2024

Read full-textEfficient optimization of polymeric materials in fused filament fabrication 3D printing (FFF 3DP) is crucial for productivity, cost reduction, resource conservation, consistency, and enhanced part performance. This study employed a multiresponse central composite design of experiments (CCD-DOE) with the desirability function algorithm (DFA) to optimize printing settings on polylactic acid (PLA) using a commercial FFF 3D printer. The goal was to identify optimal parameters for faster build time and reduced material usage in PLA part fabrication. The fabrication process involved computer-aided design and modeling of standard PLA dogbone specimens, meeting ASTM-D638 Type 1 tensile test standards. These specimens were then 3D printed using Ultimaker Green RAL 6018 PLA filament and a 2+ model printer set at varying print parameters. Reduced second-order polynomial models for printing time and PLA weight were generated using stepwise regression, eliminating noninfluential parameters. The models revealed that higher layer thickness, increased print speed, and lower infill density resulted in faster printing times, while lower infill density and higher layer thickness led to lighter PLA prints. DFA analysis determined the optimal settings as a layer thickness of 0.26–0.30 mm and an infill density of 35% for minimizing printing time and PLA weight. The stress–strain curves displayed characteristic high-strength, brittle behavior under tension, while tensile testing of optimized PLA parts revealed increased strength with low strain at the break when layers were aligned parallel to the applied force. These findings advance additive manufacturing and provide practical guidelines for high-quality 3D-printed PLA components. Optimizing FFF 3DP parameters enables efficient production with reduced time and material usage, enhancing cost-effectiveness and the fabrication of high-performance 3D printed products. © IMechE 2024.

Read full-textTeaching internship plays a crucial role in providing aspiring educators with invaluable real-world classroom exposure, translating theoretical knowledge into practical teaching skills. Hence, this qualitative research study aims to explore the teaching internship experience using metaphors among thirty (30) prospective teachers in one state university in the Philippines, framed within the context of the new normal. Through careful analysis of written metaphoric expressions and accompanying explanations, seven (7) distinctive themes emerged. These themes are enhancement of knowledge and skills, transfer of learning, building effective communication skills, management of diverse learners, facilitation of learner-centered teaching, challenges of the teaching profession, and being and becoming a beginning teacher. These themes form the basis for proposing a comprehensive model to ensure positive teaching internship experience. These underscore three key components such as pedagogical skills, inclusive classroom, communication and engagement. Tailored for practice teachers in different Teacher Education Institutions (TEIs), this model aims to equip future educators with the essential preparation and training to succeed in the dynamic educational landscape, particularly in the time of the new normal. © 2024 Kasetsart University.

Read full-textDespite growing interest in both fields, research into how positive psychology principles and gamification can be integrated in Computer-Assisted Language Learning (CALL) environments and their impact on language learning outcomes remains insufficiently explored. Thus, this study intends to contribute by implementing a 10-week gamified learning program using the Quizizz application within CALL classrooms at a university in Thailand. A total of 69 first-year students, experiencing gamified learning—which integrates game elements into educational activities—for the first time, participated in this sequential explanatory mixed-method study, which combined a single-group design with narrative frames. After a one-week introduction to the course, the study progressed through two cycles—Cycle 1 spanned five weeks, and Cycle 2 lasted four weeks. Throughout these periods, we collected data using surveys, test scores, and narrative frames. Quantitative data were analyzed using descriptive and inferential statistics, while narrative essays were examined using thematic analysis. The study found that positive psychology principles such as motivation, enjoyment, and anxiety remained stable over the two learning cycles, indicating that while gamified learning initially engages students, its effects do not significantly evolve without varied interventions. Even with high initial motivation and enjoyment, there were only modest improvements in reducing anxiety, suggesting that competitive elements in gamified environments might sustain some anxiety levels. Additionally, strengthened interrelationships among motivation, enjoyment, anxiety, and performance were observed, but these psychological factors did not directly predict grammar test outcomes. © 2024 The Authors

Read full-textThis research rigorously assessed and evaluated the MULAT SA AKLAT Reading Program, focusing on its impact, effectiveness, sustainability, and potential for continuous quality improvement. Objectives encompass analyzing participant demographics, baseline knowledge, session-wise progress, participant evaluations, and the adaptability of educational approaches. The program fosters inclusive participation, unveiling varied baseline knowledge and expectations. Positive trajectories in participant performance and confidence indicate knowledge dissemination and skill development. Consistently high ratings in participant evaluations and an upward satisfaction trend affirm sustained engagement. A positive correlation between attendance rates and session numbers highlights sustained interest, with educational approaches showcasing adaptability to diverse learning styles. The MULAT SA AKLAT Reading Program emerges as a meticulously crafted initiative with transformative impact, sustained engagement, and effective adaptation to diverse learning needs. The absence of poor ratings underscores its unwavering quality. Enhance promotional strategies, implement continuous assessments for improvement, and explore innovative educational approaches to diversify sessions. This research offers crucial insights for refining the program, ensuring sustained impact, and guiding future community-based education. © 2024, Universitas Negeri Makassar- Faculty of Languages and Literature. All rights reserved.

Read full-textIn modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN. © 2024 by the authors.

Read full-textConstruction delays pose significant challenges affecting project timelines, budgets, and quality standards globally. This study focuses on bridging the knowledge between differences of construction between cold storage facility projects among other types of projects and identifying influential factors causing delays in cold storage construction projects in the Philippines. Through a questionnaire survey including 30 critical delay factors derived from comprehensive literature review and expert consultations, data was gathered from 100 stakeholders across diverse cold storage projects via simple random sampling. Analysis using the Relative Importance Index (RII) highlighted inadequate manpower, delays in the concrete slab construction, permit acquisition delays, quality-related rework, problems arising during testing and commissioning of the temperature-controlled room, and late procurement as the primary causes of construction delays in these projects. These findings underscore the critical need for improvements to pre-emptively manage risks leading to project overruns. The study recommends a concentrated effort by construction stakeholders targeting the top 10 critical delay factors identified, to mitigate future delays and ensure smoother project execution. This approach is very crucial for meeting the country’s reliance on timely completion of cold storage facilities which is vital for accommodating the increasing demands across diverse sectors including food, pharmaceuticals, and agriculture. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

Read full-textUnderstanding people’s thinking and actions through exploring their word choice in written discourse can provide an objective analysis of their behavioural patterns in responding to life adversities. This study utilising quantitative and qualitative approaches is an attempt to investigate the coping mechanisms as response to the challenges of pursuing a bachelor’s degree of teacher education students who had postponed their college enrollment after graduating from high school. This investigation revolved around analysing students’ written essays for personality type profiling using M. A. K. Halliday’s Transitivity Th eory and exploiting their aspired professional identity employing Lakoff and Johnson’s Conceptual Metaphor Theory. The analysis of students’ wr itten essays dealing with life challenges re vealed their dominant ac tor, carrier, and senser-personality types that reinforced their self-consciousness enabling them to be reflective and strategic in addressing life challenges. Moreover, the conceptual metaphor “Teachers are Heroes”, as disclosed by the metaphorical constructs of their aspired professional identity, helped them develop motivation, as they realised the significant role which professional teachers play in the society. Th is self-identification derived from their projected personality types and the motivation they gained from realising their aspired professional identity served as a coping mechanism by stimulating their confidence in dealing with the challenges associated with continuing their college education. To enhance self-awareness and promote self-reflection among teacher education students, this study advocates the integration of the theories of transitivity and metaphor into teacher education curricula for professional identity development. © Penerbit Universiti Sains Malaysia, 2024.

Read full-textThis study aims to develop an SSI-based electronic learning material [e-LM] that will respond to the current educational landscape. Socio-scientific issues are utilized in the e-LM recognizing it can be useful in resolving societal issues and assisting learners in developing scientific habits of mind. A Mixed-method research design was employed in the development and validation of an SSI-based e-LM. Brain DeCor e-LM Framework, an SSI-based e-LM design framework highlighting lifelong learning, ethical, and sustainability issues in situations became the basis of SSI-based e-LM development. Its salient features such as the priority SSIs, e-LM design structure, appropriate teaching strategies, and suitable teaching approaches became the basis for its development. It was validated by five science facilitators considering the salient features and found out, they were highly incorporated. The study recommends its implementation on the intended participants. Furthermore, implementing the developed e-LM was also suggested to be validated considering the e-collaborative activity assessment and the result of student participant’s reflection time assessment. The student performance in science achievement will denote the effectiveness of the developed e-LM. © 2024, Duzce University, Faculty of Education. All rights reserved.

Read full-textThis study developed a Teaching and Learning Material (TLM) with Augmented Reality (AR) to enhance the students’ metacognitive knowledge, namely declarative, procedural, and conditional knowledge in General Chemistry. The developed TLM has three components: the printed learning material that contains the AR trackers; a database of tactile 3D objects and animations that served as metacognitive scaffolds; and a dedicated smartphone software application specifically created for the retrieval, display and control of AR objects. The design-based research paradigm was used in the development of the Teaching and Learning Material with Augmented Reality, while the pre-experimental single group research design was employed in the assessment of the effect of scaffolds using AR on the students’ metacognitive knowledge. A Metacognitive Knowledge Test (MKT) was administered to the student participants before and after using the AR learning material system. Using the Hake factor test, the study found that the student participants achieved high learning gains for declarative and procedural metacognitive knowledge as signified by g values of 0.73 and 0.79, respectively. In terms of conditional metacognitive knowledge, the students registered a medium learning gain given by g = 0.68. It is recommended that teachers use innovative teaching and learning materials embedded with AR to enhance students’ learning attributes like metacognitive knowledge. © 2024 by the authors.

Read full-textThis research presents a comprehensive assessment of Sepaktakraw athletes at Batangas State University (BatStateU) TNEU, aiming to enhance athlete development through evidence-based methodologies. Recognizing the global trend towards universities' pivotal role in athlete development, the study addresses the diverse landscape of sports programs, particularly focusing on Sepaktakraw in the Philippines. BatStateU's visionary 5-year development plan emphasizes creating champion athletes, aligning with global trends and emphasizing sports infrastructure and training programs. However, the study identifies an opportunity to enhance training programs by introducing the SPIKES program, incorporating kinesiological assessments for a more scientific and tailored approach. Using a descriptive survey methodology, the research integrates Physical Fitness and Skill-specific Performance Tests to evaluate Sepaktakraw athletes comprehensively. Anthropometric profiling revealed diverse body compositions, while physical fitness assessments highlighted variations in speed, agility, strength, and more. Skill-specific performance evaluations in serving, spiking, and game-specific skills provided insights into athletes' proficiency levels. Results categorized athletes into four kinesiological profiles (A, B, C, D), guiding the design of personalized training programs. The Skill-Specific Training Program, unfolding over 12 weeks, targets specific improvements in physical fitness and skills. This evidence-based approach offers a systematic framework for optimizing Sepaktakraw athletes' performance, providing valuable insights for coaches, trainers, and athletes alike. The study not only enhances BatStateU's athlete development initiatives but also serves as a model for personalized and scientifically rigorous training programs in Sepaktakraw and potentially other sports. © 2024 Nevzat Demirci. All rights reserved.

Read full-textThis study investigates the application of PID algorithm-based temperature control systems in pig house environmental management and its impact on agricultural production efficiency. Through field experiments conducted at Dongshun Aquaculture Ecological Development Co., Ltd. in Nanping City, Fujian Province, we evaluated the system's effectiveness in energy efficiency, production performance, and animal health. Results show that the PID system achieved 18.5% energy savings, a 9% increase in daily weight gain, and a 7.1% improvement in feed conversion ratio. Additionally, meat quality scores improved by 8.3%, and disease incidence rates decreased by 21.9%. Economic analysis indicates that the system begins to generate a positive return on investment in the second year, with cumulative benefits reaching 192% within five years. These findings highlight the potential of PID temperature control systems in improving pork production efficiency and sustainability, offering a valuable solution for modern pig farming. © 2024 by author(s).

Read full-textIntroduction: The use of machine learning (ML) approaches to improve energy utilization in smart urban environments has garnered significant attention in recent years. Objective: This research presents an innovative structure called a bi-fold mechanism-driven convolutional deep network (BMCDN) for estimating the energy performance of urban public facilities in urban energy management systems. Method: The suggested method includes two significant phases: (1) feature extraction and fusion, and (2) energy significance estimation. The attention fine-tuned ResNet (N1) processes street-view images to evaluate anticipated market significance levels, while the attention-based Bi-LSTM (N2) integrates cross-domain features using input attention. A decision tree (DT) is used to combine and evaluate the fused information and estimated values, serving as the energy value estimator to determine energy values. Data gathered related to public facilities’ energy efficiency from various sources is used to analyze the effectiveness of the suggested framework. Results: The research presents an analysis of the performance gains using image-only representations and a proposed approach with morphological traits. The findings demonstrate that incorporating smart urban-related façade images improves the accuracy of the proposed framework and highlights the connection between energy usage and public facilities. Conclusions: This study shows the potential for significant precision along with rapid inference time in predicting the energy performance of urban public facilities by combining data from numerous sources. © 2024; Los autores.

Read full-textCurrent remote sensing technologies employing Unmanned Aerial Vehicles (UAVs) for farm monitoring have shown promise in characterizing the environment through diverse sensor systems, including hyperspectral cameras, LiDAR, thermal cameras, and RGB sensors. However, these solutions often specialize in either activity recognition or crop monitoring, but not both. To address this limitation and enhance efficacy, we propose a multi-vision monitoring (MVM) framework capable of simultaneously recognizing farm activities and assessing crop health. Our approach involves computer vision techniques that transform aerial videos into sequential images to extract essential environmental features. Central to our framework are two pivotal components: the Farmer Activity Recognition (FAR) algorithm and the Crop Image Analysis (CIA). The FAR algorithm introduces a novel feature extraction method capturing motion across various maps, enabling distinct feature sets for each activity. Meanwhile, the CIA component utilizes the normalized Triangular Greenness Index (nTGI) to estimate leave chlorophyll levels, an important indicator for crop health. By unifying these components, we achieve dual functionality—activity recognition and crop health estimation—using identical input data, thereby enhancing efficiency and versatility in farm monitoring. Our framework employs a diverse range of machine learning models, demonstrating the potential of our extracted features to address the defined problem effectively in unison. © 2024 The Author(s)

Read full-textWith the popularization of new energy vehicles, lithium battery systems, as the main components of new energy vehicles, have the characteristics of short life cycles and harmful substances inside. The green treatment of lithium battery systems has become a research hotspot. Disassembly and recycling are essential means of reusing waste in lithium battery systems. Due to the wide variety of lithium battery systems, the lack of unified design standards, and the high flexibility requirements for disassembly, manual disassembly is currently the primary method used. However, this method can cause health hazards to oneself when dismantling some harmful components. The optimization of the dismantling process route for lithium batteries is a crucial step before dismantling, which directly determines the economic benefits of dismantling. However, unlike general electromechanical products, lithium batteries have prominent safety issues during the dismantling process, so the safety requirements for their dismantling process route are relatively high. Given the substantial absence of parametric evaluation and modification in prior research, this work investigates the influence of the most significant factors on the power density of biosensors. A conduction-based framework was employed to ascertain these variables, and the calculations were performed utilizing a neural network. The neural network was developed with Particle Swarm Optimization (PSO). Based on this, this article considers studying the optimization method of the lithium battery safety disassembly process to maximize safety and economic benefits comprehensively. Based on the essential characteristics of lithium-ion battery systems, an analysis is conducted on the allocation method of difficulty level for human-machine cooperation tasks and the impact indicators of task allocation. Then, a product disassembly hybrid diagram is established, and on this basis, multiple sets of human-machine cooperation disassembly sequences are generated. Finally, a multi-objective optimization model for disassembly cost, difficulty, and time is established. Finally, taking the Tesla Model 1sPBS waste lithium battery as an example, the safety prediction model for dismantling the waste lithium battery and the optimization model for the safety dismantling process route were solved to verify the effectiveness of the above optimization method. © 2024, Natural and Engineering Science. All rights reserved.

Read full-textThe endangered Pterocarpus indicus Willd., or narra, is one of the priority species for biodiversity conservation and protection in the Philippines. However, no studies were conducted about the impact of provenance in the seedling performance and survival of narra. The present study aimed to address this gap and study the impact of seed sources and biofertilizer amendment on the growth and survival of P. indicus seedlings. Three seed provenances (natural forest, urban forest, commercial) and three biofertilizer treatments (no amendment, uncomposted chicken manure, MYKOVAM®) were used. Two-way ANOVA and Tukey’s post hoc analysis were used to assess the differences among treatments in terms of survival and growth performance indicators. Results showed that all treatments achieved maximum survival except the seedlings treated with chicken manure. Variations in growth performances among treatments revealed that seedlings from the natural forest exhibited significantly better performance in terms of root collar diameter, seedling height and leaf count. MYKOVAM® was found to significantly improve the growth of seedlings in terms of root collar diameter, root length, seedling height, leaf count, and nodulation. Lastly, significant correlational relationships were found among all of the growth parameters. The most significant relationship was observed for root collar diameter and seedling height. The results can serve as basis for future reforestation programs and further research aiming to improve the conservation of P. indicus. © 2024 Istanbul University-CerrahpaÅŸa. All rights reserved.

Read full-textThe main problem in achieving learning outcomes in physical subjects at the primary school level is very complex. This study aimed to investigate the relationship between physical fitness, body fat, BMI, and physical activity levels with physical education learning outcome in elementary school students. A cross-sectional study was conducted to answer the research questions. This study involved 27 students (Aged 10.82+0.79) from elementary schools in Malang City, Indonesia. Instruments to determine physical fitness include endurance (PACER Test), flexibility (V sit and reach), and strength (sit-ups and squat thrust), the Indonesian Physical Fitness Test was used to determine body fat and Body Mass Index (BMI), and Physical Activity Questionnaire for Children (PAQ-C) was used to determine physical activity levels. The research result showed that physical fitness, body fat, BMI, and physical activity level were closely related to learning outcomes (R of 0.821), but only physical fitness influences physical fitness (Sig. < 0.05), while body fat, BMI, and physical activity level had no effect. In conclusion, students with good physical fitness tend to get good learning outcomes, so the physical education curriculum must accommodate physical activities that can increase the physical fitness level of students. Future research on academic success factors needs to be investigated, especially schools need to promote movement-based activities to support academic success. © 2024 Nevzat Demirci. All rights reserved.

Read full-textThe Philippines is experiencing a booming industrial sector, encompassing construction, automotive, manufacturing, petrochemicals, energy production, waste treatment facilities, and more. However, this growth brings forth a critical challenge: the need to prioritize worker safety, environmental preservation, and uninterrupted operations amid the presence and utilization of hazardous gases in production, manufacturing processes, and their by-products. This paper introduces a systems engineering approach tailored to the complexities of Philippine industries. It presents a comprehensive framework for the design, implementation, and performance evaluation of gas detection systems. These systems play a pivotal role in ensuring that the country’s manufacturing and production processes operate at peak efficiency, all the while optimizing worker safety, enhancing the efficiency of equipment and processes, and steadfastly upholding environmental integrity. By embracing systems engineering principles, this approach forms the bedrock for the meticulous design of gas detection systems, contributing to a safer, more efficient, and environmentally responsible industrial landscape in the Philippines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Read full-textThe 2022 PISA scores highlighted a significant concern in the Philippine education system, showing Filipino students ranking sixth lowest in STEM competencies among 81 participating countries. This study probes into the STEM abilities of high school students from the Bicol Region to determine if the national findings are indicative of regional educational challenges. By implementing item response theory (IRT) analysis, the research aimed to dissect student competencies in calculus, chemistry for engineers, and physics for engineers, uncovering specific areas of weakness and identifying evidence-based interventions to enhance STEM education on a regional scale. The methodology encompassed three phases: The development and pilot-Testing of a comprehensive survey, its subsequent deployment across three prominent schools in the Bicol Region, and an in-depth analysis of the gathered data using advanced IRT models, including the Rasch Model and the Partial Credit Model. A total of 162 students were surveyed, ensuring a sample representative of regional STEM competencies. Findings from the study align with the concerning PISA results, indicating areas where students struggled, particularly in understanding complex calculus concepts like the definition of definite integrals and application of electrochemical principles in chemistry. The advanced IRT analysis, especially through the lens of the Partial Credit Model, highlighted these core areas of difficulty. Moreover, the competency in solving physics problems involving interference and diffraction was also seen as challenging, mirroring the trends in national assessments and emphasizing the necessity for targeted intervention at the curriculum and instructional levels. © 2024 IEEE.

Read full-textEfficient thermal management is essential for modern electronic devices, as excessive heat poses a risk of component damage. Consequently, students utilize numerical techniques to model and understand the thermal characteristics of electronic components and address the critical need for efficient thermal management. Specifically, this paper applied Newton's law of cooling to analyze and optimize electronic component cooling, exploring heat transport concepts and demonstrating how mathematical models and numerical simulations contribute to improved temperature management. It focused on analyzing the cooling process of a Central Processing Unit, utilizing Python programming to model temperature fluctuations during operation. The students collected primary temperature data at various intervals, applied Newton's Law of Cooling to develop a mathematical model, and compared four cooling methods through simulations and visualizations. Based on the collected data, the minimal temperature variations during idle state may indicate potential benefits in terms of energy efficiency, and the faster response of heat sinks may be advantageous for rapid temperature adjustments. Also, more controlled temperature management demonstrated by air coolers could contribute to stable and reliable performance in various situations. Notably, the liquid cooler's ability to rapidly reduce CPU temperatures implies potential advantages in tasks demanding sustained high performance, such as gaming or intensive computational workloads. © 2024 IEEE.

Read full-textElectrical power systems in university campuses must have a strong infrastructure, engage in proactive maintenance, and implement efficient outage management procedures in order to be reliable. The study covered techniques used to improve the dependability of electrical power systems in university settings, including fault analysis, reliability-centered maintenance, and predictive analytics. Furthermore, minimizing carbon emissions, maximizing energy efficiency, and encouraging the use of renewable energy sources are all parts of sustainability in university electrical power systems. University communities can be encouraged to practice environmental stewardship and energy conservation through programs like student-led sustainability campaigns and campus-wide energy audits. Incorporation of renewable energy sources is investigated as a means of strengthening university power networks' resistance to disruptions and improving energy security.In university settings, improving the sustainability and dependability of the electrical power system is imperative, but there are still a number of obstacles. Financial limitations, complex technology, and organizational inertia are all part of these challenges. University stakeholders must work together, engage with industry partners, and advocate for policy reforms to ease the transition to more dependable and sustainable electrical power systems in order to overcome these obstacles.In conclusion, the optimization of electrical power system reliability and sustainability in university settings is essential for advancing operational resilience, reducing carbon footprint, and instilling a culture of sustainability within academic institutions. By embracing innovative technologies, fostering interdisciplinary collaborations, and prioritizing stakeholder engagement, universities can pave the way towards a greener, more resilient energy future. © 2024 IEEE.

Read full-textIn the perpetual challenge against infectious diseases, the implementation of effective public health strategies requires a deep understanding of how illnesses work and how they spread, accurate estimation of parameters, and strategic planning for interventions. This paper explores the fundamental principles of the Susceptible - Exposed - Infectious - Recovered (SEIR) model, a widely utilized mathematical framework employed for monitoring infectious diseases. The real-life applications of Matrices were utilized through the SEIR Model Simulation, which employs a simulation loop that constantly updates the state vector. This paper also highlights the critical role of mathematical modeling, especially the SEIR model, in advancing our comprehension of the dynamics of infectious diseases. It underscores the importance of utilizing matrix operations and examines various models and approaches, contributing to the ongoing discourse in computational epidemiology and initiatives for public health. The discourse further delves into the significance of matrix operations within the SEIR model, where matrices play a crucial role in quantifying transitions between compartments and influencing the outcomes of simulations. By drawing on insights from related literature, this paper explores the evolution of infectious disease modeling, giving emphasis to the extended SEIR model and its applications in diverse contexts, such as ACL transmission dynamics and the complexities of the SARS-Co V-2 pandemic. © 2024 IEEE.

Read full-textThis research investigates heavy rainfall dynamics in Batangas City, employing the trapezoidal rule and quadratic regression for cumulative rainfall estimation and curve fitting. Utilizing multi-year datasets sourced from the Weather and Climate website, the study not only addresses immediate impacts but also discerns long-term trends, contributing vital insights into potential indicators of climate change. The Trapezoidal Rule application reveals integral approximations, showcasing variations and trends in rainfall datasets from 2018 to 2020. Visual representations and quadratic regression aid in curve fitting, enhancing the study's precision. The development of a user-friendly Python program with a graphical interface ensures accessibility, facilitating further exploration by researchers and stakeholders. In conclusion, this research not only answers the immediate research question but significantly contributes to climate science, fostering resilience and adaptive strategies for communities facing the challenges posed by heavy rainfall events. © 2024 IEEE.

Read full-textAiming at the problem that New Energy Vehicles (NEV) charging piles are illegally occupied, this paper puts forward a design and development scheme of anti-occupation system based on deep learning. Firstly, a comprehensive system architecture is designed, including license plate recognition module and occupancy judgment module, in order to realize the recognition and state monitoring of charging piles. Secondly, a license plate recognition algorithm based on deep learning is proposed, which combines the mixed model of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to realize the accurate recognition of the license plates of electric vehicles and fuel vehicles in China. Then, a real-time evaluation system is designed to comprehensively evaluate the processing efficiency and response speed of the system. Finally, through a large number of experiments, the results show that the system has high recognition accuracy and reliable real-time performance, which can effectively prevent charging piles from being illegally occupied and improve the utilization efficiency and management effect of charging piles. This design and development scheme provides an innovative solution for the intelligent management of NEV charging piles, which has important theoretical significance and practical application value. © 2024 IEEE.

Read full-textUrban bicycle tourism refers to the recreational and enjoyable use of bicycles in navigating urban cities like Iloilo City. The city has already established an 11-kilometer cycling infrastructure within its boundaries. It is imperative to maximize its use to combat the negative impacts of rapid urbanization and promote safe and sustainable transportation. This paper shows the use of Geographic Information Systems (GIS) to assess the bike lanes network's suitability and accessibility for building a bike-sharing system that also caters to drop-off and ride-on points at each station. Deletion of duplicates and cleaning of road network data and tourist attraction point data was performed prior to any analysis. Service areas were generated to determine the number of tourist attractions catered by each potential bike share station – defined by main road intersections. Point distance analysis was performed to reduce the number of road intersections to just thirty-nine (39). Bicycle route maps and itineraries were produced to visualize the optimal routes tourists can use for exploring the city based on preferred sites category. From here, Route Analysis was conducted to create four (4) itineraries based on tourism categories: Top 10 Most Rated Tourist Attractions, Food and Dining, Museums and Churches, and Shopping and Leisure. The output maps and itineraries can be presented to the City Tourism Office of Iloilo City as a decision support tool for its programs and policies regarding bike lanes. © Author(s) 2024.

Read full-textDisease such as cancer and parameter estimation methods are major concerns in the field of medicine. Understanding the pace at which cancer spreads is essential for gaining insights into the disease's progression and devising effective treatment strategies. Cancer growth rates differ widely among individuals and types of cancer, emphasizing the need for thorough study and analysis in this area. Researchers can enhance the precision of cancer growth rate estimates and, consequently, advance the comprehension of the disease and refine treatment options for patients by employing the Newton Raphson iterative method. The Newton Raphson iterative method which is a numerical approach, can be employed to estimate cancer growth rates more precisely. Its application in determining cancer growth rate has become a pivotal tool in contemporary medical research. This potent mathematical approach enables researchers to analyze intricate nonlinear growth functions and derive precise estimations of cancer growth rates. The study aims to assess how effective Newton Raphson iterative methods can be applied to determine cancer growth rates. This gives researchers a detailed grasp of the evolution of cancer and a reliable and effective way to estimate growth rates. Researchers can overcome limitations present in traditional analytical methods by employing this method, leading to more accurate and dependable estimates of cancer growth rates. It is useful for more than just analyzing numbers; it plays a crucial role in understanding the complex workings of cancer cells, which leads to better treatment plans and happier patients. © 2024 IEEE.

Read full-textRemote work has been an integral part of business strategies across the globe since the onset of the COVID-19 pandemic. Though business operations and work functions have returned to their usual modes, remote access to business intelligence remains a crucial part of business operations, particularly in the manufacturing sector, allowing top executives and key decision makers to view real-time scenarios related to manufacturing operations and supply chains. Maintenance is another aspect of business operation that is of particular importance to the manufacturing industry, where critical equipment downtime could translate to significant economic losses. Through digital transformation, manufacturing companies can tap into technologies such as cloud computing, IoT (Internet of Things), and AI (artificial intelligence) to allow for more seamless monitoring of operating parameters and enable proactive approaches to equipment maintenance and reliability, thus ensuring continuous and efficient plant operations. © 2024 IEEE.

Read full-textThis study relates to the assessment of an ICT network infrastructure of a large university. The study seeks to identify weaknesses, improve performance, enhance security, and ensure the infrastructure aligns with current and future needs. Included in the study areas were the inventory and documentation of the major components of the ICT system, performance evaluation, adequacy, security assessment, and topology assessments. A carbon footprint evaluation was conducted to assess the carbon signature of the carbon signature of the ICT process.The inventory along with network topology assessment aims to determine the reliability and security vulnerabilities. For the adequacy, a total of 290 respondents were surveyed to among them were teaching and non-teaching personnel. A carbon footprint equation was used to calculate the footprint of the network. Based on the findings of the assessments, an action plan for the network was developed. A rehabilitation plan for the Server Room and its component were also included. © 2024 IEEE.

Read full-textFish feeding in aquaponics presents challenges in accurately estimating and distributing feed, risking the health of both fish and plants due to potential water contamination. To address these issues, our study employs computer vision and deep learning to automate feed dispensing in aquaponics farms, ensuring precise feed quantities. We utilize the Mask R-CNN algorithm to identify fish measurements in tanks, aiding in feed quantity determination and minimizing over or underfeeding. By calculating a fish-to-feed ratio, we regulate feed amounts to enhance system performance. The remaining fish segment's length and width are converted into weight, guiding feed calculations for each session. We conducted experiments with 432 Nile Tilapia, using their measurements to train the Mask R-CNN model. Results show a significant mean error of only 0.98% and 4.56% for length and weight predictions, respectively, with a high correlation coefficient of 0.97. © 2024 IEEE.

Read full-textThis study integrates the Newton-Raphson method within the PHP framework to offer an easy-to-use loan management solution for Micro, Small, and Medium Enterprises (MSMEs) in the Philippines. MSMEs, which are essential to the country's economy, frequently face financial difficulties such as a lack of specialized instruments and restricted access to loans. This research led to the development of the PHP Loan Solver, which fills these gaps by providing an intuitive user interface for accurate monthly interest rate computations. This paper focuses on practical application, where it integrates a user-centered approach and numeric accuracy so as to let MSMEs optimize their loan structure and perform an informed financial evaluation. It analyzes real-world case studies, possible applications in other financial domains, and a user-centered iterative design approach. This research holds a position where technology meets financial empowerment in a scenario that involves exceptionally easy-to-navigate financial instruments. © 2024 IEEE.

Read full-textThis research delved into the symbiotic connection between 5G technology and the Internet of Things (IoT), aiming to explore how the integration of these two cutting-edge technologies could revolutionize various industries. The primary purpose was to assess the impact of 5G on the functionality and efficiency of IoT applications, with a focus on improved connectivity, scalability, and energy efficiency. The methodology employed a multifaceted approach to ensure a comprehensive analysis. A thorough literature review laid the foundation, providing insights into existing IoT implementations and the potential advantages of 5G integration. Case studies were examined to understand real-world applications, and empirical analysis involved simulations to gauge the performance of 5G-enabled IoT devices in diverse scenarios. The key findings of the research highlighted several crucial aspects. First, the enhanced connectivity offered by 5G, characterized by higher data transfer rates and lower latency, emerged as a game-changer for IoT. This improved communication between devices, ensuring quicker response times and more efficient data exchange. The study also underscored the importance of 5G's network slicing capabilities, allowing for the creation of customized network segments tailored to the particular demands of different IoT applications. This scalability feature was identified as instrumental in accommodating the diverse requirements of various industries. Moreover, the research shed light on the potential for increased energy efficiency in 5G-enabled IoT devices. Optimizing data transmission processes and enabling devices during idle periods, to enter low-power contributed to a more sustainable IoT ecosystem. © 2024 IEEE.

Read full-textIn this study, silica (Si) nanoparticles derived from rice husk ash is coated with cetyltrimethylammonium bromide (CTAB), a cationic surfactant, and used as adsorbent of nitrate ion in wastewater. CTAB functionalized Si (CTAB-Si) nanoparticles were characterized using scanning electron microscope (SEM), energy dispersive X-Ray (EDX), dynamic light scattering (DLS) Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The synthesized CTAB-Si nanoparticles has a non-spherical shape, with visible polydispersity and is amorphous with average particle size of 72nm. It showed stretching vibrations of silanol at 963cm−1, siloxane at 1073cm−1, sharp peaks at 2852cm−1 and 2922cm−1 due to the methylene tail of CTAB and amine peak at 1,384cm−1. An ∼80% nitrate removal and adsorption capacity of 7.98 mg NO3/g CTAB-Si nanoparticles was obtained at optimized condition using Face Centered Central Composite Design (FC-CCD) at pH 4, 50 mgL-1 of adsorbate concentration, 0.2 gL-1 adsorbent dose and 30 minutes contact time. The adsorption isotherm and kinetic models fits well in Langmuir model and Pseudo second order with a R2 of 0.984 and 0.999 respectively. The efficiency of the nanoparticle after 5 adsorption cycles was ∼50% nitrate removal. © 2024 Trans Tech Publications Ltd, All Rights Reserved.