bluuebunny/crossref_metadata_2025 huggingface dataset:
Rancang Bangun Alat Pembersih dan Pengering Gabah Berbasis Arduino Uno
Authors: Pratama Sifa Ardika , Prasetyo Aries Alfian , Hajah M. Sohibul
Published: April 2024

The planning of this grain cleaning and drying machine is one of the efforts to apply appropriate technology. To assist the community in processing crop yields with a grain drying machine that is heated using a heater using a dht22 sensor. As a temperature and humidity sensor with a control system using an arduino uno as the brain of this tool . One of the reasons is that the grain drying process takes a long time because it still depends on sunny weather and requires a relatively large area. Because of that, the idea emerged to make a Design and Development of an Arduino Uno-Based Grain Cleaning and Dryer Tool. What I want to achieve from this final project is to dry grain without waiting for sunny weather first and of course it is more efficient in terms of energy and land for farmers and the community and produces good quality rice.
Perbandingan Cascade Forward Neural Network dan Feed Forward Neural Network untuk Prediksi Keluaran Daya PV di PLTH Pantai Baru Bantul
Authors: Mahmudah Norma , Millah Ibrahim Saiful , Afandi Achmad
Published: April 2024

Pembangkit listrik tenaga surya memiliki beberapa keuntungan yaitu produksi yang terus menerus, mengurangi kebutuhan listrik, pemeliharan Photovoltaic (PV) yang rendah dan umur PV yang lebih dari 30 tahun, sehingga pengunaan panel surya dapat dioptimalkan dengan mengunakan prediksi keluaran daya PV. Tujuannya adalah untuk mengetahui keluaran daya PV untuk kedepannya. Prediksi keluaran daya PV dapat mengunakan Artificial Neural Network (ANN). Pada penelitihan ini dilakukan perbandingan prediksi keluaran daya PV dengan megunakan Cascade Forward Neural Network (CFNN) dan Feed Forward Neural network (FFNN) dengan mengunakan Algoritma Levenberg-Marquard sebagai fungsi aktifasi dari proses pembelajaran prediksi keluaran daya PV. Besaran Error dihitung dengan mengunakan Mean Square Error (MSE).  Dari hasil penelitihan dengan metode Cascade Forward Neural Network (CFNN) dengan algoritma Levenberg-Marquard didapatkan hasil MSE yang lebih baik pada learning rate 0,1 dengan MSE sebesar 0,0042% sedangkan untuk Feed Forward Neural network (FFNN) juga mengunakan algoritma Levenberg-Marquard didapatkan hasil MSE sebesar 0,007% dengan learning rate 0,05. Dari hasi penelitihan menunjukkan bahwa CFNN memberikan nilai MSE terbaik, sehingga nilai error MSE yang terkecil tersebut menjadikan acuan dalam sistem manajemen energi untuk memprediksi keluaran daya PV.
Effects of Lime on Bagasse Ash-Stabilised Expansive Soils
Authors: G. O. Adunoye , K. T. Oladepo , I. Yinusa , D. E. Chibuogwu
Published: April 2024

Management of agricultural wastes and improvement of construction soils have become increasingly necessary for sustainable development. This study therefore stabilised selected expansive soils using bagasse ash (BA) and lime, with a view to determining the effect of lime on BA-stabilised soils. Two soil samples (identified as Sample A and Sample B) were collected from two identified locations characterised with expansive soils in Ile-Ife, Osun State, Southwestern Nigeria. Using standard procedures, the following preliminary and geotechnical tests were conducted on the soil samples in their natural state: moisture content determination, particle size analysis, specific gravity, Atterberg limits, compaction and unconfined compressive strength (UCS). BA was then introduced to the soils in 5 %, 10 % and 15 % proportions by weight of dry soil. Thereafter, Atterberg limits and UCS tests were conducted on the BA-stabilised soils. Also 2.5 % lime was introduced to each proportion of BA earlier used. Atterberg limits and UCS tests were then conducted on the BA-lime stabilised soils. Results showed that the two soils in their natural state have high plasticity and they both belong to the A-2-7 group. For both BA and BA-lime stabilised soil samples, plasticity reduced with increase in the stabilisers, which implies an improvement in the soil properties. Also, UCS of both BA and BA-lime stabilised soil samples increased with optimum values at 10 % BA content. Expectedly, addition of lime increased the values of UCS for each combination of stabilisers. It was concluded that BA could be used to improve the properties of expansive soils and that the addition of certain proportions of lime does not have a negative impact on the stabilisation properties of BA, but rather improves it.
Highlighting the Aspects of Women Emancipation and Liberation in Ibsen’s The Dolls House and Hedda Gabler: A Comparative Feministic Approach
Authors: Buzdar Hafsa Qadir
Published: April 2024

This research paper aims to investigate the aspects of women liberation and emancipation in Ibsen’s The Dolls House and Hedda Gabler. The Dolls House largely addresses a woman's yearning to forge her individuality and dignity in a world run by men. Review section illustrates the critical comments and reviews of notable literary authors and critics. Feministic and especially a radical feministic approach in The Dolls House and Hedda Gabler by Ibsen illustrates three goals in mind; Nora’s patriarchal struggles with the patriarchal world. To examine the types of conflict Hedda Gabler engaged in when battling over patriarchal worldview and to examine the types of challenges Hedda Gabler had in her battle with the ideology. However, the study uses content analysis to conduct a qualitative investigation with the help of textual references. Finally, the research concludes that Ibsen's unorthodox portrayal of women in one of his most notable social tragedies; The Dolls House & Gabler.
Realization of a Novel Artificial Intelligence Fuzzy Controller with Solar Irradiance-altered Maximum Power
Authors: Wadullah Jaleel Mhawes Hatem Abdullah J. H. Al Gizi,
Published: April 2024

This paper reports the realization of a novel artificial intelligence fuzzy Logic controller (AI-FLC) that attained the optimum power of the photovoltaic cell (PV array) with solar irradiance altered (from 300 to 1000 w/m2) using the boost-converter circuit. Experiments were conducted using the Simulink /MATLAB coding. The nonlinear current-voltage character of the PV array was observed to be affected by the weather conditions, solar irradiance level, and ambient temperature. In addition, unique maximum power point for each irradiance level was achieved which was ascribed to the rapid response of the FLC to the atmospheric changes without requiring any system parameters. The obtained accuracy was as much as 98.875%. The post converter circuit was connected to the buck converter for taking the advantage of the generated power, thereby stabilizing the synchronous generator-emanated voltage. The response of the proposed system was demonstrated to be better than the conventional PD control, wherein the output voltage was stable at 220 V even at low levels of the solar irradiance.
The Application of Artificial Intelligence (Ai) and Internet of Things (Iot) Middleware in Agriculture
Authors: Lee Hae-Jun
Published: April 2024

Artificial intelligence (AI) devices like master frameworks, regular language handling, discourse acknowledgment, and machine vision have modified both the amount and nature of work in the rural area because of the developing worldwide populace, the rising interest for food, as well as changes in climate and the availability of water. Specialists and researchers are presently attempting to involve new IoT advances in savvy cultivating to help ranchers in making better seeds, crop assurance, and manures utilizing AI technology. Both the nation's overall economy and the profitability of ranchers will be benefited from this. Farming robots, prescient investigation, and soil and yield observing  are the three main regions where AI is beginning to appear. In this sense, ranchers are utilizing sensors and soil test all the more often to gather information that the executives frameworks might use for more examination. Through an outline of AI applications in the farming business, this paper makes a commitment to the area. It starts with a prologue to AI and an investigation of all AI strategies utilized in the horticultural area, including AI, the Internet of Things using Universal Middleware, master frameworks, picture handling, and PC vision. This study incorporates a complete examination of the literature to research how artificial intelligence (AI) and the Internet of Things (IoT) are utilized in farming.
Deep Learning Model for Colon Cancer Classification using InceptionV3
Authors: Mayur D. Jakhete, Manoj Chavan, Milind Rane Tushar H. Jaware, Hemant Kasturiwale, Rashmi Thakur,
Published: April 2024

Colon cancer is a significant global public health concern, necessitating an accurate and timely diagnosis for effective treatment. Leveraging advancements in deep learning, this study proposes a novel approach to colon cancer classification using InceptionV3 convolutional neural network architecture. A dataset comprising 1600 colonoscopy images divided into colon_aca (adenocarcinoma) and colon_n (normal) classes was utilized. The model demonstrated promising performance, achieving a training accuracy of 98.86% and a validation accuracy of 99.74% after 100 epochs. This success was accomplished by employing a Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.0001 and momentum of 0.9, along with categorical cross-entropy loss. Our findings underscore the importance of deep learning models, specifically InceptionV3, in facilitating the precise classification of colon cancer, thus offering a valuable tool for assisting clinicians in early detection and treatment decision-making. Future research may explore the integration of additional clinical data and the evaluation of alternative deep learning architectures to further enhance diagnostic accuracy.
Design of a Basic Analytical Framework for Human Capital, Public Goods and Institutions Based on Linguistic Economics
Authors: Wang Yang
Published: April 2024

In the paper, we analyze and demonstrate the economic nature of language from the aspects of human capital, public goods and institutions, and put forward three topics in the field of Language Economics: language human capital; Language is a kind of public goods; Language is a system. As human capital, it is one of the important factors that determine people's employment and income in the labor market. It is closely related to people's social and economic activities and economic conditions; As a public product, language can directly affect the choice and formulation of language policy; As a system, language not only affects the transaction cost, but also affects the effectiveness of other institutional arrangements. As far as the existing research is concerned, the research on proposition 1 has become a very mature field of language economics. The research on propositions 2 and 3 has just started, but the prospect is very broad. This is worth studying. It is reasonable to believe that the follow-up research of language economics is usually based on or supported by these three arguments. In this sense, they can be used as the basis of linguistic economic analysis.
Exploring the Path to Improve the Quality of Student Management Education Based on Knowledge Graph and NB-loT Architecture
Authors: Jing Yang Lin Liu,
Published: April 2024

To research the effects of the smart classroom environment on college students' motivation to learn and engagement in that learning, as well as the link between those two factors in the smart classroom environment, a multi-scene student posture detection method using meta-learning is proposed. Through a combination of offline and online learning, the method develops a posture detection metamodel and a reasonable adaptation optimizer to quickly domain modify the posture detection model for certain teaching scenarios. A small amount of labelled sample data from a single teaching scene is all that is required for the metamodel to quickly adapt to the data distribution of that scene with the help of the adaptation optimizer in the online learning phase. The method simulates the process of the pose detection model in various types of teaching through two-layer training to train the parameters of the pose detection metamodel adaptation optimizer. The experimental findings demonstrate that college students' independent learning level and learning engagement are significantly higher in the smart classroom environment than they are in the traditional classroom environment. Additionally, there is a strong positive correlation between their independent learning level and learning engagement in the smart classroom environment.
Machine Learning in Teaching Piano Touch Technique and Tonal Expression
Authors: Jiang Hong
Published: April 2024

Music is the art of both sound and hearing. Among many musical instruments, the piano has the title of "king of instruments". Unlike the violin, which is delicate and soft, the piano's tone is noble and graceful. The pianist Nergoz once said that the purpose of all piano playing is to make sound and to create sound. So, how to use the piano to create a beautiful sound? This is an essential concern for all piano players. Therefore, it is important to practice tone, both in practice and in playing music, and only when the fingers touch the keys can a beautiful tone be produced. Tone is important in the whole process of piano playing. Both the expressiveness of the piano and its appeal to the listener are closely related to the quality of the tone. The part of the key that is touched, the intensity and the technique all have an important influence on the tone. The scientific approach to finger touch is a particularly important skill and technical criterion, both in the process of learning the piano and when performing it.
Discussion on Teaching Mode and Construction of International Chinese Teaching Academic Environment Under Background of Internet + Intelligent Information
Authors: Jingjing Guo Jianqiang Wang,
Published: April 2024

Online Chinese teaching has become main mode of international Chinese teaching. Therefore, new requirements and challenges have been put forward for international Chinese teaching in terms of teaching modes, teaching methods, and teaching platforms. This paper investigates current situation of online Chinese teaching in our college by means of literature analysis, questionnaire survey, interview and observation, and analyzes feasibility of applying task-based teaching method to online Chinese teaching in our college. On this basis, practice of task-based teaching method is presented through specific cases, and teaching effect is evaluated and reflected from two aspects of performance evaluation and feedback evaluation. Four teaching suggestions are put forward. Through this research, we explore application of task-based teaching method to implementation mode of online Chinese teaching and provide teaching cases and practical suggestions for development of international Chinese education under new situation.
Intelligent Integration: Harnessing Artificial Intelligence for Enhanced Performance and Efficiency in Electric Vehicles
Authors: Gopinath S., P. Vasuki, V. Arun, Purushottama TL Billa Pardhasaradhi, C. Shilaja,
Published: April 2024

The transition towards electric vehicles (EVs) necessitates the development of efficient and reliable charging infrastructure. This paper presents an AI-driven approach to optimize EV infrastructure, focusing on five key aspects: profiling, augmentation, forecasting, explainability, and charging efficiency. Profiling involves understanding EV drivers' behaviors and preferences, facilitating targeted infrastructure development. Augmentation utilizes AI algorithms to identify optimal locations for new charging stations or upgrades based on usage patterns and demand forecasts. Forecasting models leverage machine learning techniques to predict future EV adoption rates and charging demands, aiding in infrastructure planning. These datasets can be used to generate insights and decisions through the use of artificial intelligence (AI) algorithms. A thorough analysis of the usefulness of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability, and charge optimization of the EVI has not yet been conducted, despite a number of recent studies in this area. This study's goal was to create, develop, and assess a thorough AI framework that fills in this EVI gap. The findings of an empirical assessment of this AI framework on an actual EVI case study validate its usefulness in tackling the new issues surrounding dispersed energy resources in the deployment of EVs.
PV Wind Battery Based DC Microgrid with Neural Network MPPT
Authors: P. Balakrishnan, Gopinath Singaram, S. Senthil Kumar E. Vani,
Published: April 2024

This Research presents a novel approach to enhancing the performance and efficiency of a PV-wind-battery-based DC microgrid through the integration of a neural network maximum power point tracking (MPPT) system. The proposed system aims to optimize energy harvesting from photovoltaic (PV) panels and wind turbines while efficiently managing energy storage in batteries. By employing neural network algorithms, the MPPT system adapts to varying environmental conditions and load demands, thereby maximizing energy extraction and system stability. Two important RE (Renewable Energy) power sources—photovoltaic cells and wind energy systems—are examined in this technical study under a range of meteorological conditions. Initially, a state-of-the-art intelligent controller system was developed to help monitor the peak power point. For RES, an MPPT (maximum power point tracking) controller is necessary since weather patterns are unpredictable. The purpose of this work is to provide novel ways for power generation using solar and wind energy that are based on MPPT and IDNN (enhanced deep neural network). The integration of a hybrid PV/WES system into microgrids, or MGs, has the potential to lower THD values and enhance power quality. The simulation findings validated that the suggested IDNN system outperforms the current approach in various operating scenarios, as seen by decreased mean square error (MSE) rates, total harmonic distortion (THD), and computational complexity.
Unlocking Machine Learning Algorithms for Bambooshoots.AI: Revolutionizing Agricultural Applications with Computer Science
Authors: Thelma D. Palaoag Charlot L. Maramag
Published: April 2024

This research initiated a comprehensive investigation to determine the most suitable Machine Learning (ML) algorithms for bamboshoots.AI application. Convolutional neural networks (CNNs) and support vector machines (SVMs) were cited as two essential ML techniques in a comprehensive review of ten papers. To further assess these two algorithms in the context of the Bambooshoots.ai application, an experimental analysis was also carried out. The CNN model had been trained over 25 epochs with 8 batches of data per epoch, demonstrating a consistent increase in accuracy, and reached 97.94% by the end of training. In contrast, the SVM model provided an accuracy of approximately 57.14%. The experimental results indicated that the CNN model had been better at classifying bamboo shoot images, thus making it the preferred choice for the BambooShoots.AI application. The significant difference in accuracy between the two models, as well as the consistent performance of CNNs in image-based tasks, informed this decision. The results of this research were thrilling because they showed that machine learning could be used to help understand and manage bamboo shoots. This could have been a big help to farmers and could have led to new ways of doing things in agriculture.
PV-based Performance Evaluation of Zeta and Sepic Topologies for EV Applications
Authors: Bondu Pavan Kumar Reddy
Published: April 2024

In this paper, a comparative performance analysis was proposed for PV fed EV charging system for ZETA and SEPIC DC-DC converter topologies. The utilization of Electric vehicles has gained significance in recent years due to societal awareness and increasing concern for environmental sustainability. EV charging time plays a vital role in modern-day vehicle commuting with energy-efficient performance. The selection of an appropriate DC-DC converter topology plays a significant role for fast charging with improved efficiency. The paper presents an optimized parameter selection, design, simulation methodology & relative performance analysis between the ZETA and SEPIC converter topologies with and without MPPT algorithm for EV charging applications. The MPPT algorithm based on P&O and IC techniques investigates for switching time of the ZETA and SEPIC converters under standard test conditions of solar PV panel i.e., at irradiance of 1000 W/m2 and temperature of 25oC. In this article MATLAB/Simulink was developed to explore the performance of both topologies with and without MPPT approaches for battery SoC, battery voltage and charging current. The study shows that the battery charged from an SoC of 50% to 50.035% for SEPIC converter while the ZETA converter charges from 50% to 50.025% with a similar simulation time of ten seconds with Solar power input.  The results demonstrate the DC-DC SEPIC converter is the best choice for EV charging applications through the PV system.
Using Hyperspectral Images and Lidar Data to Create Models for the Classification and CAVE Visualization of Tree Species
Authors: Peter Krammer , Ladislav Hluchy Eva Pajorova
Published: April 2024

The article describes the use of artificial intelligence methods to create models for detecting and classifying tree species, using hyperspectral images and LiDAR data in the aerial photography of energy line structures. The most important output is a validated model with cloud infrastructure support for detecting and classifying objects of interest at the TRL 5 level, which is also exceptional on a global scale. The outputs of the research are also a geodatabase of reference tree characteristics, a library of spectral curves, a database of simulation of tree growth, but also a cloud infrastructure to support the development of classification models and data storage. An important output will be the visualization of the results of simulations in the "CAVE" environment.
The Impact of Russian Troll Tweets: Analyzing Political Motivation in Tweets from the Internet Research Agency
Authors: John Paul Vergara, Jose Alfredo de Vera III, M.S*
Published: April 2024

This research investigates the impact of social media trolling on political events such as elections. The study utilizes the dataset of the Internet Research Agency, a Russian "troll factory" indicted by the US Justice Department in February 2018, to analyze tweets from 2012 to 2018, with the aim of creating a classification algorithm that will predict the political motivation of future tweets. The study involves running various classification algorithms, including Naive Bayes, K Nearest Neighbors, Random Forest, Histogram-based Gradient Boosting Classification Tree, and Light Gradient Boosting Machine. Data cleaning and categorization were done, assigning numeric values to each category. The resulting model scores and a breakdown of precision per category were analyzed. Word frequency analysis was also conducted to identify the most frequent words and understand the overall sentiment of the tweets and the topics targeted by the trolls. The research findings indicate that predicting the category of tweets is challenging, but the classification algorithms were able to do so with relatively good accuracy, especially for the NewsFeed and HashtagGamer categories. The study emphasizes the need for further research to improve accuracy of tweet classification algorithms and for social media platforms to continue their efforts to identify and remove troll accounts that aim to undermine political bias.
Sentiment Analysis of Filipinos About Online Classes in the Post-Pandemic Era using Machine Learning and Code Switching
Authors: Oscar III L. Cabarron , Angie M. Ceniza-Canillo James Michael P. Bañes ,
Published: April 2024

Understanding the issue faced during and after the pandemic is a concern of the educational system in the country, particularly the feelings of teachers and students regarding the matter. In this post-pandemic era of the years 2021-present, online learning continues to be a norm and its implementation still causes issues for some students and teachers alike. Student satisfaction and Instructor preparedness are both key factors to effective delivery of education through online learning. This study aimed to develop a model that analyzes the Filipino public sentiment on the implementation of online classes using Machine Learning. Result revealed that the Filipinos sentiment towards online classes are predominantly positive. The output of the study will be used for better and improved delivery of online classes in the Philippines in the post covid-19 pandemic era.
Regional Prediction of Crop Yield Success Rate in the Philippines using Geographic Trend Analysis Algorithm
Authors: Francis Alfaro
Published: April 2024

Agriculture holds an important role in the economy of the Philippines by ensuring the food security of domestic products. It involves about 40 % of Filipino workers and it contributes an average of 20 percent to the Gross Domestic Product. Crop prediction in the Philippine’s agriculture is a big problem. At the present time, farmers are having a difficult time in choosing the right crops due to unnatural climate changes, soil type, rainfall, and other environmental factors. These can affect the economic life of farmers and can make them less familiar in forecasting future crops. This project aims to guide the farmers for sowing the reasonable crops by using geographic trend analysis algorithm. The findings from this study demonstrate the potential of the Geographic Trend Analysis Algorithm as a powerful tool for regional crop yield prediction. The success rate of each region will always equate to 100% in context with the set optimal yield prediction where the crop with highest percentage appears to be the dominant one. Irrigated palay (rice) demonstrates a greater rate of success across diverse Philippine regions when contrasted with rainfed or non-irrigated palay, as well as white and yellow corn. Additionally, the methodology employed by the researchers sets the groundwork for upcoming investigations in the domain of agricultural data analytics and geographic information science. This paves the way for a data-centered and sustainable strategy toward crop production in the Philippines.
A Numerical Investigation of Seismic Load Analysis for a Concrete Gravity Dam Utilizing Abaqus Software
Authors: Thulfiqar S. Hussein
Published: April 2024

Current research efforts focus on ensuring the safety of extant dams against seismic loads and designing earthquake-resistant new dams. Dependable analytical procedures are essential for a safe design that can withstand the forces induced by earthquakes. In this research, the Pine Flat Dam in California, United States, was subjected to modal, static, and dynamic analyses utilizing the Abaqus software to evaluate the impact of hydrostatic forces and dynamic stresses on the dam, taking into account flexible foundations. Using the vertical component and the horizontal component Taft Lincoln School Tunnel earthquake data, dynamic linear and nonlinear performance for concrete gravity dam were conducted. The results obtained from the nonlinear analysis exhibited a noteworthy decrease in the highest primary stress at the heel of the concrete dam, dropping from 3.17 MPa to 1.31 MPa. Conversely, no variation was observed in the minimum principal stress. In addition, the dam's maximum principal stress has shifted from its base to its neck. The findings from these analyses can contribute to enhancing new dam designs and evaluating seismic safety for existing dams. This study highlights the importance of accurate analytical procedures in ensuring dam safety and provides significant observations regarding the behavior of concrete dams during earthquake loading.
Productivity Improvement of Coal Hauling using Lean Techniques: A Case Study in Coal Mining Company in Indonesia
Authors: Selamat Walmanto Hia
Published: April 2024

The adoption of the lean concept as a company strategy to creates competitive advantages and provides important savings opportunities has been increasing recently. Its application has been extended from manufacturing to almost all types of industry including coal mining under the realization of the need for productivity improvement and the need for efficient operation.  However, published information and articles showing the real application of lean in the mining sectors are still scarce. The objective of this paper is to show the application of lean production method in coal hauling operations in one of coal mining company in Indonesia. The lean method with Plan, Do, Check, Act (PDCA) cycle has been utilized to identify, analyze and improve productivity. The main result of this study reveals that implementing lean increase the productivity of coal hauling operations from 115 to 129 tons per hour. Statistical test using two sample t-test confirmed the significance of the result. This paper demonstrates lean application in coal hauling operations and bring significant result.
Enhancing Online Delivery of Practical Labs for Cyber Security Course Using Virtual Laboratories
Authors: Nazleeni Samiha Haron
Published: April 2024

This research work is to create a application as web service to make it available on web for web service discovery. The most significant yoga stance is well-known on a global scale and supports the health advantages advocated by the ancient sages. OpenPose estimates that computer vision technology can help yoga practitioners achieve the greatest form and alignment. The integration of the OpenCV, Python, and MediaPipe frameworks is the main emphasis of this research project in order to develop an OpenPose estimation system for yoga poses. A camera is used to capture the yoga practitioner's motions, and a deep learning model is used to predict the important body parts. The algorithm then looks at these essential aspects to determine if the practitioner is doing the pose correctly or whether any adjustments are needed. The device gives the practitioner immediate feedback, allowing them to modify their alignment and posture as necessary. The OpenPose estimate system can help with the practice of yoga by offering detailed visualizations of the essential body parts throughout each posture in addition to providing real-time feedback Practitioners may more clearly understand the right form and alignment and make the required adjustments by utilizing this visualization. Additionally, the OpenPose estimation system enables the tracking of development over time. Practitioners who want to practice alone at home or may not have access to an instructor can also benefit from it.  This paper offers a MediaPipe, OpenCV, and Python-based OpenPose estimation system for yoga positions. For optimal form and alignment, the tool may offer real-time feedback and visual representations of key body parts throughout each position. It is useful tool for yoga teachers and students, enhancing yoga's safety and efficacy while supporting the practice.
Emotional Exhaustion in Morocco: Quantitative Analysis of Influencing Factors among Executives
Authors: Ikrame EL-Abbadi
Published: April 2024

The objective of this study is to explore the relationship between the individual and their organization, with a particular focus on emotional exhaustion. Using a mixed empirical approach, we aim to better understand the causes of emotional exhaustion, thus providing in-depth insights into this critical dimension of the connection between individuals and their professional environment.

Following the conclusions of the qualitative study conducted with fourteen executives experiencing emotional exhaustion, we implemented a questionnaire to assess the extent of this phenomenon. A survey of 327 executives in the Moroccan private sector was conducted to validate the quality of measurement scales, the overall model, and research hypotheses. The analysis of the data collected during this survey reveals the significance of workplace monotony, role ambiguity, and recognition at work in triggering emotional exhaustion.
The State of Teacher Training in the Conditions of Dual Training
Authors: A.A. Seitaliyeva
Published: April 2024

The purpose of this article is to understand and justify the conditions for the use of elements of the dual training system in the implementation of professional educational programs to improve the efficiency and quality of training of qualified pedagogical personnel in secondary education institutions.

The article discusses the advantages and disadvantages of dual education as a technology for preparing bachelor's degree programs in modern conditions of pedagogical university. Nowadays the topic of "dual training" is a very urgent problem on the pedagogical platform. Because vocational education has never been conceived without connection with the manufacturing sector. The authors describe how dual education is carried out abroad, as well as how the dual education model is being implemented in Kazakhstan. As an example to describe the introduction of elements of dual training, the system of dual training of some pedagogical universities of the Republic of Kazakhstan is presented. The article shows that this technology makes it possible to solve the tasks assigned to the pedagogical university for the training of pedagogical personnel for the system of secondary vocational education. Dual training reduces the gap between theory and practice, promotes the professional development of teaching staff. In addition, it sets new tasks for the higher education system: the development of dual training programs, the regulation of tripartite relations "university – student – employer".
Augmented Reality Based Cultural and Natural Heritage Preservation of Quirino Province: A Proposed Developmental Framework
Authors: Arsenia V. Duldulao
Published: April 2024

As the province of Quirino is the melting pot of the Region 02 and nearby provinces, consolidation of the list of all cultural properties that are deemed significant to the province’s cultural and natural heritage—both tangible and intangible is very encouraging. What people see now is the outcome of a centuries-long process of evolution, a method influenced by natural changes but was accelerated by human effort, adding, adapting, and replacing.

This study aims to design a developmental framework with augmented reality for the preservation of the cultural and natural heritage of Quirino. Design thinking methodology was used to understand the users, and to create innovative solutions to the problems derived. Systems modelling was used to represent a system based on notations from the Unified Modeling Language (UML). Survey and descriptive analysis were used to analyze results from Municipal Tourism officers and LGU employees. The proposed developmental framework promotes virtual tourism using AR, preserving the cultural and natural heritage.  The province's diverse culture is safeguarded for future generations through heritage preservation and educating people on its importance in the nation's history.
Migrant Fishers’ Cultural Funds of Knowledge and Translanguaging Praxis for the Development of Contextualized Instructional Material: A Narrative Ethnography
Authors: Joefry Q. Barcebal
Published: April 2024

Fishing is a flourishing industry in Antique, Philippines, which led to the mushrooming settlement of Cebuano-speaking migrant fishers in the coastal villages. With the implementation of the K to 12, pressing concerns about the lack of contextualized instructional materials (IM) in these trans-local spaces emerged. This study aimed to generate the cultural funds of knowledge and translanguaging praxis of migrant fishers as bases for developing contextualized instructional material. Under the lens of narrative ethnography and cultural memory banking as a mediational tool, various “seeds” about the knowledge, beliefs, and practices of migrant fishers were documented in memory bank charts. Translingual narratives were analyzed. Participants were purposively chosen and all qualitative data were analyzed qualitatively using manual coding and QDA Lite Miner software processing. Cultural beliefs and practices in fishing were presented in memory bank charts and analyzed adapting Polkinghorne’s analysis of narratives. Contextualized storybooks accompanied by a teacher’s guide were developed, validated, evaluated, and pilot-tested. It resulted in a “very acceptable” for the storybook and an “excellent” rating for the teacher’s guide. The study recommends that school heads create opportunities to involve teachers, parents, and other people in the coastal community to interact beyond the classroom setting. Contextualizing community resources and indigenous knowledge as inputs in the lessons and the IM across other subject areas may be explored.
The Impact of Online Learning on Students and Teachers: A Systematic Literature Review Investigating the Effects during the COVID-19 Pandemic
Authors: Aaron Luis Emmanuel S. Navarro
Published: April 2024

In the year 2020, the COVID-19 pandemic put the world at a standstill. The pandemic has dramatically impacted the lifestyle of many people in the world as it restricts traveling and physical contact to avoid spreading the infection of the disease. It has severely changed how education works due to shifting to online learning. This paper aims to conduct an intensive analysis of the current studies conducted in the years 2020-2022 regarding the impact of online learning on students and teachers during the COVID-19 pandemic. This paper will determine the factors that affect students' using online learning. Additionally, the effects on teachers and professors will also be included in this study. The aim of this paper is to provide a complete understanding of the effects of online learning on students and teachers during the pandemic and to provide information for educational institutions. The study found that different factors impact students and teachers in online learning, with some similarities. The sudden shift to online learning had a significant effect on students. According to the gathered literature, students' most common difficulties are technological and psychological issues, such as unstable internet connections, lack of socialization and interaction among other students, and isolation. For teachers, problems with internet connections and a lack of training in using technology for online learning also significantly impacted the delivery of lectures. Both students and teachers generally prefer traditional, face-to-face classes.
Experimental and Didactical Strategy to Improve Understanding of the Fundamental Concepts in Quantum Physics.
Authors: Andrea Carolina Sánchez Díaz
Published: April 2024

Quantum physics is an essential theory serving to explore nature at its smallest scales. Interaction between elemental particles in matter is leading toward new potential alternatives for producing decarbonized clean energies. Despite its importance, students find difficulties in understanding quantum mechanics due to a wrong categorization of the fundamental concepts. Studying quantum physics needs a new flexible ontology in which the quantum entity can have particle or wave properties depending on the context. We developed a workshop in quantum devices to put concepts into the correct conceptual categorization. With the aim of strengthening interinstitutional exchange, we realized the workshop with a population conformed by environmental engineering students of the Corporación Universitaria Uniagraria. Meeting took place in the Institución Universitaria Politécnico Grancolombiano facilities. Laser, LED (Light Emitting Diode), and photocell were the devices used to introduce the most fundamental concepts of quantum physics. The theoretical component of the workshop was divided into two main elements, the first, radiation-matter interaction, and the second, electrical transport mechanisms in materials. Pedagogical instruments employed were diagnostic proof, evaluative proof, and satisfaction inquiry. Despite the success achieved in the general realization of the workshop, students suggested some aspects for considering in future versions.
The Guidelines for Developing Competitiveness of Thai Agricultural Products: A Case Study of Durian Production in 14 Southern Provinces, Thailand
Authors: Narodom Jaemjarus
Published: April 2024

This study explores ways to make Thai durian production more competitive, concentrating on sustainable practices and difficulties experienced by durian growers. Multiple techniques were used. A thorough grasp of the market dynamics was gathered through in-depth interviews with 30 industry stakeholders, including durian producers, exporters, distributors, government officials, and industry professionals. 200 responders from 14 southern regions of Thailand, where durian production is crucial because of traditional farming practices and a favourable climate. Examining quality control practises, distribution effectiveness and sustainable farming methods revealed areas that might be improved to increase industry competitiveness. The study also revealed difficulties, including losses from post-harvest handling, barriers to entry into the market, regulatory limits, and inconsistent quality. The study also examined consumer preferences for sustainable products and their willingness to pay more. The findings reveal a sustainably produced durian goods market since they show an increasing consumer trend towards eco-friendly alternatives. Future recommendations call for incorporating technology into the supply chain, doing in-depth customer preference research, conducting a comparative analysis, and conducting long-term evaluations of sustainable practices. Policymakers, businesspeople, and researchers working to improve the competitiveness and sustainability of the Thai durian industry will find this study helpful.
The Potential of Atrium Design to Maximize the Passive Cooling Effect for Low-Rise Public Buildings in Malaysia
Authors: Noorfarhana Alia Mohd Firdaus
Published: April 2024

One of the passive design techniques is the atrium, which can have certain effects on a building inside environment. One of the key considerations when designing a building, especially in a hot, humid area, is the ventilation component. Natural ventilation has the ability to increase a building’s energy efficiency. It can be incorporated into the design of buildings to provide indoor airflow and provide comfortable experiences for the users of the buildings. The integration of an atrium which serve an area for air movement could enhance the cooling of a building. Apart from that, it is crucial to rethink the sustainable design of public buildings by reducing the energy consumption to minimal the greenhouse gas emissions for the environment. Hence, this research was executed to study the potential of atrium design to maximize the passive cooling effect for low-rise public buildings in Malaysia. It might help people appreciate the value of using natural ventilation while creating an atrium that is suitable for a hot, humid region.
Sustainable Leadership Competencies at the Top: An Agenda for the Future
Authors: Ozen Asik, Ayla Esen
Published: April 2024

The present study proposes a model to emphasize the importance of the sustainability competencies in the executive-level leadership. More specifically, our model integrates the sustainability leadership competencies model with the principles of learning organization. Our premise is that sustainable transformation in organizations can only happen with the commitment of the top levels, and the theoretical groundwork for facilitating this shift of mindset in organizational leaders can be built on the learning organization principles.
Charactonym Analysis on First Name Characters in Rowling’s Harry Potter and The Philosopher’s Stone Novel
Authors: Mudhiah Umamah
Published: April 2024

Charactonym is proper name that is used to name the characters in literary work Characteristics of literary characters in a story leads to their identity role and personality trait. Related to this case, this research is aimed to discover charactonym contribution to the character information based on the first name meaning regarding with the name function. This research uses descriptive qualitative method. The data used in this research are collected by document analysis method. This research applies the content analysis to analyze the data. The result of data analysis discovers 39 data analyzed in this research. The 17 names function as individualization, 10 names as identification, and 12 names as establishment. It can be concluded that J.K. Rowling‟s mastering of some languages, French, German, Classics with Greek and Roman studies influence her unique character‟s names. She considers the name for her characters to convey a message to the reader. J.K. Rowling tends to give the key of what her character is like and how her characters treat themselves and others. There are 56% names that reveal the character information, and 44% names are a label so far. The name function depends on what important role of each character has in the story.
Effects of Country- and Firm-Specific Factors on ESG Performance: A Cross-Country Analysis for Emerging Markets
Authors: Teerapan Ungphakorn
Published: April 2024

This paper aims to shed the light on factors influencing ESG performance in emerging markets. This paper employed data from 20 emerging markets from 2013-2022 and investigated factors which classified into 2 dimensions, country- and firm- specific factors. Country specific factors include economic growth, level of corruption, legal system, and economic equality. Firm specific factors are firm size, capital structure, performance, and free cash flows. Pooled OLS regression controlled for country- and year- fixed effect are employed. The results show that number of firms that reported ESG performance has increased as well as the average ESG score has risen over the past a decade. Results on country-specific determinants reveal that GDP growth is negatively impact on ESG performance. A company based in common law countries shows higher ESG performance. In addition, size and debt-to-equity ratio are positively influence ESG performance. These results suggest both company and country characteristics effect ESG performance in emerging market. Thus, investors who seek for ESG performance should give more attention on these factors for making their decision
Culture Internalization for AKHLAK BUMN: A Uniformized Core Values of All Indonesian State-Owned Enterprises
Authors: Abd. Wahid Wijaya, Inta Hartaningtyas Rani
Published: April 2024

In today's business landscape, companies must be increasingly agile to meet the demands of developing business potential, changing customer needs, and rising competition. This includes State-Owned Enterprises (SOEs) that aim to deliver social and economic value. Internalization of core values is crucial in creating a culture that drives the company's performance. Therefore, the activation program and measurement methods for culture are key to the success of the transformation. Through the implementation experience, this practitioner study aims to identify the necessary infrastructure for managing the uniformized culture, activation programs, and measurement methods in companies. The core value set by the Ministry of SOEs or AKHLAK BUMN, guides the behavior of employees to support company performance. Implementing core values in each SOE company will facilitate talent mobility and ensure that the transformation process aligns with the ministry's objectives.