Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Red Cell Parameters of First-time and Repeat Blood Donors: A Descriptive Study

Introduction: Blood transfusion services form an essential component of any healthcare system and it is imperative to provide adequate and safe blood for management of patients. Voluntary blood donors form the backbone of this service. However, regular donation by such voluntary donors may cause significant depletion of iron stores in the body. This has the potential to adversely affect the donor’s health, and also to lower the quality of blood being collected subsequently. The temporary deferral of such donors also causes reluctance to return for future blood donations, leading to decrease in size of the donor pool. The prompt detection of subclinical iron deficiency in voluntary blood donors is the need of the hour. Aim: To compare and analyse the difference in red cell parameters of first-time and repeat blood donors. Materials and Methods: A descriptive study was conducted by the Department of Blood Bank and Pathology at Rajarshi Dashrath Autonomous State Medical College, Ayodhya, Uttar Pradesh, India, from July 2021 to December 2022. After prospective donors were assessed for suitability of blood donation, written informed consent was obtained, and 5 mL venous blood was collected into an Ethylenediaminetetraacetic Acid (EDTA)- anticoagulated vial via the antecubital fossa. Complete blood count was performed within one hour of collection using an automated haematology analyser. The parameters analysed in the study were Red Blood Cell (RBC) count, haemoglobin, Mean Corpuscular Volume (MCV), Mean Corpuscular Haemoglobin (MCH), Mean Corpuscular Haemoglobin Concentration (MCHC), and Red cell Distribution Width (RDW). The generated data was compiled and statistical analysis, with Kruskal-Wallis test (at a p-value of 0.05) and post-hoc Dunn test was performed to determine the significant differences, if any. Results: The study included 455 participants, out of which 210 were repeat blood donors. The significant differences for MCV, MCH and RDW between groups defined by number of donations (p-value &lt;0.001) was observed. The difference was most significant between the donors having five or more donations compared to donors having no previous donations. No significant differences were observed for other parameters, with p-values for RBC count, haemoglobin, and MCHC being 0.3, 0.07, and 0.09, respectively. Conclusion: Repeat blood donors having low MCV and MCH along with high RDW, with haemoglobin values within normal range, are most susceptible to having subclinical iron deficiency, which needs to be identified and managed pre-emptively, before development of Iron Deficiency Anaemia (IDA). This is necessary in order to retain regular and repeat voluntary blood donors, and also to ensure adequate quality of collected blood.
Examining the Impact of Mobile Apps on Language Teaching and Learning in a Public University: An Experimental Study

This research article investigates the effects of mobile apps on language teaching and learning in a public university setting. The study focuses on evaluating the effectiveness and practicality of a Mobile Aided Language Learning Portal (MALLP) for teaching English as a Second Language (ESL) to English Language Learners (ELLs). A total of 186 participants from a pre-intermediate General English course at a university in Ho Chi Minh City, Vietnam, were selected using cluster sampling. The study employed a mixed-method design, including needs assessment, pre-examinations, a tryout experiment, post-examinations, and interviews. The findings indicate that a significant proportion of participants (65 percent) had a strong interest in using mobile apps for language learning. The experimental group, which utilized the MALLP, demonstrated a noticeable improvement in language proficiency compared to the control group. The engagement and usage patterns of the experimental group revealed a high level of engagement, with 70 percent regularly using the MALLP. Interviews with the experimental group highlighted their positive perceptions of the MALLP, including its user-friendliness, engaging features, availability of resources, and improvements in language skills and confidence. The post-test results showed that the experimental group achieved higher scores (passing rate of 97.8 percent) compared to the control group (passing rate of 36.6 percent). The experimental group's mean post-test score (33.76 points) was significantly higher than the control group's mean post-test score (28.52 points). These findings suggest that integrating mobile apps, such as the MALLP, in language teaching can lead to improved language learning outcomes and increased student motivation. Further research in public university settings is needed to gain a more precise understanding of the benefits and drawbacks of mobile app-based language learning.
Impacts of Digital Transformation Caused by the Growth in Online Trade Volume on Accounting Processes

In this study, the impacts of the growth in online trade volume caused by digital transformation on accounting processes, records within accounting processes, and the tracking of these records are thoroughly examined. As digital transformation accelerates and online trade becomes more prevalent, the need for businesses to transition to digital methods in accounting practices is increasingly growing.&#x0D; The aim of the study is to uncover the effects of rapid growth in online trade on accounting processes and to evaluate these changes. In this context, the transition processes of businesses from traditional accounting methods to digital accounting and the effects of digital transformation on accounting processes are discussed.&#x0D; Through an extensive literature review, a knowledge base is established on the effects of online trade on accounting processes, the impacts of digital transformation on accounting processes, and digital accounting practices. The numerical data obtained are evaluated with regression analysis, and the results reveal the effects of digital transformation on accounting processes.&#x0D; The study indicates that the effects of digital transformation on accounting processes will increase with the rapid growth of online trade. To adapt to these changes, businesses must adopt digital technologies in their accounting processes and maintain accurate and reliable financial records.&#x0D; This study can be considered an important resource for understanding the effects of online trade on accounting processes and helping businesses adapt to digital transformation. To prepare businesses for future digital transformations, further examination of the effects of digital transformation on accounting processes caused by the growth in online trade volume is necessary.
The Influence of Gender on EFL learners’ Discourse Functions in a CMC Environment

This study investigates the role of gender on EFL Learners’ output of discourse functions obtained from computer-mediated communications (CMC) via Skype. The study seeks to answer the question: Are there any statistically significant differences among the total means of discourse functions generated by gender groups (same-gender (male-male (MM), and female-female (FF)) and mixed-gender (female-male (FM), and male-female (MF)))? Sixty-four undergraduates (32 females and 32 males) participated in the study. They were assigned into two gender main groups: same-gender (MM: 16 males who chatted in pair groups with each other; FF: 16 females who chatted in pairs with each other) and mixed-gender groups (32 participants (16 females chatting with 16 males in pairs, MF (males’ output) and FM (females’ output)). Participants were asked to chat in pairs for an hour. Results revealed that FF group produced significantly more discourse functions than all the other gender groups, by having the highest total mean.
Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

Recently, multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams, which has great potential to surpass unimodal sentiment analysis. One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy. Unfortunately, existing work always considers feature-level fusion or decision-level fusion, and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion. To improve the performance of multimodal sentiment analysis, we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy (BAHFS). Firstly, we apply BiGRU to learn the unimodal features of text, audio and video. Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module. Next, BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features. Finally, BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion. Based on the CMU-MOSI and CMU-MOSEI datasets, extensive experiments have been carried out to verify BAHFS’s superiority.
Intelligent Financial Fraud Detection Using Artificial Bee Colony Optimization Based Recurrent Neural Network

Frauds don’t follow any recurring patterns. They require the use of unsupervised learning since their behaviour is continually changing. Fraudsters have access to the most recent technology, which gives them the ability to defraud people through online transactions. Fraudsters make assumptions about consumers’ routine behaviour, and fraud develops swiftly. Unsupervised learning must be used by fraud detection systems to recognize online payments since some fraudsters start out using online channels before moving on to other techniques. Building a deep convolutional neural network model to identify anomalies from conventional competitive swarm optimization patterns with a focus on fraud situations that cannot be identified using historical data or supervised learning is the aim of this paper Artificial Bee Colony (ABC). Using real-time data and other datasets that are readily available, the ABC-Recurrent Neural Network (RNN) categorizes fraud behaviour and compares it to the current algorithms. When compared to the current approach, the findings demonstrate that the accuracy is high and the training error is minimal in ABC_RNN. In this paper, we measure the Accuracy, F1 score, Mean Square Error (MSE) and Mean Absolute Error (MAE). Our system achieves 97% accuracy, 92% precision rate and F1 score 97%. Also we compare the simulation results with existing methods.
The Structure of Management Information System and its Impact on the Management of the Corona Crisis in Jordanian Universities

Purpose: This paper aims to identify the impact of structuring management information systems in managing the Corona crisis in Jordanian universities. Theoretical Framework: This paper discusses the impact of structuring management information systems in Jordanian universities in managing the Corona crisis. In addition, how has the organizational and administrative structure of Jordanian universities impacted the management of the Corona crisis? and the role of technical structuring in Jordanian university crisis management? Design/methodology/approach: The population of this study include all public and private Jordanian universities, and the sample consisted of (384) respondents from the deans of academic faculties, heads of departments, and members of the teaching staff in these universities, this paper considered one of the field research in which the researcher followed the descriptive analytical method, The data were analyzed using the statistical package for the social sciences program, and the hypotheses were tested using arithmetic means, standard deviation, and multiple and simple linear regression analysis. Findings: The results showed a statistically significant effect of the elements of the structuring of management information systems (organizational, administrative and technical structuring) in managing the Corona crisis in Jordanian universities, and it was found that the universities responded immediately to contain the Corona crisis by the way of distributing tasks in a short period when it occurs, and as periodic meetings were held to prepare to deal with the Corona crisis. Research, Practical &amp; Social implications: The structuring of management information systems plays a major role in the growth and development of the global economy. Therefore, management information systems and its various tools have become the mainstay and technical base for advancement in the field of e-business, telecommunications, e-commerce, and e-government. Originality/value: This study sheds light on the degree to which Jordanian private universities (student community) keep up with contemporary administrative systems, concepts, and models, in particular with regard to the impact of structuring management information systems in managing the Corona crisis in Jordanian private universities, which are regarded as one of the interdisciplinary research centers in the world.
Research on Text Classification Method Based on NLP

Natural language processing is a science that integrates computer knowledge, mathematical knowledge and linguistic knowledge, while text classification and recognition is considered an important research area and direction of natural language processing.In the context of the big data era, how to effectively classify text information in the face of a sea of text-based data is the focus of current research.This paper describes the theoretical knowledge of text classification concepts, text representation methods and text classifiers.Firstly, the basic concepts of text classification and the classification process are introduced.Then the model structures of convolutional and recurrent neural networks and their variants are introduced, followed by the structure and implementation principles of two classical word embedding models, Word2vec and BERT.
Malevolent WEBSITE identification using Machine Learning Techniques

Threats to network information security are currently increasing both in number and seriousness. Today's hackers are mostly focused on attacking end-to-end systems and finding weak points in people. These strategies include pharming, phishing, and social engineering. These assaults include a stage where malicious Uniform Resource Locators (URLs) are used to trick visitors. Consequently, harmful URL detection is growing in popularity. The use of machine learning and deep learning algorithms to identify malicious URLs has been proven in several academic articles. In this study, we propose a malicious URL detection system that makes use of machine learning approaches based on proposed URL behaviors and attributes. Bigdata technology is additionally being utilized to enhance the detection of risky URLs based on peculiar behavior. A novel set of URL traits and behaviors, a machine learning algorithm, and big data technologies make up the suggested detection technique. The trials' findings imply that the suggested URL characteristics and behavior can significantly improve the ability to identify potentially harmful URLs. This shows that the suggested method might be regarded as the best and most convenient way to find counterfeit URLs.
THE USE OF STATISTICAL METHODS IN PEDAGOGICAL RESEARCH IN HIGHER EDUCATION

In the article, based on the analysis of the state of the theory and practice of using statistical methods by teachers in pedagogical research, a research hypothesis is formulated: information and pedagogical support for the use of statistical methods in pedagogical research. The organizational-pedagogical and ethical-pedagogical conditions for designing information and pedagogical support for the use of statistical methods in pedagogical research are determined. The structure of information and pedagogical support for the use of statistical methods in pedagogical research has been developed. The stages are identified and the content of the system of information and pedagogical support for the use of statistical methods in pedagogical research by means of theoretical modeling is determined. The criteria of teachers' readiness to use statistical methods in pedagogical researches are revealed. The effectiveness of the system of information and pedagogical support for the use of statistical methods in pedagogical research has been proved.
TEACHING ENGLISH DURING WAR TIME

The study explores the challenges, features, and opportunities of teaching English during wartime. The authors draw from their own experiences as Ukrainian teachers of English, who survived the military events of 2022 and whose life and perspectives changed dramatically after the attack on Kyiv, the Ukrainian capital.With the outbreak of war, many schoolchildren and students lost the opportunity to attend classes. Some had to move to safer areas or go abroad., Distance learning has become the main option for access to knowledge, and universities are trying to adapt their best practices to the new conditions during the pandemic. Teachers must now not only find teaching methods but also know psychological techniques that can help students get rid of stress and psychological consequences.As today’s practice has shown, teachers must have the knowledge and skills to provide crisis psychological help to respond and refer for professional psychological help promptly.In addition, teaching a foreign language has certain specific features during the war, which are considered in our work. And the process of learning a foreign language itself has a positive effect on the psychological state of students.We want our students to understand that finding oneself in the stressful and dangerous conditions that war creates can be very difficult. However, if you feel empowered to learn new things, take advantage of at least a few of the thousand opportunities.
Moving Path Planning and Motion Coordination for Carrier-Borne Aircraft on Deck

Abstract This paper mainly discusses the planning method of shipboard transportation scheduling process on aircraft carrier from two aspects of deck moving path planning and motion coordination method for carrier-borne aircraft in deck. Two kinds of moving path planning algorithms between deck carrier aircraft stations based on standard motion pattern were proposed firstly. And the evaluation criteria of the planned moving paths were discussed. Then, basing on the network of deck moving paths generated by previous path planning algorithm, we developed a motion coordination method of deck carrier-borne aircraft basing on mixed integer programming formulations which can be used to solve the problems of coordination and collision in the transportation scheduling process of multiple carrier-borne aircraft effectively.
Privacy-preserving and verifiable IoT data aggregation scheme based on blockchain and homomorphic encryption

As an important step in the IoT workflow, IoT data aggregation transmits, filters, analyzes and synthesizes data collected by IoT devices such as sensors to help people make decisions and evaluate. However, the traditional IoT data aggregation model relies on a central data aggregator, such as a third-party cloud server, and uses traditional encryption technology to process data, which may face problems such as original data leakage and calculation errors, and it is difficult to ensure that data participants can safely participate in the aggregation. Due to its homomorphic characteristics and performance improvements in recent years, homomorphic encryption technology makes it possible to use homomorphic encryption technology to protect data privacy during data transmission and calculation. In addition, as a non-tamperable ledger technology, the blockchain can be used to store evidence, specifically to store the promises made by the commitment mechanism, and complete the verification of data consistency and correctness.
Analysis of Language Styles: Rhetoric and Figurative Language in Makeup Advertisement on Instagram

Many digital advertisements are featured on Instagram, such as an advertisement for makeup products.One factor that attracts people to purchase makeup products is how the advertisement impression leaves on people.This study attempt to examine the language style, especially rhetoric and figurative of online makeup advertisements on Instagram.The method of this research is the qualitative descriptive method.This study used the theory of language styles proposed by Keraf (2010).The result showed there were five types of rhetorical language styles, alliteration, assonance, asyndeton, polysyndeton, hyperbole, and erotic while the figurative language styles are simile, personification, synecdoche, and metonym.The result shows that some companies use rhetoric and figurative language styles to describe their products' characteristics and produce an emphasize, appealing, and strong impression advertisement.This study attempt to fill the gap by investigating the purpose of using language styles in Instagram advertisements.
A Study on the Work Status and Improvement Needs for Elementary After-School Care Program by the Elementary After-School Care Staffs

본 연구는 초등돌봄의 필요성 및 확대 요구에 부응하여 초등돌봄교실의 질 제고 방안을 모색하기 위한 목적으로 서울시 초등돌봄전담사가 바라보는 초등돌봄교실 현장의 근무실태와 직무환경 등에 대한 개선요구를 조사하여 분석하였다. 이에 초등돌봄 관련 연구보고서와 선행연구자료 등의 문헌을 탐색하여 이론을 구성하였고, 근무실태와 개선 요구 자료는 서울시 전체 초등돌봄전담사 대상의 전자설문조사, 그리고 11명의 전문가 표적집단면접(FGI)을 실시하여 수집 분석하였다. 연구결과로 첫째, 시간제전담사는 겸용교실을 사용하는 경우가 많은 것으로 나타났고, 둘째, 초등돌봄전담사는 9시-17시를 가장 적정한 근무시간으로 희망하고 있었다. 셋째, 대체로 직무수행에 대한 보람을 긍정적으로 가지고 있으나 직무환경에 대해서는 불만족한 반응을 보였다. 넷째, 초등돌봄교실의 개선요구 사항으로는 처우와 돌봄운영시간, 정책 제도 등의 개선을 가장 우선 순위로 꼽았고, 초등돌봄전담사를 위한 연수 의무화 도입에도 압도적으로 의견을 표명하였다.This study investigated and analyzed the work status and job environment of elementary after-school care program viewed by elementary after-school care staffs in Seoul, The purpose of this study is to find ways to improve the quality of elementary after-school care program in response to the need and expansion of elementary after-school care program. Accordingly, the theory was constructed by exploring literature such as elementary care-related research reports and prior research data. And the data on work status and improvement needs were collected and analyzed by an electronic survey of all elementary after-school care staffs in Seoul and 11 expert target group interviews (FGI). As the results of this study, first, it was found that part-time elementary after-school care staffs often use concurrent classrooms, and second, elementary after-school care staffs hoped that 9 a.m.-17 p.m. would be the most appropriate working hours. Third, in general, it was positively rewarding for job performance, but it showed a dissatisfied reaction to the job environment. Fourth, improvement of treatment, care operation time, and policy system were the top priorities as improvement needs for elementary after-school care program. And the elementary after-school care staffs overwhelmingly expressed their opinions on the introduction of mandatory training for them. These findings are expected to be useful basic data for gradually improving the quality of elementary after-school care program and establishing effective policies by listening to the vivid voices of elementary after-school care staffs who directly take care of school sites.