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arXiv 2025年

作者： Bonţoiu, Cristian Bonatto, Alexandre Apsimon, Öznur Bandiera, Laura Cavoto, Gianluca Drebot, Illya Gatti, Giancarlo Giner-Navarro, Jorge Lei, Bifeng Martín-Luna, Pablo Rago, Ilaria Pérez, Juan Rodríguez Nunes, Bruno Silveira Sytov, Alexei Valagiannopoulos, Costas Welsch, Carsten P. Xia, Guoxing Resta-López, Javier Department of Physics University of Liverpool Oxford Street LiverpoolL69 7ZE United Kingdom Graduate Program in Information Technology and Healthcare Management the Beam Physics Group Federal University of Health Sciences of Porto Alegre RS Porto Alegre90050-170 Brazil Department of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Cockroft Institute Keckwick Lane Daresbury WarringtonWA4 4AD United Kingdom INFN Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 2 Roma00185 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Rome00185 Italy INFN Sezione di Milano Via G. Celoria 16 Milano20133 Italy Centro de Láseres Pulsados Building M5 Science Park Calle Adaja 8 Salamanca Villamayor37185 Spain Universidad de Valencia Paterna46071 Spain Universidad de Valencia CSIC Paterna46980 Spain Nuclear and Energy Research Institute IPEN–CNEN SP São Paulo Brazil National Technical University of Athens School of Electrical and Computer Engineering Zografou157 73 Greece Departamento de Física Aplicada y Electromagnetismo Universidad de Valencia Burjassot46100 Spain

Laser wakefield acceleration (LWFA) may achieve TeV/m gradients using high-density solid-state plasmas as accelerating media. However, the application of bulk solid materials requires attosecond laser pulses, such as X-ray lasers, to drive wakefields at these high densities. Additionally, the short wakefield wavelengths associated with solid-state plasmas greatly limit the accelerating length. An alternative approach employs 2D carbon-based nanomaterials, like graphene or carbon nanotubes (CNTs), configured into structured targets. These nanostructures are designed with voids or low-density regions to effectively reduce the overall plasma density. This reduction enables the use of longer-wavelength lasers and also extends the plasma wavelength and the acceleration length. In this study, we present, to our knowledge, the first numerical demonstration of electron acceleration via self-injection into a wakefield bubble driven by an infrared laser pulse in structured CNT targets, similar to the behavior observed in gaseous plasmas for LWFA in the nonlinear (or bubble) regime. Using the PIConGPU code, bundles of CNTs are modeled in a 3D geometry as 25 nm-thick carbon tubes with an initial density of 1022 cm−3. The carbon plasma is ionized by a three-cycle, 800 nm wavelength laser pulse with a peak intensity of 1021 W cm−2, achieving an effective plasma density of 1020 cm−3. The same laser also drives the wakefield bubble, responsible for the electron self-injection and acceleration. Simulation results indicate that fs-long electron bunches with hundreds of pC charge can be self-injected and accelerated at gradients exceeding 1 TeV/m. Both charge and accelerating gradient figures are unprecedented when compared with LWFA in gaseous plasma. © 2025, CC BY-NC-ND.

关键词： X ray lasers

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Erratum: “Exploring ultra-high-intensity wakefields in carbon nanotube arrays: An effective plasma-density approach” [Phys. Plasmas 30, 033105 (2023)]

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Physics of Plasmas 2023年第4期30卷

作者： A. Bonatto G. Xia O. Apsimon C. Bontoiu E. Kukstas V. Rodin M. Yadav C. P. Welsch J. Resta-López Graduate Program in Information Technology and Healthcare Management and the Beam Physics Group Federal University of Health Sciences of Porto Alegre Porto Alegre RS 90050-170 Brazil Department of Physics and Astronomy The University of Manchester Manchester M13 9PL United Kingdom and The Cockcroft Institute Sci-Tech Daresbury Warrington WA4 4AD United Kingdom Department of Physics The University of Liverpool Liverpool L69 3BX United Kingdom ICMUV Instituto de Ciencia de los Materiales Universidad de Valencia 46071 Valencia Spain

This erratum is being issued to correct an error that affects Fig. 7 in Ref. 1. This error was caused by a problem with the processing of the PDF file. Specific

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Implementation on Indonesian property real estate industry based on service oriented architecture

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IOP Conference Series: Materials Science and Engineering 2020年第1期725卷

作者： Ahmad Nurul Fajar Nunung Nurul Qomariyah Albert Baihaqi Siregar Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department Faculty of Computing and Media Bina Nusantara University Jakarta Indonesia 11480 Department of Information Technology Universitas Sumatera Utara Medan Indonesia

This study focuses on the implementation of business property in real estate market. This is due to inability of the current technology to support the property products. Some people feel uncomfortable to buy houses from the online market. Buyers need to be very careful to deal with the sellers as the houses price is considered quite expensive. On the other hand, improving real estate market is still a challenge. This business still needs a physical representation, although it can be sold from the online market. In this paper, we propose the Service Oriented Architecture (SOA) system of virtual reality and drone. We use drone to help scanning the property units and then make 3D models with visual mapping so it can be run in a virtual reality environment, video 360. In real estate, business process can be developed in various ways. Our proposed SOA in this study can be used to integrate all of them.

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An effect of tsunami to hotel occupancy: A case of Phuket, Thailand 11

An effect of tsunami to hotel occupancy: A case of Phuket, T...

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11th Aceh International Workshop and Expo on Sustainable Tsunami Disaster Recovery, AIWEST-DR 2018

作者： Tang, J. Leelawat, N. Suppasri, A. Imamura, F. School of Management Technoogy Sirindhorn International Institute of Technology Thammasat University 131 Moo 5 Tiwanont Road Bangkadi Pathum Thani12000 Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Disaster and Risk Management Information Systems Research Group Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Risk and Disaster Management Program Graduate School Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand International Research Institute of Disaster Science Tohoku University 468-1 Aramaki-Aza Aoba Aoba-ku Sendai980-0845 Japan

As a popular city for global tourists, more than one third of the Phuket GPP depends on the hotel and restaurant sector. The 2004 Indian Ocean Tsunami caused a serious physical damage to hotels and a high cancellation of bookings in the post disaster period, which, in turn, led to most THB 3 billion drop of the hotel and restaurant sector income in 2005 in Phuket. In addition, many tourism facilities in Phuket suffered bankruptcy or a slump, because they started their business again immediately after the reconstruction phase, while tourists did not really come back. This paper is an initiative study of our research project to evaluate the effects of the 2004 Indian Ocean Tsunami on Hotel Occupancy, with a focus on the speed for the physical reconstruction and tourism market recovery. We collected the physical reconstruction period and the hotel occupancy recovery data of six hotels that located in the Patong Beach of Phuket from 2003 to 2017. Through calculation, the average of damage rate (i.e., unavailability of the rooms in this study) is 55.89%, with an average speed for the physical reconstruction to rebuild the rooms about 6.07% of the overall rooms per month. The quotient of these two values indicates that the reconstruction period is around 9.2 months. However, the average period for reconstruction and tourism recovery is about 20 months. There is an about 11 months' delay between that hotel and resorts have finished the reconstruction and the tourists start to visit Phuket as usual. Therefore, the local government, local tourist authority, and hotel entrepreneurs can consider these findings in order to prepare the recovery plan for tourism industry in the future tsunami. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Recovery

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The accuracy of the F statistic for structural health monitoring

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AIP Conference Proceedings 2020年第1期2227卷

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita 1Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 2Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 3Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 4Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

Understanding the reliability of engineering methods is crucial for its adoption and deployment. This research focuses on the reliability of the Power Spectral Density (PSD) method via the use of the F statistic for damage detection. To the author best knowledge, the method is rather classic but its realibility has not been discussed in the context of a large data size. Priory, the research anticipates that the accuracy is a function of the damage level. In this study, we evaluate 3500 cases with five levels of structural integrity, namely, healthy condition and damaged conditions with 1%, 5%, 10%, and 20% damage levels. The dataset is established via a numerical analysis of a seven degree-of-freedom system loaded with a concentrated dynamic force with random magnitude. A spring on the system is reduced in its stiffness to simulate damages. Our significant findings are the following: it is challenging for the PSD-based method to differentiate the healthy condition from the damaged conditions when the damage level is small. However, the reliability is high at 95% probability when the structural integrity has dropped by five percent.

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Numerical analysis of general-vibration-based method for structural health monitoring

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AIP Conference Proceedings 2020年第1期2227卷

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita 1Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 2Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 3Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 4Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

For damage detection, this research article discusses an easy-to-compute damage index derived from the governing dynamic of the structure that has potential practical application in Structural Health Monitoring (SHM). The research uses simplified structural models to explore the sensitivity of the index to damages, to compare the index performance with a traditional but popular damage detection method, and to understand the local/global predictive capability of the index. The research uses two simple models, namely, single- and two-degree-of-freedom systems. The results suggest that the damage index is local, that can only monitor damages occurring near the points of measurements, but it is sensitive to damages, unlike the natural frequency, which is global but less sensitive.

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Bridge Sub Structure Defect Inspection Assistance by using Deep Learning

Bridge Sub Structure Defect Inspection Assistance by using D...

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IEEE International Conference on Awareness Science and technology

作者： Pravee Kruachottikul Kittikul Kovitanggoon Nagul Cooharojananone Donnaphat Trakulwaranont Gridsada Phanomchoeng Kanokwan Atchariyachanvanich Thira Chavarnakul Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Mathematics and Computer Science Chulalongkorn University Bangkok Thailand Department of Mechanical Engineering Chulalongkorn University Bangkok Thailand Department of Commerce Chulalongkorn University Bangkok Thailand Faculty of Information Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

Road transportation is the most popular transportation in Thailand, which the top two highest traffic are the region-to-region highways;and then inter-city highways. Therefore, the regular maintenance is required to maintain the good condition due to road safety. The most significant process of bridge inspection procedures is sub structure inspection, which requires visual inspection as an initial step. This process is used to quick determine the damage severity i.e. appearance and crack that may cause damage to the structure strength. The current process requires that the experienced maintenance engineer to be on the field in order to visual inspect and estimate whether the maintenance is required. Yet, due to the limitation of number of expert engineers to be on the field, the photo verification is introduced to assist them so that they are no need on every inspection site. However, using human to verify has no standard and uncontrollable. They need to have experience and good knowledge. As well as it is highly depended on individual decisionmaking skill. Thus, in this paper, the deep learning technique will be presented to assist the expert for quality inspection process of bridge sub structure images. That is using image enhancement and then image splitting and overlapping for image pre-processing. After that applying CNNs for object classification. As a result, the total accuracy is 89% based on 3926 dataset.

关键词： Bridges Inspection Machine learning Maintenance engineering Convolution Training Concrete

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Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study

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The Lancet Digital Health 2022年第6期4卷 e455-e465页

作者： Lo-Ciganic, Wei-Hsuan Donohue, Julie M Yang, Qingnan Huang, James L Chang, Ching-Yuan Weiss, Jeremy C Guo, Jingchuan Zhang, Hao H Cochran, Gerald Gordon, Adam J Malone, Daniel C Kwoh, Chian K Wilson, Debbie L Kuza, Courtney C Gellad, Walid F Department of Pharmaceutical Outcomes and Policy College of Pharmacy University of Florida Gainesville FL United States Center for Drug Evaluation and Safety College of Pharmacy University of Florida Gainesville FL United States Department of Health Policy and Management Graduate School of Public Health University of Pittsburgh Pittsburgh PA United States Center for Pharmaceutical Policy and Prescribing Health Policy Institute University of Pittsburgh Pittsburgh PA United States Division of General Internal Medicine School of Medicine University of Pittsburgh Pittsburgh PA United States Heinz College of Information Systems and Public Policy Carnegie Mellon University Pittsburgh PA United States Department of Mathematics University of Arizona Tucson AZ United States Division of Rheumatology Department of Medicine University of Arizona Arthritis Center University of Arizona Tucson AZ United States Program for Addiction Research Clinical Care Knowledge and Advocacy Division of Epidemiology Department of Internal Medicine University of Utah Salt Lake City UT United States Department of Pharmacotherapy College of Pharmacy University of Utah Salt Lake City UT United States Informatics Decision-Enhancement and Analytic Sciences Center Veterans Affairs Salt Lake City Health Care System Salt Lake City UT United States Center for Health Equity Research Promotion Veterans Affairs Pittsburgh Healthcare System Pittsburgh PA United States

Background: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state). Methods: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013–16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017–18 Pennsylvania Medicaid data (n=318 585) and (2) 2015–17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use. Findings: A total of 8641 (1·35%) 2013–16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017–18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015–17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013–16 Pennsylvania training dataset and validated on the 2013–16 Pennsylvania internal validation dataset, 2017–18 Pennsylvania external validation dataset, and 2015–17 Arizona external validation dataset were 0·841 (95%

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Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan

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Ecotoxicology and environmental safety 2024年 284卷 116885页

作者： Cheng-Hong Yang Chih-Hsien Wu Kuei-Hau Luo Huang-Chih Chang Sz-Chiao Wu Hung-Yi Chuang Department of Information Management Tainan University of Technology Tainan 71002 Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung 80708 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung 80708 Taiwan Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung 80708 Taiwan. Electronic address: chyang@nkust.edu.tw. Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan. Electronic address: F108152123@nkust.edu.tw. Graduate Institute of Medicine College of Medicine Kaohsiung Medicine University Kaohsiung 80708 Taiwan. Electronic address: u107800007@kmu.edu.tw. Divisions of Pulmonary & Critical Care Medicine Department of Internal Medicine Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung 83341 Taiwan Ph.D Program in Environmental and Occupational Medicine and Research Center for Environmental Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Electronic address: kuan2101@yahoo.com.tw. Epidemiology in the Public Health Program College of Health Oregon State University Oregon 97331 USA. Electronic address: wusz550@***. Graduate Institute of Medicine College of Medicine Kaohsiung Medicine University Kaohsiung 80708 Taiwan Ph.D Program in Environmental and Occupational Medicine and Research Center for Environmental Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan Department of Public Health and Environmental Medicine School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan Department of Occupational and Environmental Medicine Kaohsiung Medicine University Hospital Kaohsiung Medicine University Kaohsiung 80708 Taiwan. Electronic address: ericch@kmu.edu.tw.

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used machine learning algorithms (k-nearest neighbors, random forest, gradient-boosted decision trees, eXtreme gradient boosting, and CatBoost) to investigate the association between cognitive impairment and air pollution. Data from the Taiwan Biobank and 75 air-pollution-monitoring stations in Taiwan were analyzed to determine individual levels of exposure to air pollutants. The pollutants examined were particulate matter with a diameter of ≤ 2.5 μm (PM), nitrogen dioxide, nitric oxide, carbon monoxide, and ozone. The results revealed that the most strongly correlated with cognitive impairment were ozone, PM, and carbon monoxide levels with adjustment of educational level, age, and household income. The model based on these factors achieved accuracy as high as 0.97 for detecting cognitive impairment, indicating a positive association between air pollutions and cognitive impairment.

关键词： Air pollution Carbon monoxide Cognitive impairment Dementia Ozone PM(2.5)

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Corrigendum to "Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study" [Asia-Pacific J Oncol Nurs 10 (2023) 100195]

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Asia-Pacific journal of oncology nursing 2024年第5期11卷 100437页

作者： Shuo-Chen Chien Chun-You Chen Chia-Hui Chien Usman Iqbal Hsuan-Chia Yang Huei-Chia Hsueh Shuen-Fu Weng Wen-Shan Jian Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Artificial Intelligence Research and Development Center Wan Fang Hospital Taipei Medical University Taipei Taiwan. International Center for Health Information and Technology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Radiation Oncology Wan Fang Hospital Taipei Medical University Taipei Taiwan. Office of Public Affairs Taipei Medical University Taipei Taiwan. Health ICT Department of Health Tasmania Australia. Global Health and Health Security Department College of Public Health Taipei Medical University Taipei Taiwan. Department of Artificial Intelligence in Medicine Professional Master Program Taipei Medical University Taipei Taiwan. Department of Pharmacy Taipei Veterans General Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. School of Gerontology Health Management College of Nursing Taipei Medical University Taipei Taiwan. Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan. Graduate Institute of Data Science Taipei Medical University Taipei Taiwan.

[This corrects the article DOI: 10.1016/***.2023.100195.].

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