bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence Factors of Powerline Communication by Artificial Intelligence Approach based on Rock Electric Signal
Authors: S. Srinivasan
Published: April 2024

The field of powerline communication (PLC) has been around for more than a century and uses the power line as a means of information transport. Because it utilizes the current system to transfer data using electrical power, it is advantageous because it prevents the need for new installations. The impact of coal and rock electric signals is examined using the established test methodology, taking into account the coal and rock temperature, characteristics, friction, loading speed, and load size. The main findings can be summed up as follows: Depending on the intensity of the electrical signals produced, the change rule of electrical signals in the coal rock rising with temperature can be separated into three stages. The electrical signals produced by the uniaxial compression of coal-rock mass with varying properties have distinctly different amplitudes. The friction between coal and rock can produce electrical impulses. As loading speed increases, the electrical signal that indicates when the coal rock is prepared to fracture tends to get stronger. The information bottleneck perspective is then used to study information theory and artificial intelligence. In conclusion, we go through a few concepts about the deep integration of AI methods with wireless communication networks. The fundamental aspects of AI that are pertinent to various uses have been briefly addressed at the outset of the paper.
The Importance of Setting the Classroom Learning Environment to Optimize its Function as a Learning Resource
Authors: Widiyawanti
Published: April 2024

The classroom is the main learning environment inschools where the learning process takes place. However, not only as a space where learning processes or activities take place, the classroom can actually be a source of learning if the learning environment (setting) is made in it. The purpose of this research is to examine how the learning environment (setting) in the classroom is able to maximize the potential of the classroom as a learning resource. The research method used is a literature study. The data used in this study came from textbooks, journals, scientific articles, literature reviews which contained the concepts studied. Data analysis was carried out by collecting relevant research results, sorting them, making notes, citations, and/or information which was then arranged systematically. The results of this study reveal that the arrangement of the learning environment (setting) in the classroom is able to maximize the potential of the classroom as a learning resource. Everything in the classroom should be used as a learning resource. Therefore, various equipment, objects, furniture, media and other items in the classroom should be managed, arranged, positioned and utilized appropriately and optimally as learning resources. Not only that, the arrangement of seating formations, access to various media and equipment in the classroom should also be made easier so that students can use them as learning resources more optimally. The learning environment (setting) in this class can be categorized into types of learning resources utilized (by utilization resources) and including environmental learning resources.
Consumer Perception and Purchase Intention on the Transition to Hybrid and Electric Vehicles for Sustainable Mobility and Transportation in the Philippines
Authors: Justine Roy O. Balinado
Published: April 2024

Existence of hybrid electric vehicles nowadays is one of the emerging technologies worldwide. Different countries are using this technology on their transportation, and nowadays, a lot of other countries are gradually adapting on using this kind of innovation in their transport system. Philippines is among these countries which aims to have a sustainable mobility by embracing the use of hybrid - electric vehicles but still lots of consumers are lacking awareness on this technology. Lack of information on the social, environmental, and economic potential benefits are among the main issues of Philippines consumers that really affect its uptake. The goal of this study was to evaluate the consumer’s perception and purchase intention on the transition of hybrid - electric vehicles for sustainable mobility and transportation here in the Philippines. It aimed to assess consumer awareness with hybrid - electric vehicles and its potential benefits to human beings in terms of social, environmental, and economic aspects. The study also gauged the impact of this perception on consumer’s purchase intention on hybrid - electric vehicles. These objectives were attained using the Descriptive Statistics and Correlation Analysis. Using these methods, the results revealed that majority of the consumers believed on the positive benefits of transitioning to hybrid - electric vehicles in terms of social, environmental, and economic aspects. This study also showed that these benefits have high impact on the consumer’s intention of buying or having a hybrid – electric vehicle. Electric vehicle is one of the most effective measures of decarbonizing the transport system. Furthermore, government and automotive businesses must strengthen their drive in spreading awareness of hybrid – electric vehicle’s benefits to consumers to further support country’s effort for development.
IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN
Authors: Preetha P.
Published: April 2024

Healthcare is undergoing a transformation due to predictive analytics, which offers clinical decision support in real-time, facilitating effective, tailored therapy. By lowering unfavorable events and raising the standard of care, this approach supports patients at every step of diagnosis, prognosis and therapy. Predictive algorithms driven by artificial intelligence (AI), machine learning (ML) and Internet-of-Things (IoT) handle enormous amounts of data, with cardiovascular illnesses as the primary cause of death worldwide. IoT devices make sophisticated deep learning (DL) analytics and remote patient monitoring possible. This paper presents a novel 5-stage disease prediction model as an IoT-cloud-based smart healthcare management framework, constructed using a seahorse optimization-driven mutated convolutional neural network (SHO-MuCNN). In stage-1, data is gathered from a variety of sources through data collection. The stage-2 of data pre-processing involves the use of standardization to get the data acceptable for additional processing. Using a customized principal component analysis (CPCA), the set of characteristics is determined in stage-3. Next, the stage-4 diseases are predicted by the suggested SHO-MuCNN methodology. According to the prediction results, physicians are educating patients in stage-5 on exactly what measures can be taken to reduce the risk. The smart healthcare monitoring measures for accuracy (98.56%), precision (96.69%), F1-score (97.86%), Recall (94.59%)and AUC (95.02), were employed in the models' performance. In addition, this study utilizes the SPSS and t-test for statistical analysis. The experiment's findings indicate that the SHO-MuCNN strategy outperformed the remaining techniques in an intelligent healthcare monitoring system.
Training Program for an Internet-Of-Things Assembler: A Curricular Innovation
Authors: Lea M. Gabawa
Published: April 2024

The Internet of Things (IoT) has revolutionized our interaction with objects, from daily items to industrial systems, creating a demand for skilled IoT assemblers. To address this need, a training program titled "Training Program for an Internet-of-Things Assembler: A Curricular Innovation" was developed, aiming to enhance competencies in IoT assembly. The study delves into pivotal competencies for IoT professionals, offering insights for upskilling in the domain. The IoT Training Program equips trainees with interdisciplinary proficiencies for prototyping robust IoT solutions and orchestrating end-to-end systems. Curricular innovation's significance in tackling education challenges in the digital age is underscored. Employing Research & Development (R&D) design, the study followed the Analysis, Design, Development, Implementation, Evaluation (ADDIE) methodology. The "Research" phase identified fundamental competencies for IoT assemblers through Computational Topic Modeling and Latent Dirichlet Allocation (LDA). The "Development" phase encompassed Design, Development, Implementation, and Evaluation. A comprehensive training program was created and assessed. A validated survey questionnaire gauged participants’ perception of implementation, revealing the program's efficacy. Results highlighted the program's impact, enhancing IoT assemblers' skills in assembling/disassembling IoT devices, applying competencies, and using cloud services over IP networks. Integration of Computational Topic Modeling with LDA effectively identified pivotal competencies, enabling significant skill enhancement and integration into the transformative IoT field. The study's insights facilitate continuous innovation in IoT assembly through ongoing refinement of the training program. The study underscores curricular innovation's importance in addressing education challenges in the digital era, paving the way for proficient IoT professionals.
Queer People in Business: An Exploration of their Lived Experiences in their Workplace
Authors: Zheldrick M. Villarino
Published: April 2024

The Philippines is known to have a high level of queer social acceptance, indicating higher inclusivity and equality. However, in the business sector, queer people often face unique challenges as they navigate their careers in the business world. Consequently, these queer employees pay a high price for defying gender and sexual stereotypes in their workplace. Through a phenomenological lens, this paper aims to comprehensively explore the experiences of these queer individuals in the business sector and the strategies they employ to navigate these difficulties. This study purposively selected seven participants of different work backgrounds to participate in in-depth interviews. The identities of these participants were kept confidential concerning the ethical considerations. After gathering the data, the researchers used thematic analysis to provide a deep understanding of the lived experiences of queer individuals in the business sector, encompassing issues such as discrimination, unfair treatment, and gender stereotyping. Major findings of the study revealed that while the participants perceived their workplace as inclusive, they had their fair share of negative experiences that hampered their mobility in their work environments. Furthermore, despite their negative experiences, these participants learned different coping strategies to manage their emotions and keep abreast with their work. Thus, the paper recommends that organizations with queer employees actively engage in symposiums and seminars to foster an inclusive workplace environment for all, as creating a safe and supportive workplace is essential for attracting and retaining employees, enabling them to thrive and contribute more effectively to the organization.
The Resilience of Entrepreneurship during the COVID-19 Pandemic: Evidence from Successful Entrepreneurs in Vietnam
Authors: Ha Nguyen Thi Thu
Published: April 2024

Nowadays, one of the biggest global concerns is the recovery of small and medium enterprises (SMEs) after the COVID-19 pandemic, as it has caused severe consequences to many of these businesses, leading to organization collapses and closures. To overcome this crisis, SMEs need to adapt to the new reality and plan for change. This study examines the resilience of SMEs in the post-COVID-19 context by exploring entrepreneurship, innovation adaptation and networking relationships. The authors have collected data from 100 SMEs in Northern Vietnam, with 21 questions for entrepreneurs who have experienced the pandemic. The responses were extremely positive, and the Smart PLS analysis of the SEM model showed a feasible research model with an R-squared value of 0.674, Cronbach alpha of 0.6 81 and composite reliability. The chosen factors were closely related to each other, and their relationship was quite consistent.
Usefulness and Ease of Use of E-Learning Towards the Effectiveness of Training and Development Programs for Civil Servants
Authors: Noorzihidayah Md Noor
Published: April 2024

The e-learning system is a digital technology in the world of education that is increasingly used nowadays at all academic levels, whether pedagogy or andragogy, which is now an approach used for teaching and learning electronically to deliver information through online training. The effectiveness of training and development programs in the context of this study depends on several factors in the use of e-learning. Thus the objective of this paper is to examine relationships and analyze the influence of e-learning that includes the perceived usefulness and perceived ease of use on the effectiveness of training and development programs for civil servants. The next objective is to identify the difficulty factors of participants following online training. This study used a survey method through the online questionnaire that was distributed to 152 respondents at the Department of Information Malaysia through the Google Form platform. Technology Acceptance Model (TAM) and Kirkpatrick Evaluation Model were used in this study. The results of the study show that all aspects of the use of e-learning studied have a strong positive significant relationship with the effectiveness of the training and development program. Through regression analysis proved that the use of e-learning contributes as much as 74.1% to the effectiveness of training and development programs. While technical issues and face-to-face communication gaps are the main factors that cause difficulty in attending online training.
Diabetic Drug ontology mapping for individual Diabetic Person and predict Insulin Dosage on Daily Basis
Authors: K. Pushpavathi
Published: April 2024

Managing diabetes requires a personalized treatment plan based on the patient's health records and drug usage effectively. In this paper, we have proposed accurate prediction of insulin dosage for diabetic patients daily by using ontology mapping with deep learning recurrent neural network models such as long short-term memory (LSTM), Gated recurrent unit( GRU), and Bidirectional long short-term memory(BiLSTM). The patient's information is arranged in a structured format. The structured ontology data provides a meaningful connection between patients' personal information, physical activity, insulin intake, carbohydrate intake and glucose level. The structured data is beneficial to take decisions effectively to recommend the insulin dosage level daily to avoid a sudden decrease in sugar level. The proposed LSTM model outperforms well on insulin prediction compared to GRU and BiLSTM models. The proposed model is very effective for diabetic personal care and improves patient outcomes and quality of life.
IOT Based Health Preventive Management System for Patient information discovery Using Ontology
Authors: K.Saranya
Published: April 2024

Over the years, advancements in the work have been incorporated towards the patient’s relationship and data collection techniques to model a wide variety of human   activities and behaviors. Sensor data comes from smart devices that provide the ability to manipulate data from monitoring data for patient healthcare. Due to the high popularity and use of smart devices as respondents, performance recognition systems are more accurate and easier to use. The knowledge model is based on two new approaches: to consider a functional degree scheme between measuring sensor energy and functional consciousness to mode controversial sensor data and establish the relationship between them. In this article, we make a case why ontology        can contribute to blockchain design. To support this issue, this research proposes an ontology middleware that analysis and exploits ontology and some of its representations into the smart contracts that enable it to implement patient traceability restrictions on the original traceability feature platform.
Optimizing Neural Network Inference in Edge Robotics by Harnessing FPGA Hardware Acceleration
Authors: Kolli Himantha Rao
Published: April 2024

This research explores advancements in neural network inference for edge robotics through the implementation of hardware acceleration using Field-Programmable Gate Arrays (FPGA). Leveraging FPGA's versatility in custom hardware design, the research optimizes performance and energy efficiency by utilizing features such as Digital Signal Processors (DSP), Multiply-Accumulate (MAC) arrays, parallel processing units, Convolutional Neural Networks (CNN), and custom accelerators. The computational power of FPGA-based architectures is harnessed to significantly enhance the speed and efficiency of neural network inference tasks in edge robotics applications. Key aspects include the utilization of DSP blocks for efficient signal processing, MAC arrays for high-speed matrix operations crucial in neural network computations, and parallel processing units for concurrent execution of multiple tasks, improving overall system throughput. The integration of CNN directly onto FPGA hardware allows for minimal-latency inference, making it ideal for real-time applications in edge robotics scenarios. Custom accelerators tailored to specific neural network operations further optimize performance and resource utilization. The research involves the meticulous design and synthesis of hardware modules tailored to the target FPGA platform. Through rigorous optimization and parallelization techniques, the goal is to maximize resource utilization while minimizing latency and power consumption. The experimental findings showcase remarkable results, including a substantial increase in throughput, with values reaching up to 150 images/sec, and impressive accuracy rates exceeding 95% in classification tasks.
Dynamic Neural Network-Based Resource Allocation Framework for Latency-Aware and Energy-Efficient Optimization in Wireless Communication Systems
Authors: S.Sankar Ganesh
Published: April 2024

In wireless communication, efficiency is a crucial factor due to the inherent constraints of the radio frequency spectrum and the escalating demand for connectivity. With a limited spectrum available, efficient communication protocols are vital to maximize its utilization and accommodate the increasing number of devices. This study introduces a dynamic resource allocation system for wireless communication, encompassing essential components such as Base Stations (BS), Mobile Devices (MD), and a Centralized Controller (CC). BSs, serving as access points, establish wireless communication with MD within predetermined coverage areas. The CC acts as the neural centre of the system, collecting real-time data from both BSs and MDs to facilitate an adaptive resource allocation process. The comprehensive mechanism involves a scheduling algorithm for optimized data transmission, considering priority and Quality of Service (QoS) requirements, alongside power control mechanisms that dynamically adjust power levels to optimize energy efficiency while maintaining communication quality. MDs communicate with nearby BSs, relaying data transmission information to the CC, which strategically coordinates resources based on the collected data. This integrated system enhances resource utilization, minimizes latency, and improves energy efficiency, collectively contributing to the robustness and reliability of the wireless communication infrastructure.
Dynamic Adaptive Resource Allocation for Edge Computing in Big Data Analytics Using GBDT, DQN, and GA Algorithms
Authors: Sanjay Kanth Balachandar
Published: April 2024

Edge computing in big data refers to processing and analysing data closer to its source, reducing latency and bandwidth usage. It leverages devices at the network edge to perform computations, making real-time analytics feasible. This distributed approach improves efficiency and enables faster decision-making, critical for applications like IoT, autonomous vehicles, and healthcare. The research proposes an innovative approach that harnesses three machine learning algorithms Gradient Boosting Decision Trees (GBDT), Deep Q-Network (DQN), and Genetic Algorithm (GA) to enable dynamic adaptive resource allocation within edge computing environments tailored for big data analytics. GBDT enhances classification accuracy by sequentially refining predictions through decision trees, accommodating heterogeneous data types and yielding high prediction accuracy crucial for dynamic edge environments. The GA evaluates resource allocation strategies represented as chromosomes within a population, selecting promising solutions as parents for the next generation and generating diverse offspring through crossover and mutation operations to discover optimal solutions. DQN facilitates intelligent resource allocation by iteratively refining Q-values based on experiences gathered during interactions with the environment, utilizing a neural network to determine optimal actions for a given state, thereby enhancing performance and efficiency in edge computing environments. This integrated approach ensures flexible resource allocation and fortified capabilities for big data analytics within edge computing environments. The research underscores GBDT as the most promising algorithm for resource allocation in edge computing environments, owing to its exceptional performance in resource utilization, scalability, and accuracy. This nuanced understanding of algorithmic behaviour in dynamic settings offers invaluable insights for optimizing resource allocation strategies, thereby enhancing the efficiency and effectiveness of edge computing systems in handling big data analytics tasks.
A Survey of the Mechanisms Impairing Optical fiber communications performance
Authors: Rida Jafaar Fahad A.
Published: April 2024

The growth of information technology and Internet networks, combined with nearly daily use and many subscribers, has caused the volume of data stored in communication systems to balloon to enormous proportions. Optical fiber cable is the medium for data transfer because it has a bandwidth far more significant than other transmission methods and can span significantly greater distances. The transmission of information from one location to another can be accomplished via a technique known as fiber-optic communication. This involves passing pulses of light over an optical fiber. Optical fiber performance is affected by many effects, including attenuation, dispersion, scattering, and bending. It is feasible to enhance the performance of optical fibers for communications by utilizing carbon nanotubes and multiple coding technologies. This can be accomplished via integrated optical circuits or by making the cable more advanced and developing it further. Photosynthesis is accomplished by utilizing carbon tubes and the optical property inside them.
System Design and Implementation for Machine Learning and Internet of Things Based Anomaly Detection in Patient Movement
Authors: Qassim A. Al-Jarwany
Published: April 2024

Emerging trends in healthcare have seen an increased need for improved monitoring, and as a result, machine learning (ML) and the Internet of Things have been integrated to develop smart systems that can provide rapid and accurate health assessments. With an inclusive view on mobility anomaly detection in patients, this paper aims to improve hospital patient safety. Patients wear or place these things, and the IoT sensor network then gathers real-time motion data. The information is then moved to the central server. The system was taught the machine learning part using a large number of normal and abnormal patient movement data. It also shows a way to differentiate between common activities and those that might indicate ill health. It comprises an IoT sensor network, a data processing unit, and an ML anomaly discovery module. There are sensors on the Internet of Things that have a wide range of movement data. Then, these are run through an ML model that captures the spatial-temporal dependencies between different sets based upon more sophisticated techniques like the Naive Bayes Classifier. Where there are abnormalities discovered after analyzing them, an alert is sent out; this will help in quick emergency response by saving lives even before diseases become last-stage acute conditions
Creation of a Machine Learning-Based Automated System for the Multi-Component Fault Analysis of Industrial Machines
Authors: Mohammad Ahmar Khan
Published: April 2024

The efficiency and reliability of industrial machines are paramount for ensuring smooth operations and minimizing downtime in manufacturing environments. However, the complexity of these machines and the multitude of components they consist of pose significant challenges in identifying and diagnosing faults promptly. Traditional fault analysis methods often rely on manual inspection or simplistic rule-based systems, which are time-consuming, subjective, and prone to errors. In this study, we propose the development of a novel machine learning-based automated system for the multi-component fault analysis of industrial machines. Leveraging advancements in artificial intelligence and data analytics, our system aims to revolutionize fault detection and diagnosis by efficiently processing vast amounts of sensor data to identify anomalies and pinpoint potential faults across multiple components simultaneously. The proposed system comprises several key components, including data preprocessing techniques to handle noisy sensor data and extract relevant features, machine learning algorithms for fault detection and classification, and a user-friendly interface for visualization and interpretation of results. Additionally, the system will incorporate techniques for model explain ability to enhance trust and understanding of the automated diagnostic process.
Research on Oral English Learning System Integrating AI Speech Data Recognition and Speech Quality Evaluation Algorithm
Authors: Wang Xue
Published: April 2024

The application of AI speech data recognition (SDR) and speech quality assessment algorithms in spoken English learning systems can help to realize the intelligence and automation of the process related to spoken English learning. To better assist the people concerned with the learning of spoken English, it is necessary to integrate AI speech data recognition technology in the process of learning spoken English to bring learners a learning environment and learning conditions that are not bound by time and space. As an important way to realize the natural communication of information between machines and people, AI speech recognition technology has significant application advantages and application values in eliminating the influence of the native language of English speaking learners and innovating the learning mode in the local area. In addition, the assessment of the reliability of spoken English language learning can be carried out in several dimensions. However, it is still difficult to unify the criteria of many sizes, bringing new application scenarios for AI SDR. Based on this, this paper first analyzes the theoretical basis of AI SDR. Then it investigates the speech data recognition method of the fused AI English-speaking learning mechanism and the process of fused AI English-speaking speech quality assessment. Finally, it designs and verifies the functions of the spoken English learning mechanism incorporating AI speech data identification and quality assessment.
Design of an Iterative Method for Dynamic Resource Management in 5G Networks with IoT Integration Operations
Authors: A. Venkata Mahesh
Published: April 2024

In the realm of fifth-generation (5G) wireless networks, the escalating demands for high-speed, reliable, and efficient communication are paramount, especially with the widespread deployment of Internet of Things (IoT) devices & scenarios. Despite the advancements in 5G technologies, existing network management strategies often fall short in addressing the dynamic nature of network conditions, the heterogeneity of IoT device requirements, and the need for stringent privacy measures. These limitations underscore the necessity for innovative approaches that can adapt in real-time to varying demands while ensuring optimal network performance and user privacy. This paper introduces a suite of machine learning models designed to enhance the efficiency and reliability of 5G networks, catering specifically to the diverse needs of IoT applications. At the forefront, DynamicSlicerNet, a deep reinforcement learning-based model, dynamically slices 5G network resources tailored to IoT devices' requirements, addressing device mobility, application demands, and network congestion. This model demonstrates a substantial reduction in latency by up to 30% and improvement in reliability by up to 20%, outperforming static resource allocation methods. Further enhancing edge computing capabilities, FedEdgeAI leverages federated learning to train models directly on edge devices, a move that not only slashes latency by minimizing data transmission to centralized servers but also fortifies data privacy. Experimental evaluations highlight FedEdgeAI's efficacy in maintaining model accuracy while halving communication overhead. PredictiveNetCare, employing time-series analysis and anomaly detection, anticipates network failures, facilitating preemptive maintenance strategies. This predictive approach has shown a marked precision over 90% in identifying potential disruptions, significantly reducing maintenance-related downtime by 30% and bolstering network reliability by 15%. OptiAllocRL and AdaptiveQoSDL, both harnessing reinforcement and deep learning techniques, respectively, optimize resource allocation and manage Quality of Service (QoS) parameters adaptively. OptiAllocRL's strategy results in a 40% latency reduction and a 25% throughput increase, while AdaptiveQoSDL minimizes packet loss by up to 50% and enhances end-to-end delay by up to 35%, ensuring high QoS levels under fluctuating network conditions. This comprehensive approach sets a new benchmark for future 5G network management, paving the way for a more connected, efficient, and secure digital world.
Navigating Abnormal Environmental Conditions: Traffic Signal and Obstacle Detection for Autonomous Vehicles
Authors: Mahesh Babu Basam ,
Published: April 2024

Autonomous cars are the future smart cars anticipated to be efficient and crash avoiding ideal urban car of the future. The purpose of the project is to build up a car which can move with  and without any driver. It just need a command from the user and then it will start moving towards the destination. The project aims to build a autonomous car prototype using Raspberry Pi processor. Sensor interfacing is used to provide necessary data from the real world to the car. The car is capable of reaching the given destination safely and intelligently thus avoiding the risk of human errors. Many existing algorithms like traffic light detection, obstacle detection and speed control are combined together to provide the necessary control to the car. The technology that autonomous cars and “connected vehicles” use would allow the vehicles to communicate with roadside infrastructure like traffic lights and road congestion, and then use this information to curtail fuel consumption and emissions significantly.
Design of a Booking Management System, with Responsive Design Web Appointments, to Reduce Time and Improve the Quality of Health Care Services
Authors: Enrique Lee Huamaní ,
Published: April 2024

Technology is playing an important role, especially in the current need for technological solutions focused on the health sector. There is currently much room for improvement, for example: reducing the long waiting time for patients to book an appointment according to availability in the specialty and doctor. However, there are still health centers that lack technological solutions focused on this, because most of the population does not yet have the knowledge of technology. Given this, and taking into account the needs of users with low knowledge of ICT and contribute to the health sector, will be launched the design and development of a specialized responsive website for the management of medical appointments that will solve in part the order and time of attention of each appointment requested, whose purpose is to improve the quality of care of public health services
Building a Global Research Initiative On Open Science
Authors:   Secretary General for the Committee for Public Information, Henriikka Henriikka , Naudet Naudet ,   Université de Rennes, Dacos Marin ,   CNRS - Centre National de la Recherche Scientifique
Published: April 2024

The Global Research Initiative on Open Science (GRIOS) represents a pioneering effort to systematically evaluate, promote, and implement Open Science practices worldwide. GRIOS will address the critical gaps in our understanding and application of Open Science. This initiative is a direct response to the growing recognition of Open Science's potential to democratise access to research findings, enhance the quality of scientific inquiry, and foster a more inclusive and collaborative research environment. GRIOS will catalyse the global adoption of Open Science by: - Conducting in-depth reviews of existing Open Science research to synthesise current knowledge and identify best practices and challenges. GRIOS will synergise with existing Open Science initiatives and research on open science to create a unique global understanding based on all the available knowledge. - Developing evidence-based recommendations to guide the implementation of Open Science practices and policies. - Creating a comprehensive research agenda to address knowledge gaps and promote further investigation into Open Science. - Establishing a global network of researchers and organisations committed to advancing Open Science, facilitating knowledge exchange, and fostering collaboration. A cornerstone of the GRIOS initiative is its commitment to inclusivity and diversity, ensuring that the full spectrum of the academic ecosystem is represented in its efforts, particularly emphasising the inclusion of knowledge and experience from the Global South and young academics. To achieve this, GRIOS governance reflects a broad range of perspectives and expertise to ensure that the policies, practices, and research agendas it develops are informed by a wide array of experiences and needs. This approach not only enriches the initiative's outputs with diverse insights but also fosters a more equitable and inclusive Open Science landscape. GRIOS represents a critical step forward in realising the full potential of Open Science. By systematically addressing the challenges and opportunities associated with Open Science,the initiative will play a pivotal role in shaping the future of research and its contribution to society. Stakeholders across the research ecosystem are invited to join this collaborative effort to advance Open Science and ensure that it serves the global public good. We urge research institutions, funders, policymakers, and practitioners to support and participate in the GRIOS initiative. Together, we can unlock the transformative power of Open Science and create a more open, inclusive, and impactful research landscape.
Formation of a ceramic structure of promoted iron oxide catalyst
Authors: Dvoreckaya Aleksandra , Anikanova Lyubov' , Sudzilovskaya Tat'yana , Malysheva Zoya , Dvoretsky Nikolay
Published: April 2024

The paper concerns with the mechanism of the promoters influence on the ceramic structure of the iron oxide catalyst for the dehydrogenation of olefin and alkylaromatic hydrocarbons. The research shows the dynamics of changes in the porous structure of alloyed and unalloyed catalysts as a result of heat treatment in air at several temperatures significantly exceeding the operating temperature. Moreover, the paper presents data on the mechanical strength of alloyed and unalloyed model catalysts. Indeed, the particles are sintered to each other at the contact points of the globules forming a mechanically strong and thermally stable framework. However, the particles are sintered to each other at the contact points of the globules forming. Supposedly, potassium performs the function of a kind of flux. It lowers the temperature of the melting phase formation, which ensures a strong sintering of the ceramic material (catalyst) particles to each other. Hereby, there is a formation of a stable framework without a noticeable reduction of the catalyst working surface. The addition of zirconium oxide as an alloying agent provides an increase in the depth and degree of annealing of imperfections during the restructuring of the catalyst structure. Additionally, it provides the redistribution of the released energy. This assumption is confirmed by increasing of the alloyed catalyst granules mechanical strength. The results of research can be used for development and modification of iron-oxide catalysts for dehydrogenation of olefinic and alkylaromatic hydrocarbons.
Effect of chemical modification of cotton cellulose by anthranilic acid on the sorption of Cu(II) and Fe(II) ions
Authors: Nokoforova Tatyana
Published: April 2024

The authors modified cotton cellulose with anthranilic acid to obtain a new sorbent capable of efficient extraction of heavy metal ions from aqueous solutions. We conducted the modification in two stages: at the first stage, we obtained dialdehyde cellulose by oxidation of cellulose with sodium metaperiodate; at the second stage, we treated dialdehyde cellulose with anthranilic acid to obtain a ready-made sorbent. The authors determined the optimum conditions for modifying cotton cellulose to achieve maximum sorption of iron(II) and copper(II) ions. We studied the equilibrium-kinetic characteristics of the original and modified cotton cellulose. Also, we have processed the results of the kinetic experiment within the framework of pseudo-first- and pseudo-second-order kinetics models. We selected sorption isotherms, processed them within the framework of the Langmuir model, and determined the values of the ultimate sorption capacity (A∞). The modification of cotton cellulose enables to increase its sorption capacity significantly. According to the results, the A∞ of the modified sorbent is about 4-5 times higher than the ultimate sorption capacity of native cotton cellulose to Cu(II) and Fe(II) ions. In contrast, we obtained and compared the IR spectra of anthranilic acid-modified cellulose and native cellulose. Additionally, we obtained SEM images of the modified sorbent and the native cotton cellulose surface structure.
Challenges of effective use of cold-in-place recycling technology of road pavements
Authors: Budanova Ekaterina , Yarmolinskiy Vladimir , Borisov Aleksandr , Kuchinov Nikita
Published: April 2024

The technology of Cold-in-Place recycling is an alternative to the traditional methods of highway reconditioning. The research problems are the issues of effective application of this technology due to the imperfection of the regulatory standards. An important aspect of the effectiveness of cold recycling is obtaining homogeneous and durable structural layers of road pavement. Physical and mechanical properties of dispersed materials correlate with the particle size distribution. Asphalt granulat with minimum content of dusty fractions has a great influence on the aggregate composition in organomineral mixtures. The requirements of the current normative documents for cold regeneration do not take this fact into account. It causes composing of mixtures with suboptimal composition, and, consequently, a decrease in the efficiency of the applied technology. The results of the conducted researches of organomineral mixtures allow ones’ to optimize their grain composition, and to increase physical and mechanical properties of highways’ basecourses.
EFFICIENTLY UTILIZE THE SPECTRUM AND LOAD BALANCING IN COGNITIVE RADIO NETWORK USING GENETIC ALGORITHM WITH COVERSET PREDICTION
Authors: S. Kavitha  , Dr. R. Kaniezhil
Published: April 2024

Wireless communication is one of the most in-demand fields of communication nowadays. Due to people's desire to transfer data as fast and broadly as possible, most communication processes are impacted by overloading and spectrum shortage when there is significant network usage. Make use of a cognitive radio network to keep an eye on things automatically, and use the spectrum dynamically to avoid overloading. Instead of evaluating the node's fitness, the CRN will alert the secondary user (SU) of the unused spectrum. To choose the fittest node in an environment with a coverage zone, a cognitive radio network in the proposed work uses a genetic algorithm plus coverset prediction. To address these issues, this paper offers a CRN calculation strategy.
Student Caricatures: A Teaching Tool For Character Development
Authors: Darmawan Ferry , Hakim Arif , Cholifah Yuristia Wira , Novita Sophia
Published: April 2024

Early childhood education is an important part of the 4.0 industrial era, and Information and Communication Technology (ICT) is essential to ensure that knowledge is disseminated rapidly. To keep up with scientific advances and integrate local wisdom and Islamic values into teaching materials, developing visual communication skills through the use of design software is an effective solution. This study has employed the development research method and data collection techniques such as observation, questionnaires, and focus group discussions, as well as training, to develop a visual communication model for early childhood character development. The results of the study suggest that using Inkscape software can help early childhood teachers to create independent teaching materials effectively. The tracing image method used in the visual communication model for teachers to create illustrated stories is simple to understand and uses the Inkscape application for drawing. This model can enable early childhood teachers to gain the skills required to better integrate Islamic values and local wisdom into their teaching materials and to create visual materials suited to the 4.0 industrial era
Conversations about reading (Analysis of data from interviews with students)
Authors: Vasileva Despina
Published: April 2024

The problem of developing students' reading skills has been extensively discussed in the scientific literature. The statements are diverse—from the conviction that Bulgarian students do not read enough or do not have sufficiently developed skills, to the opposite opinion that reading has not been ignored, but the approach, patterns, and ways in which it is carried out have changed. The article presents data from a qualitative study on five group interviews, with six students participating in each. The data obtained are heterogeneous. Factors such as family environment, type of school, family reading practices have a significant impact on students' attitudes towards reading and their reading practices. Differences are observed in terms of students' motivation to read and educate themselves, in terms of the type of school, in attitudes towards reading. The data show that students do not prefer fiction, but choose short texts with more visual content associated with easy to perceive and process information.
CLASSROOM PARTICIPATION AND PERSONALITY DEVELOPMENT OF STUDENTS AT HIGHER LEVEL: A CORRELATIONAL STUDY
Authors: Munir Hina , Qureshi Aleeza , Ali Mahwish Muhammad
Published: April 2024

The objective of current research was to examine the correlation between classroom participation and personality development of students in higher education. The population of the study included all social sciences students from 2-universities (Bahauddin Zakariya University & Women University) in Multan district. Two universities were selected from Multan district, only faculty of social sciences was selected, 25 students were selected from each department so total samples of study were 300 students. The questionnaire was designed by using 5-point Likert scale. Data was entered on SPSS sheet after completion of the data collection. Data was analyzed by using diverse statistical techniques, means, SD, correlation, regression, ANOVA and t-test. In data analysis, it was found that participants see classroom participation as enhancing effective management with peers, reflecting prime viewpoint and impact of strong communication skills helps peers on engaging with classmates. Students should participate in workshops or seminars that focus on personal growth including communication skills, emotional intelligence and self-awareness.
Dampak Pertumbuhan Ekonomi terhadap Kemiskinan di Provinsi Sulawesi Tenggara
Authors: Muslim Caesar , Nur Syamsir , Hastuti  , Muadsin Ikrar
Published: April 2024

Kemiskinan masih menjadi permasalahan yang signifikan di banyak daerah di Indonesia, termasuk Sulawesi Tenggara, meskipun telah terjadi berbagai upaya pengentasan dari pemerintah maupun lembaga swadaya masyarakat. Dalam beberapa kasus, pertumbuhan ekonomi juga tidak selalu diikuti dengan penurunan tingkat kemiskinan. Penelitian ini bertujuan untuk menganalisis dampak pertumbuhan ekonomi terhadap kemiskinan di Provinsi Sulawesi Tenggara. Data yang digunakan adalah data sekunder dari Badan Pusat Statistik (BPS) Provinsi Sulawesi Tenggara, yang mencakup rentang tahun 2013 hingga 2022. Analisis dilakukan menggunakan regresi linier sederhana. Hasil penelitian menunjukkan adanya hubungan searah antara pertumbuhan ekonomi dan tingkat kemiskinan di Provinsi Sulawesi Tenggara, di mana pertumbuhan ekonomi berkorelasi positif dan signifikan terhadap tingkat kemiskinan di wilayah tersebut. Sebagai hasilnya, dampak dari pertumbuhan ekonomi tersebut tidak membantu mengurangi kemiskinan, melainkan malah memperparahnya.
Systematic Literature Review (SLR) Peran Asuransi Syariah dalam Membantu Perkembangan UMKM
Authors: Ghofur Umrotul
Published: April 2024

Artikel ini memaparkan tentang peran dari pada asuransi syariah dalam membantu perkembangan UMKM. yang mana asuransi syariah memiliki dua akad yakni tijarah dan tabarru’. Sebagai upaya dalam membantu perkembangan UMKM asuransi syariah memiliki terobosan baru yakni dengan akan tabbaru’ saling tolong menolong dengan antara pelaku UMKM dan pihak asuransi. Dan keuntungan yang akan dibagi hasil karena dilakukannya dari pihak asuransi yakni investasi dengan investasi yang menggunakan prinsip syari’ah. Dalam asuransi syariah terdapat akan tijarah yang mana juga memberikan keuntungan pada UMKM, serta dalam asuransi syariah sudah adanya jaminan halal oleh Majelis Ulama Indonesia atau MUI melalu fatwa DSN (Dewan Syariah Nasional dengan Fatwa No. 21/DSN-MUI/X/2001 tentang pedoman umum asuransi syariah. Metode yang digunakan dalam penelitian ini yakni metode Systematic Literature Review (SLR) oleh Bettany-Saltiknov (2012) dengan cara meringkas temuan dari penelitian terdahulu yang relevan dengan topik penelitian. Sehingga topik yang dirasa relevan dengan penelitian ini yang akan dibahas, dikritik dan disimpulkan.