. In addition, a fault-tolerant mechanism makes the entire system robust to component failure. These results may open the way to a new generation of distributed soft skins that are versatile due to material design and processing.
Substance Use among Belgian Higher Education Students before and during the First Wave of the COVID-19 Pandemic

The COVID-19 pandemic prompted many countries to issue far-reaching policy measures that may have led to increased substance use. Higher education students may have been disproportionally affected due to the rearrangement of educational life and their susceptibility to psychosocial distress and substance use. The current study examined associations between pandemic-related stressors, psychosocial distress, and self-reported alcohol, tobacco, and cannabis use before and during the first wave of the pandemic. Data were collected in Belgium as part of the COVID-19 International Student Well-being Study (C19 ISWS) and analyzed using multinomial logistic regression analyses. The sample contained 18,346 higher education students aged 17 to 24 (75% women). Overall use of alcohol, tobacco, and cannabis as well as binge drinking decreased during the pandemic, perhaps due to limited social gatherings. Moving back to the parental home was associated with decreased substance use, while depressive symptoms were associated with increased substance use. Perceived threat and academic stress were associated with increased binge drinking among heavy bingers and increased tobacco use. Decreases among students who moved back to their parental home may be explained by increased informal social control. Increased substance use was associated with a number of stressors and psychosocial distress, which suggests that some students may have been self-medicating to manage their mental health amidst the pandemic. Public health policy concerning substance use may prove to be less effective if not tailored to particular subgroups within the student population.
Interference Mitigation Based on Intelligent Location Selection in a Canonical Communication Network

Abstract In this letter, the interference mitigation in a canonical communication network is discussed from the perspective of intelligent location selection. A potential game model is constructed and a location-selection algorithm is designed combining no-regret procedure. With the proposed algorithm, all nodes can update their strategies with limited information exchange. Specifically, our proposed algorithm can converge to a set of correlated equilibria which are the globally or locally optimal solution to the problem of interference minimization. Moreover, our proposed algorithm can achieve distributed implementation without a central node. Simulation results demonstrate that the total interference can be mitigated efficiently with our proposed algorithm. And the proposed algorithm can converge fast.
The Law of Requisite Variety and the Organizational Performance: An Empirical Analysis of the Pakistani Cellular Sector

D’Aveni (1994) states that environmental uncertainty is creating such a situation where means and ends relationships are unknown and survival is in danger. Now question arises how firms can route towards better performance and sustainability? The top management demography (Milliken and Martins, 1996), learning (Dodgson, 1993 and Meyer, 1982), and innovation (Russell and Russell, 1992), researchers endorse that in volatile environment creating and maintaining requisite variety is pre requisite for learning, innovation and better performance. Current study is an effort to check out the effect of requisite variety on organizational learning and innovation directly and on performance directly as well as indirectly through mediation of learning and innovation in cellular sector of Pakistan. Results show that requisite variety has direct and significant impact on learning and innovation whereas, direct effect of requisite variety could not be verified on performance, however model highlights that it influences the performance through the mediation of learning and innovation.
Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure

Maintenance and repairing in actual engineering for long-term used structures, such as pipelines and bridges, make structural damage detection indispensable, as an unanticipated damage may give rise to a disaster, leading to huge economic loss. A new approach for detecting structural damage using transmissibility together with hierarchical clustering and similarity analysis is proposed in this study. Transmissibility is derived from the structural dynamic responses characterizing the structural state. First, for damage detection analysis, hierarchical clustering analysis is adopted to discriminate the damaged scenarios from an unsupervised perspective, taking transmissibility as feature for discriminating damaged patterns from undamaged ones. This is unlike directly predicting the structural damage from the indicators manifestation, as sometimes this can be vague due to the small difference between damaged scenarios and the intact baseline. For comparison reasons, cosine similarity measure and distance measure are also adopted to draw out sensitive indicators, and correspondingly, these indicators will manifest in recognizing damaged patterns from the intact baseline. Finally, for verification purposes, simulated results on a 10-floor structure and experimental tests on a free-free beam are undertaken to check the suitability of the raised approach. The results of both studies are indicative of a good performance in detecting damage that might suggest potential application in actual engineering real life.
Design of ultrasonic flowmeter for wet gas measurement

When an ultrasonic flowmeter (UFM) is applied in the measurement of wet gas, the ultrasonic signal attenuates and fluctuates strongly because of the liquid film and droplets in gas. It is hard to obtain the accurate time of flight (TOF) of ultrasound. Besides, the gas velocity distribution on a pipe cross section changes with different flow patterns, making it difficult to determine the measurement model. In order to solve these problems, a dual-channel UFM is specially designed and a measurement system is built. Then, a dynamic threshold method based on statistical average signal amplitude is proposed to obtain the TOF of ultrasound in wet gas. Combined with the classical void fraction model, the wet gas velocity measurement model for the dual-channel UFM is established based on a neural network. Finally, experiments are carried out on a DN50 wet gas device, where pressure is 0.1 MPa, gas superficial velocity is 5–20 m s−1 and liquid volume fraction is 0.2%–5%. The results show that the average relative error of gas velocity is 0.52% and the maximum relative uncertainty is 0.491%.
Community Detection Based on Multiobjective Particle Swarm Optimization and Graph Attention Variational Autoencoder

Community detection is an important research direction in complex network analysis that can help us discover valuable network structures. The community detection algorithms based on multiobjective particle swarm optimization encode community membership of nodes in particles and employ evolutionary strategies to search for the optimal community division. Existing algorithms face two challenges: (1) they are inapplicable to large networks because the evolution process is time-consuming; (2) they are easy to fall into local optima. In this paper, we propose a novel algorithm that combines a label-propagation-based multiobjective particle swarm optimization algorithm with a graph attention variational autoencoder to realize community detection. On the one hand, the label propagation strategy is involved in the update of a swarm's particles to speed up its evolution. The optimal solutions found by the particle swarm optimization algorithm are embedded into the objective of the autoencoder to improve the embedding vectors’ quality. On the other hand, the embedding vectors are used to improve the solutions of the particle swarm optimization algorithm to avoid its early convergence. The experiments on artificial and real-world networks demonstrate the feasibility and effectiveness of our algorithm compared with some state-of-the-art algorithms.
Nonparametric monitoring of sunspot number observations

Abstract Solar activity is an important driver of long-term climate trends and must be accounted for in climate models. Unfortunately, direct measurements of this quantity over long periods do not exist. The only observation related to solar activity whose records reach back to the seventeenth century are sunspots. Surprisingly, determining the number of sunspots consistently over time has remained until today a challenging statistical problem. It arises from the need of consolidating data from multiple observing stations around the world in a context of low signal-to-noise ratios, non-stationarity, missing data, nonstandard distributions and errors of different kind. The data from some stations experience therefore severe and various deviations over time. In this paper, we apply a systematic statistical approach for monitoring these complex and important series. It consists of three steps essential for successful treatment of the data: smoothing on multiple time-scales, monitoring using block bootstrap calibrated CUSUM charts and classifying of out-of-control situations by support vector techniques. This approach allows us to detect a wide range of anomalies (such as sudden jumps or more progressive drifts), unseen in previous analyses. It helps us to identify the causes of major deviations, which are often observer or equipment related. Their detection and identification will contribute to improve future observations. Their elimination or correction in past data will lead to a more precise reconstruction of the world reference index for solar activity: the International Sunspot Number.
Hypervelocity impacts in the Solar System : an experimental investigation on the fate of the impactor

Collisions is one of the most important processes in the Solar System that have played a significant role in its evolution for 4.5 Gy. They are responsible for the formation of asteroid families, craters and regolith production on bodies surfaces. Moreover they pose a hazard for our planet's environment, human civilisation and space assets. Impacts have shaped the asteroids and their surfaces and recently there are indications that they are also responsible for the creation of multi-lithology asteroids. The effectiveness of this process lies, apart from the collisional speed and angle, on the physical parameters of both the target and the impactor. A plethora of laboratory experiments are devoted to study the outcome of impacts, from low speeds of a few m/s to greater speeds of several km/s. In addition space missions; such as Deep Impact (NASA) in the past and AIDA (ESA/NASA) hopefully in the near future, are aiming to perform hyper-velocity impact experiments at large scales. Although there is advance in our understanding of crater formation, target fragmentation and ejecta speeds, however the fate of the impactor is still very poorly constrained. Experiments so far were focused using materials not directly relevant to the composition of asteroids. We start an investigation for the impactors' fate, by using lithological projectiles that impacted three different types of targets with different material and bulk porosities. For this experimental campaign was used the Light Gas Gun (LGG) of the Impact Group at the University of Kent. The study was focused on three main topics: i) the fragmentation of the impactor, ii) the implantation of exogenous material onto the target and iii) the inspection of the final state of the projectile. \\ This Thesis is divided in six Chapters. The first two, Chapters 1 and 2, are giving a review of recent advances of small bodies studies, the importance of collisions in the Solar System, and a brief description of the laboratory impact experiments, providing the current state of research on the fate of projectiles. Some open questions lead to the explanation of the aim of this study. In Chapter 3 are described the series of experiments performed, explaining the analysis methods were developed and the way that the main topics of fragmentation, implantation and characterisation of the impactor were studied. All the results for each one of these topics, along with the difficulties during the experimental procedure are provided in Chapter 4. In Chapter 5 we discuss the results giving the implications, attempting to place the outcome in the big picture of the small bodies collisions. In the last Chapter 6 there is a summary of this work, providing also possible future ideas for the continuation of this study.
Arc crust formation of Lesser Antilles revealed by crustal xenoliths from Petit St. Vincent

The Lesser Antilles volcanic arc is known for its magmatic diversity and unusually abundant plutonic xenoliths. Xenoliths from Petit St. Vincent (Grenadines archipelago) are particularly interesting because of their textural and petrogenetic range. Here we combine petrographic observations, Electron Backscatter Diffraction (EBSD) analysis, major and trace element chemistry of xenoliths and lavas, and geochemical and thermal modelling to explore the construction of arc crust beneath Petit St. Vincent. Petit St. Vincent xenoliths are dominated by calcic plagioclase, clinopyroxene and amphibole, and can be divided into two main categories, igneous and meta-igneous. Igneous xenoliths typically have cumulate textures; meta-igneous xenoliths range texturally from those that preserve vestiges of primary magmatic fabrics to intensely deformed varieties characterised by grain-size reduction and foliation development. Meta-igneous xenoliths also contain the most calcic plagioclase (An98-100).
Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Electromyograms (EMG) are a recorded galvanic action of nerves and muscles which assists in diagnosing the disorders associated with muscles and nerves. The efficient discrimination of abnormal EMG signals, myopathy and amyotrophic lateral sclerosis, engage crucial role in automatic diagnostic assistance tools, since EMG signals are nonstationary signals. Hence, for computer-aided identification of abnormalities, extraction of features, selection of superlative feature subset, and developing an efficient classifier are indispensable. Initially, time domain and Wigner-Ville transformed time-frequency features were extracted from abnormal EMG signals for experiments. The selection of substantial characteristics from time and time-frequency features was performed using bat algorithm. Extensively, deep neural network classifier is modelled for selected feature subset using bat algorithm from extracted time and time-frequency features. The performance of deep neural network exerting selected features from bat algorithm was compared with conventional artificial neural network. Results demonstrate that the deep neural network modelled with layers 2 and 3 (neurons = 2 and 4) using time domain features is efficient in classifying the abnormalities of EMG signals with an accuracy, sensitivity, and specificity of 100% and also exhibited finer performance. Correspondingly, the developed conventional single layer artificial neural network (neurons = 7) with time domain features has shown an accuracy of 83.3%, sensitivity of 100%, and specificity of 71.42%. The work materializes the significance of conventional and deep neural network using time and time-frequency features in diagnosing the abnormal signals exists in neuromuscular system using efficient classification.