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Enhancement of Self-Trapped Exciton and Near-Infrared Emission in Bi3+/Er3+ Co-Doped Cs2Ag0.4Na0.6InCl6 Double Perovskite for Optical Telecommunications

SSRN

引用

SSRN 2022年

作者： Dave, Kashyap Huang, Wen-Tse Leśniewski, Tadeusz Lazarowska, Agata Grzegorczyk, Maciej Mahlik, Sebastian Leniec, Grzegorz Kaczmarek, Slawomir M. Liu, Ru-Shi Department of Chemistry National Taiwan University Taipei106 Taiwan Nanoscience and Technology Program Taiwan International Graduate Program Academia Sinica National Taiwan University Taipei115 Taiwan Institute of Experimental Physics Faculty of Mathematics Physics and Informatics University of Gdańsk Gdańsk80-308 Poland Department of Technical Physics Faculty of Mechanical Engineering and Mechatronics West Pomeranian University of Technology in Szczecin Piastow 48 Szczecin70-311 Poland Advanced Research Center of Green Materials Science and Technology National Taiwan University Taipei106 Taiwan

Erbium (Er) complexes are used as optical gain material for signal generation in the telecom C-band at 1540 nm, but they need a sensitizer to enhance absorption. Na+ substitution at Ag+ and Bi3+ doping at the In3+ site are possible strategies to enhance broadband emission in Cs2AgInCl6, which could be used as sensitizers to energy transfer into rare-earth elements. Herein, self-trapped exciton (STE) energy transfer to Er3+ at 1540 nm for low loss optical telecommunication applications is reported. An acid precipitation method is used to synthesize Cs2AgInCl6 and its derivative with Er3+, Bi3+, and Na+. Bare Cs2AgInCl6:Er emission signals are found to be weak at 1540 nm, but Bi3+ doping increased by 12 times, and Bi3+ and Na+ doping increased by up to 25 times signal intensity. Electron paramagnetic resonance spectroscopy characterized a decrease in axial symmetry over the Er3+ ion after substitutions of Na+ and Bi3+ in Cs2AgInCl6 at low temperature ( © 2022, The Authors. All rights reserved.

关键词： Bismuth compounds

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Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

arXiv

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arXiv 2023年

作者： Song, Andrew H. Williams, Mane Williamson, Drew F.K. Jaume, Guillaume Zhang, Andrew Chen, Bowen Serafin, Robert Liu, Jonathan T.C. Baras, Alex Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Department of Pathology The Ohio State University ColumbusOH United States

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3–6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7–18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12–14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, M

关键词： Pathology

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Entropy and Information Gain Analysis on Low Cost BCI for Motorbike Users to Prevent Accident

Entropy and Information Gain Analysis on Low Cost BCI for Mo...

引用

International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Rolly Maulana Awangga Syafrial Fachri Pane Dinda Majesty Asep Setiawan Moch Yusuf Asyhari Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Magister Program of Informatics Engineering Universitas Islam Indonesia Yogyakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

The phenomenon of mothers giving the wrong vehicle signal lights to turn left and right reminds the effect of giving the right signal lights to other road users. Weak human concentration while driving can cause errors when giving vehicle signal lights or not giving vehicle signal lights. Mistakes when giving a signal light a vehicle has a risk of an accident. The driver's brain wave data will show the results of the rider's brain thinking when turning. This study aims to analyze the concentration of the driver when driving. This research will obtain driver brain wave data when driving and analyze human brain waves while riding a motorcycle, especially when turning left and right, to reduce the risk of accidents while driving. The driver will use brain waves tool while driving, and the computer will detect the driver's brain waves, and then send brainwave data to the computer. Driver brain waves are analyzed using the formula entropy and gain. The results of the driver's brain waves become a decision whether the driver is allowed to drive on the highway based on the detected waves (alpha, beta, gamma, theta, and delta) to reduce the chance of an accident.

关键词： Electroencephalography Entropy Turning Tools Calibration Motorcycles

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Mini shufflenet for efficient parking space classification

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AIP Conference Proceedings 2023年第1期2480卷

作者： Sayuti Rahman Marwan Ramli Fitri Arnia Rusdha Muharar Haida Dafitri Dharmawati Mahyus Ihsan Doctoral Program School of Engineering Syiah Kuala University Banda Aceh Indonesia Department of Mathematics Syiah Kuala University Banda Aceh Indonesia Department of Electrical and Computer Engineering Syiah Kuala University Banda Aceh Indonesia Department of Informatics Technology Universitas Harapan Medan Medan Indonesia

Motorists driving around looking for parking spaces has negative impacts such as increasing traffic jams, noise, air pollution, wasting fuel, and panic. A smart parking system providing information on the availability of parking spaces is needed. This study aims to develop a parking system by providing information on the availability of car parking spaces. Classification of car parking spaces using Sufflenet V2 is by trimming it. Based on the tests carried out, pruning was on the number of Sufflenet V2 output channels. This trimming produces the smallest Mini Shufflenet size with almost half the parameter number of mAlexnet and less than 1/10 times the number of FLOPs of mAlexnet. Mini Shufflenet has better accuracy than mAlexnet in processing time per image that is almost the same as mAlexnet. Mini Shufflenet implemented is on low-end computing devices such as the Raspberry Pi for real-time data classification. So this architecture can be implemented in a low-cost parking system.

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Evaluating an e-Learning Platform at Graduate School Based on User Experience Evaluation Technique

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Journal of Physics: Conference Series 2021年第1期1737卷

作者： M S Abubakari Nurkhamid G Hungilo Department of Electronics Engineering Education Postgraduate Program Universitas Negeri Yogyakarta Indonesia Department of Informatics Engineering Graduate Program University Atma Jaya Yogyakarta Indonesia

This study was aimed to evaluate the e-learning platform in a graduate school by using User Experience Questionnaire (UEQ). The data were collected from 65 respondents and analyzed using an excel data analysis tool. The results indicate that all six scales of the instrument are consistent except the novelty scale. Also, the calculated means of the scales are slightly positive as all values are greater than 0.8 compared to the UEQ benchmark. Although the respondents show a little positive impression with the platform, all scales means are below average category except efficiency scale which is above average as compared to the benchmark. This indicates that the platform needs more improvement in all aspects of user experience for it to be at least in a good category. The findings discussed in this paper are valuable for all academic stakeholders in various educational institutions which implement interactive e-learning systems as well as practitioners of user experience and developers of e-learning systems.

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Crowdsourcing Urban Narratives for a Post-Pandemic World

Crowdsourcing Urban Narratives for a Post-Pandemic World

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Ramon Chaves Daniel Schneider Claudia Motta António Correia Hugo Paredes Bárbara Caetano Jano Moreira de Souza Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) Federal University of Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro UTAD Vila Real Portugal Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil

ISBN: (纸本)9781728165974;9781728165981

Over the past decades, the use of digital technologies to support participatory urban planning and design has been repeatedly described as a crucial instrument and critical building block for tackling historical problems of participation in such processes. Social media, e-participation platforms, and crowdsourcing applications are examples of technologies that can involve citizens in decision-making processes and thus leverage the benefits of collective intelligence. However, despite the extensive use of social media platforms, old problems related to engagement and participation still occur in digital initiatives. Successful collaboration examples between citizens, policymakers, and strategic stakeholders are still scarce based on online social practices. This study aims to introduce a collective intelligence model, which combines crowdsourcing and social storytelling to support participatory urban planning and design from a bottom-up perspective. The paper concludes by discussing a scenario where citizens can engage in mapping, taking photos, sending ideas, or even creating collective stories about their university issues in a post-pandemic future.

关键词： Crowdsourcing Social networking (online) Smart cities Instruments Urban planning Collective intelligence Tools

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WaiterBot: Comparison of Fuzzy Logic Approaches for Obstacle Avoidance in Dynamic Unmapped Environments Using a Laser Scanning System (LiDAR)

WaiterBot: Comparison of Fuzzy Logic Approaches for Obstacle...

引用

IEEE Latin American Robotics Symposium, LARS

作者： Jonathan Cerbaro Dieisson Martinelli André Schneider de Oliveira João Alberto Fabro ederal University of Technology - Paraná (UTFPR) Graduate Program on Electrical Engineering and Applied Informatics (CPGEI) Advanced Laboratory of Embedded Systems and Robotics (LASER) Curitiba Paraná Brazil

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

Fuzzy logic is a control technique commonly applied in mobile robotics. This paper contains the analysis of three implementations of control systems for obstacle avoidance of a mobile robot. The simulation environment simulate situations with both fixed and dynamic obstacles - like people walking - aiming to compare the fuzzy control approach most suitable to avoid different kinds of obstacles. The analyzed approaches differ by segmenting the sensor data (provided by a LiDAR sensor) into three regions, five regions or by considering only the position of the nearest obstacle. The three approaches were tested in scenarios containing different obstacles and in a simulation where the robot delivers to tables in a dynamic environment, simulating a social event as an application example of a waiter robot.

关键词： Robot sensing systems Robots Collision avoidance Robot kinematics Laser radar Fuzzy logic Input variables

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No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases.

arXiv

引用

arXiv 2024年

作者： Li, Thomas Z. Xu, Kaiwen Krishnan, Aravind Gao, Riqiang Kammer, Michael N. Antic, Sanja Xiao, David Knight, Michael Martinez, Yency Paez, Rafael Lentz, Robert J. Deppen, Stephen Grogan, Eric L. Lasko, Thomas A. Sandler, Kim L. Maldonado, Fabien Landman, Bennett A. Medical Scientist Training Program Vanderbilt University NashvilleTN37235 United States Biomedical Engineering Vanderbilt University NashvilleTN37235 United States Computer Science Vanderbilt University NashvilleTN37235 United States Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Digital Technology and Innovation Siemens Healthineers PrincetonNJ08540 United States Division of Allergy Pulmonary and Critical Care Medicine Department of Medicine Vanderbilt University Medical Center NashvilleTN37232 United States Department of Thoracic Surgery Vanderbilt University Medical Center NashvilleTN37232 United States Biomedical Informatics Vanderbilt University Medical Center NashvilleTN37232 United States Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN37232 United States

Purpose: Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. Materials and Methods: This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. Results: We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. Conclusion: These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis. © 2024, CC BY-NC-SA.

关键词： Biopsy

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Development of a Lightweight Deformable Surface Mechanism (DSM) by Applying Shape-Memory Alloy (SMA) and the Sponge for Handling Objects

Development of a Lightweight Deformable Surface Mechanism (D...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Peizhi Zhang Namiko Saito Hiroki Shigemune Shigeki Sugano Graduate Program for Embodiment Informatics for Leading Graduate Schools Waseda University Tokyo Japan Collage of Engineering Shibaura Insitute of Technology Tokyo Japan School of Creative Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

In this paper, we present a lightweight Deformable Surface Mechanism (DSM) by applying shape-memory alloy (SMA) and sponge for moving objects as a soft actuator. The SMA is driven by heating and cooling processing with the cur-rent flowing. For the SMA, cooling is a process for recovering to original length which consumes time. In order to decrease the recovering time and making the surface deformable, a sponge sheet is applied in the mechanism. We used the cotton thread to sew the SMA into the sponge to manufacture the mechanism. The DSM contains a multi-triangle structure, and each triangle works as an individual actuation unit. By applying this structure and special sewing technique, the sponge sheet can be deformed in a vertical direction when the SMA contracted. While, when the current is turned off, the SMA can be stretched to the original length by the pushing force generated by the sponge. Therefore, a deformable surface mechanism with a rapid response can be achieved. We simulated the changing of uni-Deformable Surface Mechanism (uniDSM), and the experiments were followed to compare with the analyzed results. Additionally, different objects were examined on the DSM to test the conveyance ability.

关键词： Heating systems Cooling Force Metals Yarn Surface treatment Cybernetics

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A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

Artificial Intelligence Chemistry

引用

Artificial Intelligence Chemistry 2024年第2期2卷

作者： Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Ryo Maezono Study Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R 2 ) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1) , methyl quinoxaline-6-carboxylate (Q2) , (2 E ,3 E )-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3) , (2 E ,3 E ) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4) , ( E )-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5) , and 2-(4-methoxyphenyl)-7-methylthieno[3,2- b ] quinoxaline (Q6) , show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

关键词： Quantum neural network QSPR Corrosion inhibition Quinoxaline

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