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Vertex
Published by Institute of Computer Science
ISSN : 2089385X     EISSN : 28296761     DOI : https://doi.org/10.35335/Vertex
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
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).
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 36 Documents
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Advancements in radiochemistry and nuclear methods of analysis for safer and sustainable applications Sosuke Han Sanada; Richard Nichida Shrestha
Vertex Vol. 12 No. 2 (2023): June: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/v4t2dh66

Abstract

This research investigates radiochemistry and nuclear analysis technologies to make applications safer and more sustainable. A mathematical optimization approach optimizes radiopharmaceutical synthesis parameters for positron emission tomography (PET) imaging to maximize yield and minimize radioactive waste. Optimizing critical parameters improves the efficiency, safety, and sustainability of radiochemistry and nuclear technologies in medical imaging, nuclear energy, and environmental monitoring. The numerical example shows that optimization achieves the study goal. Optimization strategies improve medical imaging by increasing radiopharmaceutical yield and decreasing radioactive waste volume. Real-world implementation requires cost-effectiveness, safety restrictions, and numerous synthesis factors. Researchers, policymakers, and industry professionals must collaborate to enhance human and environmental welfare, according to the report. In conclusion, this research advances radiochemistry and nuclear procedures for safer and more sustainable applications, mitigating hazards and environmental effect and ensuring a safer and more sustainable future for nuclear technology.
Advanced integration of stereotaxis and real-time MRI for precise and safe medical navigation: a future paradigm for minimally invasive interventions Lorenzo Söderholm Raygo; Wojcieszynski Puder Tonutti
Vertex Vol. 12 No. 2 (2023): June: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/x5n5be87

Abstract

Minimally invasive techniques have transformed medicine by improving patient outcomes and reducing invasiveness. Existing navigation methods, which use fluoroscopy or pre-operative imaging, lack real-time visualization and precision during complex surgeries. Fluoroscopy may also expose patients and medical staff to ionizing radiation. We propose enhanced stereotaxis and real-time magnetic resonance imaging (MRI) integration to overcome these problems and improve minimally invasive intervention precision and safety. Stereotactic guiding and high-resolution real-time MRI imaging are combined in this research to improve medical navigation. The conceptual framework includes modeling the stereotactic system's magnetic field, real-time tracking of magnetic-sensored medical devices, and dynamic MRI imaging for continuous visibility throughout treatments. Stereotactic and MRI data can be fused for simultaneous vision and navigation, and adaptive path planning algorithms allow real-time targeting and avoidance of key structures. A simulated cardiac electrophysiology catheter ablation treatment shows the combined approach's potential benefits. Real-time adaptive navigation reduces radiation exposure and problems while targeting precisely. This research establishes a new medical navigation paradigm that improves precision, patient safety, and radiation exposure. This integrated method could revolutionize minimally invasive procedures across medical disciplines, despite limitations in patient-specific data integration and real-time algorithm development. This new navigation approach needs further research, validation, and clinical trials to confirm its feasibility and efficacy and improve medical patient care
AI-Driven approach for enhancing nuclear reactor safety predictive anomaly detection and risk assessment Qureshi Sethu Russell; Nichols Peng Linzi
Vertex Vol. 12 No. 2 (2023): June: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/eh0bph05

Abstract

Nuclear power plays a vital role in meeting global energy demands, but ensuring the safety of nuclear reactors remains a paramount challenge. In recent years, the emergence of artificial intelligence (AI) technologies has opened new avenues to significantly enhance nuclear reactor safety through predictive anomaly detection and risk assessment. This research proposes an innovative AI-driven approach that integrates machine learning techniques and data analytics to monitor, detect, and assess potential anomalies in nuclear reactors. The research begins with a comprehensive literature review on nuclear reactor safety and the application of AI in various industrial domains, emphasizing predictive maintenance and anomaly detection. It highlights the need for an AI-driven approach to enhance nuclear reactor safety proactively. In conclusion, this research establishes the transformative potential of AI in enhancing nuclear reactor safety. The proposed AI-driven approach empowers operators with powerful tools to ensure the safe and efficient operation of nuclear power plants. As AI technologies continue to advance, the research opens doors for further exploration and development, paving the way for a more sustainable and secure future in nuclear energy production.
Nuclear energy in the era of climate resilience: advancing long-term scenarios with the world-times model Fankhauser Doyle Edenhofer; Lehtveer Loulou Parikh; Sen Zhu Wang Yu
Vertex Vol. 12 No. 2 (2023): June: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/eeqhzn35

Abstract

Sustainable energy routes that improve climate resilience are needed because climate change affects global energy systems. Nuclear energy's low-carbon electricity could mitigate climate change. This study uses the World-TIMES Model to assess its climatic resilience. A mathematical optimization model is used to discover the best energy mix, including nuclear power, to minimize greenhouse gas emissions and meet energy demand and cost limitations. We use a simplified numerical example to demonstrate the concept and assess nuclear energy, renewable sources, and cost-effectiveness trade-offs. Wind and solar electricity are better in the scenario, reducing greenhouse gas emissions and mitigating climate change. This conclusion is scenario-specific, and real-world difficulties demand more thorough models. Thus, the study emphasizes regional-specific data, dynamic dynamics, and sensitivity analysis. This work improves our understanding of nuclear energy's potential in climate-resilient energy systems and aids policymakers in developing evidence-based energy strategies. The report also emphasizes the importance of renewable energy sources in reaching climate targets and urges future research to solve real-world difficulties and maximize nuclear energy integration in long-term energy planning
Innovative approaches to nuclear energy density optimization for enhanced power generation and waste minimization Muellner Gufler Kromp; Frechette Pascual Zambetakis; Wood Wood
Vertex Vol. 12 No. 2 (2023): June: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/5jd0hm75

Abstract

Nuclear energy has emerged as a viable low-carbon option for electricity generation, but traditional reactor designs face challenges regarding energy density and nuclear waste management. This research explores innovative approaches to optimize nuclear energy density while minimizing long-term environmental impact through reduced waste production. The study compares a traditional Pressurized Water Reactor (PWR) with an advanced Molten Salt Reactor (MSR) as the innovative technology. The objectives are to maximize energy density and minimize nuclear waste. A multi-objective optimization model is formulated, incorporating safety, operational, and environmental constraints. Numerical results demonstrate that the MSR achieves a higher energy density (30 MW/kg) than the PWR (20 MW/kg) and produces less waste (0.2 kg/MW vs. 0.5 kg/MW). The research highlights the potential benefits of innovative nuclear technologies and emphasizes the importance of safety evaluations, regulatory considerations, and economic viability for practical implementation. Collaborative efforts and supportive policies are crucial to realizing a sustainable and low-carbon energy future through advanced nuclear solutions
Exploring exotic nuclei: advancing the nuclear shell model beyond traditional magic numbers using the generalized interacting boson model Krücken Cao Möller; Sachs Sherrill Porquet
Vertex Vol. 12 No. 1 (2022): December: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/3g3k3a86

Abstract

The study of exotic nuclei and advancements in the nuclear shell model beyond traditional magic numbers have been at the forefront of nuclear physics research. This research aims to understand the nuclear structure and properties of exotic nuclei with extreme neutron-proton imbalances, going beyond the conventional understanding based on the well-established magic numbers. The theoretical framework of the Generalized Interacting Boson Model (GIBM) is employed to capture collective degrees of freedom and describe nuclear shapes and interactions more accurately. A numerical example of a hypothetical exotic nucleus with 8 protons and 20 neutrons is presented, showcasing the GIBM's ability to predict low-energy states and explore deviations from traditional magic numbers. The results demonstrate potential shell evolution and the interplay between spherical and deformed bosons in exotic nuclei. The implications of these findings for astrophysical processes, particularly in stellar nucleosynthesis, are discussed. The research opens new avenues for understanding the behavior of exotic nuclei and their relevance to the origin of elements in the universe. Collaborative efforts between experimentalists and theorists continue to shape this exciting frontier of nuclear physics, paving the way for deeper insights into the fundamental nature of matter
Towards sustainable nuclear energy: innovations and solutions for the future Zohuri Cruickshank Vujić; Sovacool Ritch Hammad
Vertex Vol. 12 No. 1 (2022): December: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/1dq2vk79

Abstract

Nuclear energy is a potential answer to climate change and energy security. This study examines technical breakthroughs, economic viability, environmental impacts, social acceptance, and policy factors for sustainable nuclear energy. The paper overviews nuclear energy's history, highlighting successes and challenges that have affected public opinion and regulatory frameworks. To get public approval, it stresses safety and waste management. This research's conceptual framework sets goals and variables to optimize sustainable nuclear energy technology implementation. The multi-objective optimization model considers budget, waste management, and electricity consumption to minimize greenhouse gas emissions, increase resource usage, and minimize total costs. The paper uses a numerical example to explain how the optimization model may be used to deploy nuclear power stations to meet numerous goals. The ideal solution shows how modern reactor designs and fuel recycling reduce environmental impacts and increase resource efficiency. To successfully integrate nuclear energy, scientific improvements, economic feasibility, appropriate waste management, and public engagement are crucial. It highlights nuclear power's ability to help solve global energy issues and create a low-carbon energy system. The study concluded that sustainable nuclear energy requires international cooperation, reduced rules, and continual research and innovation. Nuclear energy can help create a cleaner, more sustainable energy future by taking a holistic approach
Advancements in predictive modeling of nuclear magnetic resonance parameters: integrating quantum mechanics, machine learning, and quantum computing Jonhariono Sihotang; Patrisius Michaud Felix Marsoit
Vertex Vol. 12 No. 1 (2022): December: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/qc6shb61

Abstract

This research explores the integration of quantum mechanics, machine learning, and quantum computing to advance predictive modeling of nuclear magnetic resonance (NMR) parameters. The aim is to develop a hybrid quantum-enhanced machine learning model that combines the accuracy of quantum calculations with the efficiency of machine learning techniques for predicting NMR chemical shifts. The conceptual framework involves quantum mechanical calculations for accurate reference NMR parameters, supervised machine learning models trained on diverse molecular datasets, and hybrid quantum-classical algorithms to leverage quantum computing resources. A simplified numerical example demonstrates the potential of the proposed model for predicting NMR chemical shifts for small molecular systems. The results showcase the model's ability to capture underlying relationships between molecular features and NMR observables, indicating promise for larger and more complex systems. This interdisciplinary approach opens new avenues for advancing NMR spectroscopy and understanding molecular structures, dynamics, and interactions in various scientific domains. The research also discusses challenges and opportunities in integrating quantum mechanics, machine learning, and quantum computing, emphasizing the importance of diverse datasets and quantum algorithm selection. The proposed model holds significant implications for transforming NMR parameter predictions and contributing to chemistry, biochemistry, and materials science research
Quantum-assisted NMR data processing: enhancing sensitivity and resolution with quantum computing algorithms Vaara Bugay Russell; Outeiral Strahm Sushkov; Koch Bohnet Böhm
Vertex Vol. 12 No. 1 (2022): December: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/a79g5q93

Abstract

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.
Next-Generation hyperpolarization techniques for NMR: amplifying signal sensitivity and resolving complex molecular systems Schmidt Laustsen Vorm; Rößler Rößler; Pajvani Duckett
Vertex Vol. 12 No. 1 (2022): December: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/vm8x6r30

Abstract

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

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