Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-consistent models for the X-ray emission from supernova remnants

A tool for exploring the evolution of X-ray emission from young SNRs is presented which employs a novel approach to the problem of time-dependent ionization in a shock-heated plasma. The solution of this problem is coupled to a spherically symmetric hydrodynamic calculation for the evolution of a point explosion in a uniform medium. The method is applied to Kepler's SNR, and two narrowly constrained classes of models which can simultaneously fit the spectral and morphological features of the object are found. One of these is a Sedov model in which the emission arises from shocked ambient gas, and the other is a reverse-shock model in which the SN ejecta is the dominant source of radiation. The emission from one specific model in each class is compared with the radial surface brightness profile, the 0.2-4.5 keV broadband spectrum, and the 1-3 keV moderate-resolution spectrum of the remnant. Reasonable fits are obtained in both cases. 48 references.
Characterization of the Elastic, Piezoelectric, and Dielectric Properties of Lithium Niobate from 25 °C to 900 °C Using Electrochemical Impedance Spectroscopy Resonance Method

Lithium niobate (LiNbO3) is known for its high Curie temperature, making it an attractive candidate for high-temperature piezoelectric applications (>200 °C); however, the literature suffers from a paucity of reliable material properties data at high temperatures. This paper therefore provides a complete set of elastic and piezoelectric coefficients, as well as complex dielectric constants and the electrical conductivity, for congruent monocrystalline LiNbO3 from 25 °C to 900 °C at atmospheric pressure. An inverse approach using the electrochemical impedance spectroscopy (EIS) resonance method was used to determine the materials’ coefficients and constants. Single crystal Y-cut and Z-cut samples were used to estimate the twelve coefficients defining the electromechanical coupling of LiNbO3. We employed an analytical model inversion to calculate the coefficients based on a linear superposition of nine different bulk acoustic waves (three longitudinal waves and six shear waves), in addition to considering the thermal expansion of the crystal. The results are reported and compared with those of other studies for which the literature has available values. The dominant piezoelectric stress constant was found to be e15, which remained virtually constant between 25 °C and 600 °C; thereafter, it decreased by approximately 10% between 600 °C and 900 °C. The elastic stiffness coefficients c11E, c12E, and c33E all decreased as the temperature increased. The two dielectric constants ϵ11S and ϵ33S increased exponentially as a function of temperature.
Multi-head self-attention bidirectional gated recurrent unit for end-to-end remaining useful life prediction of mechanical equipment

In order to reduce error accumulation caused by multistep modeling and achieve a generally accurate model, this paper proposes an end-to-end remaining useful life (RUL) prediction model based on a multi-head self-attention bidirectional gated recurrent unit (BiGRU). Taking multivariable samples with long time series as the model input and multistep RUL values as the model output, the BiGRU model is constructed for continuous prediction of RUL. In addition, single-head self-attention models are applied for time series and variables of samples before or after the BiGRU, which can be fused into a multi-head attention BiGRU. Aeroengines and rolling bearings are selected to testify the effectiveness of the proposed method from the system level and component level respectively. The results show that the proposed method can achieve end-to-end RUL prediction efficiently and accurately. Compared with single-head models and individual deep learning models, the prediction mean square error of the proposed method is reduced by 20%–70%.
Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

The Internet of Things (IoT) era is mainly dependent on the word “Smart”, such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the result is the Cognitive IoT (CIoT). In the automotive industry, many researchers worked on self-diagnosis systems using deep learning, but most of them performed this process on the cloud due to the hardware limitations of the end-devices, and the devices obtain the decision via the cloud servers. Others worked with simple traditional algorithms of machine learning to solve these limitations of the processing capabilities of the end-devices. In this paper, a self-diagnosis smart device is introduced with fast responses and little overhead using the Multi-Layer Perceptron Neural Network (MLP-NN) as a deep learning technique. The MLP-NN learning stage is performed using a Tensorflow framework to generate an MLP model’s parameters. Then, the MLP-NN model is implemented using these model’s parameters on a low cost end-device such as ARM Cortex-M Series architecture. After implementing the MLP-NN model, the IoT implementation is built to publish the decision results. With the proposed implemented method for the smart device, the output decision based on sensors values can be taken by the IoT node itself without returning to the cloud. For comparison, another solution is proposed for the cloud-based architecture, where the MLP-NN model is implemented on Cloud. The results clarify a successful implemented MLP-NN model for little capabilities end-devices, where the smart device solution has a lower traffic and latency than the cloud-based solution.
Looking beyond primary barriers: Support workers’ perspectives on school dropout among students with a migration background

ABSTRACT Worldwide, students with a migration background are over-represented in statistics of early school leavers. Too often, educational inequalities are attributed to culture as the primary barrier to successful educational outcomes. This group tends to be regarded as potential dropout students, culminating in stigmatization and stereotyping biases. This qualitative study evaluates the perspectives of support workers using 15 in-depth interviews that reveal influencing factors. These results highlight that for many of these students, “education indeed can become secondary” in light of the many struggles and structural barriers they face. The need for a more holistic perspective on the challenges faced by students with a migration background throughout education is emphasized.
A Batch-dynamic Suitor Algorithm for Approximating Maximum Weighted Matching

Matching is a popular combinatorial optimization problem with numerous applications in both commercial and scientific fields. Computing optimal matchings w.r. t. cardinality or weight can be done in polynomial time; still, this task can become infeasible for very large networks. Thus, several approximation algorithms that trade solution quality for a faster running time have been proposed. For networks that change over time, fully dynamic algorithms that efficiently maintain an approximation of the optimal matching after a graph update have been introduced as well. However, no semi- or fully dynamic algorithm for (approximate) maximum weighted matching has been implemented. In this article, we focus on the problem of maintaining a \( 1/2 \) -approximation of a maximum weighted matching (MWM) in fully dynamic graphs. Limitations of existing algorithms for this problem are (i) high constant factors in their time complexity, (ii) the fact that none of them supports batch updates, and (iii) the lack of a practical implementation, meaning that their actual performance on real-world graphs has not been investigated. We propose and implement a new batch-dynamic \( 1/2 \) -approximation algorithm for MWM based on the Suitor algorithm and its local edge domination strategy [Manne and Halappanavar, IPDPS 2014]. We provide a detailed analysis of our algorithm and prove its approximation guarantee. Despite having a worst-case running time of \( \mathcal {O}(n + m) \) for a single graph update, our extensive experimental evaluation shows that our algorithm is much faster in practice. For example, compared to a static recomputation with sequential Suitor, single-edge updates are handled up to \( 10^5\times \) to \( 10^6\times \) faster, while batches of \( 10^4 \) edge updates are handled up to \( 10^2\times \) to \( 10^3\times \) faster.
Long-term ENSO prediction with echo-state networks

The El Niño-Southern Oscillation (ENSO) is a climate phenomenon that profoundly impacts weather patterns and extreme events worldwide. Here we develop a method based on a recurrent neural network, called echo state network (ESN), which can be trained efficiently to predict different ENSO indices despite their relatively high noise levels. To achieve this, we train the ESN model on the low-frequency variability of ENSO indices and estimate the potential future high-frequency variability from specific samples of its past history. Our method reveals the importance of cross-scale interactions in the mechanisms underlying ENSO and skilfully predicts its variability and especially El Niño events at lead times up to 21 months. This study considers forecasts skillful if the correlation coefficients are above 0.5. Our results show that the low-frequency component of ENSO carries substantial predictive power, which can be exploited by training our model on single scalar time series. The proposed machine learning method for data-driven modeling can be readily applied to other time series, e.g. finance and physiology. However, it should be noted that our approach cannot straightforwardly be turned into a real-time operational forecast because of the decomposition of the original time series into the slow and fast components using low-pass filter techniques.
Is the development of the stock market a barometer of China's economy?

The rise of China and the five-fold increase in the expanse of its stock market over the previous decade has spurred a rising body of research in financial economics on the marketplace. The link between the development of the Chinese stock market and China’s economy has always been an eternal topic. The existing literature has rarely systematically analyzed whether the stock market development is a barometric measure of China's economy. To this end, this paper examines the role of using the Chinese stock market’s development as a barometer of China's economy by studying the relationship between stock price changes in the stock market and China's Industrial Development Index.
Measurement of currents and voltages non-sinusoidal parameters in power supply systems with rectifier load

The use of frequency-controlled electric drives in industry and municipal services is accompanied by the problem of their negative impact on the distribution network. As examples, the results of measurements of power quality indices in the power supply system of an oil producing enterprise and on the supply input of a railway traction substation are given. It is shown that the voltage subgroup total harmonic distortions (THDS) can exceed their rated permitted values in 100% of the measurement time.
Revisiting the security–development nexus: Human security and the effects of IMF adjustment programmes

The concept of ‘human security’ holds promise of capturing the strong connections between economic development and personal security that has ushered in the debate about the ‘security−development nexus’ in policy circles. Human security is understood as the ability of states to protect the security of their citizens and to cater to their basic socioeconomic needs. However, the literature on human security is fragmented, leaving applied researchers without a workable definition and a convincing measurement strategy. Part of this problem is due to the fuzziness of the concept and the ambition to associate an ever-increasing set of dimensions with the concept, which renders empirical analysis moot. Following a review of the related literature, we collect measures aligning with ‘freedom from fear’ and ‘freedom from want’, representing the varied discourse on the human security concept. Employing confirmatory factor analysis on 11 indicators, we demonstrate the existence of a single latent factor and extract their common variation to generate a new Human Security Index. We validate the index by comparison against known proxies and related measures. We then present a first empirical application examining the human security implications of IMF programmes, finding that IMF programmes undermine human security, accounting for non-random selection into IMF programmes.
The use of play in English as a foreign language classrooms: Chinese teachers’ perspectives

This semi-structured interview study seeks to describe Chinese teachers’ understanding and concerns about the use of play in supporting young children’s English learning. Eight English as a foreign language (EFL) teachers of children aged between 3 to 8 years were involved, including both local teachers and international teachers. Findings reveal that because of the unique feature of EFL learning, structured play-based learning was mainly discussed and favored by teachers. Although teachers showed an inclination of separating learning from play, they believed that play serves a supporting role in maintaining students’ interest and motivation in English learning, which is especially crucial important for young children. However, teachers are concerned that play’s benefits would be considered being neutralized when children get older from parents’ perspectives which will eventually negatively impact teachers’ teaching practices. Teachers also face the barriers of balancing the gap between child’s language abilities and cognitive development in EFL teaching. The findings and discussions raise implications for researchers and practitioners to rethink how to define play. A broader definition of play-based learning will help balance both the needs for academic teaching and the benefits of incorporating play, as well as provide implications for integrating play pedagogy in public school systems.
Pippenger's Exponentiation Algorithm

Pippenger’s exponentiation algorithm computes a power, or a product of powers, or a sequence of powers, or a sequence of products of powers, with very few multiplications. Pippenger’s algorithm was published twenty-five years ago, but it is still not widely understood or appreciated, although certain parts of it have recently been reinvented, republished, and popularized. This paper is an exposition of the state of the art in generic exponentiation algorithms: in particular, Pippenger’s algorithm.
GENERAL HEALTH STATUS AND ONLINE TEACHING READINESS AMONG EDUCATORS DURING COVID-19 PANDEMIC

The current COVID-19 outbreak has resulted in several life adjustments, including modifications to education. Online learning has become the primary mode of teaching and learning. Consequently, the purpose of this study is to determine the level of educators' overall health and its impact on their readiness for online teaching. The study used a total of 220 educators from primary level to tertiary level, both private and public. These characteristics were evaluated using the General Health Questionnaire and the Online Teaching Readiness Questionnaire. The results indicate that the participants' general health is excellent and that they are highly prepared for online teaching. According to correlational research, the two variables are also significantly connected. A few suggestions for future research are offered at the conclusion of the publication.
SOCLP: A Set Oriented Calculus for Logic Programming

For identifying one of a plurality of known gases in a gaseous mixture based on air a single catalytic transducer is placed in the gaseous medium. The catalytic transducer is heated to a lower threshold temperature at which the oxidizable gas is oxidized little or not at all and a first value of a parameter characteristic of the thermal state of the transducer is captured. The catalytic transducer is then heated to an upper threshold temperature at which the oxidizable gas oxidizes significantly in contact with the catalytic transducer and a second value of the parameter characteristic of the thermal state of the transducer is captured. Using a predetermined law, a response signal representative of the gaseous medium is produced from the difference between the second and first values of the parameter characteristic of the thermal state of the catalytic transducer. The catalytic transducer is allowed to cool below the lower threshold temperature and the above operations are repeated cyclically.
The Impact of Foreign Ownership on Chinese Stock Liquidity

This paper investigates the relationship between foreign ownership and stock liquidity in China during 2010-2018. We find that foreign ownership has a negative effect on stock liquidity in the main board and SMEs board, while no impact on the GEM board. Foreign investors as informed traders can reduce the stock liquidity due to adverse selection by their counterparties, while they may also increase the stock liquidity due to competitive effect. We find that the negative impact of adverse selection dominates the competitive effect by foreign shareholding.
Peer Tutoring and Math Digital Tools: A Promising Combination in Middle School

Peer tutoring in combination with math digital tools was employed with middle school students learning mathematics. A total of 112 students in 9th grade (14 to 15 years old) participated in the study. A pretest–posttest with control group design was used. Students worked with systems of linear equations during the experience. The effects of the intervention of peer tutoring in combination with math digital tools on students’ mathematics achievement were examined using quantitative methods. The way students in the experimental group learned and their motivation towards using digital tools compared with students in the control group were analysed qualitatively. Statistically significant improvements and a large effect size were reported for students’ mathematics achievement in the experimental group. No statistically significant differences were reported between the pretest and the posttest for the control group. The qualitative analyses revealed that students in the experimental group achieved a higher level of autonomous learning, showed a greater association of mathematical concepts, helped their peers more, did more exercises and problems than students in the control group, and enjoyed the experience.
An Empirical Study on the Impact of Corporate Greenwashing Behaviours on Financial Performance

In the context of sustainable development, governments, consumers and other external institutions have become increasingly concerned about corporate social responsibility (CSR) issues. The behavior of companies attempting to improve their corporate image with social responsibility investment, but not actually implementing or exaggerating the effect is called greenwashing. Such behavior is extremely harmful to consumers, companies and the development of green markets. This paper explores the relationship between corporate greenwashing and financial performance. In this paper, the degree of corporate greenwashing is measured by the company's peer-relative greenwashing score. Based on this, we examined the negative relationship between corporate greenwashing behaviors and financial performance using a sample of 349 A-share listed companies in China from 2017 to 2020, and made suggestions to enterprises, government departments and regulators to reduce the occurrence of corporate greenwashing.
Rendering Immersive Haptic Force Feedback via Neuromuscular Electrical Stimulation

Haptic feedback is the sensory modality to enhance the so-called “immersion”, meant as the extent to which senses are engaged by the mediated environment during virtual reality applications. However, it can be challenging to meet this requirement using conventional robotic design approaches that rely on rigid mechanical systems with limited workspace and bandwidth. An alternative solution can be seen in the adoption of lightweight wearable systems equipped with Neuromuscular Electrical Stimulation (NMES): in fact, NMES offers a wide range of different forces and qualities of haptic feedback. In this study, we present an experimental setup able to enrich the virtual reality experience by employing NMES to create in the antagonists’ muscles the haptic sensation of being loaded. We developed a subject-specific biomechanical model that estimated elbow torque during object lifting to deliver suitable electrical muscle stimulations. We experimentally tested our system by exploring the differences between the implemented NMES-based haptic feedback (NMES condition), a physical lifted object (Physical condition), and a condition without haptic feedback (Visual condition) in terms of kinematic response, metabolic effort, and participants’ perception of fatigue. Our results showed that both in terms of metabolic consumption and user fatigue perception, the condition with electrical stimulation and the condition with the real weight differed significantly from the condition without any load: the implemented feedback was able to faithfully reproduce interactions with objects, suggesting its possible application in different areas such as gaming, work risk assessment simulation, and education.
Machine Learning Safety System for Treadmill Users

In this paper, we present an AI-based treadmill assistant that employs Computer vision and Deep learning algorithms to offer a comprehensive safety net for treadmill users. The system is designed and implemented with minimal components including an off-the-shelf camera, speaker, an IoT smart plug, and Jetson Nano (a single board GPU-based embedded computing device). To provide an extensive set of safety features such as face recognition, facial stress detection, object detection, and distance and aspect measurement to the treadmill user, the system uses CNN architectures based on ResNet, MobileNet SSD, and customized fully connected neural networks. This set of features monitored in real-time will play a crucial role in mitigating treadmill user falls and injuries.
Testing Solid State DC Circuit Breakers for Electrified Aircraft Applications

With the ever increasing power demand for next generation aircraft, hybrid propulsion and more electrical systems, fault detection and protection are crucial to flight safety and reliable operations, especially for DC distribution network. Solid State Circuit Breakers (SSCB) using state of the art (SOA) wide bandgap (WBG) semiconductor devices are potential candidates to meet the fast action, high power density and high efficiency requirements for aerospace electrical systems. The objective of this paper is to introduce test methods to evaluate the operation and performance of these fast-acting protection devices. A literature review of the current SSCB technology is included in the introduction, followed by the design phase test criteria. Test circuits and experimental setup for both the dynamic breaking and steady state efficiency evaluations are described in detailed. Fault current interruption capability and to verify efficiency at nominal operating conditions of a medium voltage DC (MVDC) SSCB were demonstrated and verified through the test results.