Miracle-dz/UniFAR-FTUs huggingface dataset:
Empowering Education through Generative AI: Innovative Instructional Strategies for Tomorrow's Learners

As the educational landscape endures continuous change, artificial intelligence (AI) has presented unprecedented opportunities to revolutionize instructional methods. Among these cutting-edge AI technologies, Generative AI has emerged as a promising instrument with the potential to empower educators and students through innovative instructional strategies. This article aims to investigate the various applications of Generative AI in education and cast light on its role in shaping the future of education. The objectives of this study are twofold: first, to investigate the various instructional strategies that can be enhanced by employing Generative AI, and second, to assess the potential impact of these strategies on student learning outcomes. To accomplish these goals, a comprehensive literature review was conducted analyzing existing studies and applications of Generative AI in educational settings. The results and discussions emphasize the numerous educational benefits of Generative AI. Educators can personalize learning experiences, create interactive content, and facilitate adaptive assessments by leveraging the capabilities of Generative AI. This individualized strategy has the potential to boost learner engagement and knowledge retention. However, despite the numerous advantages, ethical concerns and difficulties arise. The responsible incorporation of Generative AI in education requires addressing issues such as data privacy, algorithmic bias, and the educator's role in directing AI-driven learning experiences. The research concludes by emphasizing that Generative AI holds enormous promise for empowering education and transforming instructional practices. The findings highlight the importance of ongoing collaboration between educators, policymakers, and AI developers to ensure the ethical and equitable integration of Generative AI into educational environments. By embracing the potential of Generative AI while remaining vigilant regarding its challenges, the field of education can unlock novel opportunities to nurture an inclusive, adaptive, and learner-centric pedagogical landscape for tomorrow's learners.
A Cross-Cultural Comparison of Self-Efficacy as a Resilience Measure: Evidence From PISA 2018

This study explored the equivalence of resilience across countries and economies that participated in PISA 2018. A total of 79 countries and economies were divided into ten sub-groups based on their socio-demographic characteristics. Analysis of the comparability of the PISA self-efficacy scale as a measure of resilience across the participating countries/economies in the study was conducted using multi-group confirmatory factor analysis (MG-CFA). The results demonstrated that across all countries and economies, the configural invariance level, which is the lowest level of invariance, has been reached but the metric and scalar invariance levels have not been reached. Within-group results showed that all sub-groups presented a model fit for the metric level of invariance. However, only the Anglo countries were able to reach the strict invariance level. This finding indicates that the Anglo countries were more homogeneous in terms of their interpretation of self-efficacy in PISA, whereas other sub-groups were more heterogeneous. Confirming the notion of cultural affiliation of resilience, it was concluded that self-efficacy by itself might not be an adequate indicator of resilience. The current study has some recommendations for future research and how PISA can be more inclusive about the constructs it employs.
Performance Analysis of 5G New Radio LDPC over Different Multipath Fading Channel Models

The creation and developing of a wireless network communication that is fast, secure, dependable, and cost-effective enough to suit the needs of the modern world is a difficult undertaking. Channel coding schemes must be chosen carefully to ensure timely and error-free data transfer in a noisy and fading channel. To ensure that the data received matches the data transmitted, channel coding is an essential part of the communication system's architecture. NR LDPC (New Radio Low Density Parity Check) code has been recommended for the fifth-generation (5G) to achieve the need for more internet traffic capacity in mobile communications and to provide both high coding gain and low energy consumption. This research presents NR-LDPC for data transmission over two different multipath fading channel models, such as Nakagami-m and Rayleigh in AWGN. The BER performance of the NR-LDPC code using two kinds of rate-compatible base graphs has been examined for the QAM-OFDM (Quadrature Amplitude Modulation-Orthogonal Frequency Division Multiplexing) system and compared to the uncoded QAM-OFDM system. The BER performance obtained via Monte Carlo simulation demonstrates that the LDPC works efficiently with two different kinds of channel models: those that do not fade and those that fade and achieves significant BER improvements with high coding gain. It makes sense to use LDPC codes in 5G because they are more efficient for long data transmissions, and the key to a good code is an effective decoding algorithm. The results demonstrated a coding gain improvement of up to 15 dB at 10-3 BER.
Carbon-Neutrality Research in China—Trends and Emerging Themes

Carbon neutrality is a key human endeavor to deal with global climate while China is the country producing the most publications on carbon neutrality. However, what are the focuses of carbon-neutrality research in China? To answer such an important question, this study adopts a bibliometric approach to analyze carbon-neutrality journal publications from China-based researchers during the period of 2008–2022 using CNKI and Scopus. Results showed that carbon-neutrality publications in Chinese and English journals by Chinese-based researchers increased from 4 in 2008 to 2879 in 2022. In Chinese journals, X.D. Hao was the most productive author with 10 publications. In English journals, Y.K. Zhou was the most productive author with 14 publications. As a whole, the Chinese Academy of Sciences was the most productive institution with 376 publications. Co-occurrence of keywords analysis revealed seven themes in Chinese journal articles, namely, “carbon neutrality and climate change”, “energy transformation”, “peak carbon emission”, “carbon emission and low carbon economy”, “realization path in China”, “hydrogen energy and clean energy”, and “green finance and sustainable development”. In English journals, three major clusters were formed—“carbon, carbon neutralities, emission control, and energy utilization”, “carbon dioxide, carbon neutrals, biomass, and electrocatalysts”, and “China, carbon neutrality, sustainable development, and economic development. ” Most extant publications focused on policy and technological development. Emphasis shall be paid to social change and changes in people’s behavior, sectoral carbon emissions, and carbon leakage in future research.
Generating random numbers by means of nonlinear dynamic systems

To introduce the randomness of a physical process to students, a chaotic pendulum experiment was opened in East China University of Science and Technology (ECUST) on the undergraduate level in the physics department. It was shown chaotic motion could be initiated through adjusting the operation of a chaotic pendulum. By using the data of the angular displacements of chaotic motion, random binary numerical arrays can be generated. To check the randomness of generated numerical arrays, the NIST Special Publication 800-20 method was adopted. As a result, it was found that all the random arrays which were generated by the chaotic motion could pass the validity criteria and some of them were even better than the quality of pseudo-random numbers generated by a computer. Through the experiments, it is demonstrated that chaotic pendulum can be used as an efficient mechanical facility in generating random numbers, and can be applied in teaching random motion to the students.
The Development of Teacher and Student’s Book Based on Realistic Mathematics Education in Statistics for A package Program

Development research demands a improvement in the implementation of learning by developing products based on learning needs. The products of this development are teacher book and student book based on the realistic mathematic education (RME) approach for package A in statistics material. Validity testing in this study includes instrument validation, self-evaluation, expert validation, one-to-one evaluation. Aiken's V and Intraclass Correlation Coefficient (ICC) are used to determine the validity and reliability of the product. The result of research shows that the instruments and prototype are valid and feasible. Then, the ICC obtained moderate stability, it also categorize reliable. In terms of context and hypothetical learning trajectory (HLT) developed, the products should be revised to achieve meaningful learning.
Prediction of Residues in Protein-RNA Interaction Sites by Neural Networks

Structural studies of protein-RNA interactions have been focused on analyzing intraand intermolecular interactions or recognizing binding patterns [3, 4, 5]. It has been examined that specific nucleotide-protein secondary structures are favored in protein-RNA interactions. The primary focus of our study is to investigate a predicting problem of the protein-RNA interaction sites upon a protein chain. A prediction system based on artificial neural networks was developed to identify accurate residues involved in protein-RNA interaction sites from combining the residue sequence and the protein secondary structure information.
Electromagnetic instability and Schwinger effect in holographic QCD with a topological charge

Using the D0-D4/D8 brane model holographically, we compute the vacuum decay rate for the Schwinger effect in the bubble and black brane configuration which corresponds to the confined (zero temperature) and deconfined (finite temperature) phase respectively in the large $N_{c}$ QCD. Our calculation contains the influence of the D0-brane density which could be holographically related to $\theta$ angle or chiral potential in QCD. Under the strong electromagnetic fields, the creation of a pair of quark-antiquark introduces the instability and the decay rate can be obtained by evaluating the imaginary part of the probe brane Lagrangian with a constant electromagnetic field. In the bubble D0-D4 configuration, we find the decay rate decreases when the $\theta$ angle increases and it is consistent with the result in the black D0-D4 configuration at zero temperature limit. According to the calculations, we additionally find an instability from the vacuum of this system and interpret it as the description of the vacuum decay triggered by the $\theta$ angle as it is known in the $\theta$-dependent QCD.
A MapReduce-based improvement algorithm for DBSCAN

This paper proposes an improved adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm based on genetic algorithm and MapReduce parallel computing programming framework to improve the poor clustering effect and low efficiency of the DBSCAN algorithm, which due to experiential solving parameters. The size of Intensive Interval Threshold minPts and Scan Radius Eps would be rational planned by genetic algorithm iterative optimization, and it is secondary statute processing with the similarity and variability of the dataset and the efficient computing power of Hadoop Cluster. The data could be reasonable serialization, and the efficient adaptive parallel clustering could be achieved ultimately. Through the experimental results, it is shown that the proposed algorithm in this paper has higher clustering accuracy and execution efficiency than that of the comparison baselines. The trend will continue to grow with the increased volume of dataset. The improved algorithm provides a more accurate implementation method for the threshold of DBSCAN algorithm, and realizes the specific calculation process, which provides practice support for the realization of DBSCAN.
College Students’ Experiences in the Use of Technology in Virtual Classrooms

This study explores the use of technology and social media during the pandemic among the first-year students of a higher education institution (HEI). This study employed a mixed-methods research design that embodies the principles of descriptive communication research, which entails a systematic examination of the communication process and its essential components. It was discovered that the abilities that the research participants are most familiar with include Word processors, E-mail, Search Engines, and Social Networking, demonstrating how technology may also influence the degree of knowledge in both students and the instructor. The college does a good job of offering services including virtual classrooms, computer labs, learning management systems, ePortfolios, online technologies, software access, and open-source software. However, students still need more time or to be given more opportunities to explore these skills so they can advance their user level. This also reveals their dependency on social media platforms to satisfy a variety of needs.
Sentiment Analysis of Code-mixed Social Media Data on Philippine UAQTE using Fine-tuned mBERT Model

—The Universal Access to Quality Tertiary Education (UAQTE) marks a significant policy change in the Philippines. While the program’s objective is to offer free higher education and tertiary education subsidies to eligible Filipino students, its viability and effectiveness have been subject to scrutiny and continuous evaluation. This study explores the sentiments of Filipinos towards UAQTE. Leveraging a fine-tuned multilingual Bidirectional Encoder Representations from Transformers (mBERT) model, we conducted sentiment analysis on code-mixed data. With minimal preprocessing, our model achieved an accuracy of 80.21% and an F1 score of 81.14%, surpassing previous related studies and confirming its effectiveness in handling code-mixed data. The results reveal that the majority of social media users view UAQTE positively or beneficially. However, negative sentiments highlight concerns related to subsidy delays, alleged fund misuse, and application challenges. Additionally, neutral sentiments center around subsidy-related announcements. These findings provide valuable insights for its key stakeholders involved in the implementation, enhancement, and evaluation of UAQTE.
Fiber ring laser sensor based on Fabry–Perot cavity interferometer for temperature sensing

A ring laser temperature sensor based on a novel reflective fiber Fabry–Perot (F–P) interferometer air cavity is proposed and experimentally demonstrated. The reflective F–P air cavity, which consists of a segment of glass capillary inserted between two single–mode fibers, is utilized as a sensing element as well as as a filter in the fiber ring cavity. As temperature increases, the reflection spectra of the F–P sensor move towards the longer wavelength, and then cause lasing wavelength shifts. By monitoring the variation of lasing wavelength, we obtain a temperature sensor system with a high temperature sensitivity of 0.249 nm °C−1, a narrow 3 dB bandwidth of 0.1514 nm, and a high signal-to-noise ratio of 52 dB. Moreover, it is convenient to fabricate the sensor head, and the stability is very good, giving it a wide range of applications.
Probabilistic models for invariant representations and transformations

This thesis deals with machine learning models that either provide invariant representations for data or provide representations for transformations in the dataset. The first algorithm proposed is based on a sparse coding model with discrete coefficients. The following algorithm, extends the discrete sparse coding method to deal with one dimensional translations. Furthermore, a Neural Network for image transformations is proposed and analyzed. Lastly, a nonlinear dynamical system designed to separate transformations at different points in time is introduced and discussed. For the most part the afore mentioned algorithms are experimentally evaluated on synthetic and real data to quantify effectiveness and general applicability.
Application of the RBF collocation method to transient coupled thermoelasticity

Purpose In this study, the authors aim to upgrade their previous developments of the local radial basis function collocation method (LRBFCM) for heat transfer, fluid flow, electromagnetic problems and linear thermoelasticity to dynamic-coupled thermoelasticity problems. Design/methodology/approach The authors solve a thermoelastic benchmark by considering a linear thermoelastic plate under thermal and pressure shock. Spatial discretization is performed by a local collocation with multi-quadrics augmented by monomials. The implicit Euler formula is used to perform the time stepping. The system of equations obtained from the formula is solved using a Newton–Raphson algorithm with GMRES to iteratively obtain the solution. The LRBFCM solution is compared with the reference finite-element method (FEM) solution and, in one case, with a solution obtained using the meshless local Petrov–Galerkin method. Findings The performance of the LRBFCM is found to be comparable to the FEM, with some differences near the tip of the shock front. The LRBFCM appears to converge to the mesh-converged solution more smoothly than the FEM. Also, the LRBFCM seems to perform better than the MLPG in the studied case. Research limitations/implications The performance of the LRBFCM near the tip of the shock front appears to be suboptimal because it does not capture the shock front as well as the FEM. With the exception of a solution obtained using the meshless local Petrov–Galerkin method, there is no other high-quality reference solution for the considered problem in the literature yet. In most cases, therefore, the authors are able to compare only two mesh-converged solutions obtained by the authors using two different discretization methods. The shock-capturing capabilities of the method should be studied in more detail. Originality/value For the first time, the LRBFCM has been applied to problems of coupled thermoelasticity.
: Gut endocrine cells operate under immune control

Background and aims: Gastrointestinal inflammation reduces food intake, but the biological mechanisms explaining suppressed feeding during inflammation are unknown. We have used a model of upper gut infection ( Trichinella spiralis in the mouse) to study the effect of inflammation on food intake, and explored the role of a key enteroendocrine cell (EEC) in the regulation of feeding by the immune response. Methods: Food intake of NIH mice infected with the intestinal nematode Trichinella spiralis was measured. Duodenal cholecystokinin (CCK) cells were counted. Plasma CCK was measured. Infected mice were treated with a specific CCK 1 receptor antagonist, and food intake reassessed. The influence of the immune response on food intake and CCK was mechanistically examined by treating mice with CD4 or mast cell neutralising antibodies. The role of the T helper 2 response was further explored in mice genetically deficient for IL-4, IL-13 or IL-4R α. Results: Food intake of infected mice was significantly reduced at the temporal peak of intestinal inflammation. Cholecystokinin (CCK)-expressing EEC were upregulated in infected mice, and plasma CCK levels were increased. A CCK 1 receptor antagonist restored the food intake of infected mice to a significant degree. Furthermore, suppression of food intake was completely abolished in the absence of CD4 + T-lymphocytes or IL-4R α . Conclusions: The data show for the first time that intestinal inflammation results in reduced food intake due to up-regulation of CCK. Moreover, following infection, food intake and CCK expressing cells are under the specific control of CD4 + T-cells, via the release of IL-4 and IL-13.
SIKA—the multiplexing cold-neutron triple-axis spectrometer at ANSTO

SIKA is a new cold-neutron triple-axis spectrometer receiving neutrons from the cold source CG4 of the 20MW Open Pool Australian Light-water reactor. As a state-of-the-art triple-axis spectrometer, SIKA is equipped with a large double-focusing pyrolytic graphite monochromator, a multiblade pyrolytic graphite analyser and a multi-detector system. In this paper, we present the design, functions, and capabilities of SIKA, and discuss commissioning experimental results from powder and single-crystal samples to demonstrate its performance.
Vertical Profile of Wind Speed Over the Open Sea

In the pre-GARP experiments in 1969 and 1970 , we measured mean profiles of wind speed, air temperature and humidity over the open sea. Many abnormal wind profiles were obtained under the conditions of high wind speeds and swell wave. The wind profiles did not have a logarithmic shape, but had a jet-like flow at about twice as high as wave height . All of the wind profiles were observed over the open sea about 200-300km away from the land by the use of R/V Hakuho-Maru. Wind speeds were measured by five small threecup anemometers installed along the pole 9.6m away from the bow, and wave heights were observed by the gyroscope, accelerometer and sonic wave gauge. Overspeeding of the cup anemometers, ship movements and thermal stratification of the air layer were taken into account for obtaining the wind profiles. Our conclusions are as follows: As a mean wind speed increases on the open sea with significant swell waves , a departure from a logarithmic wind profile becomes larger, and obvious two peaks in the profile are recognized corresponding to the wave height distribution with two maxima.
COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. We also designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.