bluuebunny/crossref_metadata_2025_split huggingface dataset:
Examining the relationship between peer support and foreign language emotions in online learning: the mediating effect of self-efficacy
Authors: Huang Yujie 
Published: October 2023

IntroductionWith the proliferation of online learning, understanding students’ affective experiences in digital foreign language courses has become imperative.MethodsThis mixed-methods study investigates how peer support and self-efficacy shape the emotional experiences of 502 Chinese undergraduate students in online English courses. Quantitative analyses using surveys were conducted to examine the relationships between peer support, self-efficacy, enjoyment, and anxiety. Qualitative analyses of interviews provided additional insights.ResultsQuantitative analyses reveal that peer support positively predicts higher levels of foreign language enjoyment and anxiety. Self-efficacy was found to partially mediate this relationship, with peer support strengthening self-efficacy, which, in turn, positively influences enjoyment and anxiety. Qualitative analyses provide converging yet nuanced insights into how peer support enhances self-efficacy and emotions beyond the surveys.DiscussionThe findings provide insight into optimizing online language courses through peer support and self-efficacy development tailored to diverse student needs. Limitations around sample selection, self-reported measures, and scope of relationships examined present opportunities for future research.
Evolution of storytelling pedagogy in global health course at a U.S. Native American-Serving Nontribal Institution from Fall 2019 to Spring 2023
Authors: Dutta Tapati , Keith Camille 
Published: September 2023

Background and purposeResponding to COVID-19-induced disruptions to traditional teaching methodologies, and considering the relevance of narratives among indigenous populations, “storytelling as pedagogy” was developed and implemented in the undergraduate Global Health course in a Native American-Serving Nontribal Institution (NASNTI) in Colorado.MethodsWe describe the evolving pedagogic adjustments and storytelling strategies incorporated into the global health course from Fall 2019 to Spring 2023. This entailed before the COVID-19 in-person format, online digital storytelling during the pandemic emergency, the HyFlex and hybrid classes with the emergence of “new normals,” and finally the gradual move to in-person classes. The story arc in the course included the following: (1) Course learning outcomes revisited and the course syllabus language framed based on the native philosophies of empowerment education and experiential learning, (2) students’ inputs sought to incorporate socioculturally responsive topics in the course syllabus (e.g., dental health disparities among indigenous populations), (3) strategic and non-threatening shifts such as “no textbooks” and “no finals” introduced, (4) global health thought partners invited by the course instructor and coached to use story-based teaching methods, (5) use of first-person trauma-informed storytelling methods to teach specific global health topics, and (6) students undertook gratitude journaling, a scaffolding exercise of writing letters on global health topics to global health thought partners.ResultsStorytelling as pedagogy was most effective in the in-person format, while digital storytelling during the COVID-19-induced online classes was extremely challenging considering the stark digital divide in the Navajo Nation. First-person, trauma-informed storytelling is a helpful approach to discuss insider–outsider perspectives and can potentially establish sustainable trustworthy relationships among the students, instructor, and global health thought leaders. Gratitude journaling and photovoice can be tweaked as powerful storytelling methods to build students’ interaction-based critical thinking, intercultural humility, and professional networking.ConclusionMapping storytelling pedagogies’ best practices can be useful in developing a granulated understanding of this strategy and utilizing them across diverse disciplines in higher education. Faculty capacity building is recommended to enable the former to conceptualize culturally responsive storytelling pedagogies and create assessment plans to assess students’ learning outcomes through the utilization of this method.
Perceção e estratégias do sector do turismo no município de San Gil
Authors: Diaz María Alejandra Prieto , García Manuel Oswaldo Castillo , Rojas Maria Alix Quintero , Monsalve Karen Rossana Céspedes , Muñoz Cindy Yurany Colmenares , Ardila Luz Daniela Quintero , Maldonado Yuly Jimena Vásquez , Rincón Brayan Steven 
Published: September 2023

El siguiente documento tiene por objeto mostrar el resultado del estudio realizado por un selecto grupo de docentes y estudiantes del programa de Ingeniería Industrial pertenecientes a la UNIVERSIDAD DE INVESTIGACIÓN Y DESARROLLO UDI.
Las metodologías utilizadas brindan un valor significativo para la realización de capacitación y talleres en materia de turismo. El estudio consiste en exponer el desarrollo local y regional enfocado en el turismo, ya que este integra factores influyentes, destacando la economía y cultura presentes en el municipio de San Gil, Santander, con el objetivo de poder identificar la viabilidad y diferentes estrategias metodológicas utilizadas que implica la realización de dicha actividad.
La investigación sobre el turismo surge debido al cambio constante de intereses en la sociedad ya que esto representa amenazas para el sector turístico al no contar con la debida participación de los visitantes y un buen plan estratégico que permita minimizar el impacto económico al que se enfrentaría el sector.
Architect Hans Poelzig: a project for Ukraine
Authors: Smolenska Svitlana , Nägelke Hans-Dieter 
Published: October 2023

The paper examines one of the fragments of the creative heritage of the famous German architect Hans Poelzig: his competition project of the State Ukrainian Theatre of Mass Musical Action for 4000 seats for Kharkiv. The project is viewed through the prism of little-known competitive events of 1930, which makes it possible to decipher its architectural, compositional, urbanistic and functional ideas, to reveal its symbolism. The study is based on the architect’s original drawings preserved in the archive, primary sources of the early 1930s, biographical and critical works of other authors dedicated to the nominees of the Kharkiv competition, as well as on a comparative and meaningful analysis of architectural graphics and textual documentation.
Codebook Design and Autoencoder-based Codeword Selection for RIS-assisted Communications
Authors: Sahu Khushboo , Sonwani Bhavesh , Shrivastava Amit Kumar , Kherani Arzad Alam 
Published: October 2023

<p>In this paper, we designed a one-bit beamforming codebook for reconfigurable intelligent surfaces (RIS). Each codeword in a codebook is a phase pattern at RIS elements that reflects the signal to a desired angle. Those codewords are computed for a specific reflection angle that maximizes the signal-to-noise ratio (SNR) at the receiver (Rx). Since multiple codewords can maximize the SNR, an algorithm to reduce the size of the codebook is proposed. It ensures that the codebook does not contain repetitive codewords. Then, a deep neural network-based codeword selection procedure is performed using an autoencoder (AE) that minimizes the bit error rate (BER) at the receiver. The proposed method shows promising results compared to existing techniques regarding BER with reduced codebook size. </p>
An Algorithm for Constructing Random Inverses of Non-Square Matrices Across Arbitrary Fields
Authors: Haidary Makoui Farshid , Gulliver Aaron , Dakhilalian Mohammad 
Published: October 2023

<p>In the realm of linear algebra, the notion of matrix inversion plays a crucial role. While the inversion of square matrices is well-known and results in a unique inverse, however, the non-square inverse matrice is not unique and in fact, the number of inverses for a non-square matrix can be as vast as q^m(n−m), where q signifies the order of the underlying field. In this paper, we embark on a journey to construct these elusive inverse matrices, harnessing the power of arbitrary fields. Arbitrary fields, including prime fields, finite fields, real fields, and complex fields. These fields find practical applications that are essential to contemporary technology. I have written 5 MATLAB programs that able to construct random inverses in different fields based on the given algorithm. </p>
An Attention-based Context Fusion Network for Spatiotemporal Prediction of Sea Surface Temperature
Authors: Shi Benyun , Hao Yingjian , Feng Liu , Ge Conghui , Peng Yue , He Hailun 
Published: October 2023

<p>Sea surface temperature (SST) is a fundamental parameter in the field of oceanography as it significantly influences various physical, chemical, and biological processes within the marine environment. In this study, we propose an Attention-based Context Fusion Network (ACFN) model for short-term prediction of SST based on the Operational SST and Sea Ice Analysis (OSTIA) data. The ACFN model combines an attention-based context fusion block with the Convolutional Long Short-Term Memory (ConvLSTM) model, enabling the exploration of intricate spatiotemporal correlations between the previous context state and the current input state in ConvLSTM. To assess the performance of the ACFN model, we apply it to predict SST in the Bohai Sea over lead times spanning from 1 to 10 days. The results demonstrate that our proposed model outperforms several state-of-the-art models, i.e., ConvLSTM, PredRNN, and MoDeRNN, in terms of mean absolute error and coefficient of determination. In particular, our analysis reveals that the prediction errors near the coastal areas exhibit relatively higher values compared to those in the central Bohai Sea.</p>
Transformer-CNN Hybrid Network for Improving PET Time Of Flight Prediction
Authors: Feng Xuhui 
Published: October 2023

<p>In Positron Emission Tomography (PET) reconstruction, utilizing Time of Flight (TOF) information can significantly enhance the signal-to-noise (SNR) ratio, posing a greater challenge for the precision of TOF. To address this, we employed two distinct waveform datasets for training our developed network. One dataset comprises simulated waveform data obtained through a comprehensive simulation process established using Geant4 and GosSip. The other dataset consists of real waveform data collected from lutetium yttrium orthosilicate (LYSO) scintillators and silicon photomultiplier (SiPM) detectors placed at various positions. Our network, a combination of Transformer and Convolutional Neural Network (CNN), was developed for predicting the TOF of coincidence events based on waveform data from PET detectors. Our network achieved average full width at half maximum (FWHM) of 189 ps, with reductions of 82 ps and 13 ps compared to constant fraction discriminator (CFD) and CNN, across multiple positions. Additionally, there was an average bias reduction of 10.3 ps compared to CNN. We visualized the attention map, revealing the remarkable enhancement of Transformer on the rising edge of waveforms. We also demonstrated the robustness of our proposed network by including waveforms with scattered events in the real training dataset. Data augmentation through translation and flip was investigated and resulted in an improvement of 5 ps. Furthermore, we analyzed the characteristic differences between real and simulated waveform data, providing valuable insights for generating more realistic simulated data in the future. Our network improved the average FWHM and bias, leading to enhanced SNR and clearer imaging. Data augmentation effectively expanded the dataset and facilitated the data collection process.</p>
Stream Concept-cognitive Computing System for Streaming Data Learning
Authors: Mi Yunlong 
Published: October 2023

<p>People can often acquire knowledge dynamically and rapidly from different types of data, yet existing incremental learning algorithms are still computationally time consuming and most of stream learning methods are mainly designed for streaming data while ignoring other types of data. Hence, this paper proposes a novel dynamic concept learning (CL) algorithm by imitating human cognitive learning processes from the perspective of brain logical cognition, which is named stream concept-cognitive computing system (streamC3S). For streamC3S, it mainly consists of three aspects: the concept space, CL process, and model update process. Moreover, considering the concept drift frequently occurs in the streaming data over time, an extended version of streamC3S (namely, streamC3S<sub>E</sub>) is also proposed in this work. Specifically, we first show the related theories for streamC3S and streamC3S<sub>E</sub> on the basis of the concept space. Then an overall framework and its corresponding algorithm are shown. Finally, experimental results on various types of datasets, including the standard machine learning datasets, streaming datasets, image datasets, and two traffic data streams, validate the effectiveness of our streamC3S and streamC3S<sub>E</sub> compared to the state-of-the-art incremental learning and stream learning algorithms. </p>
A Non-Iterative Design Method for Output LCL Filter with RC Damping in Grid-Connected Inverters
Authors: Mondal Bishal , Karuppaswamy B Arun 
Published: October 2023

<p><em>LCL</em> filters with passive damping are widely used for grid-connected inverter (GCI) applications. This work proposes a novel non-iterative component selection procedure for the <em>LCL</em> filter with passive <em>RC</em> damping. While the primary goal of the filter is to effectively attenuate the switching frequency components of the inverter, there are several other desirable constraints that make the selection procedure challenging. In this work, eight such desirable constraints are identified from the existing literature and the proposed method meets all the identified constraints. Besides being non-iterative, the novelty of the work primarily lies in the selection of the damping resistor <em>R</em><sub><em>d</em></sub> and the ratio <em>n</em> in which the total shunt capacitor is split. The primary intent of <em>R</em><sub><em>d</em></sub> being to damp the resonance, a methodical approach that ensures a high impact of the resistance at the resonance frequency is proposed. The proposed approach also ascertains a minimum possible quality factor. The selection of the ratio <em>n</em> is leveraged as an additional degree of freedom. An analytical method to find the range for <em>n</em> that offers a critically flat resonance peak is proposed. A critically flat resonance peak is important for control stability. The final value of <em>n</em> is chosen from the available range to meet all the constraints. </p>
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CroCoS-v1: Enhancing Mask Leakage and Bounding Box Localization for Real-Time Crop/Weed Instance Segmentation
Authors: Franco Robles Jesus , Aviles Mejia Jorge Eduardo , Labbani-Igbida Ouiddad 
Published: October 2023

<p>Precision agriculture brings together most innovative topics from research in the fields of vision, artificial intelligence and robotics aimed at developing sustainable and resilient solutions. In this context, crop identification is a crucial issue. This paper exposes a new algorithm to enhance real-time crop/weed instance segmentation, merging a learning-based method for instance segmentation and a feature model-based image processing strategy that relies on the vegetation characteristics of the crops. The proposed algorithm compensates for the shortcomings of each method performing independently. That is, the image processing strategy fulfils crop segmentation by generating finely refined masks; however, some weeds are segmented incorrectly. On the other hand, instance segmentation methods perform well on weeds and crops but, when inaccurate bounding boxes are present, imperfect masks are generated. The experiments are conducted in different evaluation campaigns within the ACRE international competition framework. </p>
Assessment and Evaluation of Student Participation and Achievement in a Circuit Theory Course
Authors: Enochoghene Samuel 
Published: October 2023

<p>Circuit theory cuts across every part of electrical engineering and is a fundamental skill for electrical engineers. However the abstractions involved can present challenges for students. In this work, a problem solving approach was employed to help students motivation and interest in class attendance. This study looks at performances in the final examination and analyses them with a view to developing better approaches and tools for future delivery of such a course. </p>
AI - Assisted Learning for NVIDIA SDKs and Toolkits
Authors: Kalakonda Sujith Sai , Srikanth Chaitanya , Deepesh Gondimalla Veera , Bavana Sreevaatsav , Vamshi Bodla Krishna 
Published: October 2023

<p>This paper introduces an AI-driven language model (LLM) designed to facilitate users’ comprehension and adept usage of different NVIDIA SDKs (Software Development Kits) and toolkits. The central goal is to provide a LLM which establishes an interactive and intuitive platform that offers inclusive insights, practical illustrations, and expert direction on NVIDIA’s diverse SDKs and toolkits. Harnessing the capabilities of language models and NVIDIA’s toolkits, this study strives to streamline devel- opers’ learning process, enabling them to harness NVIDIA’s technological advancements with enhanced proficiency. </p>
Characterization of Commercial QKD Systems for Multicore Fiber-Based Hybrid Networks
Authors: Fritsch Samuel , Dugre Joshua , Mohan R. Krishna 
Published: October 2023

<p>The feasibility of a quantum-classical hybrid network using multicore fiber is explored and key performance metrics of the quantum channel with respect to co-propagating classical light are examined. Multiple commercial time bin quantum key distribution systems from ID Quantique are characterized using direct injection techniques to determine optimal operational parameters for the classical optical channels in order to minimize the quantum bit error rate and maximize secret key generation. A phenomenological model that determines the expected quantum bit error rate for a wide range of injected input wavelengths and powers is utilized to evaluate the quantum key distribution system performance. Analysis shows that the performance limitations exhibited by the tested commercial quantum key distribution systems in a hybrid network based on multicore fiber could be overcome by a strategic choice of the classical channel parameters. </p>
Reliability Assessment Combining Importance Resampling and the Cross Entropy Method
Authors: Bjerkebæk Ivar , Toftaker Håkon 
Published: October 2023

<p>In the ongoing transition towards a sustainable energy system, the electric power system increases in complexity and must adapt to rapid changes and new uncertainties. Thus, development and application of appropriate probabilistic methods is all the more important. Reliability analysis based on Monte Carlo simulation (MCS) has become increasingly popular, and its strength lies in the ability to account for a large number of random variables, general stochastic processes and assessing the probability distribution of the output. However, power system reliability is governed by relatively rare interruption events which poses a fundamental challenge to MCS. This paper presents a variance reduction technique for Monte Carlo based reliability analysis which combines resampling and the cross entropy (CE) method. The motivation for the work is to mitigate the computational burden related to rare event sampling, while at the same time preserving the flexibility of MCS by introducing few assumptions on the stochastic model. The method is demonstrated on a synthetic test system and gives a speedup of about 10 times compared to a crude simulation. </p>
Corporate Asset Management in the Digital Age: A Blockchain Perspective
Authors: Bodemer Oliver 
Published: October 2023

<p>The digital transformation has ushered in a new era of asset management, particularly with the advent of blockchain technology. This study explores the integration and implications of blockchain technology in corporate asset management. Through a comprehensive literature review, theoretical framework development, and case studies of two corporations, the research unveils the potential of blockchain in enhancing transparency, security, and efficiency in asset management processes. A comparative analysis between traditional asset management systems and blockchain-based systems reveals significant advancements in real-time asset tracking, reduced operational costs, and enhanced trust among stakeholders. However, challenges such as technological maturity, regulatory acceptance, and initial implementation costs are identified. The findings suggest that with a strategic approach, corporations can overcome these challenges and significantly benefit from blockchain-enabled asset management systems. This study contributes to the growing body of knowledge on blockchain technology’s application in corporate sectors and provides a foundation for future research in this realm. The implications of these findings are discussed, providing a roadmap for corporations aiming to transition to blockchain-based asset management systems. </p>
Visual Outcomes of Phacoemulsification in Senile Cataract Patients in Purworejo Indonesia
Authors: Widagdo The Maria Meiwati , Andrea Patricia Dissy , Khrismasari Yediva , Jayadi Tejo , Wibowo Edy 
Published: September 2023

Purpose: There was still limited study about the success rate of phacoemulsification in Indonesia, although it has been widely used. This study aimed to assess the visual acuity based on the characteristics of patients with senile cataract who underwent phacoemulsification.
Study Design: Descriptive observational.
Place and Duration of Study: Panti Waluyo Hospital, Purworejo from January to November 2019.
Methods: This included 58 senile cataract patients (70 eyes) who underwent phacoemulsification. Basic patient characteristics such as: age, gender, cataract stages, comorbidity and pre- and postoperative visual acuity were taken from medical records. The visual acuity was measured pre-and postoperatively using Snellen Chart. The data were analyzed using a descriptive analysis method.
Results: There were 23 men and 35 women who underwent phacoemulsification during the study period. Seventy six percent were 60 – 74 years old. Eighty percent had immature cataract. Half of the patients did not have any comorbidity of diabetes mellitus or hypertension. There were 45 (64.3%) eyes with moderate to severe visual impairment (vision 6/18 – ≥ 3/60) and 25 (35.7%) eyes with blindness (vision < 3/60). The postoperative vision of 57 eyes (81.4%) improved to a good outcome. Thirteen eyes (18.6%) were categorized as borderline outcome. No eyes were reported to have poor outcome post-surgery.
Conclusion: The postoperative visual acuity of cataract patients who underwent phacoemulsification was classified as good outcome (81.4%) and borderline outcome (18.6%). In this study, there was no poor visual outcome found.