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Experimental study of natural circulation in Kartini reactor core: Effect of primary flow rate and power

AIP Conference Proceedings 2024年第1期2967卷

作者： Edi Sugianto Alexander Agung Anhar Riza Antariksawan Zulfikar Elran Bhagaskara Tri Nugroho Hadi Susanto Umar Sahiful Hidayat Argo Satrio Wicaksono Wahyu Nur Hidayat Aldhan Dewanto Putra Master Program in Engineering Physics Faculty of Engineering Gadjah Mada University Yogyakarta 55281 Indonesia National Research and Innovation Agency Yogyakarta 55281 Indonesia Nuclear Engineering and Physics Engineering Department Faculty of Engineering Gadjah Mada University Yogyakarta 55281 Indonesia

The Kartini research reactor core cooling uses natural convection. Primary cooling system is used to cool coolant by circulating coolant in reactor tank to a heat exchanger dissipating the heat to the secondary cooling system and back to the reactor tank. The coolant is injected to the reactor tank through a diffuser located above the core in order to dilute the N-16 radionuclide. However, it turns out that the primary coolant flow affected the natural circulation in the reactor core. This study was conducted to study the effect of primary coolant flow on natural circulation in the reactor core at various reactor power, as well as to determine the characteristics of natural circulation in the core. The experimental study was focused in the hot channel (ring B). The reactor was operated with a power of 50 kW, 75 kW, and 100 kW. At each power, the flow rate of the primary coolant changed from 100% to 0% flow rate with decrement of 20%. The instrumentation to measure coolant temperature were used to provide the axial coolant temperature distribution in the hot channel. From this temperature distribution, the flow pattern in the channel was predicted. An analytical sub-channel analysis was performed to calculate the natural circulation flow rate and DNBR. The experimental results showed that in all cases the primary flow influenced the flow in the core; the natural circulation was seemed balanced by forced circulation coming from primary flow. It was also observed that at low power, the temperature of the cooling fluid tends to be relatively stable compared to high power. When the primary flow rate is 0%, natural convection occurs in the reactor core with the velocity (u) and DNBR of 9.22 cm/s and 9.02, 9.24 cm/s and 6.02, and 11.55 cm/s and 4.51 at the power of 50 kW, 75 kW and 100 kW, respectively. The values of DNBR are still larger than safety requirement. It is concluded that the flow in the core of Kartini reactor is mixed convection due to primary coolant forced

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Gelatine-Based Phantom For Simulating A-Line Reverberations in Lung Ultrasonogram

Gelatine-Based Phantom For Simulating A-Line Reverberations ...

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International Conference on Instrumentation Control and Automation (ICA)

作者： Citra Alfa Nabila Saeful Bahri Damar Rastri Adhika Suprijanto Suprijanto Master Program of Instrumentation and Control Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Doctoral Program of Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Advanced Functional Material Research Group Faculty of Industrial Technology Research Center of Nanosciences and Nanotechnology Institut Teknologi Bandung Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institute Teknologi Bandung Bandung Indonesia

To understand typical features that could be imaged using ultrasonography (USG) in the training phases of medical students or practitioners, a lung phantom is required. The research team suggests using a gelatine-based phantom to demonstrate A-line reverberations in lung ultrasound. The ordinary gelatine-based phantom has the disadvantage of being too fragile and prone to breaking. The specific process was attempted on gelatine with a plasticizer and carrier agent to increase the gelatine's strength and elasticity. To simulate the A-lines, two different textures of gelatine layers are used, with other processes in between. A special procedure was used to set off the various acoustic impedances affecting reverberation physics with those varied textures. Based on USG image analysis, the proposed lung phantom material has some advantages: it is inexpensive and easy to find, despite its complicated procedures. Furthermore, it could be processed to have the desired properties and recycled after hardening with water bath melting.

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programming Variational Quantum Circuits with Quantum-Train Agent

Programming Variational Quantum Circuits with Quantum-Train ...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks programming Predictive models Quantum circuit physics Long short term memory

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Editorial: Biophotonics for cancer diagnostics and treatment

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Frontiers in physics 2022年 10卷

作者： Gogoi, Ankur Kao, Fu-Jen Liu, Yen-Liang Zhuo, Guan-Yu Department of Physics Jagannath Barooah College Jorhat India Institute of Biophotonics National Yang Ming Chiao Tung University Taipei Taiwan Master Program of Biomedical Engineering China Medical University Taichung Taiwan Institute of New Drug Development China Medical University Taichung Taiwan

In this context, innovations in biophotonics, which uses photons to interrogate tumor microenvironments and eliminate cancer cells and tissues, have significantly improved diagnostic acumen by improving the existing procedures and reducing the side effects of anti-cancer treatments. As laser light can be focused onto diffraction-limited spots, the induced light scattering and absorption deliver the information on cellular and sub-cellular levels, available for early detection of cancers. Cancer treatment relies on advanced laser technology with precise guidance to the surgical site and tumor mass. Ultra-short laser bursts allow for clear-cutting and drilling in tissues, which reduces time spent on surgery, bleeding, and patient's pain. Besides, new light sources characterized by low cost, miniaturization, and high flexibility have drawn much attention from medical doctors in cancer screening. On the other hand, the revolution in artificial intelligence (AI) and machine learning (ML) further enhances the efficiency and accuracy of image qualification and quantification, enabling differentiation between normal and malignant tissues and staging cancers [5][6][7][8].In this special issue of Frontiers in physics entitled "Biophotonics for cancer diagnostics and treatment," we gathered four articles exploring the state-of-the-art biophotonic techniques and machine learning methodologies for cancer diagnosis and treatment. In addition to cancer diagnostics, these articles demonstrated the trajectories of anti-cancer biophotonic methods for the phenotyping of cancer cells or tissues and projected the future direction of therapeutic innovations. The first article by Ellas Spyratou reviewed the role of optical tweezers as an efficient biophotonic tool in cancer theranostics. The article highlights the use of optical tweezers in determining cancer cells' biomechanical, biochemical, and biophysical properties by monitoring the cell-cell and/or protein-protein

关键词： imaging cancer diagnosis targeting probe surgery clinical translation

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programming Variational Quantum Circuits with Quantum-Train Agent

arXiv

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arXiv 2024年

作者： Liu, Chen-Yu Chen, Samuel Yen-Chi Chen, Kuan-Cheng Huang, Wei-Jia Chang, Yen-Jui Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Research Institute Taipei Taiwan Wells Fargo New YorkNY United States Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

In this study, the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advantage over conventional hybrid quantum-classical models by optimizing both quantum and classical parameter management. The framework has been benchmarked across several time-series prediction tasks, including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW), demonstrating its ability to reduce parameters by roughly 70-90% compared to Quantum Long Short-term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) without compromising accuracy. The results show that QT-QFWP outperforms related models in both efficiency and predictive accuracy, providing a pathway toward more practical and cost-effective quantum machine learning applications. This innovation is particularly promising for near-term quantum systems, where limited qubit resources and gate fidelities pose significant constraints on model complexity. QT-QFWP enhances the feasibility of deploying VQCs in time-sensitive applications and broadens the scope of quantum computing in machine learning domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Generating Synthetic Values on Automatic Weather Station Dataset using GAIN

Generating Synthetic Values on Automatic Weather Station Dat...

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International Conference on Instrumentation Control and Automation (ICA)

作者： Bayu Santoso Muhammad Ryan Faqihza Mukhlish Deddy Kurniadi Master Program in Instrumentation and Control Institut Teknologi Bandung Bandung Indonesia Department of Instrumentation and Calibration Badan Meteorologi Klimatologi dan Geofisika Medan Indonesia Center of Aviation Meteorology Badan Meteorologi Klimatologi dan Geofisika Jakarta Indonesia Engineering Physics Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

The Automatic Weather Station (AWS) is an automated instrument designed to gather meteorological parameters within a given environment. The dataset obtained from AWS often contains missing values due to various factors such as sensor malfunction or transmission issues. Nonetheless, the data produced by AWS plays a vital role in weather analysis and prediction. In this study, the authors employ the Generative Adversarial Imputation Network algorithm to generate synthetic values for the missing data in the AWS dataset. The dataset is divided into four schemes, each exhibiting different distributions of missing values at 5, 25, 50, and 80 gaps, respectively. The synthetic values generated by the algorithm are subsequently compared to the ground truth values using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The findings demonstrate that for the air pressure parameter, both the 5 gaps and 25 gaps models exhibit RMSE and MAE values below 2 hPa. Similarly, the models for the air temperature parameter demonstrate RMSE and MAE values below 2°C. As for the relative humidity parameter, the models achieve RMSE and MAE values below 15%. Therefore, both the 5 gaps and 25 gaps models satisfy the error threshold established by the Badan Meteorologi Klimatologi, dan Geofisika for AWS data quality control.

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Efficient Bitcoin Address Classification Using Quantum-Inspired Feature Selection

arXiv

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arXiv 2024年

作者： Sie, Ming-Fong Chang, Yen-Jui Lin, Chien-Lung Chang, Ching-Ray Liao, Shih-Wei Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei City10617 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 499 Xingzhong N. Rd. Taoyuan City320317 Taiwan Department of Physics National Taiwan University Taiwan Quantum Information Center Chung Yuan Christian University No. 499 Xingzhong N. Rd. Taoyuan City320317 Taiwan

Over 900 million Bitcoin transactions have been recorded, posing considerable challenges for machine learning regarding computation time and maintaining prediction accuracy. We propose an innovative approach using quantum-inspired algorithms implemented with Simulated Annealing and Quantum Annealing to address the challenge of local minima in solution spaces. This method efficiently identifies key features linked to mixer addresses, significantly reducing model training time. By categorizing Bitcoin addresses into six classes—exchanges, faucets, gambling, marketplaces, mixers, and mining pools—and applying supervised learning methods, our results demonstrate that feature selection with SA reduced training time by 30.3% compared to using all features in a random forest model while maintaining a 91% F1-score for mixer addresses. This highlights the potential of quantum-inspired algorithms to swiftly and accurately identify high-risk Bitcoin addresses based on transaction features. © 2024, CC BY.

关键词： Simulated annealing

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A Review of Carbon dots (CDs) Application in Sensing and Removing Medical Waste 5

A Review of Carbon dots (CDs) Application in Sensing and Rem...

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5th International Conference on Energy, Environmental and Information System, ICENIS 2020

作者： Alkian, Ilham Sutanto, Heri Aprimanti Utami, Bella Rafika Duri, Inten A'Yuni, Dewi Qurrota Hadiyanto, H Master Program of Environmental Science School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Smart Materials Research Center Diponegoro University Semarang Indonesia

Carbon dots materials (CDs) have the capability in the management of medical waste problem because of its advantage in the detection and degradation of pollutants. The synthesized CDs through various processes and raw materials have been applied to medical wastes, especially antibiotics such as tetracycline (TC), amoxicillin (AMX), para-Nitrophenol (p-NP), doxycycline (DC), ciprofloxacin (CIP), and oxytetracycline (OTC). These antibiotics are detected by utilizing changes in the luminescence property of CDs from the turn-off and turn-on mechanisms. Afterward, the degradation process is conducted using CDs as support materials agent semiconductors in the photosynthesis scheme. In general, CDs can be referred to as multifunction smart materials with the ability to detecting the presence of the antibiotics in simple, sensitive, and selective ways, and potency of medical pollutant removal clearly and effectively. © The Authors, published by EDP Sciences, 2020.

关键词： Antibiotics

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Counting of Aedes Aegypti Eggs using Image Processing with Grid Search Parameter Optimization

Counting of Aedes Aegypti Eggs using Image Processing with G...

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2019 International Conference on Sustainable engineering and Creative Computing, ICSECC 2019

作者： Bandong, Steven Joelianto, Endra Institut Teknologi Bandung Engineering Physics Master Program Bandung Indonesia Instrumentation and Control Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781728151922

Dengue fever is an epidemic brought by infected Aedes Aegypti. This disease has often threatened public health and even caused death. One way to control it is by monitoring the number of its eggs. In this paper, a model for calculating the number of Aedes Aegypti eggs using a camera is made by applying image processing. Steps for processing this image consists of several stages, namely the transformation to HSV, segmentation of objects, labeling of objects, removing small objects, closing holes in objects, and calculating many eggs. To improve accuracy, the parameters in the segmentation, cleaning and closing processes are optimized using Grid Search. The results obtained showed a better average accuracy of 99%. © 2019 IEEE.

关键词： Image segmentation

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Cold atmospheric plasma-enabled platelet vesicle incorporated iron oxide nano-propellers for thrombolysis

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Materials Today Bio 2023年 23卷 100876页

作者： Jheng, Pei-Ru Chiang, Chia-Che Kang, Jiunn-Horng Fan, Yu-Jui Wu, Kevin C.-W. Chen, Yan-Ting Liang, Jia-Wei Bolouki, Nima Lee, Jyh-Wei Hsieh, Jang-Hsing Chuang, Er-Yuan Graduate Institute of Nanomedicine and Medical Engineering Graduate Institute of Biomedical Materials and Tissue Engineering Institute of Biomedical Optomechatronics International Ph.D. Program in Biomedical Engineering School of Biomedical Engineering College of Biomedical Engineering Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Institute of Biomedical Engineering & Nanomedicine National Health Research Institute Keyan Road Zhunan Miaoli City350 Taiwan Department of Chemical Engineering National Taiwan University No.1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan Department of Plasma Physics and Technology Faculty of Science Masaryk University Brno Czech Republic Center for Plasma and Thin Film Technologies Ming Chi University of Technology New Taipei City Taiwan Department of Materials Engineering Ming Chi University of Technology New Taipei City Taiwan Cell Physiology and Molecular Image Research Center Taipei Medical University-Wan Fang Hospital Taipei11696 Taiwan

A new approach to treating vascular blockages has been developed to overcome the limitations of current thrombolytic therapies. This approach involves biosafety and multimodal plasma-derived theranostic platelet vesicle incorporating iron oxide constructed nano-propellers platformed technology that possesses fluorescent and magnetic features and manifold thrombus targeting modes. The platform is capable of being guided and visualized remotely to specifically target thrombi, and it can be activated using near-infrared phototherapy along with an actuated magnet for magnetotherapy. In a murine model of thrombus lesion, this proposed multimodal approach showed an approximately 80 % reduction in thrombus residues. Moreover, the new strategy not only improves thrombolysis but also boosts the rate of lysis, making it a promising candidate for time-sensitive thrombolytic therapy. © 2023 The Authors

关键词： Infrared devices

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