Reviewerly/exHarmony huggingface dataset:
Analysis and prospect of power transformer fault diagnosis technology

The transformer stands as a vital component within the power system, serving a crucial role in voltage regulation and energy transmission throughout the entire process of electricity generation, transmission, distribution, and consumption. Furthermore, it holds a pivotal position in facilitating Alternate Current (AC) and Direct Current (DC) conversion within DC transmission. This paper begins by offering an overview of common anomalies and fault categories that arise during transformer operation, focusing on the role of power transformers within system operations. It subsequently delves into the contemporary methods and diagnostic processes used for identifying and diagnosing transformer faults. The paper then proceeds to expound upon cases involving winding deformation and insulating oil failure, illustrating instances of these particular types of faults. Finally, the paper outlines two prospective avenues for transformer fault diagnosis technology. The first entails the incorporation of artificial intelligence theories in the realm of transformer fault diagnosis. The second involves the development of an expert system for comprehensive transformer fault diagnosis, grounded in artificial intelligence technology.
Education and Training Program to Improve Preparedness and Increased Access to Energy Workforce for Engineering Technology Graduates

Abstract Elizabeth City State University (ECSU) implemented a comprehensive education and training program to strengthen the renewable energy curriculum and increase the pipeline of qualified workers, especially underrepresented minorities for the growing energy industry. The overarching goal is to meet the current and future energy industry workforce needs, especially in the renewable energy sector. The project activities were designed based on three tenets, which include mentoring, research, and education/training. The project components included, enhancing renewable energy courses, establishing a wind and solar energy systems training laboratory, a year-round hands-on project-based training, a residential summer bridge program, undergraduate research experience, and summer internships. The activities afforded through the project will help ensure that the energy sector has a sustained pipeline of a qualified workforce. The project implementation and challenges, project delivery components, and results from pretests and posttests and participant surveys are presented in the paper.
Analysis of Complete Blood Count Data and Use in Classification Machine Learning Models to Predict Icans Incidence Following CAR-T Cell Therapy

CD19 directed CAR-T cell therapies are increasingly used for treatment of hematological malignancies, however immune cell-associated neurotoxicity syndrome (ICANS), remains a challenge to predict and treat. We retrieved demographic and complete blood count (CBC) data from electronic health records (EHR) to evaluate whether this simple, accessible, and routinely collected data could be used to train classification machine learning (ML) models for prediction of ICANS. Demographic and CBC measures from n=96 patients treated for B-cell malignancies at the University of Michigan (UM) were analyzed for univariate significance at baseline (prior to lymphodepletion) and through day (D) 3 post CAR-T infusion. Complete datasets were available for n=76 patients. These were then used for feature selection and ML model training. Features found significant were used as inputs in binary classification models to predict ICANS incidence within 20 days post-infusion. Models were validated using data from a test set of n=29 patients treated at Johns Hopkins Hospital (JHH). Data analysis was completed in RStudio-4.1.3, R Core Team 2022. Overall incidence of ICANS was 45% in the training set (UM) and 34% in the test set (JHH). While baseline data alone was not predictive, between D0 and D3, univariate analysis revealed lymphocyte count, monocyte count, red blood cell count, hemoglobin, hematocrit, C-reactive protein, ferritin, and white blood cell count as differentially associated with patients that developed Grade 3 ICANS versus those that developed Grade 1-2 or No ICANS. On D0 (after lymphodepletion and prior to CAR-T cell infusion), the combination of C-reactive protein, monocyte count, red blood cell count, and age used in ML models were able to predict incidence of any grade ICANS with an AU-ROC of 0.80-0.82, MCC=0.51-0.54, and F1=0.74-0.76. This model was robust to validation in the independent test set cohort (i.e., JHH patients), with an AU-ROC of 0.92-0.95, MCC=0.63-0.85, and F1=0.67-0.90. ML-based classification using CBC data and commonly measured clinical blood analytes holds promise for ICANS prediction. Future work may integrate additional blood- clinical-, and/or digital-based biomarkers from other predictive models of ICANS to develop further improved models. Our findings suggest that ML-based models may be able to risk-stratify patients receiving CAR-T therapy, to identify those who may benefit from close monitoring and/or early therapeutic intervention to prevent or mitigate severity of ICANS.
Logical activation functions for training arbitrary probabilistic boolean operations

In this work, we introduce a family of novel activation functions for deep neural networks that approximate n-ary, or n-argument, probabilistic logic. Logic has long been used to encode complex relationships between claims that are either true or false. Thus, these activation functions provide a step towards models that can efficiently encode information. Unfortunately, typical feedforward networks with elementwise activation functions cannot capture certain relationships succinctly, such as the exclusive disjunction (p xor q) and conditioned disjunction (if c then p else q). Our n-ary activation functions address this challenge by approximating belief functions (probabilistic Boolean logic) with logit representations of probability and experiments demonstrate the ability to learn arbitrary logical ground truths in a single layer. Further, by representing belief tables using a basis that associates the number of nonzero parameters with the effective arity of each belief function, we forge a concrete relationship between logical complexity and sparsity, thus opening new optimization approaches to suppress logical complexity during training. We provide a computationally efficient PyTorch implementation and test our activation functions against other logic-approximating activation functions on both traditional machine learning tasks as well as reproducing known logical relationships.
Unleashing the power of AI: a systematic review of cutting-edge techniques in AI-enhanced scientometrics, webometrics and bibliometrics

Purpose: The study aims to analyze the synergy of Artificial Intelligence (AI), with scientometrics, webometrics, and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields. Design/methodology/approach: By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends, and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science, and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles. Findings: (i) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis, and knowledge mapping, in a more objective and reliable framework. (ii) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis, and recommender systems. (iii) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining, and recommender systems are considered as the potential of AI integration in the field of bibliometrics. Originality/value: This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics, and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.
The Influence of Knowledge Sharing, Transformational Leadership on Organizational Citizenship Behaviour (OCB) and Job Satisfaction in the Msmes Sector in Minahasa

Purpose: The purpose of this research is to analyze the relationship between transformational leadership on organizational citizenship behavior (OCB), transformational leadership on job satisfaction, job satisfaction on OCB, knowledge sharing on OCB, knowledge sharing on job satisfaction. Theoretical Framework: Several research studies have explored the correlation between job satisfaction and knowledge sharing within the tourism industry, hese findings indicate that the sharing of knowledge can have a beneficial effect on job satisfaction within the MSMEs Sector. Design/Methodology/Approach: This research method is quantitative through surveys, data obtained by distributing online questionnaires via social media to respondents. The respondents of this study were 378 MSMEs Sector in Minahasa who were determined by simple random sampling. Findings: Employee trust in superiors has been proven to mediate the relationship between transformational leadership style and OCB, so management or the authorities are advised to be more selective in giving promotions to get officials who have competencies that are in accordance with their positions, have high integrity and have concern for their subordinates. In addition, officials or leaders whose competence is still lacking need to be improved through training. Job satisfaction has been proven to mediate the relationship between transformational leadership style and OCB, so that leaders or management is advised to pay more attention to aspects of job satisfaction, both intrinsic and extrinsic job satisfaction of employees. Research, Practical &amp; Social Implications: We suggest a future research agenda and highlight the contribution it makes to job satisfaction. Originality/Value: The results indicate to deepen the study on OCB, it is also recommended for further research to examine each dimension of OCB.
Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection

Online payment fraud detection is crucial for safeguarding e-commerce transactions against sophisticated fraudsters who exploit system vulnerabilities. This paper proposes an efficient framework for predicting online payment fraud, employing six diverse machine learning algorithms, namely constant, CN7Rule induction, KNN, Tree, Random Forest, Gradient boosting, SVM, Logistic regression, Naive Bayes, Ada boost, Neural network, and stochastic gradient descent, on three distinct datasets. The gradient-boosting algorithm consistently outperformed others through rigorous testing, achieving an impressive accuracy rate of 99.7%. This algorithm demonstrated resilience across various testing scenarios, establishing itself as the most effective online payment fraud detection solution. With the highest accuracy score of 99.7% in all testing phases, gradient boosting is optimal for preemptive measures against fraudulent activities in electronic transactions, providing a robust defense mechanism for e-commerce platforms.
Signature Scheme Based on Alternant Codes

In this paper, we present a version of the signature on subcodes of generalized Reed-Solomon codes defined on a subfield. We show that the use of alternating codes reduces the key size and the use of subcodes has the caracteristic of hiding the code structure. This makes the system more secure.
Generation of Revision Identifier (rsid) Numbers in MS Word

The 2007 implementation of the Office Open XML standard for Microsoft Word introduced the assignation of individual revision save identifiers (Rsid) to document editing sessions that end in a save action. The relevant standards ECMA (2016) and ISO/ IEC 29500-1:2016 (2016) stipulate that these Rsid should be allocated randomised but with increasing numerical value, thereby documenting the progress of the editing. As MS Word is the most ubiquitous word processing software, Rsid appear to be a useful tool to examine and provide evidence for a wide range of common document generation editing and modification processes and file management operations, with implications for document analysis including, but not limited to academic integrity issues in student assignment submissions (e.g. contract cheating). This paper presents the results of a series of experiments conducted to assess whether and how well MS Word implements the ECMA and ISO/ IEC standards. The results show that the number of allocated Rsid indeed increases with each edit and save action, with the previous Rsids carried over and retained. The newly allocated Rsid, however, do not conform to the standard as the numerical value of a Rsid associated with a save action may be larger or smaller than any or all of those allocated during that of the previous save actions. The allocation of a new Rsid is not necessarily caused by an edit event but that a new Rsid can also be generated if a file is saved as rtf or if it is sent as an e-mail from within MS Word, although the file was not edited in any way. Rsid numbers are not generated if a person opens a MS Word document, reads it and closes the file without saving, making this action impossible to detect. MS Word template files on a given machine contain document (root) Rsid numbers that are generated when a newly installed application is launched for the first time. As these will be embedded as legacy Rsid into every new file generated from that template file, they act as signatures for all MS Word documents that are created. The experiments have shown that user behaviour has a direct influence on the number of Rsid represented in a given file. Although the implementation of Office Open XML chosen by Microsoft is not compliant with the relevant standards, and thus Rsid cannot be used determine the exact chronological order of all editing sequences within a given document, the Rsid retain their value for document forensics as they are associated with specific edit events, and illuminate the document writing and editing process.
Advancements and Challenges in 360° Augmented Reality Video Streaming: A Comprehensive Review

As the demand for immersive multimedia experiences continues to grow, 360° augmented reality (AR) video streaming has emerged as a promising technology to deliver interactive and engaging content to users. This review paper provides a comprehensive overview of the current state of 360° AR video streaming, focusing on recent advancements, challenges, and potential future directions. The paper begins by presenting an in-depth exploration of the fundamental concepts and technologies underpinning 360° AR video streaming, including camera capture techniques, video encoding and compression methods, and the integration of augmented reality overlays. Subsequently, it delves into a critical analysis of the existing literature, highlighting key developments in the field. This encompasses advancements in video quality, real-time rendering, and the integration of interactive AR elements within the 360° video environment. Furthermore, the review addresses the challenges and limitations associated with 360° AR video streaming, ranging from bandwidth constraints and latency issues to hardware requirements and user experience considerations. Solutions and strategies proposed in the literature to mitigate these challenges are discussed, providing insights into the ongoing research efforts aimed at enhancing the overall performance and user satisfaction. In addition to technical aspects, the paper explores diverse applications of 360° AR video streaming across various domains, such as entertainment, education, healthcare, and industry. This section highlights successful implementations, innovative use cases, and potential benefits realized in each domain. The review concludes by outlining potential future trends and research directions in the dynamic field of 360° AR video streaming. Anticipated developments in hardware, software, and content creation are discussed, along with their potential impact on the widespread adoption of this technology. By synthesizing and critically examining the existing body of knowledge, this review paper serves as a valuable resource for researchers, practitioners, and industry professionals interested in gaining a comprehensive understanding of the current landscape, challenges, and opportunities in 360° augmented reality video streaming.
Nonprofit Learning Lab' Essential HR Practices for Mission Driven Leaders workshop

Nonprofit Business AdvisorVolume 2024, Issue 414 p. 4-4 Conferences & Events Nonprofit Learning Lab' Essential HR Practices for Mission Driven Leaders workshop First published: 10 February 2024 https://doi.org/10.1002/nba.31655Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2024, Issue414March 2024Pages 4-4 RelatedInformation
Pengembangan Kemampuan Bahasa Ekspresif Anak Melalui Membaca Nyaring Buku Berbasis Nilai Agama dan Moral di TKIT Permata Bunda Merauke

The development of expressive language needs to be developed so that children can express their opinions and thoughts to other people. Children's expressive language development can be developed through the reading aloud method. This research aims to examine the development of children's expressive language skills through reading aloud at TKIT Permata Bunda. The research method used is descriptive qualitative. The subjects of this research were group A children aged 4-5 years and two teachers. Data collection techniques include interviews, documentation and direct observation. The results of the research show that developing children's expressive language skills can be done through the activity of reading aloud books based on religious and moral values. The implementation stage includes planning by selecting books according to the child's development level and containing religious and moral values. The next stage is implementation in the form of introducing the book, reading the story book aloud with the right intonation, holding a discussion and asking stimulating questions and motivating the child to retell the story. Next, the teacher carries out an assessment to determine the achievement of expressive language development.
Peningkatan Hasil Belajar Menulis Recoun Text pada Siswa Kelas Viiig Melalui Model Pembelajaran Problem-Based Learning di SMP Negeri 1 Sambungmacan

This research aims to enhance the learning outcomes of recount text writing for kelas VIIIG SMP Negeri 1 Sambungmacan in the academic year 2023—2024 through the implementation of the problem-based learning instructional model. The objectives of this research are (a) to ascertain the effectiveness of the problem-based learning model in improving the ability to write recount texts, (b) to understand the process of students' learning outcomes improvement, and (c) to determine the extent of the improvement in recount text writing skills for eighth-grade students at SMP Negeri 1 Sambungmacan in the academic year 2023—2024 before and after the implementation of the problem-based learning model. This study follows a Classroom Action Research (CAR) design, conducted over two cycles, each involving four stages: planning, implementation, observation, and reflection. The research subjects are 30 students kelas VIIIG at SMP Negeri 1 Sambungmacan in the academic year 2023—2024. Both quantitative and qualitative analyses were employed for data analysis. The results of the analysis indicate an improvement in recount text writing learning outcomes in the first cycle with the topic of "unforgettable experiences," where the average score was 73.53, falling into the less satisfactory category. However, in the same material in the second cycle, the average score increased to 84.03. Based on these findings, it is concluded that the problem-based learning model is suitable for enhancing students' learning outcomes in recount text writing with the topic of "unforgettable experiences." Moreover, the problem-based learning model can be applied to other English language topics and across various subjects.
PENERAPAN MODEL PEMBELAJARAN ACTIVE LEARNING DALAM MENINGKATKAN HASIL BELAJAR PESERTA DIDIK PADA MATA PELAJARAN AQIDAH AKHL.pdf

This research discusses the implementation of the Active Learning model in improving students' learningoutcomes in the subject of Islamic faith and morals at MTs Alkhairaat Madinatul Ilmi, Dolo District, Sigi Regency. Themain issues addressed in this thesis are: How is the implementation of the Active Learning model in the subject ofIslamic faith and morals? What steps are used in implementing the Active Learning model in the subject of Islamic faithand morals? What are the obstacles and solutions to the Active Learning model in the subject of Islamic faith andmorals?The research approach is qualitative, employing data collection techniques such as observation, interviews,documentation, followed by data reduction, data presentation, and verification through triangulation methods.The results of this thesis research indicate that the implementation of the Active Learning model greatlyassists educators and students in the teaching and learning process. Students are encouraged to create an atmospherewhere they actively ask questions, inquire, and develop ideas, thereby becoming the center of learning activities.Educators act as facilitators, motivators, and inspirators, controlling, supervising, and providing opportunities forstudents to think critically, develop inspiration, and creativity. The implementation involves designing learningactivities, motivating students, organizing them, and presenting the material. Challenges in implementing the ActiveLearning model include students' lack of confidence and limited time allocation, while solutions involve motivatingstudents and training them to articulate thoughts and manage time effectively.