Reviewerly/exHarmony huggingface dataset:
Types of Figurative Language Found in Coldplay Song Lyrics

This study intended to analyze figurative language found in the Coldplay song lyrics. The objectives of this research were to find out types of figurative language and their meanings in Coldplay song lyrics. There were two theories that used in this research such as the first theory proposed by Knickerbocker and Reninger (1974) entitled Interpreting Literature used to find out types of figurative language found in Coldplay song lyrics, and the second theory proposed by Leech (1981) entitled Semantics: The Study of Meaning used to find out the meaning of figurative language found in Coldplay song lyrics. The method used in collecting the data was observation method. The writer collected the data of song lyrics by browsing and downloading song lyrics from the internet, reading and understanding all the song lyrics and taking note and classifying the phrase and senteces that contain of figurative language. The writer analyzed the data descriptively by using qualitative method. After analyzing the sentences in the song lyrics, the writer found several finding of figurative language found in Coldplay song lyrics. There were five types of figurative language that found in song Coldplay song lyrics, they are: 2 of simile (16%) 2 of metaphor(16%) 1 of personification (8%) 2 of metonymy(16%), 4 of hyperbole(33%), and 1 of allusion(8%). All the figurative language has a connotation that implicitly conveys hidden message and values of life.
DIGITAL CONTACT TRACING LANDSCAPE DURING COVID-19 PANDEMIC: ROLE OF MOBILE APPLICATIONS IN CONTACT TRACING

Disha Agarwalla; Dr. Piyusha Majumdar
STUDY ON UTILIZATION OF OPERATION THEATRE

Gaurav Loona; Dr. P.R. Sodani
IMITATION 3D-MODEL OF THE MOTION OF A GROUP OF AUTONOMOUS OBJECTS-DRONES

Purpose. To develop a simulation model of the motion of a group of objects in three-dimensional space to generate datasets and individual samples for further use in training neural networks and developing a mutual positioning system. Methodology. Analytical review, the automated control theory, imitation modelling. Results. Using Python, a 3D model has been developed. Digital and graphical representations of the linear trajectory of movement for a group of 4 drones based on input data, including coordinates of initial and final points and an error range, have been received. Trajectories with compensated and uncompensated deviations have been compared. Approximation and simplifications have been justified by the goal of enabling the use of the model by those without fundamental knowledge of control theory, system modelling or advanced mathematics. Code testing and benchmarking have also been conducted, proving the financial feasibility of the solution. The model will be used by the authors for further research, including assessing the likelihood of obtaining biassed data from sensors of some drones in a swarm and finding ways to correct it. The source code of the developed model is provided for open access. Scientific novelty. An imitation model of interaction and movement in three-dimensional space for a group of autonomous objects has been proposed. These objects, capable of collective decision-making based on swarm intelligence, aim to compensate for errors or interference. Practical significance. Testing in the AWS cloud environment has demonstrated that even with minimal computational power the proposed imitation model yields adequate results, suitable for neural network training.
Construction and standardization of tools for measuring the tendency to modernity in students

Background and Aim: The issue of transition from traditional society to modern society has been one of the main topics of sociology since the end of World War I. The aim of this study was to construct and standardize the modernity tendency questionnaire by analyzing the factors and measuring its validity and reliability. Methods: The present study was exploratory. The statistical population of this study was all students studying in the academic year of 2008-2009 in Payame Noor Universities in Tehran province. For this purpose, 895 students of Payame Noor University of Tehran completed the designed tool in the academic year of 2008-19. For this purpose, first, the theoretical foundations of previous researches were studied and then a questionnaire was designed based on the areas of the mentioned structure obtained from the study of the resources. After examining the face and content validity of the questions made in relation to each subtest and removing inappropriate questions from it, a total of 58 questions were selected for final implementation on the sample by multistage cluster random sampling. Principal component analysis and varimax rotation method and its reliability with Cronbach's alpha coefficient and retesting were investigated. Results: The results showed that the sampling quality index (KMO) was 0.828 and the significance level of Bartlett's spherical test was less than 0.001. Five factors were extracted from the set of questions, of which 48.2% of the total variance is explained. The first factor with a specific value of 10.8 equal to 22.5% of the total variance and finally the fifth factor with a special value of 2.4 about 5.1% justifies the variance of the total variables. Cronbach's alpha coefficient was the components of the tendency to modernism questionnaire between 0.69 and 90, indicating the appropriate validity of this tool. Conclusion: It can be concluded that the modernity tendency questionnaire in students has good validity and reliability for future research in Iran.
Mediating Role of Self-Efficacy in the Relationship between Psychological Capital and Innovative Job Performance of Teachers

Background and Aim: Education, as one of the most important and broadest social organizations, plays a very important role in the process of socialization and socialization of the society. Therefore, the current research was conducted to determine the mediating role of self-efficacy in the relationship between psychological capital and innovative job performance of primary school teachers in Karaj. Methods: This research is practical in terms of purpose; In terms of the type of data, little; In terms of the nature of the research, it was descriptive-correlational. The socio-statistics of the research were all primary school teachers in Karaj city. 242 people were selected based on the calculation of the sample size in structural equations and the stepwise cluster random sampling method. Data collection tools were the researcher-made questionnaire. Data analysis was carried out in descriptive statistics (mean, standard deviation, etc.) and inferential (Pearson correlation and structural equation modeling) through Spss-V23 and LISREL-V8.8 software. Results: It showed that there is a relationship between psychological capital (0.63) and self-efficacy (0.57) on innovative job performance; Also, self-efficacy plays a mediating role in the relationship between psychological capital and self-efficacy (0.74). Conclusion: It can be concluded that self-efficacy plays a mediating role in the relationship between psychological capital and innovative job performance of teachers.
An exploratory study on the questionnaire measuring EFL teachers' attitudes towards information and communication technology (ICT) integration in primary school classrooms

This study aimed to examine the reliability and robustness of a questionnaire developed based on the Teachers' Attitudes Towards Computers (TAC) Questionnaire (version 6) by Christensen and Knezek (2009) to measure EFL primary school teachers’ attitudes towards ICT use in teaching practice in Vietnam. Exploratory Factor Analysis (EFA) was conducted on a pilot study sample (n = 202) to refine the factor model. The identified factor structure was then used to collect data for the main study (n = 598). Cronbach's alpha and McDonald’s omega coefficients were computed to evaluate the internal consistency of the newly identified factors in both the pilot study and main study samples. The findings revealed consistent reliability in the factor structure across the samples, reinforcing the robustness of the questionnaire and its reliability for future use. Implications for both researchers and educational organizations are also presented in this study.
Can organisational culture increase innovative behaviour A serial mediation effect of job competency and job performance: comparing before and after pandemic

How to successfully apply organisational culture to employees can raise a problem for decision-makers. Though organisational culture may have positive effects, little research has been conducted into how to voluntarily involve employees without huge costs. This article uses spillover theory to examine how organisational culture conveys the organisation's vision of employees' work-related (job competency and job performance) and off-duty results (innovative behaviours). Using public data in South Korea, the serial mediation findings support that: 1) job competency mediates the relationship between organisational justice and job performance; 2) job competency mediates the relationship between job performance and innovative behaviour; 3) job competency and job performance serially mediate the effect of organisational culture and innovative behaviour. The results support the notion that organisational culture may spillover into employee behaviour without huge costs and increase both job-related outcomes and innovative behaviour. Therefore, this study provides a solution for decision makers and employees. In addition, the study also compared how serial mediation changes before and after the pandemic.
FUNDAMENTALS OF CONTENT MODERNIZATION AND TEACHING METHODOLOGY SCHOOL BIOLOGICAL EDUCATION

The article discusses the development of new generation standards within the framework of the National Development Strategy of the Kyrgyz Republic for 2021-2040, updating state and subject standards of biological education, confirming that, in educational institutions, the knowledge, skills, and abilities provided for by the previous standard will replace key and subject competencies, the formation of which provided for in the new standards and are essential to the educational process. The ways of developing the competencies of students and teachers are determined. Information was also provided on science literacy and preparation for the upcoming international PISA study. Within the framework of this article, we presented the development of a lesson developed by us based on the materials of the subject “Biology” 8th grade for the course “Human and his health”, using the learning technologies “challenge - comprehension - reflection”.
Predicting failures in electronic water taps in rural sub-Saharan African communities: an LSTM-based approach

This chapters in this book highlight the breadth of smart water technologies and applications. From the use of classical machine learning and data transformation methods for process improvement, to data management and assimilation in models for better monitoring control, a selection of these chapters describes the acquisition and preparation of data as an initial step in its use as part of a digital framework for smart water applications. Importantly, several chapters examine the use of smart tools and contemporary AI technologies, such as neural networks and Internet of Things, that demonstrate value in non-conventional or remote environments. This collection demonstrates the innovation possible through the entire ‘pipeline’ of the process of applying smart water techniques, from data measurement and collection, through initial analysis to the application of machine learning and AI techniques and finally through to system deployment. Each of these steps plays an important role in the application of data science and AI techniques to water problems.Collectively, the book showcases the innovation required to leverage modern data science and AI approaches in the water sector and collectively point the way towards a future of new measurement techniques, innovative methodologies, and intuitive human interaction to truly ‘Make Water Smart’.In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.
Research on ecological environment impact assessment based on PSR and cloud theory in Dari county, source of the Yellow River

Climate change has altered hydrologic conditions, which directly affects water resources. In addition, increasing human activities have an impact on water resources, water environment and water ecology. Rivers are important channels of water cycle, the main source of fresh water for human consumption and an important renewed resource, which play essential roles in material cycle, energy flow and climate regulation. Reservoirs are important components of water conservancy constructions, and play vital roles in regional flood interception, water storage, and the regulation of water flow. It is of great significance to study the mechanism of the water cycle under the changing environment and build an optimized water resource management based on it.Rivers and reservoirs are faced with many problems, and it is necessary to study the optimal management of water resources in rivers, lakes and reservoirs, the hydrological mechanism and ecological environment process (including experimental and numerical simulation methods), the optimal operation of engineering facilities and their natural and social effects, as well as the mitigation of water security problems and ensuring the sustainability of the ecosystem through the integrated management of rivers and reservoirs.In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.
Impact assessment of water and nutrient reuse in hydroponic systems using Bayesian Belief Networks

The pressure on available freshwater resources due to urbanization and climate change impacts is increasing worldwide; so follows the need to explore unconventional resources. Water reuse and desalination are viable options for alternative water supplies, also for regions that had no need to consider these approaches in the past. However, these solutions need to be tailored to local conditions, be balanced against conventional supply options while minimizing any adverse health and environmental impacts. There is increasing interest in developing new planning tools, technological solutions and approaches in this field to foster safe and economically viable water reuse and desalination applications.The German Federal Ministry of Education and Research (BMBF) realized these needs at an early stage and launched the major funding initiative ‘WavE’ (www.bmbf-wave.de/en) in 2016 entitled ‘Future-oriented Technologies and Concepts to Increase Water Availability by Water Reuse and Desalination’ within the framework program ‘Research for Sustainable Development (FONA)’ and the research agenda on Green Economy. WavE is aimed at developing innovative technologies and management concepts to foster a sustainable increase in water availability. The aim is to provide sustainable solutions for national and international applications.This book reports research highlights from comprehensive research projects addressing contemporary approaches to evaluate new technologies and concepts in non-potable water reuse applications, multi-barrier concepts for potable and non-potable reuse applications, concepts for industrial reuse and resource recovery approaches for brine streams, as well as onsite desalination technologies for developing communities.In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector
A comprehensive review of deep learning applications in hydrology and water resources

This chapters in this book highlight the breadth of smart water technologies and applications. From the use of classical machine learning and data transformation methods for process improvement, to data management and assimilation in models for better monitoring control, a selection of these chapters describes the acquisition and preparation of data as an initial step in its use as part of a digital framework for smart water applications. Importantly, several chapters examine the use of smart tools and contemporary AI technologies, such as neural networks and Internet of Things, that demonstrate value in non-conventional or remote environments. This collection demonstrates the innovation possible through the entire 'pipeline' of the process of applying smart water techniques, from data measurement and collection, through initial analysis to the application of machine learning and AI techniques and finally through to system deployment. Each of these steps plays an important role in the application of data science and AI techniques to water problems.Collectively, the book showcases the innovation required to leverage modern data science and AI approaches in the water sector and collectively point the way towards a future of new measurement techniques, innovative methodologies, and intuitive human interaction to truly 'Make Water Smart'.In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.
AI-Driven Automated Feature Engineering to Enhance Performance of Predictive Models in Data Science

In the rapidly evolving landscape of data science, predictive modeling stands as a cornerstone for deriving actionable insights from vast amounts of data. Central to the success of predictive modeling is the process of feature engineering, which involves selecting, transforming, and creating features to improve model performance. With the advent of artificial intelligence (AI) and machine learning (ML), automated feature engineering has emerged as a promising approach to streamline and enhance this critical process. This paper explores the role of AI-driven automated feature engineering techniques in augmenting the performance of predictive models in data science.The paper begins with an overview of predictive modeling in data science, highlighting the significance of feature engineering in model development. [1] Traditional approaches to feature engineering often rely on manual experimentation and domain expertise, which can be time-consuming and prone to human bias. In contrast, AI-driven automated feature engineering leverages ML algorithms and techniques to automate and optimize the feature engineering process, reducing the need for manual intervention and accelerating model development. Various AI-driven automated feature engineering techniques are examined, including machine learning-based feature selection algorithms, automated feature transformation methods, generative adversarial networks (GANs) for feature creation, and deep learning-based feature extraction techniques. These methods offer advantages such as improved model performance, time and resource efficiency, and reduced human bias. The paper also discusses challenges and limitations associated with AI-driven automated feature engineering, such as data quality requirements, interpretability of automated features, and the risk of overfitting. Additionally, case studies and applications demonstrate the practical utility of automated feature engineering across diverse domains, including finance, healthcare, marketing, and more. The paper explores future directions in automated feature engineering, including emerging trends, integration of domain knowledge, and ethical considerations. By providing valuable insights into the methodologies, tools, benefits, challenges, and future prospects of AI-driven automated feature engineering, this paper aims to guide practitioners and researchers in harnessing advanced techniques to enhance predictive modeling in data science.
Pengaruh Model Discovery Elerning terhadap Materi Sistem Pernapasan untuk Meningkatkan Kecerdasan Sosial Peserta Didik

Creative thinking must be developed in the learning process, so that students are accustomed to developing their creativity. According to Fadly (2014: 166) media is a component of learning resources or physical vehicles that contain instructional material among students which can stimulate students to learn. to convey messages or learning materials. In addition, learning media is useful for attracting students' interest in learning material and increasing student understanding. The goal is to apply an interesting and student-centered learning model with learning activities that activate students to construct subject matter so as to improve learning outcomes. One learning model that can overcome these problems is Discovery Learning. Discovery is a learning model that was developed based on a constructivism view.&#x0D; Keywords: discovery elerning model, respiratory system material, social intelligence&#x0D;
An Analysis of Recruitment Policies Practiced in Large Enterprises

This paper focuses on the recruitment policies that are practiced by four multinational companies. The objective of this research is to analyse how these companies are responding to these international trends. The study is designed to help HR professionals within large enterprises to understand best recruitment practices that can be applied in their organisations in order to attain employer of choice status. It provides an account of current recruitment policies practiced in the multinational companies (MNCs) and to identify the global recruitment trends adopted by these companies. A comprehensive review of related literature was carried out in four leading chemical and petrochemical MNCs. Data was obtained from in-depth interviews with some of the staffs of the HR Department. The results of the study show that the companies surveyed have adopted some global recruitment practices to gain competitive advantage and to keep up with dynamic changes. By implementing methodologies to identify and recruit talent faster, automate recruitment processes, and reduce recruitment costs, it has helped establish the HR department as a strategic contributor to organisational success.
The Impact of Unit Membership, Discipline, and Age on Team Effectiveness

The research on the idea of teams as dynamic systems that transcend boundaries is increasingly attempting to better understand how changes in team members (i.e., team members joining and/or leaving) shape organizational team performance and operation. The purpose of this study was to determine the impact that unit membership, discipline and various demographic characteristics such as age and gender have on levels of stress, coping, self-esteem and estimates of team effectiveness. Stress from patient behaviors was influenced by team and the psychiatric and Alzheimer's units had the highest levels and the lowest levels were in the geriatric and nursing home units. The amount of emotion related coping was significantly affected by age and the youngest participants had the greatest amount. Unit membership and discipline have a significant impact on the level of stress and coping.