bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis and Design of Enterprise Architecture in Cooperatives Using TOGAF Adm Framework (Case Study: Cooperative Mandiri XYZ)
Authors: Dinar Rahayu,  Rifki Nugraha Nurilahi Sukrina Herman, Yopi Nugraha,
Published: April 2024

Cooperatives in Indonesia have an important role, one of which can be seen in everyday life, the existence of cooperatives that help build the community's economy. Current digital transformation, it will have an impact on the design of the company's organization, employee management, data collection patterns and transaction patterns. If this Digital transformation is not carried out, there will be a lameness in developing cooperatives. Many cooperatives still use the conventional system, using manual logbooks in depositing cow's milk products by each member of the cooperative and collecting data requirements using excel documents which require more time to process, thus slowing down the performance of the data exchange process in cooperatives. In Maximizing the utilization of the system itself, the Enterprise Architecture design of a cooperative management information system is carried out using the TOGAF ADM approach. Enterprise Architecture can help in aligning various business needs, data, applications and technology. Designing the Enterprise Architecture cooperative management information system at TOGAF ADM, it is carried out with the Preliminary Phase, architecture vision, Business Architecture, Information System Architecture, Technology Architecture, Opportunity and solution and migration planning stages. Final result of this study is in the form of a proposed architecture model for Cooperative XYZ, and blueprint.
Using Virtual Laboratory to Improve Student's Critical Thinking Ability in Learning Chemistry in High School
Authors: Cepi Rahmansyah Nuraeni, Deni Darmawan,
Published: April 2024

Education in the digital era requires innovation in the use of technology as a learning medium that can improve student’s critical thinking skills. One of the promising technologies is the virtual laboratory, which can be used as a medium of learning in chemistry lessons in senior high schools. This study aims to present an overview of the use of virtual laboratories to improve students' critical thinking skills in high school chemistry learning. In this study, an analysis of relevant literature was carried out to identify the benefits and potential of virtual laboratories in chemistry learning. The results of the study show that the use of a virtual laboratory can effectively improve student’s critical thinking skills in high school chemistry learning. Virtual laboratories can help students to develop skills in analyzing, evaluating and interpreting information in a chemical context, as well as increasing their understanding of concepts and problem-solving skills. To optimize the use of virtual laboratories in chemistry learning in high school, good planning, development and implementation is required, as well as an evaluation of the effectiveness and efficiency of using virtual laboratories. This research is expected to provide insights and recommendations for educational practitioners, especially in the development of chemistry learning that is innovative and oriented towards students' critical thinking skills.
The Effectiveness of the Glenn Doman Method in Improving a Student with Mild Intellectual Disability’s Reading Skills
Authors: Sheila Amalia Jati Pertiwi Lina Siti Nurwahidah, Deasy Aditya D.,
Published: April 2024

Low cognitive function or intellectual ability in mentally disabled children causes them to have difficulty accepting and mastering the lessons given by teachers at school. Lessons mastery is inseparable from reading skills. Therefore, in their learning, mentally disabled children need special educational services that can be adapted to their development, one of which is the Glenn Doman method with flashcard media. This study aims to describe: (1) reading skills of mild mentally disabled children at SLB C YKB Garut before and after using the Glenn Doman method; (2) the effectiveness of the Glenn Doman method in improving reading skills in children with mild mental disability at SLB C YKB Garut. This study uses a quantitative approach with a single subject design A-B-A. The participant of this study is one mentally disabled child in the school. Interviews, tests, and observations are employed as the data collecting techniques. The results show that the Glenn Doman method can improve reading skills in children with mental retardation at SLB C YKB Garut. This is indicated by an increase in each phase from baseline A-1, intervention B, to baseline A-2. In the baseline phase A-1, the scores are in the range of 44-71 with a stability percentage of 71%. In the intervention phase B, there is an increase in the range, 62-96, with a stability percentage of 84%. Meanwhile, in the baseline phase A-2, the scores are in the maximum range, 81 – 100, with a stability percentage of 100%. Based on the results the Glenn Doman method can be used in the implementation of learning in schools. The reading skills of mentally disabled children can improve if the children are continuously trained to read using the Glenn Doman method.
Language Attitudes of Teacher Students Towards Indonesian
Authors: Arief Loekman, Ninah Hasanah Umi Kulsum, Ari Kartini,
Published: April 2024

The language attitude of teaching students must be positive towards Indonesian, considering that Indonesian is the language of instruction that must be used at all levels of education. A positive language attitude is manifested by loyalty, pride, and language awareness. The research method used is a descriptive method with a quantitative approach. This research was conducted using a research instrument in the form of questionnaires. The population of this study is all teacher training students in Indonesia, the research sample is 98 teacher training students from 8 universities in Indonesia. The results showed that most students had a positive language attitude. This is shown by the loyalty, pride, and awareness of speaking Indonesian. Of course, as a prospective teacher in any study program, it is necessary to be certified for language skills through UKBI as a step that can ensure the competence and language attitude of prospective teachers.
Coping with Technology Issues in Synchronous Virtual Speaking Classroom
Authors: Anne Ratna Suminar
Published: April 2024

The present study seeks to answer the problem regarding the appropriate media to teach speaking in synchronous virtual classroom when the students have technology issues. The forementioned technology issues are related to remote internet speed and connection, electronic devices, and technological literacy issues. Through purposive sampling technique, 30 EFL students who are selected as sample should proceed a set of speaking tests and questionnaire after being taught in synchronous virtual classroom for one semester using various remote Instructional media when Covid-19 pandemic as a hinder to face-to-face interaction. The result will contribute to teachers and other researchers for selecting the appropriate synchronous virtual classroom media as the solution to teach speaking to EFL students with technology issues.
Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey
Authors: Hind Ali abdul Hassan
Published: April 2024

The proliferation of Internet users has coincided with a commensurate increase in the amount of very important, sensitive, and private information being transferred across the Internet. Malicious actors are increasingly targeting networks to breach them and obtain illegal access to critical information since this trend has revealed holes in security systems. In addition to endangering the privacy of the data concerned, these breaches disrupt the smooth functioning of systems. Therefore, in light of these dangers, intrusion detection systems (IDSs) are now an essential part of any cybersecurity program. The goal of these systems is to detect and report any suspicious activity by constantly monitoring and analyzing network traffic. Numerous review articles have investigated various methods for network intrusion detection. To improve detection accuracy while keeping computing efficiency high, this survey study investigates lightweight deep learning techniques for intrusion detection systems. These techniques include pruning, quantization, clustering, and collaborative optimization. This study analyzes five different types of new real-world traffic datasets (i.e., CSE-CIC- IDS2018, NSL-KDD, Bot-IoT, ToN IoT Network, and UNSW-NB15) and evaluates the performance of several machine learning and deep learning techniques. This survey provides metrics for measuring the accuracy of intrusion detection across various systems, which may be used to assess performance.
An evaluation of the Efficacy of the Human Resource Data Analytics on Artificial Intelligence Program
Authors: Abdul Wahab Samad, Zahera Mega Utama Mas Wigrantoro Roes Setiyadi,
Published: April 2024

The primary purpose of this study is to investigate the advancement of data analytics application programs in human resources, along with the challenges faced by data analytics in significant artificial intelligence throughout the industrial era 4.0 in Indonesia. Data collection is facilitated through the administration of questionnaires, conducting interviews, and administering surveys. This inquiry aims to establish the feasibility of substituting human intelligence with computer intelligence. This undertaking is predicated on the idea that individuals and machines possess commensurate abilities to handle vast quantities of data. The chosen strategy for analytics is problematic from a qualitative standpoint. The reason for this phenomenon is the adoption of the method. Based on the research outcomes, it has been observed that within the context of the fourth industrial revolution, data analytics can be effectively managed by robots possessing extensive data storage capabilities comparable to human counterparts. This applies to all facets of human intelligence, particularly weak Artificial Intelligence.
Strengthening Human Resource Management in a Competitive Market to Assess Cooperative Opportunities and Challenges 4.0 in 2045 Indonesian Economy Battle
Authors: Mas Wigrantoro Roes Setiyadi, Abdul Wahab Samad Zahera Mega Utama,
Published: April 2024

Through strengthening the role of human resource management in dealing with changes in the market environment facing the 4.0 competition war in the Indonesian economy in 2045, which causes changes in all aspects of digitalization that occur in cooperative organizations, the purpose of this study is to conduct a study of the opportunities and challenges facing cooperatives in the industrial era 4.0. The alterations also call for business intelligence in evaluating high-tech developments in the interchange of knowledge relating to design and innovation, as well as information collected from the behaviour of suppliers and customers, which will be continuously transmitted in Industry 4.0. The research method that was used for this study was the troubling method approach, which involves an in-depth examination of causal relationships, listening to information from a variety of parties, and the discovery of appropriate solution ideas through a narrative study that is articulated qualitatively. According to the findings of this study, the formation of cooperatives in Indonesia has yet to reach its full potential and still needs to be strengthened in terms of planning and funding. This breakthrough will become the foundation upon which the Indonesian economy will be built in 2045 to attain economic goals based on cooperative endeavours. Because of this, cooperatives want confidence regarding their place in the ecosystem of the digital platform transformation, which necessitates tight collaboration that is mutually advantageous for both the suppliers and the customers.
Automotive Remanufacturing Capability for End of Life Vehicle (ELV) in Malaysia
Authors: Md Raziff Zainal Abidin
Published: April 2024

The Malaysia Automotive Remanufacturing Roadmaps (MAAR) outline policies and technologies to transform the automotive used part sector into remanufacturing. Remanufacturing processes offer significant benefits, hence the need for transformation, despite the substantial availability of used automotive products. The automotive industry in Malaysia has experienced considerable growth and development, leading manufacturers to incorporate remanufacturing into their business operations. Recently, research has concentrated on end-of-life vehicle (ELV) management. Malaysia has no specific period for the end-of-life (ELV) regulation policy. It is essential to consider the proper disposal of these vehicles once they have reached the end of their useful life. The number of abandoned cars is increasing, leading to significant costs for society regarding removal and disposal expenses and other indirect costs due to environmental degradation. Addressing this issue will positively impact both the environment and the economy. This study explores how industries’ capabilities can face sustainability goals under the National Automotive Policy (NAP) 2020 and gain insights into how the remanufacturing process works. Therefore, it is imperative to establish remanufacturing as a norm within automotive practices.
The Research on Mongolian Dialects and Their Vitality
Authors: Badamsuren Yadamjav
Published: April 2024

The research purpose is to study Mongolian dialects within the framework of the language-saving policy and to define their language vitality and endangerment based on some of the international criteria. In this research, we used several sociolinguistics methodologies such as sociological survey, collecting and developing the materials, investigative and statistical methods, comparing logical, correlation, analytic, synthetic methods, induction, and deduction. Here the authors tried to study the Language Vitality and Endangerment/Language Vitality Assessment (UNESCO 2003), which is used more commonly and frequently at the international level, to define Mongolian dialects’/languages’ vitality,

Finally, our research was made on eighteen Mongolian dialects such as khotong, darkhad, khalkh, Kazakh, bayad, uzemchin, torguud etc…. As a result of the research, most of the Mongolian dialects included in the categories such as don’t heritage their languages to the next generation or severely endangered. During the research, we observed that several factors such as demography, politics, geography, social and economic situation, psychology, population movement, government policy on language, and others are influencing the extinction of the minority languages of the nations and ethnic groups.

Eventually, it is necessary to study, save, revitalize, and document the dialects and pass them to on the next generation.
A Deep Reinforcement Learning Approach for Optimizing Inventory Management in the Agri-Food Supply Chain
Authors: B. Murugeshwari
Published: April 2024

This research aims to improve inventory management throughout the agri-food supply chain, through the use of Deep Reinforcement Learning algorithms. Three Deep Reinforcement Learning algorithms, including Deep Q-Networks, Deep Deterministic Policy Gradient, and Proximal Policy Optimization algorithms were implemented and tested in order to evaluate their ability to actively manage inventories and improve the performance of supply chains. The results of the experimental phase offered important information regarding the performance of each Deep Reinforcement Learning algorithm. The Deep Deterministic Policy Gradient algorithm was identified as a viable choice, offering the best results in terms of accuracy to set the optimal inventories for the supply chain and improving the efficiency of the supply chain. The percentage of cost efficiency improved from 92.5% to 95.8% in the case of the DDPG model. The inventory turnover was also improved, surpassing the level of 8.1 units from the original level of 7.3 units which means that the system converts the inventor into sales in less time. The metric for on-time delivery also beneficiated from several improvements, reaching 96.5% form the level of 93.2% return. The quality metrics also registered a significant improvement and reported level of 96.2% after the implementation of the DDPG algorithm and compared to the level of 94.6% prior to the implementation of the algorithm. These results suggest that using such a system will bring beneficial changes to the supply chain and will offer the possibility of implementing a data-driven inventory system based on Deep Reinforcement Learning.
Development of an Elderly Care Rehabilitation System Utilizing Machine Learning for Energy Monitoring and Transmission using Wireless Sensor Networks
Authors: S. Srinivasan
Published: April 2024

The combination of robotic exoskeleton systems and machine learning (ML) techniques present great potential for significantly improving rehabilitation outcomes in patients with mobility impairments. This particular study examines whether ML algorithms can predict torque and angular inputs required for joint rotation using electromyography (EMG) data obtained during patient lower-limb rehabilitation. A new exoskeleton was designed for personalized and adaptable assistance in rehabilitation of walking. The predictive power of four different ML models, Artificial Neural Network (ANN), k-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM), is evaluated. Of these, the ANN model is the most effective with an accuracy rate of 98.87%, followed by KNN (94.55%), DT (91.2%) and SVM (89.45%). Subsequent patient tests confirm significant improvements in flexion and extension angles of the knee, suggesting improved mobility and restored natural gait mechanics. These results stress the potential uses of ML-enhanced exoskeleton systems in personalised rehabilitation therapy. Proposals for further work include improving model performance, tackling challenges for real-time processing, and evaluating ML performance over the long term as it affects the quality of life in individuals with mobility impairments or changes in their condition over time. This investigation into rehabilitation aims at applying technology-driven solutions to help people regain their freedom and ability to move in a way that pleases them.
Convey: Developing a Facial Emotion Recognition System for Enhancing Interpersonal Interactions of Children with Autism Spectrum Disorders
Authors: Fernandez, Janah Rose R.
Published: April 2024

Children with Autism Spectrum Disorder (ASD) oftentimes have difficulty in both interpersonal and intrapersonal interactions. Most children with ASD lack emotional intelligence and cannot properly comprehend social cues and responses of the people around them. These social impairments are causing them to have difficulty in maintaining relationships thus, to combat these issues, the researchers presented an application with Facial Emotion Recognition (FER) technology using the deep-learning approach namely Convolutional Neural Network (CNN). Using the FER allows the children to perceive the emotions of their interaction partner at face value. The model used in developing the application sits at an accuracy level of 98%. In addition to the FER technology, the research implements a kind of mood meter that enables the children to plot their mood and a dashboard that tracks the accumulated plots. The tracker lets the children convey their emotions as well as help their interaction partner to understand them better. Furthermore, the application includes a recommendation system that suggests various relaxation and meditation techniques that the child can use to manage outbursts or spontaneous emotional activity. The Convey mobile application aimed to empower children that has ASD and help them lead a normal life.
A Critical Appraisal: Elementary School Coding Education Effectiveness with Scratch in Alignment with Cognitive Development, Analyzed through Piagetian Lens
Authors: Sri Utami
Published: April 2024

The digitalization has revolutionized the education sector by integrating technology and bringing about significant changes. Computational Thinking (CT), an indispensable skill for the 21st century, has now become an integral part of school curricula in several countries worldwide. Focused on enhancing CT skills, programming education, especially through Scratch, Scratch simplifies programming concepts, making it suitable for elementary school children aged 7 to 11 years. Aligned with Piaget's concrete operational stage, emphasizing organized and rational thinking. Hence, this study aims to delve deeper into how Scratch programming education can meet the cognitive developmental needs of children in this phase. The research methodology used is a literature review, employing data from textbooks, journals, scholarly articles, and literature reviews that contain concepts central to the research focus. The data analysis involves collecting relevant research findings, organizing, and systematically recording information. The results indicate that Scratch not only provides a platform for learning programming but also supports the development of concrete operational cognitive skills taught in Piaget's theory. Through creativity in designing Scratch projects, children can apply and strengthen their understanding of concepts such as seriation, classification, reversibility, conservation, and the elimination of egocentrism. But, it's essential to consider an approach tailored to the developmental characteristics of children when designing programming education at the elementary level. This study is expected to provide concise guidance for educators and policymakers in formulating programming education that optimally fosters cognitive development in elementary school children.
Assessment of Physicochemical Parameters in Selected Water Bodies in Laguna, Philippines
Authors: Khristine L. Sandoval
Published: April 2024

This study was aimed to examine the frequent variations in four selected physico-chemical parameters of surface water of two lakes in Laguna: Laguna de Bay in Calamba City and Sampaloc Lake in San Pablo City. Parameters such as conductivity, dissolved oxygen (DO), pH, and temperature were determined using in-situ water quality tester. The two lakes are both freshwater body, different data are collected and observed throughout the sampling. Due to various anthropogenic activities, the lakes are biotically affected. The water quality of the two important lakes in Laguna have been compared in different parameters like pH, DO, conductivity, water temperature and color. The result indicates that both lakes are within the standard (25-31°C) of DENR Administrative Order (DAO 2016-08 for Class C), but except for water temperature which exceeds the standard value (36.37°C mean values). The results support insights about the future effects of increased water temperature on fish management and the lake ecosystem for numerous aquatic purposes. This is also for the management and call on action for the residents and local government unit in said municipalities of Laguna especially after pandemic season.
Relationship Between Workplace Isolation, Age and Turnover Intention amongst Malaysian Seafarers
Authors: Ramesh Babu Amathalai
Published: April 2024

The study was carried out for the Malaysian Seafarers those having intention to quit their current career as a seafarer by discovering the relationships between the Workplace Isolation (WI) with the Turnover Intentions (TOI) concept and further to examine the moderation effect of age category over the correlation that showed significant relationships. Three sub workplace isolation elements are also tested (Social Isolation, Physical Isolation, and Informational Isolations) were assessed using the dimensional Impacts of Workplace Isolations. The P – E Fit Theory is then applied. All the measurements were collected from a sample of 400 registered Malaysian seafarers. There is a growing concern about shortage of Seafarers, particularly in Malaysia. This dissertation intends to clarify that being and feeling isolated is the main reason for these Seafarers to leave the seafaring career. This study also examines the seafarers age category as the moderating effects between the Workplace Isolation towards turnover intention among the seafarers in Malaysia. Moderation is divided in two distinct subgroups of ages. This study will help employers or the shipping companies in Malaysia to find solutions to this problem faced by the Seafarers by recommending certain measurers to counteract against turnover intention of the Seafarers.
Enhanced Detection of Chronic Heart Disease: A Novel Approach With Extreme Gradient Boosting-Logistic Regression Algorithm
Authors: Kiruthiga G.
Published: April 2024

In recent years, biomedical healthcare applications for managing and treating chronic heart diseases have increased. Chronic heart disease is a serious and sometimes deadly ailment that affects millions of individuals globally. Therefore, timely and effective detection of heart disease is crucial in medicine, particularly in cardiology. These complications can lead to reduced quality of life, increased medical costs, and increased risk of death. Obesity, smoking, and high blood pressure are some of the risk factors that worsen chronic heart disease. However, challenges occur in accurately and sensitively classifying heart disease in performance evaluation. To resolve this problem, we propose an Extreme Gradient Boosting-Logistic Regression (EGBLR) algorithm to accurately and quickly predict chronic heart disease. Furthermore, pre-processing can be performed using the Normalized Minimum-Maximum Scaling Feature (NMMSF) technique to estimate missing or duplicate values. Next, the Inter-Quartile Range Optimization (IQRO) technique can analyze heart disease data to identify and remove outliers. After that, the Relief Feature Vector (RFV) algorithm can be used to find each feature weight. Finally, the proposed EGBLR method can be employed to detect normal or abnormal chronic heart diseases and increase accuracy in diagnosis. The evaluation of the proposed techniques can be predicted using performance matrices such as recall, sensitivity score, precision, false rate, and precision. The accuracy of the proposed EGBLR detection approach exceeds that of previous techniques. Moreover, the proposed method for diagnosing chronic heart disease can be easily adapted to biomedical healthcare applications.
Single Source Switched Capacitor Based Multilevel Inverter With Reduced Number of Components
Authors: K. Rajalashmi
Published: April 2024

Multilevel inverters find extensive use across various applications owing to their capability to generate voltage waveforms of superior quality. thereby minimizing harmonic distortion. Traditional multilevel inverters require a substantial number of power semiconductor devices and passive components, resulting in heightened costs, intricacy, and physical dimensions. These multilevel inverters play a pivotal role in the electric vehicle sector, serving as a critical component in EV power trains for converting Convert DC power from batteries into AC power to operate electric motors.. The research methodology involves the following steps: Design and configuration of a multilevel inverter utilizing switched capacitor technology from a single source.. Design proposed topologies for multilevel inverter design. Design a circuit according topologies in MATLAB. Comparative analysis with existing multilevel inverter designs. Evaluation of the inverter's performance in terms of harmonic distortion, output voltage quality, and efficiency. Rather than employing a multilevel inverter with fewer switches that operates from a single source the highest possible levels are preferred. This paper introduces an innovative 9-level multilevel inverter based on a single source and switched capacitors, which significantly reduces the number of switches required, utilizing only three capacitors and a single DC source. This design approach serves to streamline the inverter's intricacy, decrease costs, and minimize its physical footprint. Furthermore, it elevates the voltage levels by up to 96%, enhancing the overall performance and efficiency of the inverter. Additionally, this technology can be applied in electric vehicles, integrated with renewable energy sources, and utilized in industrial settings. The novelty of this research lies in the utilization of a single-source switched capacitor configuration to achieve multilevel inverter functionality. This approach reduces the number of components required, leading to potential cost savings and improved system reliability. The novel features of this study include: Integration of switched capacitors in a single-source multilevel inverter design. Achieving multilevel inverter functionality with fewer components. Improved performance and efficiency compared to traditional multilevel inverter designs.
Decentralized Machine Learning for Dynamic Resource Optimization in Wireless Networks using Reinforcement Learning
Authors: K.Shantha Shalini
Published: April 2024

Efficient allocation of resources is crucial for optimizing wireless networks that face constraints in bandwidth, power, and spectrum. This paper proposes a decentralized reinforcement learning (RL) model that departs from traditional centralized paradigms to revolutionize resource optimization. The proposed model empowers individual wireless devices with autonomous decision-making capabilities, enhancing adaptability and scalability by leveraging Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). The innovative integration of memory mechanisms facilitates learning from past experiences, addressing the dynamic nature of wireless environments. This decentralized RL model offers practical implications for improved efficiency, adaptability, and reliability in wireless network resource optimization. By transforming individual devices into collaborative decision-makers, our proposed model contributes to a resilient and responsive wireless communication infrastructure. The specific contributions of this paper include the pioneering use of DQN and PPO algorithms within a multi-agent system, offering a groundbreaking solution for dynamic resource optimization in wireless networks.
Blockchain-enabled Data Governance Framework for Enhancing Security and Efficiency in Multi-Cloud Environments through Ethereum, IPFS, and Cloud Infrastructure Integration
Authors: Sanjay Kanth Balachandar
Published: April 2024

In today's digital landscape, the exponential growth of big data demands secure and efficient processing, particularly in complex multi-cloud environments. This paper proposes an innovative blockchain-enabled data governance framework, revolutionizing data management, processing, and security across diverse cloud infrastructures. The framework integrates cutting-edge technologies, including the Ethereum blockchain, the InterPlanetary File System (IPFS) protocol, and cloud solutions like OpenStack and Red Hat OpenShift. At its core, the framework utilizes Ethereum's robust smart contracts and consensus mechanisms to establish a decentralized and secure data governance model. This ensures data integrity, transparency, and immutability, mitigating risks associated with centralized storage and processing. The IPFS protocol complements blockchain by offering efficient data sharding and retrieval mechanisms, enhancing data accessibility and fault tolerance in distributed cloud environments. Through comprehensive testing and analysis, the proposed framework's value is demonstrated. Performance metrics, including throughput, latency, CPU utilization, and memory utilization, were meticulously evaluated to assess system efficiency and scalability. Results indicate high performance, with Ethereum's Proof of Authority (PoA) consensus mechanism enabling efficient transaction throughput of up to 1000 transactions per second. Additionally, the IPFS protocol exhibits effective data retrieval capabilities, with an average latency of 15 milliseconds for data access operations.
Modelling and Analysis of High Performance for Solar Power Injection with Distribution Networks
Authors: Abdullah Sami Assaf
Published: April 2024

Given the growing emphasis on environmental consciousness, there is a spike in the integration of solar photovoltaic energy into contemporary distribution networks. Interestingly, there is a non-linear relationship between the energy production of solar modules and changing external environmental conditions. In response, this study aims to increase the effectiveness of solar photovoltaic systems (SPVS) by putting out a careful analysis to ascertain the ideal performance criteria for the integration of renewable energy sources into distribution networks. The use of a Maximum Power Point Tracker is crucial to this assessment (MPPT). The model provides important insights into the possible increases in energy efficiency, making it a strong tool in this optimization process. In addition, the research conducts a comparison analysis, examining the distribution system properties in relation to different levels of solar PV system penetration. This aspect of the research illuminates the consequences of harmonic-induced distortions in current and voltage on the feeder networks in the distribution system. The simulation findings clearly show that as the penetration capacity of the PV system increases, so does the amount of harmonic dispersion that is introduced into the network. This suggests that it would be wise to incorporate the photovoltaic (PV) array only as far as the network can support it in order to prevent any possible performance reduction. Using MATLAB/SIMULINK as a computational tool, the research carefully examines the important characteristics from the technical data in order to examine the overall model. Further testing of the model's flexibility and resilience under a range of weather scenarios and partial shade conditions offers a thorough assessment of the model's performance dynamics. Positively, the investigation concluded with results that were satisfactory and confirmed the system's exceptional performance capabilities, which are supported by the MPPT. This result reflects a positive trend toward maximizing the integration of renewable energy sources into distribution networks, which will help to create a more sustainable and environmentally friendly energy landscape.
A NEW Technique for Disseminating Knowledge Concerning VANET Traffic Using Crocodile Hunting Search
Authors: Raghad Ghalib Saadallah
Published: April 2024

VANETs or Vehicular Ad hoc Networks represent the most rapidly emerging application of Ad hoc Networks. It shares traffic condition information and is one of the attempts to improve road safety and traffic management. This paper develops a new technique in the name of "Crocodile Hunting Search" (CHS) to assist information dissemination in Vehicular hoc networks (VANETs). It is named after hunting a crocodile, methodically moving to catch prey, which CHS bases many of its principles upon. The CHS solution that we use sends important traffic data in a decentralized, flexible way within the network. From the point of view of VANET, we consider each vehicle as a mobile node that carries the packets with the information contained on the traffic conditions. These packets are solicited and forwarded to the various vehicles in proportion to closeness and usefulness. The point here is that the search perimeter would have to be adapted to the variation in the quantity of vehicles and their actual traffic. If times are off-peak and there is less traffic, the cars would therefore widen their searching area, which could not be the case for other solutions during that time of less traffic. On the other side, in localities with narrow narrowing information-seeking to find only what is essential and related becomes increasingly important. We show through large-scale simulations and real-world experimentation that our method can work in sending information over Vehicular Ad Hoc Networks (VANETs). This perspective, the current research, clearly demonstrates that knowledge propagation is way more effective in spreading information than traditional methods. It causes low packet loss, and the communication overhead is also low. The Crocodile Hunting Search is another practicable method of solving the problems that arise while trying to send information effectively in VANETs.
Research on Dynamic Analysis and Prediction System of Energy Economic Efficiency under Fusion Algebraic Equations
Authors: Xiaoke Cui
Published: April 2024

Energy is an important material basis for national economic development, and it plays a vital role in social development. As China's industrialization process and urbanization construction continues to accelerate, energy demand is also increasing, and the dependence on electricity is getting higher and higher. How to improve the electric energy efficiency under the guarantee of stable quality of power generation, transmission and distribution and meeting the requirements of users' electricity consumption is an important issue to be solved in the current power grid construction. However, the serious defects of the traditional binary hierarchy and the lack of scientific understanding have led to many current new problems with more and more new and complex features. This is accompanied by the emergence of a large number of nonlinear term coefficients and an increasing research workload, and the traditional linear programming methods cannot meet the practical needs. Therefore, this paper analyzes the eigenvalues of energy economic efficiency based on algebraic equations and predicts energy economic efficiency using EMD-BP neural network algorithm, and then carries out the design and research of economic energy system based on these two points.
EWFAIGF: Design of an Efficient Model for Enhancing Energy Efficiency in IoT-Based Wireless Sensor Networks through Fuzzy AHP and Iterative Grey Wolf Jelly Fish Optimization
Authors: Vaibhav V. Deshpande
Published: April 2024

In the realm of Wireless Sensor Networks (WSNs), the quest for enhanced energy efficiency remains paramount, given their pivotal role in facilitating the Internet of Things (IoT) applications. Existing methodologies often grapple with the dual challenge of optimizing energy consumption while ensuring robust data transmission, thereby limiting their efficacy in dynamic, resource-constrained environments. This work introduces a novel paradigm that meticulously addresses these constraints, thereby heralding a significant leap towards optimizing WSNs' operational efficiency. At the core of our proposed model lies the integration of Fuzzy Analytic Hierarchy Process (Fuzzy AHP)-based clustering, which ingeniously segregates nodes by leveraging multi-criteria such as location, energy efficiency, and temporal performance. This clustering serves as a precursor to the application of an innovative Iterative Grey Wolf Jelly Fish Optimizer (GWJFO). The GWJFO stands out by its strategic prowess in delineating optimal routing paths, thus minimizing energy expenditure and enhancing data relay efficiency. Furthermore, our model is fortified with a Q Learning Method, ingeniously designed to identify and execute optimal alternate routing paths through a Make Before Break strategy. This addition not only mitigates potential faults but also significantly boosts computational efficiency and packet delivery performance. Empirical validation through real-time network simulations underscored the model's superiority, demonstrating a 9.5% improvement in communication speed, an 8.5% increase in energy efficiency, a 4.5% improvement in packet delivery performance under fault conditions, a 10.4% rise in throughput, and a 5.9% enhancement in network consistency over existing benchmarks. This groundbreaking work not only paves the way for more energy-efficient WSNs but also sets a new standard in achieving high-performance metrics essential for the next generation of IoT applications. The implications of these advancements extend beyond mere technical enhancements, offering a beacon for future research in the domain, potentially revolutionizing the way we deploy and manage sensor networks in an array of applications.
Design of a Mobile Application for Geolocation and Medical Information for University Students in Emergency Situations
Authors: Lida Violeta Asencios-Trujillo
Published: April 2024

The present research work focuses on the design of a mobile application to geolocate and provide medical information to university students in emergency situations. The growing interest in the use of mobile technologies is recognized, highlighting the need for innovative solutions to improve the safety and well-being of students on campus. The methodology used was based on a literature review and analysis of previous studies that highlight the importance of technology in promoting the health and safety of university students. Research highlighting the use of mobile applications and information and communication technologies to address challenges related to health and physical activity among college students was discussed. In addition, the need to ensure the accuracy and reliability of the information provided through the mobile application was highlighted. The conclusion highlights the potential of the proposed application to improve the safety and health of college students while promoting a safer, more connected university environment. The importance of actively promoting the use of the app among the student community is emphasized and the need for collaboration between different actors to ensure the success of the project is underlined. In summary, this project has the potential to positively impact students' lives and contribute to the advancement of research in the field of digital health and campus safety.
Türk Ordusunda Küçük Birlik Uyumu: Lider, Görev ve Kültürel Değerler
Authors: Ateş Barış , Akal Mustafa Yücehan
Published: April 2024

Bu makalede Türk Ordusunda küçük birlik uyumunun hangi faktörler sonucunda ortaya çıktığı incelenmektedir. Türkiye’de bu konuda daha önce yapılmış bir çalışma tespit edilememiştir. Dolayısıyla bu konudaki veri toplama güçlüğü ve konuya dair akademik alt yapı eksikliği araştırmayı metodolojik açıdan sınırlamaktadır. Ancak nitel araştırma deseninden faydalanarak araştırmada hem farklı bölükleri temsil eden katılımcılarla yapılan derinlemesine görüşmeler hem de alana ilişkin olarak yazarların yakın gözlem ve notları kullanılarak bu sınırlılık aşılmaya çalışılmıştır. Ayrıca Türkçe literatüre sağlıklı bir başlangıç yapabilmek adına geniş bir literatür taraması ile kavramların tartışması yapılmıştır. 1980’ler ve 1990’larda farklı bölge ve şartlarda görev yapmış iki tip bölük üzerinde yapılan çalışma neticesinde Türk Ordusunda küçük birlik uyumunun liderlik, görevin önemi ve askerliğe atfedilen kutsal değerlerden etkilendiği sonucuna varılmıştır. Bu üç faktörden askerliğe atfedilen önem yaygın bir kültürel değer olarak genellemeye açık olduğundan dolayı askerî uyum seviyesi yüksek birlikler yaratmak için liderlik ve görevin öneminin baskın faktörler olduğu ancak bu ikisi arasında da liderliğin belirleyici olduğu değerlendirilmektedir.
Measuring Qualitative Characteristics of Internet Financial Reporting: Evidence from Malaysia
Authors: Lajuni Nelson , Mathew Kevin Bosi  , Mohd Noor Azli bin Ali Khan  , Melissa Della Joy
Published: April 2024

As the world economy undergoes a rapid digital transformation, Internet financial reporting (IFR) has evolved into an important platform for the dissemination of information to investors. It has also been a central topic of discussion among practitioners and researchers due to the lack of rigorous regulations to govern IFR practices, resulting in disclosures of financial reports varying across listed companies. This study aims to assess the level of compliance with the qualitative characteristics of IFR for companies listed on Bursa Malaysia. This study involves adopting 34 constructed index items based on prior literature and the use of a 5-point Likert scoring scale anchored from "very poor" (1) to "excellence" (5) to measure the fundamentals and enhance the qualitative characteristics of IFR, which is also in line with the "Revised Conceptual Framework for Financial Reporting" issued by the Malaysian Accounting Standards Board (MASB) in 2018. Non-probability purposive sampling was employed to select the companies from 11 relevant industries, comprising 160 listed companies from the main market of Bursa Malaysia. Annual reports and corporate governance statements were extracted from corporate websites for 2018. Findings suggest that most corporations that are listed in the main market of Bursa Malaysia have yet to fully comply with the MASB’s financial reporting framework, notably timeliness, which requires urgent improvement. Without strict IFR regulations, managers may also be free to act opportunistically by planning their IFR disclosure in a way that benefits both the company's reputation and their interests. This research enriches the body of literature on IFR and provides a measuring mechanism that can gauge the level of compliance with the qualitative characteristics of IFR. It acknowledges the importance of collective efforts from regulatory bodies to implement accounting reforms and the best IFR governance practices to harness information's usefulness in the Industrial Revolution era. 4.0.
The PRINCIPAL INSTRUCTIONAL LEADERSHIP IN SEKOLAH PENGGERAK AS A CATALYST IN REALIZING THE PANCASILA STUDENT PROFILE
Authors: Istiqomah Ms , Arifin Mr. Imron , Burhanuddin Mr. , Timan Mr. Agus
Published: April 2024

Program Sekolah Penggerak (PSP) is one of the policies of the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia in the transformation of education. PSP places the principal's instructional leadership as a determinant of the success of the Sekolah Penggerak in transforming education and realizing the Pancasila Student Profile. The purpose of this study is to describe the strategy, implementation, and role of the principal's instructional leadership in the Sekolah Penggerak to realize the Pancasila Student Profile. This research uses a qualitative approach using a multicase study design. Data collection was done by in-depth interview, observation, and documentation study. The result of this study is the instructional leadership strategy carried out by the principal by adjusting the existing school vision with the Pancasila Student Profile then socializing it to all school members, promoting a child-friendly, harmonious, and family-like school climate, dividing tasks according to the competence of teachers and employees, and facilitating professional development for teachers and employees. The implementation of instructional leadership is carried out through child-centered learning in intracurricular, extracurricular learning, Pancasila Student Profile strengthening projects, as well as through fostering school culture with the stages of planning, implementation, monitoring, and evaluation. To realize the role of the Sekolah Penggerak as a catalyst in realizing the Pancasila Student Profile, the school principal.