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Alert and Response of Earthquake and Tsunami for Community Based Disaster Risk Reduction

Journal of Physics: Conference Series 2021年第1期1811卷

作者： Rr M I Retno Susilorini Andre Kurniawan Pamudji Ridwan Sanjaya Amrizarois Ismail Ardhito Hayyu Amasto Department of Infrastructure and Environmental Engineering Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Master Program of Environmental and Urban Studies Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Department of Information System Faculty of Computer Science Soegijapranata Catholic University Semarang

Indonesia had been suffered by earthquake and tsunami for many centuries. Since many lives have taken by tsunami strikes, alert and response system are very important as important part of Disaster Risk Reduction, especially in area with high population density. This research conducted in Kangkung fishing village in Bandar Lampung coastal area which is an earthquake and tsunami prone area. The aim of the research is to study alert and response of Community Based Disaster Risk Reduction. The research conducted by qualitative method and purposive-descriptive approach. There were 53 respondents which participated in the survey and interview. It is reported that 'Smart Resilient City' model suitable for community needs of alert and response of earthquake and tsunami disaster. This research meets conclusion that 'Smart Resilient City' model may become a great solution in Community Based Disaster Risk Reduction to cope with the hazard of earthquake and tsunami comprehensively and sustainably.

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Retraction notice to "Synthesize palladium nanoparticles from the macroalgae Sargassum fusiforme: An eco-friendly tool in the fight against plasmodium falciparum?" [Sci. Total Environ. 857 Part 3, (2022) 159517]

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The Science of the total environment 2024年 957卷 177660页

作者： Chinnaperumal Kamaraj Chinnasamy Ragavendran R C Satish Kumar Sarvesh Sabarathinam Cittrarasu Vetrivel Mariyappan Vaithiyalingam Guilherme Malafaia Interdisciplinary Institute of Indian System of Medicine (IIISM) Drug Testing Laboratory Directorate of Research SRM Institute Science and Technology Kattankulathur 603 203 Tamil Nadu India. Department of Cariology Saveetha Dental College and Hospitals Chennai Tamil Nadu India. Carbon Capture Lab Department of Chemical Engineering SSN College of Engineering Kalavakkam Chennai 603110 Tamil Nadu India Department of Anatomy Saveetha Dental college and Hospital Saveetha institute of medical and Technical science Chennai Tamil N 600 007 India. Department of Chemistry SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 Tamilnadu India. Laboratory of Toxicology Applied to the Environment Goiano Federal Institute Urutaí GO Brazil Post-Graduation Program in Conservation of Cerrado Natural Resources Goiano Federal Institute Urutaí GO Brazil Post-Graduation Program in Ecology Conservation and Biodiversity Federal University of Uberlândia Uberlândia MG Brazil Post-Graduation Program in Biotechnology and Biodiversity Federal University of Goiás Goiânia GO Brazil.

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National Science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Preoperative parietal TMS-EEG hyperexcitability in patients with subsequent postoperative delirium

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Brain Stimulation 2025年第1期18卷 609-610页

作者： Tseng, Yu-Hsuan Ozdemir, Recep Rodionov, Andrei Ross, Jessica M. Klooster, Debby Cavallari, Michele Dickerson, Bradford C. Fong, Tamara G. Jones, Richard N. Libermann, Towia A. Marcantonio, Edward R. Santarnecchi, Emiliano Schmitt, Eva M. Touroutoglou, Alexandra Travison, Thomas G. Inouye, Sharon Pascual-Leone, Alvaro Shafi, Mouhsin M. Berenson-Allen Center for Noninvasive Brain Stimulation Beth Israel Deaconess Medical Center USA Department of Neurology Beth Israel Deaconess Medical Center USA Department of Neurology Harvard Medical School USA BioMag Laboratory HUS Medical Imaging Center Helsinki University Hospital Finland Faculty of Educational Sciences University of Helsinki Finland Veterans Affairs Palo Alto Healthcare System and the Sierra Pacific Mental Illness Research Education and Clinical Center USA Department of Psychiatry and Behavioral Sciences Stanford Medical School USA Center for Care and Cure Technology Department of Electrical Engineering Eindhoven University of Technology The Netherlands Ghent Experimental Psychiatry lab Department of Head and Skin Ghent University Belgium Department of Radiology Beth Israel Deaconess Medical Center USA Center for Neurological Imaging Department of Radiology Brigham and Women’s Hospital USA Department of Neurology Massachusetts General Hospital USA Hinda and Arthur Marcus Institute for Aging Research Hebrew SeniorLife USA Department of Psychiatry and Human Behavior Department of Neurology Warren Alpert Medical School Brown University USA Harvard Medical School USA Department of Medicine Beth Israel Deaconess Medical Center USA Divisions of General Medicine and Gerontology Department of Medicine Beth Israel Deaconess Medical Center USA Precision Neuroscience & Neuromodulation Program (PNN) Gordon Center for Medical Imaging Department of Radiology Massachusetts General Hospital USA Hinda and Arthur Marcus Institute for Aging Research & Deanna and Sidney Wolk Center for Memory Health Hebrew SeniorLife USA

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Comprehensive High Impedance Fault Model for Real-Time Environment 45

Comprehensive High Impedance Fault Model for Real-Time Envir...

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45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019

作者： Wontroba, Aldair De Morais, Adriano Peres Rossini, Jean Pereira Gallas, Mairon Cardoso, Ghendy Vieira, Joao Paulo Abreu Santos, Marcelo Costa Farias, Patrick Escalante Federal University of Santa Maria Santa Maria RS Brazil Federal University of Pará Electrical Engineering Graduate Program Belém PA Brazil CELPA -Electric Distribution Utility of Pará Distribution System Operation Management Belém PA Brazil Federal Institute of Education Science and Technology of Rio Grande Do sul IFRS Farroupilha Brazil

ISBN: (纸本)9781728148786

This paper presents a comprehensive high impedance fault model for real-time environment. This model offers different types of ground surface and high impedance faults. Experimental results show that it is suitable for relays and algorithms commissioning in closed loop environment. The model is incorporated into OPAL-RT simulator and implemented in Hypersim software. Finally, the performance of the model is validated under a real case of high impedance fault by comparison with real-time simulation. © 2019 IEEE.

关键词： Computer software

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Range Verification using Prompt Gamma-rays with Energy Spectral Analysis in Geant4 Based Proton Therapy Simulation

Range Verification using Prompt Gamma-rays with Energy Spect...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Tsukasa Aso Hiroki Oke Teiji Nishio Department of Electronics and Computer Engineering National Institute of Technology Toyama College Japan Control Information System Engineering Program Advanced Course National Institute of Technology Toyama College Japan Department of Medical Physics Tokyo Women’s Medical University Japan

ISBN: (数字)9781728141640

ISBN: (纸本)9781728141657

Range verification of therapeutic proton beam is crucial in proton therapy to confirm the irradiation field for ensuring effects and safety of the treatment. The irradiation field reconstruction using prompt gamma-rays generated from excited nuclides by proton beam has being studied for the purpose of precision medicine. A simulation tool based on fundamental physics processes employs an important role to understand intermediate relationship between physics interactions and detected observables, and to optimize a detector system with its analysis method. Previously, we reported on the simulation study about an irradiation field reconstruction by using prompt gamma-rays of whole energy range. In this study, we analyzed the energy spectrum and reconstructed the image of irradiation field by using prompt gamma-rays corresponding to a particular energy peak. The simulation was performed for proton beam with a target placed at the isocenter by using Geant4 based particle therapy system simulation framework. A simple tubular detector was placed around the target, in which the kinetic energy and the detected position of each gamma-ray were recorded. The irradiation field was then reconstructed in the ordered subset expectation maximization method. The reconstructed irradiation fields were compared with the dose distribution in the target. The peak positions of these depth distributions were compared each other and discussed about the range verification of therapeutic proton beam.

关键词： Image reconstruction Radiation effects Gamma-rays Particle beams Medical treatment Detectors

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Modeling and Real-Time Simulation of High Impedance Faults for Protection Relay Testing and Methods Validation

Modeling and Real-Time Simulation of High Impedance Faults f...

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2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019

作者： Wontroba, Aldair De Morais, Adriano Peres Rossini, Jean Pereira Gallas, Mairon Cardoso, Ghendy Vieira, João Paulo Abreu Farias, Patrick Escalante Santos, Marcelo Costa Federal University of Santa Maria Santa Maria-RS Brazil Federal University of Pará Electrical Engineering Graduate Program Belém PA Brazil Grande Do sul - IFRS Federal Institute of Education Science and Technology of Rio Farroupilha Brazil Electric Distribution Utility of Pará Distribution System Operation Management CELPA Belém PA Brazil

ISBN: (纸本)9781538695678

This work presents a real-time model of high impedance fault. The model allows full relay validation including different types of ground surface. The model is incorporated into OPAL-RT simulator and implemented in Hypersim software, a comparison with ATPdraw is performed. The model and its theoretical basis are presented;the real-time implementation, its results and the issues are discussed. © 2019 IEEE.

关键词： Computer software

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Aloe vera and beeswax based coating to maintain shelf life of salak cv. Madu

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IOP Conference Series: Earth and Environmental Science 2020年第1期542卷

作者： P R P Sari E Darmawati U Ahmad Postharvest Technology Master Program Graduate School IPB University Bogor Indonesia 16680 Departement of Mechanical Engineering and Bio-system Faculty of Agricultural Engineering and Technology IPB University Indonesia 16680

The coatings on fruits after harvested was applied to maintain the appearance and quality up to the consumer's hands. This study aimed to maintain color and reduce the level of decay of salak madu during storage. Salak madu was obtained from Madding Fresh, trader and supplier salak from Sleman, Yogyakarta. The salak was coated with Aloe vera gel (20% and 50%) and beeswax (3, 6, and 10%). All samples were stored at 25–28 °C. The parameters used to measure the effect of treatment are skin color, percentage of decay, and weight loss. Observations were carried out for 12 days. The results of this study were found that dyeing on a coating made from Aloe vera 50% was able to slow down weight loss and percentage of decay until the 6 days, while Aloe vera 30% was better at maintaining color changes during storage. Beeswax 3% coating can slow down discoloration and percentage of decay until 6 days of storage. Long-term storage (12 days) with beeswax 10% was better in maintaining changes in the quality of salak madu, while for short-term storage (6 days), beeswax 3% gives better results.

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Synthesize palladium nanoparticles from the macroalgae Sargassum fusiforme: An eco-friendly tool in the fight against Plasmodium falciparum?

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The Science of the total environment 2023年第PT 3期857卷 159517页

作者： Chinnaperumal Kamaraj Chinnasamy Ragavendran R C Satish Kumar Sarvesh Sabarathinam Cittrarasu Vetrivel Mariyappan Vaithiyalingam Guilherme Malafaia Interdisciplinary Institute of Indian System of Medicine (IIISM) Drug Testing Laboratory Directorate of Research SRM Institute Science and Technology Kattankulathur 603 203 Tamil Nadu India. Electronic address: kamarajc@srmist.edu.in. Department of Cariology Saveetha Dental College and Hospitals Chennai Tamil Nadu India. Interdisciplinary Institute of Indian System of Medicine (IIISM) Drug Testing Laboratory Directorate of Research SRM Institute Science and Technology Kattankulathur 603 203 Tamil Nadu India. Carbon Capture Lab Department of Chemical Engineering SSN College of Engineering Kalavakkam Chennai 603110 Tamil Nadu India Department of Anatomy Saveetha Dental college and Hospital. Saveetha institute of medical and Technical science ChennaiTamil N- 600 007 India. Department of Chemistry SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 Tamilnadu India. Laboratory of Toxicology Applied to the Environment Goiano Federal Institute Urutaí GO Brazil Post-Graduation Program in Conservation of Cerrado Natural Resources Goiano Federal Institute Urutaí GO Brazil Post-Graduation Program in Ecology Conservation and Biodiversity Federal University of Uberlândia Uberlândia MG Brazil Post-Graduation Program in Biotechnology and Biodiversity Federal University of Goiás Goiânia GO Brazil. Electronic address: guilhermeifgoiano@***.

Although numerous drugs are practiced to control malaria and its vectors, more recently, eco-friendly control tools have been proposed to battle its etiologic agents. Thus, using green bionanotechnology approaches, we aimed to synthesize palladium nanoparticles (Pd NPs) from the macroalgae Sargassum fusiforme (Sf), its potential antiparasitic activity against P. falciparum, as well as its possible cytotoxicity, in HeLa cells. After the biosynthesis of the PdSf NPs, their characterization was carried out by UV-Vis, FESEM, and EDX analyses, and their hydrodynamic size, zeta potential, and surface area were determined. Furthermore, the functional groups of the PdSf NPs were analyzed by FT-IR and GC-MS. While PdSf NPs had an IC of 7.68 μg/mL (Chloroquine (CQ)-s) and 16.42 μg/mL, S. fusiforme extract had an IC of 14.38 μg/mL (CQ-s) and 35.27 μg/mL (CQ-r). With an IC value of 94.49 μg/mL, PdSf NPs exhibited the least toxic effect on the HeLa cells. The Lipinski rule of five and ADMET prediction were used to assess the in silico models of caffeine acid hexoside and quercetin 7-O-hexoside for the presence of drug-like properties. Pathogenic proteins, primarily responsible for motility, binding, and disease-causing, were the target of the structurally based docking studies between plant-derived compounds and pathogenic proteins. Thus, our study pioneered promising results that support the potential antiplasmodial activity of eco-friendly synthesized PdSf NPs using S. fusiforme extract against P. falciparum, opening perspectives for further exploration into the use of these NPs in malaria therapy.

关键词： Bionanotechnology Brown algae Molecular docking PdSf NPs Plasmodium falciparum Sargassum fusiforme

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