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arXiv 2025年

作者： Gupta, Nitant Akepati, Sri V.V.R. Bianco, Simona Shah, Jay Adams, Dave J. Jayaraman, Arthi Department of Chemical and Biomolecular Engineering University of Delaware NewarkDE19716 United States Data Science Program University of Delaware NewarkDE19716 United States School of Chemistry University of Glasgow GlasgowG12 8QQ United Kingdom Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States

In this paper, we extend a recently developed machine-learning (ML) based CREASE-2D method to analyze the entire two-dimensional (2D) scattering pattern obtained from small angle X-ray scattering measurements of supramolecular dipeptide micellar systems. Traditional analysis of such scattering data would involve use of approximate or incorrect analytical models to fit to azimuthally-averaged 1D scattering patterns that can miss the anisotropic arrangements. Analysis of the 2D scattering profiles of such micellar solutions using CREASE-2D allows us to understand both isotropic and anisotropic structural arrangements that are present in these systems of assembled dipeptides in water and in the presence of added solvents/salts. CREASE-2D outputs distributions of relevant structural features including ones that cannot be identified with existing analytical models (e.g., assembled tubes’ cross-sectional eccentricity, tortuosity, orientational order). The representative three-dimensional (3D) real-space structures for the optimized values of these structural features further facilitate visualization of the structures. Through this detailed interpretation of these 2D SAXS profiles we are able to characterize the shapes of the assembled tube structures as a function of dipeptide chemistry, solution conditions with varying salts and solvents, and relative concentrations of all components. This paper demonstrates how CREASE-2D analysis of entire SAXS profiles can provide an unprecedented level of understanding of structural arrangements which has not been possible through traditional analytical model fits to the 1D SAXS data. © 2025, CC BY-NC-SA.

关键词： X ray scattering

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Application of expert system for oil palm smallholder-owned plantations

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Umami H. G. Mawandha D. D. Puruhito Purwadi T. Suparyanto D. Sudigyo B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Most of the oil palm smallholders operate independently. As a result of this lack of support and assistance, oil palm owned by independent smallholders has the lowest productivity compared to large private (corporation) plantations and large state plantations. In addition, smallholder-owned plantations often operate without paying attention to sustainability aspects. There is a need for an effective, efficient, and user-friendly mentoring tool for oil palm farmers that are accessed independently. The Android-based platform was developed by applying an expert system to support increased production of oil palm cultivation for smallholders. Several stages of the expert system implementation, including the identification of planters and land profiles, were carried out using the direct interview method. The expert system includes land preparation management, planting material selection, seeding, weeds control, pests and diseases, fertilization, harvesting and transport, plantation administration, chat platforms, and data scrapping from data providers. The type of expert system is a data-driven Decision Support System (DSS).

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Sleep Quality and Daily Activity Association Assessment From Wearable Device data

Sleep Quality and Daily Activity Association Assessment From...

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International Conference on Information Management and Technology (ICIMTech)

作者： Bharuno Mahesworo Arif Budiarto Alam Ahmad Hidayat Haryono Soeparno Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Having good sleep is important to maintain physical and mental health. However, many young adults suffer poor sleep due to work or academic demands. Even more, sleep is associated with stress which is many young adults experienced it. To solve this problem, many epidemiologic studies suggest that exercise may improve sleep quality. On the other hand, several studies show that daily activity has no association with sleep quality. In this study, we tried to find more evidence of whether the daily activity has an association with sleep quality or not. We used a commercially available wearable device to capture the subject's heart rate, step count and deep sleep duration for 7 months. Then we used linear regression, XGBoost, and Long Short-Term Memory to find an association between these variables. The results show that there was no association between these variables.

关键词： Sleep Heart rate Biomedical monitoring Computer science Predictive models Bioinformatics data science

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Learning management system for oil palm smallholder-owned plantations

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AIP Conference Proceedings 2023年第1期2594卷

作者： E. Firmansyah D. Nurjannah S. I. Dinarti D. Sudigyo T. Suparyanto B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The production gap between oil palm smallholder-owned plantations and corporations shows a real problem in the national palm oil industry. To reduce the gap, user-friendly assistance for smallholder-owned plantations is needed for the cultivation of sustainable palm oil production. Android-based software can be a platform for bridging the transfer of knowledge and technology, transfer of problems, and transfer of solutions between planters and experts. The platform development was formulated in five stages which began with Learning Management System (LMS) application development, identification of Good Agricultural Practices (GAP) and Best Management Practices (BMP) materials, development of digital contents, uploading the digital content to the LMS server, and dissemination. The curriculum in the application platform was prepared based on the needs of the growers according to the identification results of the farmers' profiles. The curriculum consists of GAP and BMP material blocks. Each block was divided into discussion topics. The digital contents related to GAP and BMP were set in the LMS, which was built using Moodle platform.

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Effect of foliar nutrients application to the growth and yield of Physalis peruviana and Physalis alkekengi

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IOP Conference Series: Earth and Environmental science 2022年第1期978卷

作者： Diana Nurus Sholehah Dini Ermavitalini Miratul Khasanah Edy Setiti Wida Utami Hery Purnobasuki Doctoral Study Program of Mathematics and Natural Science Faculty of Science and Technology Airlangga University Indonesia Study Program of Agroecotechnology Faculty of Agriculture University of Trunojoyo Madura Indonesia Departement of Biology Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Indonesia Departement of Chemistry Faculty of Science and Technology Airlangga University Indonesia Departement of Biology Faculty of Science and Technology Airlangga University Indonesia

Physalis Sp. is a potential medicinal plant and functional food. The use of appropriate nutrients can gain optimal plant growth and productivity. This study aimed to determine the macro and micronutrients that can provide optimal growth and yield of Physalis sp. This research was conducted at a greenhouse in Bangkalan Madura. A factorial randomized block design with four replications was used in this study. The first factor was the species of Physalis Sp., namely P. peruviana and P. alkekengi. The second factor is nutrition at four levels: a spray of water only as control; foliar application of macronutrients at a dose of 2 g/L; foliar application of micronutrients at a dose of 0,125 g/L; foliar Application of a combination of macronutrients 1 g/L and micronutrients 0,0625 g/L. The data obtained were analyzed using analysis of variance (ANOVA) followed by Least Significant Difference (LSD) at the 5 % level. Nutrition significantly affected growth (plant height, stem diameter, and number of leaves) and yield (number of fruit). The interaction between treatments occurred in the number of fruits. Micronutrients gave the highest number of fruit, both species of Physalis sp.

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Spatiotemporal dilated convolution with uncertain matching for video-based crowd estimation

arXiv

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arXiv 2021年

作者： Ma, Yu-Jen Shuai, Hong-Han Cheng, Wen-Huang Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Institute of Electronics National Chiao Tung Univresity Hsinchu300 Taiwan Artificial Intelligence and Data Science Program National Chung Hsing University Taichung400 Taiwan

In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decomposition of 3D convolution and the 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of the model size caused by the Conv3D layer. Moreover, since the dilated convolution extracts the multiscale features, we combine the dilated convolution with the channel attention block to enhance the feature representations. Due to the error that occurs from the difficulty of labeling crowds, especially for videos, imprecise or standard-inconsistent labels may lead to poor convergence for the model. To address this issue, we further propose a new patch-wise regression loss (PRL) to improve the original pixel-wise loss. Experimental results on three video-based benchmarks, i.e., the UCSD, Mall and WorldExpo’10 datasets, show that STDNet outperforms both image- and video-based state-of-the-art methods. The source codes are released at https://***/STDNet/STDNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Automatic detection of large-scale flux ropes and their geoeffectiveness with a machine learning approach

arXiv

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arXiv 2024年

作者： Pal, Sanchita dos Santos, Luiz F.G. Weiss, Andreas J. Narock, Thomas Narock, Ayris Nieves-Chinchilla, Teresa Jian, Lan K. Good, Simon W. NASA Postdoctoral Program Fellow NASA Goddard Space Flight Center GreenbeltMD20771 United States SETI Institute Mountain ViewCA94043 United States Center for Natural Computer and Data Sciences Goucher College BaltimoreMD United States ADNET Systems Inc. 7515 Mission Drive LanhamMD20706 United States Heliophysics Science Division NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Physics University of Helsinki P.O. Box 64 HelsinkiFI-00014 Finland

Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary-classification machine learning (ML) models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of auto-detected FRs. Using the properties of southward magnetic field the second model determines the geoeffectiveness of FRs. Our method identifies 88.6% and 80% large-scale ICMEs (duration ≥ 1 day) observed at 1 au by Wind and Sun Earth Connection Coronal and Heliospheric Investigation (STEREO) mission, respectively. While testing with a continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during 2008 - 2014 period (recall= 0.875) but many false positives (precision= 0.56) as we do not take into account any additional solar wind properties than the magnetic properties. We found an accuracy of 0.88 when estimating the geoeffectiveness of the auto-detected FRs using our method. Thus, in space weather now-casting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and geoeffectiveness. © 2024, CC BY.

关键词： Magnetic fields

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Detecting Hemileia vastatrix using Vision AI as Supporting to Food Security for Smallholder Coffee Commodities

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Teddy Suparyanto Erick Firmansyah Tjeng Wawan Cenggoro Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The loss of food crops during the COVID-19 pandemic threatens food security in Indonesia as one of the world's top coffee producers. This affects the insecurity of coffee commodities which is influenced by several factors other than the pandemic such as pests, plant diseases, and extreme weather. Plant diseases, such as leaf rust, are a significant factor in the insecurity problem in coffee commodities. The fungus Hemileia vastatrix B et Br causes leaf rust disease, which is a pest that frequently damages coffee plants. This disease not only interferes with plant growth but also causes a decrease in coffee quality and quantity. This initial research aims to carry out early prevention of these diseases as supporting to food security in smallholder coffee commodities. An AI-based visual detection application is the result of this research. We collected 100 images of coffee leaves from various coffee plants. The image is reshaped to 256 x 256 pixels and randomly trimmed to 224 x 224 pixels to fulfill the size requirements of a standard Deep Learning technique. Each image was classified into two classes by a plant pest and disease specialist. The dataset was divided into training, validation, and testing series with a ratio of 60:20:20 for training procedures. The Convolutional Neural Network (CNN) research method used a variation of the ResNet CNN model with 18 layers. The best model validation was 59%.

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Variational methods and deep Ritz method for active elastic solids

arXiv

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arXiv 2022年

作者： Wang, Haiqin Zou, Boyi Su, Jian Wang, Dong Xu, Xinpeng Physics Program Guangdong Technion - Israel Institute of Technology 241 Daxue Road Guangdong Shantou515063 China Technion - Israel Institute of Technology Haifa32000 Israel School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Variational methods have been widely used in soft matter physics for both static and dynamic problems. These methods are mostly based on two variational principles: the variational principle of minimum free energy (MFEVP) and Onsager's variational principle (OVP). Our interests lie in the applications of these variational methods to active matter physics. In our former work [Soft Matter, 2021, 17, 3634], we have explored the applications of OVP-based variational methods for the modeling of active matter dynamics. In the present work, we explore variational (or energy) methods that are based on MFEVP for static problems in active elastic solids. We show that MFEVP can be used not only to derive equilibrium equations, but also to develop approximate solution methods, such as Ritz method, for active solid statics. Moreover, the power of Ritz-type method can be further enhanced using deep learning methods if we use deep neural networks to construct the trial functions of the variational problems. We then apply these variational methods and the deep Ritz method to study the spontaneous bending and contraction of a thin active circular plate that is induced by internal asymmetric active contraction. The circular plate is found to be bent towards its contracting side. The study of such a simple toy system gives implications for understanding the morphogenesis of solid-like confluent cell monolayers. In addition, we introduce a so-called activogravity length to characterize the importance of gravitational forces relative to internal active contraction in driving the bending of the active plate. When the lateral plate dimension is larger than the activogravity length (about 100 micron), gravitational forces become important. Such gravitaxis behaviors at multicellular scales may play significant roles in the morphogenesis and in the up-down symmetry broken during tissue development. © 2022, CC BY-NC-ND.

关键词： Deep neural networks

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Projected Time for the Elimination of Cervical Cancer Under Various Intervention Scenarios: Age-Period-Cohort Macrosimulation Study

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JMIR Public Health and Surveillance 2024年 10卷 e46360页

作者： Chen, Yi-Chu Chen, Yun-Yuan Su, Shih-Yung Jhuang, Jing-Rong Chiang, Chun-Ju Yang, Ya-Wen Lin, Li-Ju Wu, Chao-Chun Lee, Wen-Chung Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Head Office Taiwan Blood Services Foundation Taipei Taiwan Master Program in Statistics National Taiwan University Taipei Taiwan Institute of Statistical Science Academia Sinica Taipei Taiwan Taiwan Cancer Registry Taipei city Taiwan Health Promotion Administration Ministry of Health and Welfare Taipei Taiwan Institute of Health Data Analytics College of Public Health National Taiwan University Taipei Taiwan

BACKGROUND: The World Health Organization aims for the global elimination of cervical cancer, necessitating modeling studies to forecast long-term outcomes. OBJECTIVE: This paper introduces a macrosimulation framework using age-period-cohort modeling and population attributable fractions to predict the timeline for eliminating cervical cancer in Taiwan. METHODS: data for cervical cancer cases from 1997 to 2016 were obtained from the Taiwan Cancer Registry. Future incidence rates under the current approach and various intervention strategies, such as scaled-up screening (cytology based or human papillomavirus [HPV] based) and HPV vaccination, were projected. RESULTS: Our projections indicate that Taiwan could eliminate cervical cancer by 2050 with either 70% compliance in cytology-based or HPV-based screening or 90% HPV vaccination coverage. The years projected for elimination are 2047 and 2035 for cytology-based and HPV-based screening, respectively;2050 for vaccination alone;and 2038 and 2033 for combined screening and vaccination approaches. CONCLUSIONS: The age-period-cohort macrosimulation framework offers a valuable policy analysis tool for cervical cancer control. Our findings can inform strategies in other high-incidence countries, serving as a benchmark for global efforts to eliminate the disease. ©Yi-Chu Chen, Yun-Yuan Chen, Shih-Yung Su, Jing-Rong Jhuang, Chun-Ju Chiang, Ya-Wen Yang, Li-Ju Lin, Chao-Chun Wu, Wen-Chung Lee. Originally published in JMIR Public Health and Surveillance (https://***), 18.04.2024.

关键词： Age-period-cohort model Cancer screening Cervical cancer Cervical screening HPV Human papillomavirus Intervention Macrosimulation Population attributable fraction Public health intervention Women

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