Vertex
type: channel
description: Indexing: Vertex (ISSN 2089-385X (Print) | 2829-6761 (Online)) at https://pub.iocscience.org/index.php/Vertex/index) is a peer-reviewed journal that publishes scientific articles from various engineering disciplines (Preferably from the Nuclear Field). Here are the fields that we highly prioritize to publish:. Computer Science AI Quantum Computation Instrumentation Electronics Electrical Engineering all fields of science that are relevant to the field of technology Articles published in Vertex include original scientific research results (top priority), new scientific review articles (non-priority), or comments or criticisms on scientific papers published by Vertex. The journal accepts manuscripts or articles in the field of engineering from various academics and researchers both nationally and internationally. The journal is published every June and December (2 times a year). Articles published in Vertex are those that have been reviewed by Peer-Reviewers. The decision to accept a scientific article in this journal is the right of the Board of Editors based on recommendations from the Peer-Reviewers. Since 2011, Vertex only accepts articles derived from original research (top priority), and new scientific review articles (non-priority).
publisher: Institute of Computer Science (IOCS)
language: en
publication name: Vertex
issn: 2089-385X
items: Enhancing Rice Disease Identification using Hybrid GLCM-XGBoost with SMOTE Imbalance Handling
items: Analysis of the Technology Acceptance Model (TAM) in the Acceptance of Smartboards in Elementary Schools
items: Obesity risk estimation using ensemble learning and synthetic data augmentation techniques
items: Anomaly Detection of Parasitic Plankton in Brebes Eco-Waters Using Vision-Based Autoencoder AI
items: Implementation of UI/UX Design in the Development of Profile Website Using the User Centered Design Method
items: Implementation of the Topsis Method in Determining Online Shopping Options in the Marketplace
items: Development of distance measurement accuracy technology in physical activity tracking applications with a reward point system
items: Performance Analysis of Wet Gas Flow in Up and Down Transmission Pipelines
items: Analysis of the Effect of Pretreatment of Empty Palm Oil Bunches (TKKS) on Biogas Production
items: Analysis of the Impact of Paving Block Material Variations on Urban Traffic Flow, Safety, and Sustainability
items: Performance Analysis of Insulation Materials in Earthquake Resistant Buildings
items: Analysis of Picohydro Power Plant Design on Water Flow and its Utilization
items: Optimizing Evacuation Routes During Volcanic Eruptions: A Comparative Analysis of Pathfinding Algorithms around Mount Sinabung
items: Optimizing Wind Energy Generation: Wind Speed Forecasting Using Elman Recurrent Neural Networks for Enhanced Power Generation in Turbines
items: Optimizing Anthurium Cultivation: Advancements in Greenhouse Design for Enhanced Plant Quality in Controlled Environments
items: Analyzing the Impact of Occupational Safety Programs on Work Productivity Using Fault Tree Analysis
items: Advancing Fault Diagnosis in BLDC Motors: Surge Test-Based Diagnostic Equipment for Phase to Ground Faults
items: Enhancing Hydrogen Production Efficiency in Water Electrolysis through Polycarbonate Integration from CD-R Waste
items: Men's facial foam selection decision support system based on skin type
items: Fake news detection using naive bayes classifier and forward selection in the digital era
items: Design of an expert system for diagnosing eye disease using the certainty factor method
items: Optimizing face sensor-based attendance system using wavelet method for enhanced security and efficiency
items: Adaptive traffic control at complex intersections using fuzzy logic multi-agent approach
items: Autonomous and sustainable air navigation technologies shaping the future of aeronautical engineering
items: Advancing air navigation engineering for autonomous and sustainable aviation: navigating technological innovation and regulatory frameworks for sustainable flight
items: Decision support system for selection of major concentration using fuzzy logic
items: Web-Based on-line learning (e-learning) decision support system
items: Optimization-based decision support system for accurate earthquake epicenter determination
items: Next-Generation hyperpolarization techniques for NMR: amplifying signal sensitivity and resolving complex molecular systems
items: Quantum-assisted NMR data processing: enhancing sensitivity and resolution with quantum computing algorithms

Enhancing Rice Disease Identification using Hybrid GLCM-XGBoost with SMOTE Imbalance Handling
type: item
description: Rice (Oryza sativa ) is a major food staple, which is prone to multiple diseases that will dramatically decrease the harvest yield. Disease identification is time consuming and is usually subject to subjective errors in a manual approach. The following research will seek to increase the level of precision of automatic rice plant disease detection, namely the Brown Spot, Hispa, and Leaf Blast classes. The suggested method combines both the Gray Level Co-occurrence Matrix (GLCM) to extract texture features and the Extreme Gradient Boosting (XGBoost) classification algorithm. Furthermore, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to address class imbalance within the dataset of 5,548 images. Preprocessing steps include resizing, grayscale conversion, and Min-Max normalization. Experimental results demonstrate that the model trained on SMOTE-balanced data with optimized XGBoost parameters achieved a superior accuracy of 98%, outperforming the imbalanced scenario (97%) and previous studies. This research confirms that the combination of GLCM, SMOTE, and XGBoost constitutes a robust and high-precision method for rice disease identification
subject: Articles
  topic: section
subject: Disease Classification
  topic: keywords
subject: GLCM
  topic: keywords
subject: Rice Disease
  topic: keywords
subject: SMOTE
  topic: keywords
subject: XGBoost
  topic: keywords
creator: Anwar Sadad
rights: Copyright (c) 2026 Anwar Sadad (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 1
ending page: 6
doi: 10.35335/z9q64978

Analysis of the Technology Acceptance Model (TAM) in the Acceptance of Smartboards in Elementary Schools
type: item
description: The use of Smartboards in primary school learning has become an essential part of digital transformation in Indonesian education. However, the success of this implementation strongly depends on user acceptance. This study aims to analyze the factors influencing Smartboard acceptance using the Technology Acceptance Model (TAM), which includes Perceived Usefulness (PU), Attitude Toward Using (ATU), and Intention to Use (IU). A quantitative explanatory correlational design was employed, involving primary school teachers in West Jakarta who have used Smartboards in classroom instruction. Data were analyzed using Structural Equation Modeling (SEM). The findings indicate that all indicators of PU, ATU, and IU demonstrate valid and reliable loading factor values. Structurally, PU has a positive and significant effect on IU, and ATU also shows a significant positive influence on IU. These results highlight that perceived usefulness and positive attitudes are the primary determinants shaping teachers’ intention to use Smartboard technology. The study implies that enhancing digital competencies, providing structured Smartboard training, and ensuring technical support are crucial to optimizing the implementation of Smartboard-based learning
subject: Articles
  topic: section
subject: Intention to use
  topic: keywords
subject: Perceived Usefulness
  topic: keywords
subject: Smartboard,
  topic: keywords
subject: Technology Acceptance Model
  topic: keywords
creator: Ririn Restu Aria
creator: Helina Apriyani
creator: Enok Tuti Alawiah
rights: Copyright (c) 2026 Ririn Restu Aria, Helina Apriyani, Enok Tuti Alawiah (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 7
ending page: 17
doi: 10.35335/f8fpy424

Obesity risk estimation using ensemble learning and synthetic data augmentation techniques
type: item
description: Obesity has become a primary global health concern due to its strong association with various chronic diseases such as diabetes, cardiovascular disorders, and certain types of cancer. Accurate and early risk prediction of obesity is essential for effective prevention and intervention strategies. However, predictive modeling in this domain often encounters two critical challenges: the presence of imbalanced datasets and the complex, nonlinear nature of behavioral and anthropometric features. This study aims to address these challenges by developing a robust classification model that integrates ensemble learning with synthetic data augmentation techniques. The research utilizes the Obesity Dataset from Kaggle, which comprises 2,111 records labeled into seven obesity levels, reflecting a realistic class distribution imbalance. Preprocessing steps included data cleaning, encoding, and stratified splitting. To enhance class representation, two augmentation methods were applied: SMOTE for synthetic oversampling and Generative Adversarial Networks (GANs) for generating realistic minority samples. A stacking ensemble model was constructed using Random Forest and XGBoost as base learners, with Logistic Regression serving as the meta-learner. Hyperparameter optimization was conducted using both grid and randomized search methods. Evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess performance. The proposed model achieved a 91% accuracy and an F1-score of 0.89, significantly outperforming models from previous studies. These findings suggest that combining ensemble learning with hybrid augmentation strategies effectively addresses class imbalance and improves predictive reliability in obesity risk estimation. The developed model holds practical value as a decision-support tool for early screening and targeted intervention in obesity prevention programs.
subject: Articles
  topic: section
subject: Augmentation
  topic: keywords
subject: Classification
  topic: keywords
subject: Ensemble Learning
  topic: keywords
subject: Imbalanced Data
  topic: keywords
subject: Obesity
  topic: keywords
creator: Nur Tulus Ujianto
creator: Gunawan Gunawan
creator: Wresti Andriani
creator: Ivan Rizky Ramadhani
creator: Nasichatun Nasichatun
rights: Copyright (c) 2023 Nur Tulus Ujianto, Gunawan Gunawan, Wresti Andriani, Ivan Rizky Ramadhani, Nasichatun Nasichatun (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 96
ending page: 105
doi: 10.35335/1bg4ws75

Anomaly Detection of Parasitic Plankton in Brebes Eco-Waters Using Vision-Based Autoencoder AI
type: item
description: The escalating impact of environmental stress on coastal ecosystems necessitates reliable, scalable tools for monitoring marine biodiversity. This study proposes an unsupervised anomaly detection framework to identify parasitic and morphologically abnormal plankton in the waters of Brebes, Indonesia. The primary aim is to develop an interpretable, vision-based system capable of detecting visual anomalies without relying on labeled anomaly data. The research integrates convolutional autoencoders for reconstructing normal plankton images, Principal Component Analysis (PCA) for feature extraction, and One-Class Support Vector Machines (OC-SVM) for classification. Monthly microscopic images were obtained from selected mangrove and aquaculture pond sites in Brebes, Central Java, using portable digital microscopy under standardized field conditions. Images that exceeded a dynamic reconstruction threshold were flagged as anomalous and validated by marine biology experts. The system achieved an F1-score of 86.1%, a precision of 85.3%, and an AUC of 0.94, demonstrating high effectiveness in distinguishing between normal and anomalous plankton. With an average inference time of 0.37 seconds per image, the system supports near real-time monitoring. These results confirm the potential of the proposed method as a low-latency, field-deployable solution for aquatic ecosystem surveillance. By integrating AI-based detection with ecological expert validation, this research offers a scalable approach for marine biodiversity assessment and establishes a foundation for future adaptive environmental monitoring systems.
subject: Articles
  topic: section
subject: Anomaly Detection
  topic: keywords
subject: Autoencoder
  topic: keywords
subject: Marine Ecology
  topic: keywords
subject: Plankton Monitoring
  topic: keywords
subject: Unsupervised Learning
  topic: keywords
creator: Gunawan Gunawan
creator: Wresti Andriani
creator: Sesilia Putri Maryanto
creator: Restu Abi Mustaqiim
rights: Copyright (c) 2025 Gunawan Gunawan, Wresti Andriani, Sesilia Putri Maryanto, Restu Abi Mustaqiim (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 79
ending page: 95
doi: 10.35335/75mwxm55

Implementation of UI/UX Design in the Development of Profile Website Using the User Centered Design Method
type: item
description: The evolution of UI/UX design on the profile webpage is covered in this study. A community called Omah Cikal Kreatif aims to empower housewives by fostering their creativity via handicrafts made from secondhand items. The primary obstacle is the limitations of digital platforms' user interface (UI) design and user experience (UX), which affect user engagement and low exposure. This research aims to design and develop an attractive, informative and easy-to-use website by applying the UCD approach. The development process includes user needs analysis, prototype design, and design evaluation using the System Usability Scale (SUS) method. The results of this research provide strategic insights in improving the quality of website design and strengthening Omah Cikal Kreatif's identity in the digital realm
subject: Articles
  topic: section
subject: User Centered Design
  topic: keywords
subject: System Usability Testing
  topic: keywords
subject: Website Profile
  topic: keywords
creator: Savanda Harianty
creator: Sri Wahyuni
creator: Ruly Dwi Arista
rights: Copyright (c) 2025 Savanda Harianty, Sri Wahyuni, Ruly Dwi Arista (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 69
ending page: 78
doi: 10.35335/14gh9964

Implementation of the Topsis Method in Determining Online Shopping Options in the Marketplace
type: item
description: The development of information technology changes the way customers view shopping. Currently, customers are more likely to make online shopping transactions through the marketplace. Significantly increased internet penetration, ease of transactions, seller reputation, speed of service, ease of access are factors that support customers in making shopping transactions in the marketplace. However, customers need to decide wisely before making a shopping transaction so that the products obtained are in accordance with expectations. This study uses the TOPSIS method, a method in the decision-making process to choose an ideal solution based on the criteria offered. The results of the study obtained a result of 0.85 for product reviews as an alternative preference in shopping online through the marketplace.
subject: Articles
  topic: section
subject: Decision Support System
  topic: keywords
subject: Marketplace
  topic: keywords
subject: Online Shopping
  topic: keywords
subject: TOPSIS
  topic: keywords
creator: Tuslaela Tuslaela
creator: Enok Tuti Alawiah
creator: Helina Apriyani
rights: Copyright (c) 2025 Tuslaela Tuslaela, Enok Tuti Alawiah, Tuslaela (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 60
ending page: 68
doi: 10.35335/ckpd8y89

Development of distance measurement accuracy technology in physical activity tracking applications with a reward point system
type: item
description: In the digital era, physical activity tracking applications have become increasingly popular as tools to monitor body health and encourage healthy habits. However, the accuracy of distance measurements used by many of these applications still faces challenges, especially in environments with GPS signal interference. This research aims to develop a system that integrates GPS technology and accelerometer sensors to improve the accuracy of distance measurements in physical activity tracking applications. The developed system was tested through a prototype to evaluate the effectiveness of combining these two technologies in improving measurement results. Additionally, this research also designs a database system for efficient physical activity data management, enabling real-time monitoring. To enhance user motivation, a reward point system was applied as a gamification element to encourage further engagement in physical activities. The results of this research show that the combined use of GPS and accelerometers was able to improve measurement accuracy, with errors ranging from 2.4% to 4.2%, depending on the type of activity performed. Walking activities demonstrated higher accuracy compared to running. The reward point system was also proven to be effective in motivating users to be more active. This research provides an important contribution to the development of more accurate, efficient health applications that can improve both physical and mental well-being
subject: Articles
  topic: section
subject: Accelerometer
  topic: keywords
subject: Distance Measurement
  topic: keywords
subject: GPS
  topic: keywords
subject: Physical Activity
  topic: keywords
subject: Reward Point System
  topic: keywords
creator: Oki Setiono
creator: Nurjanah Nurjanah
creator: Kismi Mubarokah
creator: Haikal Haikal
creator: Muhammad Iqbal
rights: Copyright (c) 2025 Oki Setiono, Nurjanah Nurjanah, Kismi Mubarokah, Haikal Haikal, Muhammad Iqbal (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 50
ending page: 59
doi: 10.35335/85rbrg31

Performance Analysis of Wet Gas Flow in Up and Down Transmission Pipelines
type: item
description: This research examines the performance of wet gas flow in up and down transmission pipelines, addressing the critical challenges and dynamics associated with fluid behavior in varying orientations. Through extensive empirical analysis and modeling, the study identifies key factors influencing slugging, liquid accumulation, and flow efficiency. Findings reveal that upward transmission systems are particularly prone to slugging, leading to operational instability and increased energy demands, while downward flow systems benefit from gravitational assistance, resulting in enhanced reliability and reduced maintenance needs. Additionally, effective liquid management strategies and advanced monitoring technologies are essential for mitigating adverse effects and optimizing system performance. The research also addresses gaps in existing literature by providing new insights into flow management and environmental considerations, offering practical recommendations for pipeline design and operation. Ultimately, this study underscores the importance of ongoing research to refine understanding and improve practices in the transport of wet gas, contributing to the development of sustainable energy solutions.
subject: Articles
  topic: section
subject: Wet Gas Flow
  topic: keywords
subject: Pipeline Transmission
  topic: keywords
subject: Slugging
  topic: keywords
subject: Liquid Management
  topic: keywords
subject: Flow Efficiency
  topic: keywords
creator: Silvester Adi Surya Herjuna
creator: Andrieu Labriet
creator: Bima Akbar
creator: Johannes Dimara
creator: Frans Kaisiepo
rights: Copyright (c) 2024 Silvester Adi Surya Herjuna, Andrieu Labriet, Bima Akbar, Johannes Dimara, Frans Kaisiepo (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-11-30
publication date: 2024-11-30
starting page: 118
ending page: 128
doi: 10.35335/apjc7f13

Analysis of the Effect of Pretreatment of Empty Palm Oil Bunches (TKKS) on Biogas Production
type: item
description: This study investigates the effect of different pretreatment methods mechanical, chemical, and biological on biogas yield and quality from TKKS. Mechanical pretreatment involves shredding and grinding TKKS to increase surface area and enhance enzymatic accessibility, facilitating microbial degradation of cellulose and hemicellulose. Chemical pretreatment employs acid hydrolysis to disrupt lignocellulosic bonds, releasing fermentable sugars for improved substrate availability in anaerobic digestion. Biological pretreatment utilizes enzymatic or microbial processes to enhance biomass deconstruction and accelerate methane production. Experimental results demonstrate that all pretreatment methods enhance biogas production compared to untreated TKKS. Mechanical pretreatment yields significant improvements in biogas yield and methane content, owing to enhanced substrate accessibility. Chemical pretreatment shows comparable efficacy, albeit with considerations for optimal acid concentration and microbial inhibition. Biological pretreatment exhibits superior methane production rates, underscoring its potential in maximizing biogas recovery from TKKS. The implications of these findings extend to sustainability benefits, including reduced greenhouse gas emissions and enhanced waste management practices within the palm oil industry. Techno-economic feasibility and scalability considerations highlight mechanical and biological pretreatment methods as viable options for industrial-scale biogas production from TKKS.
subject: Articles
  topic: section
subject: TKKS
  topic: keywords
subject: Biogas production
  topic: keywords
subject: Pretreatment methods
  topic: keywords
subject: Sustainable energy
  topic: keywords
subject: Palm oil industry
  topic: keywords
creator: David Manuel
creator: Nasihin Rahman
rights: Copyright (c) 2024 David Manuel, Nasihin Rahman (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-06-27
publication date: 2024-06-27
starting page: 109
ending page: 117
doi: 10.35335/0zwrjp26

Analysis of the Impact of Paving Block Material Variations on Urban Traffic Flow, Safety, and Sustainability
type: item
description: This study investigates the impact of varying paving block materials on traffic flow, safety, durability, and environmental sustainability in urban settings. By analyzing data from multiple field studies, laboratory experiments, and performance models, we compared traditional materials such as asphalt and concrete with innovative options like porous asphalt, permeable pavers, and recycled composites. Our findings reveal that smoother materials, including asphalt and certain concrete types, enhance traffic flow by reducing rolling resistance, while textured surfaces offer superior skid resistance, thereby improving safety. Concrete demonstrated notable durability and lower long-term maintenance costs compared to asphalt, while sustainable materials like recycled composites and permeable pavers exhibited reduced carbon footprints and enhanced stormwater management capabilities. The implications of this research suggest that informed material selection can significantly improve urban traffic efficiency, safety, and sustainability, providing a roadmap for urban planners and policymakers aiming to develop resilient and environmentally friendly infrastructure.
subject: Articles
  topic: section
subject: Paving Materials
  topic: keywords
subject: Traffic Flow
  topic: keywords
subject: Road Safety
  topic: keywords
subject: Urban Infrastructure
  topic: keywords
subject: Environmental Sustainability
  topic: keywords
creator: Rivaldi Miwa
creator: Aswinda Aswinda
rights: Copyright (c) 2024 Rivaldi Miwa, Aswinda Aswinda (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-06-26
publication date: 2024-06-26
starting page: 98
ending page: 108
doi: 10.35335/bfmnfy19

Performance Analysis of Insulation Materials in Earthquake Resistant Buildings
type: item
description: This research investigates the performance of insulation materials in enhancing the seismic resilience of buildings against earthquake-induced forces. The study focuses on evaluating various insulation materials including fiberglass, mineral wool, and foam boards (EPS and XPS) through rigorous experimental testing under simulated seismic conditions. Findings reveal significant differences in the seismic response of insulation materials based on their damping capacities, stiffness characteristics, and resilience post-seismic event. Mineral wool demonstrates superior energy dissipation properties, effectively reducing structural vibrations and enhancing building stability. Conversely, certain foam board insulations exhibit high compressive strength, maintaining structural integrity and controlling deformations under dynamic loading conditions. The study's results align with theoretical expectations in earthquake engineering, validating the importance of material properties in enhancing building performance under seismic hazards. The implications of this research extend to informing building codes, standards, and design practices aimed at promoting sustainable and resilient urban infrastructure in earthquake-prone regions.
subject: Articles
  topic: section
subject: Insulation materials
  topic: keywords
subject: Earthquake resilience
  topic: keywords
subject: Seismic performance
  topic: keywords
subject: Building design
  topic: keywords
subject: Structural dynamics
  topic: keywords
creator: Fabrio Situmorang
creator: Rifanro Situmorang
rights: Copyright (c) 2024 Fabrio Situmorang, Rifanro Situmorang (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-06-25
publication date: 2024-06-25
starting page: 88
ending page: 97
doi: 10.35335/w79fz846

Analysis of Picohydro Power Plant Design on Water Flow and its Utilization
type: item
description: Picohydro power plants represent a promising solution for decentralized energy generation in remote and underserved regions, leveraging the natural energy potential of small water flows. This research investigates the design principles, efficiency considerations, and socio-economic impacts of picohydro systems to assess their viability and potential contributions to sustainable development. The study begins with a comprehensive review of existing literature to establish foundational knowledge on picohydro technology and its applications. It then proceeds with empirical analyses, including case studies and field surveys, to gather firsthand data on system performance, water flow dynamics, and community perceptions. Computational simulations further optimize design parameters, such as turbine selection and system configuration, to maximize energy extraction efficiency under varying operational conditions. Key findings highlight the critical role of tailored engineering solutions in enhancing picohydro system performance and reliability. Socio-economic analyses underscore the transformative impact of picohydro installations on improving energy access, supporting local livelihoods, and stimulating economic growth in rural areas. Environmental assessments emphasize the importance of eco-friendly design practices to minimize ecological impacts and ensure sustainable operation.
subject: Articles
  topic: section
subject: Picohydro
  topic: keywords
subject: Small-scale hydropower
  topic: keywords
subject: Renewable energy
  topic: keywords
subject: Rural electrification
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Gv Rian Simare-mare
creator: Bintra Rotua Simbolon
rights: Copyright (c) 2024 Gv Rian Simare-mare, Bintra Rotua Simbolon (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-06-25
publication date: 2024-06-25
starting page: 77
ending page: 87
doi: 10.35335/zhkrss73

Optimizing Evacuation Routes During Volcanic Eruptions: A Comparative Analysis of Pathfinding Algorithms around Mount Sinabung
type: item
description: This research explores the optimization of evacuation routes amid volcanic eruptions around Mount Sinabung, employing sophisticated pathfinding algorithms the Bellman Ford and Floyd Warshall algorithms. Leveraging diverse datasets encompassing geographical, demographic, and historical information, the study aims to identify optimal evacuation paths considering factors such as terrain conditions, population distribution, and real-time adaptability. The study's methodology involves the integration of geospatial data, historical eruption records, and infrastructure details into structured graph representations, enabling algorithmic computations to determine efficient evacuation routes. Comparative analyses of the Bellman Ford and Floyd Warshall algorithms highlight their strengths, limitations, and applicability in dynamic volcanic scenarios, offering nuanced insights into their performance. The findings reveal optimized evacuation routes that prioritize safety, efficiency, and inclusivity, catering to diverse demographic needs. Additionally, future research directions outlined for refining pathfinding algorithms stress the importance of interdisciplinary collaboration, technological advancements, and community-centric approaches in enhancing disaster preparedness and response strategies. This research contributes to the evolving landscape of disaster management by offering evidence-based insights, actionable recommendations, and a roadmap for policymakers, emergency responders, and local authorities.
subject: Articles
  topic: section
subject: Disaster Preparedness
  topic: keywords
subject: Evacuation Routes
  topic: keywords
subject: Mount Sinabung
  topic: keywords
subject: Pathfinding Algorithms
  topic: keywords
creator: Erikson Sinukaban
creator: Franky Sotar Sitohang
rights: Copyright (c) 2024 Erikson Sinukaban, Franky Sotar Sitohang (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-06-24
publication date: 2024-06-24
starting page: 63
ending page: 76
doi: 10.35335/scdp7k30

Optimizing Wind Energy Generation: Wind Speed Forecasting Using Elman Recurrent Neural Networks for Enhanced Power Generation in Turbines
type: item
description: This research delves into the realm of wind energy by exploring the accuracy of wind speed forecasting using the Elman Recurrent Neural Network (RNN) and its direct influence on the power generation of wind turbines. Leveraging historical wind speed data and employing the Elman RNN, this study demonstrates the model's precision in forecasting wind speeds, capturing temporal dependencies, and elucidating their impact on electricity output. Correlating these forecasts with actual power generation records, the research establishes a profound relationship, showcasing how even minor variations in predicted wind speeds significantly influence the amount of electricity produced by wind turbines. The study's findings underscore the critical role of accurate wind speed predictions in optimizing wind farm operations, enhancing energy capture efficiency, and contributing to grid stability. Furthermore, the research sets the stage for practical applications in renewable energy planning and policy-making, offering insights that shape the future trajectory of wind energy utilization. The research concludes by proposing avenues for further refinement in predictive models, real-time integration strategies, and long-term forecasting, guiding the path towards a sustainable and resilient energy landscape.
subject: Articles
  topic: section
subject: Elman Recurrent Neural Network
  topic: keywords
subject: Power Generation
  topic: keywords
subject: Renewable Energy Optimizations
  topic: keywords
subject: Wind Speed Forecasting
  topic: keywords
subject: Wind Turbines
  topic: keywords
creator: Anthony Brandon
creator: Richrad Xavier
creator: Cley Valentine Ludwig
rights: Copyright (c) 2023 Anthony Brandon, Richrad Xavier, Cley Valentine Ludwig (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 51
ending page: 62
doi: 10.35335/zgnvvy88

Optimizing Anthurium Cultivation: Advancements in Greenhouse Design for Enhanced Plant Quality in Controlled Environments
type: item
description: Anthuriums, renowned for their captivating blooms and lush foliage, thrive in controlled environments. This research delves into the nuanced intricacies of greenhouse design, aiming to optimize growth conditions and enhance the quality of anthurium plants. Through meticulous experimentation and comparative analysis of various greenhouse configurations, the study explores the impact of structural elements, environmental variables, lighting strategies, and soil management on anthurium growth and flowering patterns. Distinct greenhouse structures, including glass, polycarbonate, and advanced plastics, were scrutinized for their effects on anthurium health and quality. Findings reveal the significance of elements such as insulation, humidity regulation, ventilation, and balanced lighting strategies in fostering superior plant vitality. Polycarbonate or advanced plastic structures emerged as optimal environments, ensuring stable conditions conducive to vibrant foliage, prolific blooms, and overall plant vigor. The research extends beyond plant quality, emphasizing economic viability and sustainability. While aesthetics traditionally favored glass structures, the evidence supporting energy efficiency and reduced maintenance costs of alternative materials underscores their practicality in commercial anthurium cultivation. These insights not only redefine greenhouse design paradigms but also serve as a guiding beacon for future research and industry practices. They pave the way for innovative approaches and sustainable cultivation methods, heralding a new era of precision and excellence in anthurium cultivation within controlled greenhouse environments.
subject: Articles
  topic: section
subject: Anthurium Cultivation
  topic: keywords
subject: Greenhouse Design
  topic: keywords
subject: Plant Quality
  topic: keywords
subject: Controlled Environments
  topic: keywords
subject: Ornamental Horticulture
  topic: keywords
creator: Albertus Bahri
creator: Chairi Kamoga
creator: Kinza Adiguna Pallawarukka
rights: Copyright (c) 2023 Albertus Bahri, Chairi Kamoga, Kinza Adiguna Pallawarukka (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 40
ending page: 50
doi: 10.35335/sm80xp89

Analyzing the Impact of Occupational Safety Programs on Work Productivity Using Fault Tree Analysis
type: item
description: This research delves into the intricate relationship between the implementation of an occupational safety program and its influence on work productivity within organizational settings. Employing fault tree analysis as a primary methodology, the study aims to scrutinize the efficacy of safety initiatives and delineate their direct correlation with productivity outcomes. Through a mixed-method approach integrating qualitative and quantitative analyses, the research examines the effectiveness of safety programs in mitigating workplace hazards and fostering a culture of safety. The fault tree analysis dissects causal factors, identifying critical pathways and root causes impacting work productivity. Findings reveal a symbiotic relationship between safety measures and productivity metrics. A well-executed safety program emerges as a catalyst for improved operational efficiency, employee engagement, and quality of work. The implications drawn from this study advocate for a paradigm shift, where safety initiatives are integrated strategically, nurturing an environment where safety and productivity coalesce seamlessly. This research contributes actionable insights for organizations to optimize safety measures, bolster productivity, and foster a culture where safety isn't just a priority but a fundamental value. Ultimately, the findings offer a compass guiding organizations towards a future where safety and productivity converge synergistically, shaping a landscape of sustained success and employee well-being.
subject: Articles
  topic: section
subject: Fault Tree Analysis
  topic: keywords
subject: Occupational Safety Program
  topic: keywords
subject: Productivity Impact
  topic: keywords
subject: Workplace Safety
  topic: keywords
subject: Work Productivity
  topic: keywords
creator: Alex Christhoper
creator: Dareen Dominic
rights: Copyright (c) 2023 Alex Christhoper, Dareen Dominic (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 26
ending page: 39
doi: 10.35335/c10g3861

Advancing Fault Diagnosis in BLDC Motors: Surge Test-Based Diagnostic Equipment for Phase to Ground Faults
type: item
description: This research introduces a groundbreaking approach to fault diagnosis in Brushless DC (BLDC) motors through the design and validation of specialized diagnostic equipment utilizing surge tests for phase to ground faults. The study focuses on identifying, localizing, and classifying various fault types, including insulation breakdowns, erosion, penetration, and partial grounding within BLDC motors. The research methodology encompasses theoretical frameworks, experimental validations, and comparative analyses with existing diagnostic methods. Surge tests conducted on BLDC motors with induced faults revealed distinct fault signatures, providing precise fault localization and aiding in the establishment of diagnostic criteria and thresholds. The findings showcased the developed equipment's precision, reliability, and automated fault classification capabilities, surpassing the limitations of traditional diagnostic methods. Comparisons with conventional techniques highlighted the advantages of the developed approach, emphasizing its heightened sensitivity, objectivity, and potential for predictive maintenance strategies. The equipment's ability to offer quantifiable fault parameters and establish diagnostic thresholds presents a transformative potential for proactive maintenance, minimizing downtime, and enhancing operational efficiency in BLDC motor-driven systems. The research findings underline the significance of surge tests and specialized diagnostic equipment in revolutionizing fault diagnosis practices for BLDC motors. The implications extend to industry-wide adoption, offering a pathway for enhanced reliability, safety, and operational continuity in various industrial applications.
subject: Articles
  topic: section
subject: BLDC Motors
  topic: keywords
subject: Diagnostic Equipment
  topic: keywords
subject: Fault Diagnosis
  topic: keywords
subject: Phase to Ground Faults
  topic: keywords
subject: Surge Tests
  topic: keywords
creator: Stephen Larigaduelle
creator: Jeseph Patrick William
rights: Copyright (c) 2023 Stephen Larigaduelle, Jeseph Patrick William (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 13
ending page: 25
doi: 10.35335/p5j7yp39

Enhancing Hydrogen Production Efficiency in Water Electrolysis through Polycarbonate Integration from CD-R Waste
type: item
description: This research explores the integration of polycarbonate derived from CD-R (Compact Disc-Recordable) waste into water electrolysis for hydrogen production, aiming to assess its catalytic potential and influence on electrolysis efficiency. The study investigates the effects of waste-derived polycarbonate on hydrogen evolution rates, electrolysis kinetics, and operational parameters. Experimental trials reveal a notable enhancement in hydrogen production rates with the incorporation of waste-derived polycarbonate, suggesting its potential catalytic influence within the electrolysis process. However, observed variations and nonlinear concentration-efficiency relationships underscore the complexities and challenges in achieving consistent performance. Limitations related to material dispersion and compatibility highlight critical areas requiring optimization for practical implementation. The findings offer insights into the nuances of waste-derived material integration in electrochemical systems, emphasizing the need for further research to address challenges and optimize the utilization of waste-derived materials in advancing clean energy technologies.
subject: Articles
  topic: section
subject: Catalytic Efficiency
  topic: keywords
subject: CD-R Waste
  topic: keywords
subject: Electrolysis
  topic: keywords
subject: Hydrogen Production
  topic: keywords
subject: Polycarbonate
  topic: keywords
creator: Matthews Romagnoli
creator: Boiral Lymberopoulos Sevastya
creator: Vanechka Valery
rights: Copyright (c) 2023 Matthews Romagnoli, Boiral Lymberopoulos Sevastya, Vanechka Valery (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 01
ending page: 12
doi: 10.35335/7ds16z37

Men's facial foam selection decision support system based on skin type
type: item
description: This research presents the development of a Decision Support System (DSS) aimed at assisting men in selecting facial foam products based on their skin types. The growing interest in skincare and grooming among men has led to an abundance of facial care products in the market, making it challenging for consumers to choose the most suitable option for their individual needs. The WDSS addresses this predicament by intelligently analyzing user input, classifying skin types, and generating personalized product recommendations. The conceptual framework of the WDSS combines content-based filtering and collaborative filtering techniques to ensure accuracy and relevance in recommending facial foam products. The Decision Support System offers a valuable tool for men seeking the most suitable facial foam products based on their individual skin types. The system's ability to provide personalized recommendations contributes to improved self-confidence and promotes proactive self-care practices among users. Continuous efforts in refining algorithms and updating the product database are essential to ensure the DSS's accuracy and relevancy as the skincare industry continues to evolve. The research seeks to empower men in their skincare journey, fostering a positive impact on their overall well-being and self-image.
subject: Articles
  topic: section
subject: Facial Foam Products
  topic: keywords
subject: Men's Skincare
  topic: keywords
subject: Personalized Recommendations
  topic: keywords
subject: Skin Type Classification
  topic: keywords
creator: Jonhariono Sihotang
creator: Roma Sinta Simbolon
creator: Amran Manalu
rights: Copyright (c) 2022 Jonhariono Sihotang, Roma Sinta Simbolon, Amran Manalu (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 71
ending page: 79
doi: 10.35335/vxydbw52

Fake news detection using naive bayes classifier and forward selection in the digital era
type: item
description: This study investigates the application of the Naive Bayes Classifier Method with the Forward Selection Technique in an effort to detect hoax news. Through a hypothetical numerical example, this study illustrates the basic steps involved in this approach. The Naive Bayes method is used to estimate class probabilities based on the selected text features, while the Forward Selection technique is used to select the most informative features. The results and implications of this approach are discussed in the context of potential real-world applications. This research provides an initial understanding of the use of these techniques in dealing with the challenge of detecting fake news in the digital age. While this research does not describe the more complex aspects of real-world fake news detection, it highlights a foundation that can be developed for further efforts to improve information integrity in an increasingly complex digital environment.
subject: Articles
  topic: section
subject: Digital Era
  topic: keywords
subject: Fake News Detection
  topic: keywords
subject: Forward Selection
  topic: keywords
subject: Information Reliability
  topic: keywords
subject: Naive Bayes Classifier
  topic: keywords
creator: Traore Lei Ogilvie
creator: Monti Sharma
creator: Zhou Xei Huu
rights: Copyright (c) 2022 Traore Lei Ogilvie, Monti Sharma, Zhou Xei Huu (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 64
ending page: 70
doi: 10.35335/rxg67q35

Design of an expert system for diagnosing eye disease using the certainty factor method
type: item
description: Accurate eye disease diagnosis is essential for therapy and vision preservation. Complexity and diversity of symptoms often confound established diagnostic methods. We suggest designing an expert system for eye illness diagnosis utilizing the certainty factor method, which handles ambiguous and imprecise medical diagnoses. A systematic technique to improve diagnostic accuracy is proposed in this research to bridge medical experience and computational reasoning. The research begins with a mathematical framework for symptoms, test data, and diagnostic conclusions. The system uses medical-inspired rule-based inference to aid evidence-based reasoning. The certainty factor technique quantifies diagnosis confidence, ensuring transparency and justifiability. A numerical example shows how to apply the strategy. The simple example shows how the expert system can analyze various criteria and make well-supported diagnoses. It emphasizes symptom analysis, test results, and the certainty factor method's capacity to handle uncertainty. This research emphasizes the interaction between artificial intelligence and medical competence and is conceptual. Real-world application involves medical practitioner participation, intensive testing, and patient data validation. This research advances medical diagnostic tools by combining computational and clinical knowledge. It symbolizes a shift toward efficient, accurate, and transparent diagnostic methods to improve patient care and healthcare.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Certainty Factor Method
  topic: keywords
subject: Diagnosing Eye Diseases
  topic: keywords
subject: Expert System
  topic: keywords
subject: Medical Diagnosis
  topic: keywords
creator: Birch Shah
creator: Zeeshan
creator: Asghar Sadiq
creator: Übeyli Ung
creator: Shanbhag Gilmore
rights: Copyright (c) 2022 Birch Shah , Zeeshan , Asghar Sadiq, Übeyli Ung , Shanbhag Gilmore (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 56
ending page: 63
doi: 10.35335/vw11e535

Optimizing face sensor-based attendance system using wavelet method for enhanced security and efficiency
type: item
description: In today’s digital era, efficient and accurate attendance management is essential in various sector. This research presents an innovative approach in the development of facial sensor-based attendance systems using the wavelet method. This approach aims to address the challenges of recognizing faces under varying lighting conditions, rotations, and facial details. Image pre-processing is used to improve the quality of the face image, followed by a discrete wavelet transform to decompose the image into wavelet coefficients at various scales. This model is strengthened by strong security techniques, considering the importance of individual data privacy. This research offers a modern alternative in attendance management by utilizing facial recognition technology and the wavelet method. Although the mathematically formulated model provides a clear framework, practical implementation requires advanced techniques in image processing and artificial intelligence.
subject: Articles
  topic: section
subject: Absence
  topic: keywords
subject: Face recognition
  topic: keywords
subject: Face sensor
  topic: keywords
subject: Security and privacy
  topic: keywords
subject: Wavelet method
  topic: keywords
creator: Priya Shimizu
creator: Hagihara Kobatake
creator: Subasi Tziritas Ezema
creator: Maiti Eneh Bhanot
creator: Das Junior
rights: Copyright (c) 2022 Priya Shimizu , Hagihara Kobatake, Subasi Tziritas Ezema, Maiti Eneh Bhanot, Das Junior (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 50
ending page: 55
doi: 10.35335/629k6b98

Adaptive traffic control at complex intersections using fuzzy logic multi-agent approach
type: item
description: In an increasingly dense urban environment, efficient and adaptive traffic management is essential to maintain smooth mobility and reduce congestion. In this effort, the Multi-Agent Fuzzy Logic method emerges as a promising approach to overcome the complexity and fluctuation of traffic conditions. This study aims to investigate the potential application of the Multi-Agent Fuzzy Logic method in controlling traffic lights at complex crossroads. Within this conceptual framework, a mathematical formulation model and a programming algorithm are developed that enable the simulation of traffic light settings with the Multi-Agent Fuzzy Logic approach. By fuzzifying input variables to convert numeric data into linguistic variables. Furthermore, applying fuzzy rules to make adaptive decisions based on traffic conditions and coordination between agents in a multi-agent system. The results of this system are then validated through simulations, with evaluation metrics such as average waiting time, energy efficiency, and traffic smoothness. The results of this study indicate that the Multi-Agent Fuzzy Logic method is capable of producing traffic light settings that are responsive to changes in traffic conditions on each lane. By coordinating between agents, the average waiting time can be reduced, energy efficiency can be increased, and traffic flow can be improved.
subject: Articles
  topic: section
subject: Adaptive Traffic Management
  topic: keywords
subject: Fuzzy Logic
  topic: keywords
subject: Multi-Agent System
  topic: keywords
subject: Traffic Control
  topic: keywords
subject: Traffic Intersections
  topic: keywords
creator: Zainal Yang Xu
creator: Rivera Smith Ager
creator: Niu Wylie Sjödin
creator: Mubashar Wylie Pintrich
rights: Copyright (c) 2022 Zainal Yang Xu, Rivera Smith Ager, Niu Wylie Sjödin, Mubashar Wylie Pintrich (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 43
ending page: 49
doi: 10.35335/0meps156

Autonomous and sustainable air navigation technologies shaping the future of aeronautical engineering
type: item
description: Technology, regulation, operations, and environmental awareness are transforming the aviation business. This study examines aeronautical technologies, focusing on paradigm-shifting autonomous and sustainable air navigation. A multidimensional conceptual framework encompassing unmanned aerial vehicles (UAVs), electric and hybrid-electric propulsion, Urban Air Mobility (UAM), sustainable aviation fuels (SAFs), advanced avionics, AI, noise reduction technologies, and collaborative stakeholder engagement emerges from a comprehensive review of existing research. In a numerical route planning scenario, optimal route optimization reduces fuel consumption, emissions, and operational costs while meeting energy restrictions. This shows how mathematics helps solve air navigation's modern problems. A holistic, interdisciplinary strategy is needed to address the numerous, interwoven aspects of autonomous and sustainable air navigation. Technology and regulation must work together to integrate autonomous and sustainable technology. The research shows how air traffic management systems must balance safety, efficiency, and environmental stewardship. Aviation engineers, environmental specialists, regulatory authorities, lawmakers, and industry stakeholders must work together to make skies safer, more accessible, and environmentally friendly. This research helps aviation engineering enter a new era of autonomous and sustainable flight navigation. As the aviation industry moves toward this transformative horizon, it promises improved operational efficiency, environmental sustainability, and societal advancement, ushering in an age of aviation that harmonizes human aspirations with the vast sky
subject: Articles
  topic: section
subject: Aeronautical Innovations
  topic: keywords
subject: Autonomous Aviation
  topic: keywords
subject: Sustainable Air Navigation
  topic: keywords
subject: Technological Advancements
  topic: keywords
subject: Urban Air Mobility
  topic: keywords
creator: Ribeiro Karanjikar Rae
creator: Murashov Sutton Görür
creator: Babiceanu Babiceanu
creator: Sándor Sándor
rights: Copyright (c) 2021 Ribeiro Karanjikar Rae, Murashov Sutton Görür, Babiceanu Babiceanu, Sándor Sándor (Author) https://creativecommons.org/licenses/by/4.0
date: 2021-12-30
publication date: 2021-12-30
starting page: 34
ending page: 42
doi: 10.35335/m44pf111

Advancing air navigation engineering for autonomous and sustainable aviation: navigating technological innovation and regulatory frameworks for sustainable flight
type: item
description: Autonomous flying and sustainability are transforming the aviation business. This study examines how technology, policy, and society affect air navigation engineering for autonomous and sustainable aviation. A thorough literature analysis shows how navigation algorithms, sensor systems, and communication networks enable safe and efficient autonomous operations. To address autonomous flight's specific problems, aviation authorities, stakeholders, and researchers must collaborate on regulatory evolution. To gain public trust, safety, privacy, and ethics must be addressed. These observations suggest integrating technical innovation, regulatory frameworks, and public education. Sustainable aviation techniques emphasize aircraft route optimization and emissions reduction. The research highlights the collaborative efforts needed to negotiate the complicated landscape of autonomous and sustainable aviation, resulting in an efficient, safe, and environmentally responsible sector. This study guides industry stakeholders, politicians, and researchers toward reimagined flight operations and aeronautical advancement as the aviation sector navigates autonomy and sustainability
subject: Articles
  topic: section
subject: Air Navigation Engineering
  topic: keywords
subject: Autonomous Aviation
  topic: keywords
subject: Regulatory Frameworks
  topic: keywords
subject: Sustainable Flight
  topic: keywords
subject: Technological Innovation
  topic: keywords
creator: Zhaoxuan Ma Yang
creator: Feng Chao Jung
rights: Copyright (c) 2021 Zhaoxuan Ma Yang, Feng Chao Jung (Author) https://creativecommons.org/licenses/by/4.0
date: 2021-12-30
publication date: 2021-12-30
starting page: 26
ending page: 33
doi: 10.35335/716k5h38

Decision support system for selection of major concentration using fuzzy logic
type: item
description: The choice of major concentration at the tertiary level is an important stage in one's educational development, involving various subjective factors such as interests, abilities, career goals, and subject preferences. To overcome the complexity of this process, we propose the development of a Fuzzy Logic-based Decision Support System (SPK) that can provide recommendations for major concentrations that are more in line with student profiles. In this study, we designed and implemented a DSS model that uses the Fuzzy Logic method to overcome uncertainty and ambiguity in concentration selection. The membership function that has been defined describes the degree of membership in each relevant set. Fuzzy rules are formed based on domain knowledge and historical data, and are applied in the inference process to produce recommendations for major concentrations. Fuzzy Logic in building a Decision Support System that can improve the process of selecting major concentrations
subject: Articles
  topic: section
subject: Fuzzy Logic
  topic: keywords
subject: Decision Support System
  topic: keywords
subject: Selection of Major Concentration
  topic: keywords
subject: Student Interests
  topic: keywords
subject: Academic Ability
  topic: keywords
creator: Heo Wang Jee
rights: Copyright (c) 2021 Heo Wang Jee (Author) https://creativecommons.org/licenses/by/4.0
date: 2021-12-30
publication date: 2021-12-30
starting page: 19
ending page: 25
doi: 10.35335/f1j7ys60

Web-Based on-line learning (e-learning) decision support system
type: item
description: The rapid advancement of technology has revolutionized education, paving the way for innovative learning methods such as E-Learning. However, optimizing the effectiveness of online education poses challenges in data management and decision-making processes. This research investigates the integration of Web-Based Decision Support Systems (DSS) in E-Learning to enhance learning outcomes. The study develops a mathematical formulation that quantifies the impact of DSS by considering student engagement, knowledge retention, and academic achievement. A numerical example is presented to demonstrate the application of the formulation, showcasing the positive influence of the DSS on individual students and the overall cohort. The results emphasize the potential benefits of personalized learning experiences, data-driven insights, and informed decision-making facilitated by the DSS. Nonetheless, the limitations of the study are acknowledged, warranting further research with larger and more diverse samples. Overall, this research contributes to the discourse surrounding the role of Web-Based DSS in shaping the future of online education, empowering educators and learners to unlock the full potential of E-Learning in the digital age
subject: Articles
  topic: section
subject: Web-Based Decision Support; Systems
  topic: keywords
subject: E-Learning
  topic: keywords
subject: Learning Outcomes
  topic: keywords
subject: Personalized Learning
  topic: keywords
subject: Data Analytics
  topic: keywords
creator: Firta Sari Panjaitan
creator: Sonya Enjelina Gorat
rights: Copyright (c) 2021 Firta Sari Panjaitan, Sonya Enjelina Gorat (Author) https://creativecommons.org/licenses/by/4.0
date: 2021-12-30
publication date: 2021-12-30
starting page: 10
ending page: 18
doi: 10.35335/kpwbk147

Optimization-based decision support system for accurate earthquake epicenter determination
type: item
description: Accurate determination of earthquake epicenters is vital for effective disaster response and risk mitigation. This research proposes a Decision Support System (DSS) that leverages optimization techniques to enhance earthquake epicenter determination accuracy. The DSS combines seismic data processing, geospatial analysis, and advanced optimization algorithms to pinpoint epicenters with high precision. The study presents a mathematical formulation to minimize the misfit between observed and theoretical travel-time data using optimization algorithms. A numerical example showcases the DSS's effectiveness in accurately localizing seismic events. The research demonstrates that the DSS outperforms traditional methods, providing valuable insights for seismic monitoring agencies and disaster response teams. The DSS offers a promising solution for real-world applications, contributing to community safety and disaster preparedness in seismic-prone regions
subject: Articles
  topic: section
subject: Accurate Determination
  topic: keywords
subject: Earthquake Epicenter
  topic: keywords
subject: Geophysical Modeling
  topic: keywords
subject: Optimization Algorithms
  topic: keywords
subject: Seismic Localization
  topic: keywords
creator: Roma Sinta Simbolon
creator: Methodius Tigor
rights: Copyright (c) 2021 Roma Sinta Simbolon, Methodius Tigor (Author) https://creativecommons.org/licenses/by/4.0
date: 2021-12-30
publication date: 2021-12-30
starting page: 01
ending page: 09
doi: 10.35335/hd5are53

Next-Generation hyperpolarization techniques for NMR: amplifying signal sensitivity and resolving complex molecular systems
type: item
description: Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical tool used to investigate the structure and dynamics of molecules at the atomic level. However, its application to complex molecular systems, such as large biomolecules and diluted chemical mixtures, is often hindered by limited NMR signal sensitivity. To address this challenge, next-generation hyperpolarization techniques have emerged, offering the potential to enhance NMR signals significantly. This research explores the dynamic hyperpolarization enhancement process for NMR sensitivity through a mathematical formulation and a numerical example. The proposed model describes the transfer of polarization from polarizing agents to target molecules and its impact on nuclear spin polarization. The numerical example demonstrates how hyperpolarization techniques can amplify nuclear spin polarization over time, leading to improved NMR signal sensitivity. The research highlights the optimization of key parameters, such as relaxation time constants and polarization transfer rates, for achieving maximum sensitivity enhancements. The results underscore the transformative potential of hyperpolarization techniques in expanding the scope of NMR applications, enabling the study of complex molecular systems with unparalleled precision, and advancing scientific discoveries in biochemistry, materials science, and medical research. The conclusion emphasizes the ongoing efforts to develop next-generation hyperpolarization methods and their implications for fundamental and applied research. Ultimately, this research opens new frontiers in NMR spectroscopy, providing researchers with a powerful tool to explore intricate molecular systems and resolve scientific challenges across diverse disciplines
subject: Articles
  topic: section
subject: Complex molecular systems
  topic: keywords
subject: Hyperpolarization
  topic: keywords
subject: Next-generation techniques
  topic: keywords
subject: NMR spectroscopy
  topic: keywords
subject: Signal sensitivity
  topic: keywords
creator: Schmidt Laustsen Vorm
creator: Rößler Rößler
creator: Pajvani Duckett
rights: Copyright (c) 2022 Schmidt Laustsen Vorm, Rößler Rößler, Pajvani Duckett (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-12-30
publication date: 2022-12-30
starting page: 40
ending page: 50
doi: 10.35335/vm8x6r30

Quantum-assisted NMR data processing: enhancing sensitivity and resolution with quantum computing algorithms
type: item
description: Quantum computing is used to improve NMR spectroscopy sensitivity and resolution. Many scientific fields employ NMR to analyze molecular structures and interactions. Typical NMR data processing algorithms have limitations, especially in recognizing low-abundance compounds and resolving overlapping signals. To solve NMR data processing problems, the proposed research uses quantum algorithms, simulations, and a hybrid quantum-classical approach. Quantum Fourier transform (QFT) enhances sensitivity and quantum phase estimation (QPE) improves resolution. The QFT accelerates data analysis using quantum parallelism to detect low-concentration chemicals. QPEs accurately estimate phases, resolve overlapping peaks, and improve peak assignments. Quantum-assisted NMR data processing improvements are shown numerically. Quantum algorithms improve sensitivity and resolution, allowing delicate signals and correct structural assessments. This study also addresses quantum hardware restrictions, noise, and efficient quantum algorithm design. Quantum-assisted NMR data processing has the potential to transform NMR spectroscopy. Researchers can acquire new accuracy and sensitivity into molecule structures, interactions, and dynamics by linking quantum computers and NMR data analysis. This research advances quantum computing and NMR spectroscopy and lays the groundwork for future studies on quantum-assisted approaches in real-world NMR applications. Quantum-assisted data processing will enable novel molecular characterisation methods and groundbreaking scientific discoveries as quantum technologies advance.
subject: Articles
  topic: section
subject: Data Processing
  topic: keywords
subject: NMR Spectroscopy
  topic: keywords
subject: Quantum Computing
  topic: keywords
subject: Resolution Improvement
  topic: keywords
subject: Sensitivity Enhancement
  topic: keywords
creator: Vaara Bugay Russell
creator: Outeiral Strahm Sushkov
creator: Koch Bohnet Böhm
rights: Copyright (c) 2022 Vaara Bugay Russell, Outeiral Strahm Sushkov, Koch Bohnet Böhm (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-12-30
publication date: 2022-12-30
starting page: 30
ending page: 39
doi: 10.35335/a79g5q93