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arXiv 2024年

作者： Singh, Avinash Teja, Rishabh Singh Moriya, Takashi J. Maeda, Keiichi Kawabata, Koji S. Tanaka, Masaomi Imazawa, Ryo Nakaoka, Tatsuya Gangopadhyay, Anjasha Yamanaka, Masayuki Swain, Vishwajeet Sahu, D.K. Anupama, G.C. Kumar, Brajesh Anche, Ramya M. Sano, Yasuo Raj, A. Agnihotri, V.K. Bhalerao, Varun Bisht, D. Bisht, M.S. Belwal, K. Chakrabarti, S.K. Fujii, Mitsugu Nagayama, Takahiro Matsumoto, Katsura Hamada, Taisei Kawabata, Miho Kumar, Amit Kumar, Ravi Malkan, Brian K. Smith, Paul Sakagami, Yuta Taguchi, Kenta Tominaga, Nozomu Watanabe, Arata Hiroshima Astrophysical Science Centre Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Department of Astronomy The Oskar Klein Center Stockholm University AlbaNova University Center StockholmSE 106 91 Sweden Indian Institute of Astrophysics II Block Koramangala Karnataka Bengaluru560034 India Pondicherry University UT of Puducherry R.V. Nagar Kalapet Pondicherry605014 India National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Department of Astronomy Kyoto University Kitashirakawa-Oiwake-cho Sakyo-ku Kyoto606-8502 Japan Department of Physics Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Astronomical Institute Tohoku University Aoba Sendai980-8578 Japan Department of Physics Graduate School of Advanced Science and Engineering Hiroshima Astrophysical Science Centre Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Graduate School of Science and Engineering Kagoshima University 1-21-35 Korimoto Kagoshima Kagoshima890-0065 Japan Department of Physics Indian Institute of Technology Bombay Powai Mumbai400076 India South-Western Institute for Astronomy Research Yunnan University Yunnan Kunming650504 China Steward Observatory University of Arizona 933N Cherry Avenue TucsonAZ85721 United States Observation and Data Center for Cosmosciences Faculty of Science Hokkaido University Kita-ku Sapporo-shi Hokkaido060-0810 Japan Nayoro Observatory 157-1 Nisshin Nayoro-shi Hokkaido096-0066 Japan Indian Centre for Space Physics 466 Barakhola Netai Nagar West Bengal Kolkata700099 India Cepheid Observatory India Fujii Kurosaki Observatory 4500 Kurosaki

We present a detailed investigation of photometric, spectroscopic, and polarimetric observations of the Type II SN 2023ixf. Earlier studies have provided compelling evidence for a delayed shock breakout from a confined dense circumstellar matter (CSM) enveloping the progenitor star. The temporal evolution of polarization in SN 2023ixf revealed three distinct peaks in polarization evolution at 1.4 d, 6.4 d, and 79.2 d, indicating an asymmetric dense CSM, an aspherical shock front and clumpiness in the low-density extended CSM, and an aspherical inner ejecta/He-core. SN 2023ixf displayed two dominant axes, one along the CSM-outer ejecta and the other along the inner ejecta/He-core, showcasing the independent origin of asymmetry in the early and late evolution. The argument for an aspherical shock front is further strengthened by the presence of a high-velocity broad absorption feature in the blue wing of the Balmer features in addition to the P-Cygni absorption post 16 d. Hydrodynamical light curve modeling indicated a progenitor mass of 10 M☉ with a radius of 470 R☉ and explosion energy of 2 × 1051 erg, along with 0.06 M☉ of 56 Ni, though these properties are not unique due to modeling degeneracies. The modeling also indicated a two-zone CSM: a confined dense CSM extending up to 5×1014 cm, with a mass-loss rate of 10−2 M☉ yr−1 and an extended CSM spanning from 5×1014 cm to at least 1016 cm with a mass-loss rate of 10−4 M☉ yr−1, both assuming a wind-velocity of 10 km s−1. The early nebular phase observations display an axisymmetric line profile of [O I], red-ward attenuation of the emission of Hα post 125 days and flattening in the Ks-band marking the onset of dust formation. Copyright © 2024, The Authors. All rights reserved.

关键词： Supernovae

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Strategic forecasting of electricity demand for 100 % electrification in Malawi by 2063: A data-driven ECEEMDAN-BiGRU and quantile regression approach

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Energy 2025年 332卷

作者： Sylvester William Chisale Han Soo Lee Manuel Alejandro Soto Calvo Coastal Hazards and Energy System Science Laboratory Transdisciplinary Science and Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-5-1 Kagamiyama Higashi-Hiroshima 739-8529 Hiroshima Japan Department of Applied Studies Malawi University of Science and Technology (MUST) P. O. Box 5196 Limbe Malawi Center for the Planetary Health and Innovation Science (PHIS) The IDEC Institute Hiroshima University 1-5-1 Kagamiyama Higashi-Hiroshima 739-8529 Hiroshima Japan Coastal Hazards and Energy System Science Laboratory Graduate School of Innovation and Practice for Smart Society Hiroshima University 1-5-1 Kagamiyama Higashi-Hiroshima 739-8529 Hiroshima Japan

Malawi faces significant challenges in transitioning to renewable energy due to a low electrification rate, rapid urbanization, and population growth, which drive increased electricity demand, particularly in urban transportation and residential sectors. This study uses the enhanced complete ensemble empirical mode decomposition with adaptive noise (ECEEMDAN) and bidirectional gated recurrent unit (BiGRU) models to forecast electricity demand in Malawi under three scenarios: business as usual (BAU), low demand, and high demand. These scenarios consider varying industrialization rates and policy effectiveness. By 2060, electricity demand is expected to range from 8219.47 MW in the low demand scenario to 9492.14 MW in the high demand scenario, with the BAU scenario estimating 8544.64 MW. To achieve full electrification by 2060, the BAU scenario predicts an annual increase of about 201.7 MW, while the low and high demand scenarios suggest yearly increases of 193.31 MW and 224.69 MW, respectively. These findings underscore the urgent need for strategic energy planning, infrastructure enhancement, and sustainable economic growth. The study provides valuable insights to guide policymakers and stakeholders in supporting Malawi's Agenda 2063 through effective energy development and planning.

关键词： Malawi'S agenda 2063 Electricity demand forecasting Renewable energy transition ECEEMDAN Quantile regression Sustainable energy planning

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Four-layer Convnet to Facial Emotion Recognition With Minimal Epochs and the Significance of Data Diversity

Research Square

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Research Square 2021年

作者： Debnath, Tanoy Mahfuz Reza, Md. Rahman, Anichur Band, Shahab S. Alinejad-Rokny, Hamid Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Tangail Bangladesh Dhaka Savar Bangladesh Future Technology Research Center College of Future National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliu64002 Taiwan BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Emotion recognition defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communication and many more. The purpose of this study is to propose a new facial emotional recognition model using convolutional neural network. Our proposed model, "ConvNet", detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. This research focuses on the model’s training accuracy in a short number of epoch which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this work focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases, and we test the system’s success by identifying facial expressions in the real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. ConvNet also achieved validation accuracy of 65% to 70% (considering different datasets used for experiments), resulting in a higher classification accuracy compared to other existing models. We also made all the materials publicly accessible for the research community at: https://***/Tanoy004/Emotion-recognition-through-CNN. © 2021, CC BY.

关键词： Emotion Recognition

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Long-Term Care Risk Prediction by Claims Data Analysis

Long-Term Care Risk Prediction by Claims Data Analysis

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Yuan Luo Masahiro Kubo Yasuki Kobayashi Hiroaki Fukunishi Data Science Research Labs. NEC Corporation Kawasaki Japan Graduate School of Medicine The University of Tokyo Tokyo Japan School of Computer Science Tokyo University of Technology Tokyo Japan

ISBN: (数字)9781728153827

ISBN: (纸本)9781728153834

In an aging society such as Japan and other OECD countries, the medical and non-medical costs of long-term care are increasing every year, and it is becoming more difficult for local governments to bear the costs. It is necessary to identify high-risk persons before they need long-term care. We built a prediction model for seven scenarios regarding the need for long-term care using medical claims, pharmacy claims, diagnosis procedure combination (DPC) payment system data, health examination results, and long-term care claims from a local government database. Based on the number of actual data, our proposed long-term care risk prediction is targeted at the elderly over 75 years old. Because there are many variables in the data, especially in medical claims, we used the heterogeneous mixture learning (HML) model because it can automatically optimize all combinations of explanatory variables. The explanatory variables we used are age, sex, 533 diagnosis codes as listed in ICD-10, 108 prescription drug groups under the therapeutic category of drugs in Japan, and 28 special health examination items. The results showed that the area under the curves (AUCs) for all scenarios was above 0.7. Since the recalls of HML were larger than that of other machine learning models, more high-risk persons can be identified by our model.

关键词： Drugs Local government Senior citizens Medical services Predictive models Data models Medical diagnostic imaging

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Towards unbiased COVID-19 lesion localisation and segmentation via weakly supervised learning

arXiv

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arXiv 2021年

作者： Yang, Yang Chen, Jiancong Wang, Ruixuan Ma, Ting Chen, Jie Zheng, Wei-Shi Zhang, Tong Wang, Lingwei Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China School of Computer Science and Engineering Sun Yat-sen University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China Shenzhen People’s Hospital Shenzhen Institute of Respiratory Diseases Shenzhen China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School China

Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images. Due to the nature of blurred boundaries, the supervised segmentation methods usually suffer from annotation biases. To support unbiased lesion localisation and to minimise the labelling costs, we propose a data-driven framework supervised by only image level labels. The framework can explicitly separate potential lesions from original images, with the help of an generative adversarial network and a lesion-specific decoder. Experiments on two COVID-19 datasets demonstrates the effectiveness of the proposed framework and its superior performance to several existing methods. © 2021, CC BY.

关键词： Generative adversarial networks

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Combinatorial Engineered Immuno-Fragments Targeting SARS-CoV-2 Nucleocapsid Protein for Epitope Complementarity

SSRN

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SSRN 2024年

作者： Dlamini, Nhlanhla Benedict Lee, Chi-Hsin Wang, Wei-Chu Huang, Peng-Nien Leu, Sy-Jye Tseng, Sung-Nien Shih, Shin-Ru Chiang, Liao-Chun Mao, Yan-Chiao Tsai, Bor-Yu Yang, Yi-Yuan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei110301 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei110301 Taiwan Research Center for Emerging Viral Infections Chang Gung University Taoyuan333423 Taiwan Division of Infectious Diseases Department of Pediatrics Linkou Chang Gung Memorial Hospital Taoyuan333423 Taiwan Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei110301 Taiwan Department of Microbiology and Immunology School of Medicine College of Medicine Taipei Medical University Taipei110301 Taiwan Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan333423 Taiwan College of Life Sciences National Tsing Hua University Hsinchu300040 Taiwan Division of Clinical Toxicology Department of Emergency Medicine Taichung Veterans General Hospital Taichung407219 Taiwan Navi Bio-Therapeutics Inc. Taipei10351 Taiwan Core Laboratory of Antibody Generation and Research Taipei Medical University Taipei110301 Taiwan

Recombinant avian phage display immunoglobulin Y derivative single chain variable fragment (IgY-scFv) antibodies have evolved as robust single-chain antigen recognizing IgY analogues due to the advent of immuno-nanotechnology and other immunomolecular biotechnology applications. This academic study reports on the engineering and characterization of severe acute respiratory syndrome coronavirus 2 nucleocapsid (SARS-CoV-2 N) monoaffinity recombinant chicken IgY derivative single chain variable fragment (IgY-scFv) antibody fragments as versatile tools for surrogate immunological identification of the SARS-CoV-2 virus nuclear biomarker. To improve the immunopanning selection for specific binders, layer hens were immunised with the SARS-CoV-2 nucleocapsid protein to pre-enrich higher affinity B cell gene repertoires prior to library construction. Pre-enrichment humoral egg yolk polyclonal IgY antibodies were isolated following the PEG-8000 precipitation protocol. An experimental based modelling approach was applied in order to create IgY derivative N protein single chain variable fragments (IgY-scFv N) M13KO7 phage antibody libraries, an immune phage display library constructed system that supported the generation of functional anti-SARS-CoV-2 N protein IgY-scFv affinity monospecific fragment immunomolecules. After the immunopanning enrichment selection process three distinct positive sequence family IgY-scFv fragments obtained demonstrated epitope specific affinity (micromolar range kinetics) towards the targeted recombinant SARS-CoV-2 N protein and also positively identified native SARS-CoV-2 (COVID-19) N protein in formalin-fixed paraffin-embedded (FFPE) 293T cell pellets. © 2024, The Authors. All rights reserved.

关键词： Coronavirus

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Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things

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InfoMat 2020年第6期2卷 1131-1162页

作者： Qiongfeng Shi Bowei Dong Tianyiyi He Zhongda Sun Jianxiong Zhu Zixuan Zhang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore117576Singapore Center for Intelligent Sensors and MEMS National University of SingaporeSingapore117608Singapore Hybrid-Integrated Flexible(Stretchable)Electronic Systems Program National University of SingaporeSingapore117608Singapore NUS Suzhou Research Institute(NUSRI) Suzhou215123China NUS Graduate School for Integrative Science and Engineering National University of SingaporeSingapore117456Singapore

The past few years have witnessed the significant impacts of wearable electronics/photonics on various aspects of our daily life,for example,healthcare monitoring and treatment,ambient monitoring,soft robotics,prosthetics,flexible display,communication,human-machine interactions,and so *** to the development in recent years,the next-generation wearable electronics and photonics are advancing rapidly toward the era of artificial intelligence(AI)and internet of things(IoT),to achieve a higher level of comfort,convenience,connection,and ***,this review provides an opportune overview of the recent progress in wearable electronics,photonics,and systems,in terms of emerging materials,transducing mechanisms,structural configurations,applications,and their further integration with other ***,development of general wearable electronics and photonics is summarized for the applications of physical sensing,chemical sensing,humanmachine interaction,display,communication,and so *** self-sustainable wearable electronics/photonics and systems are discussed based on system integration with energy harvesting and storage ***,technology fusion of wearable systems and AI is reviewed,showing the emergence and rapid development of intelligent/smart *** the last section of this review,perspectives about the future development trends of the next-generation wearable electronics/photonics are provided,that is,toward multifunctional,self-sustainable,and intelligent wearable systems in the AI/IoT era.

关键词： artificial intelligence energy harvesting human-machine interface internet of things wearable electronics wearable photonics

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Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Research Square

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Research Square 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, Di Zhang, Shuo Mura, Cameron Bourne, Philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Spatial hearing and virtual auditory display 49

Spatial hearing and virtual auditory display

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49th International Congress and Exposition on Noise Control Engineering, INTER-NOISE 2020

作者： Xie, Bosun Vigeant, Michelle C. South China University of Technology Acoustic Lab. School of Physics and Optoeletronics Wushan Road 381 Guangzhou510641 China Pennsylvania State University Graduate Program in Acoustics College of Engineering 201 Applied Science Bldg. University ParkPA16802 United States

ISBN: (纸本)9788994021362

Spatial hearing refers to the subjective perceptions of the spatial attributes of sound and is one of the important functions of human hearing. Based on the principles of spatial hearing, virtual auditory displays simulate the binaural signals in various acoustic environments and then create desired spatial auditory events via headphone or sometimes loudspeaker presentations. HRTF-based (head-related transfer functions) binaural synthesis is the core of virtual auditory displays over headphones. As a special spatial sound technique, virtual auditory displays are not only applicable to conventional audio reproduction, but also to a wide field of research areas and engineering applications, such as multimedia, virtual reality, communication, and acoustic environment assessments. During the past two or three decades, virtual auditory displays have become a hot topic in the field of acoustics and signal processing. This presentation first outlines the principles of spatial hearing, and then comprehensively reviews the principles, progresses, and applications of virtual auditory displays, including some recent works from the authors' research groups. Finally, the presentation will conclude with some prospective future works. © Proceedings of 2020 International Congress on Noise Control Engineering, INTER-NOISE 2020. All rights reserved.

关键词： Loudspeakers

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CCL5 Protects Cortical Neuron Function by Regulating M2 Microglia Activation After Mild Traumatic Brain Injury

SSRN

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SSRN 2022年

作者： Tsai, Yih-Jeng Ho, Man-Hau Yen, Chia-Hung Wang, Jia-Yi Burnouf, Thierry Chen, Chia-Yen Wang, Yun Chiang, Yung-Hsiao Hoffer, Barry J. Chou, Szu-Yi Department of Otolaryngology Head and Neck Surgery Shin Kong Wu HoSu Memorial Hospital Taipei Taiwan School of Medicine Fu Jen Catholic University New Taipei City Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University National Health Research Institute Taiwan Graduate Institute of Neural Regenerative Medicine College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Biological Science and Technology National Pingtung University of Science and Technology Neipu Pingtung91201 Taiwan Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei11031 Taiwan Department of Neurosurgery Taipei Medical University Hospital Taipei11031 Taiwan Neuroscience Research Center Taipei Medical University Taipei11031 Taiwan International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan NeuroTMULille International Laboratory Taipei Medical University Taipei Taiwan International PhD Program in Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei Taiwan Brain and Consciousness Research Centre TMU Shuang Ho Hospital New Taipei City Taiwan Center for Neuropsychiatric Research National Health Research Institutes Zhunan Miaoli County Taiwan Department of Surgery School of Medicine College of Medicine Taipei Medical University 11031 Taiwan Department of Neurosurgery Case Western Reserve University School of Medicine ClevelandOH United States Scientist Emeritus National Institutes of Health United States International Master Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan

Cytokines and chemokines play important roles in inflammation and repair following brain injury. M1 microglia are pro-inflammatory which produce cytokines and lead to neural injury;in contrast, M2 microglia are anti-inflammatory, release neurotrophic factors, and promote neural repair processes. Chemokine CCL5, increased after TBI, shows a neuroprotective function by reducing oxidative stress. CCL5 may contribute to the balance between oxidative stress and immune responses after brain injury. Herein, we investigated the role of CCL5 in microglial polarization after mild traumatic brain injury, focusing on the cerebral cortex.C57BL/6 and CCL5 knockout (CCL5-KO) mice were given a mild traumatic brain injury (TBI) using weight-drop. Neurological parameters such as motor and sensory functions were analyzed by mNSS score, accelerating rotarod, beam walking, and adhesive removal tests. Oxidative stress and neuron damage were measured by NADPH oxidase activity, hypoxyloprobe staining and FJC. Performance of motor and sensory functions in both WT and CCL5-KO mice were impaired after brain injury which recovered after 7 days-post-injury (dpi) in the WT group but only after 14 days in the CCL5-KO mouse group. RT-qPCR analysis revealed that pro-inflammation cytokines - IL-1β, TNF-α, and IL-6 were higher in CCL5-KO mice compared to WT mice at 4 and 14 dpi. In contrast, M2-like microglia markers - IL-10 and Agr-1 were increased in WT mouse cortical tissue at 4 dpi. Oxidative stress increased both M1 and M2- related cytokine expression in BV2 cells treated with H 2 O 2;CCL5 treatment increased M2- but suppressed M1- related cytokine gene activation. Intranasal delivery of CCL5 reduced neuronal oxidative stress, increased IL-10 expression and improved motor and sensory functions in CCL5-KO mice after brain *** summary, CCL5, which alters immune responses and protects neurons from TBI damage, has an important function in regulating M2-like microglial polarization during post-

关键词： Polarization

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