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Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

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

作者： Erick Firmansyah Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (AI)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

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Smart IoT CPO Storage Tank

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

作者： Rengga Arnalis Renjani Hermantoro Prima Okta Adi Nugraha Kartika Purwandari Teddy Suparyanto Bens Pardamean Department of Agricultural 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 Bina Nusantara University Jakarta Indonesia 11480

Indonesia's crude palm oil (CPO) production from year to year continues to increase, at the end of 2020 it reached 17.35 million tons, up 3.6% from the previous year. Increasing production will result in more CPO stock and require good storage. The storage process that occurs is to maintain the temperature of the CPO so that the quality is not damaged. This temperature regulation is still done manually and raises the risk of work accidents. The purpose of this research is to create a temperature control system and automatic volume measurement that can be monitored from a smartphone. The manufacture of a control system used ESP8266 NodeMCu microcontroller, temperature sensor, proximity sensor, and 1000-Watt heater. programming used the Arduino IDE and C++. The result of this study was an IoT CPO Storage Tank design equipped with sensors and microcontrollers. The temperature was measured with the DS18B20 sensor had a data accuracy of 99.19% while the volume measured with the HC-SR04 sensor had an accuracy of 99.78%. Data obtained from the sensor could be seen through the Thingspeak application from a computer or smartphone.

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Information system model for maize research to anticipate the impact of climate change on Indonesian food security

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. S. Perbangsa F. A. Munawwar M. B. Pabendon B. Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Indonesia Cereals Research Institute Maros South Sulawesi 90514 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

As a rich country in genetic resources, Indonesia has the potential for strengthening food security. Maize is one of the commodities that greatly affect food security in Indonesia. Maize is also widely used as animal feed. The high demand for maize requires efforts to increase the productivity of sustainable cultivation. Various research efforts that have been carried out require the support of a comprehensive information system. This study aimed to build a research model for maize genetic resource information systems that can help search for superior varieties. The data collection method used was collecting genotype, phenotype, climate data and interviewing the maize breeders or researchers. As for the design of the model, this study used the Object-Oriented Analysis & Design (OOAD) method with notation from Unified Modelling Language (UML). The result of this research was a web-based model of information systems for maize research.

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Evaluation of Frequency-Dependent Effects of Deep Brain Stimulation in a Cortex-Basal Ganglia-Thalamus Network Model of Parkinson’s Disease*

Evaluation of Frequency-Dependent Effects of Deep Brain Stim...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Marcelo R. Romano Renan C. Moioli Leonardo A. Elias Neural Engineering Research Laboratory School of Electrical and Computer Engineering University of Campinas Campinas SP Brazil Graduate Program in Bioinformatics Digital Metropolis Institute Federal University of Rio Grande do Norte Natal Brazil Center for Biomedical Engineering University of Campinas Campinas SP Brazil

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Parkinson's disease (PD) is a chronic neurodegenerative disease whose motor symptoms are accompanied by an exaggerated power in the alpha-beta (7-35Hz) band and an increased synchronization of neurons encompassing the cortex-basal ganglia-thalamus network. Currently, deep brain stimulation (DBS) is used as an effective therapy for reducing the excessive power and synchrony observed in brain circuits, thereby ameliorating the PD symptoms. In the present study, we used a biologically plausible computational model of cortex-basal ganglia-thalamus network, which represents both healthy and PD conditions, to systematically investigate the effects of DBS frequency on the model outputs. DBS was applied to the subthalamic nucleus (STN) at different stimulation frequencies (40Hz to 300Hz). Spike train variability and spectral power in the 7-35Hz band were measured from the several nuclei represented in the model. In addition, the magnitude squared coherence between the nuclei was assessed. An increased DBS frequency tended to produce interspike intervals (ISIs) with higher variability as compared to PD condition. Also, DBS significantly reduced the alpha-beta power for almost all brain nuclei. The median of the magnitude-squared coherence matrix (which is a metric of global network synchronization) decreased significantly with the increase of DBS frequency.

关键词： Satellite broadcasting Brain modeling Neurons Computational modeling Coherence Partial discharges Synchronization

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Multi-stream deep learning framework to predict mild cognitive impairment with Rey Complex Figure Test

arXiv

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arXiv 2024年

作者： Park, Junyoung Seo, Eun Hyun Kim, Sunjun Yi, SangHak Lee, Kun Ho Won, Sungho Department of Neurology and Neurological Sciences Stanford University School of Medicine StanfordCA94305 United States RexSoft Inc Seoul08826 Korea Republic of Gwangju Alzheimer's & Related Dementia Cohort Research Center Chosun University Gwangju61452 Korea Republic of Premedical Science College of Medicine Chosun University Gwangju61452 Korea Republic of Neurozen Inc. Seoul06160 Korea Republic of Department of Neurology Wonkwang University Hospital Wonkwang University School of Medicine Iksan15865 Korea Republic of Department of Biomedical Science Chosun University Gwangju61452 Korea Republic of Korea Brain Research Institute Daegu41068 Korea Republic of Department of Public Health Sciences Graduate School of Public Health Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of Institute of Health and Environment Seoul National University Seoul08826 Korea Republic of

Drawing tests like the Rey Complex Figure Test (RCFT) are widely used to assess cognitive functions such as visuospatial skills and memory, making them valuable tools for detecting mild cognitive impairment (MCI). Despite their utility, existing predictive models based on these tests often suffer from limitations like small sample sizes and lack of external validation, which undermine their reliability. We developed a multi-stream deep learning framework that integrates two distinct processing streams: a multi-head self-attention based spatial stream using raw RCFT images and a scoring stream employing a previously developed automated scoring system. Our model was trained on data from 1,740 subjects in the Korean cohort and validated on an external hospital dataset of 222 subjects from Korea. The proposed multi-stream model demonstrated superior performance over baseline models (AUC = 0.872, Accuracy = 0.781) in external validation. The integration of both spatial and scoring streams enables the model to capture intricate visual details from the raw images while also incorporating structured scoring data, which together enhance its ability to detect subtle cognitive impairments. This dual approach not only improves predictive accuracy but also increases the robustness of the model, making it more reliable in diverse clinical settings. Our model has practical implications for clinical settings, where it could serve as a cost-effective tool for early MCI screening. © 2024, CC BY.

关键词： Convolutional neural networks

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Astrocytic Cebpd Regulates Pentraxin 3 Expression to Promote Fibrotic Scar Formation After Spinal Cord Injury

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Molecular neurobiology 2023年第4期60卷 2200-2208页

作者： Shao-Ming Wang Jung-Yu C Hsu Chiung-Yuan Ko Hsiang-En Wu Yu-Wei Hsiao Ju-Ming Wang Graduate Institute of Biomedical Sciences China Medical University Taichung 404333 Taiwan. wangs@mail.cmu.edu.tw. Neuroscience and Brain Disease Center China Medical University Taichung Taiwan. wangs@mail.cmu.edu.tw. Department of Neurology China Medical University Hospital Taichung Taiwan. wangs@mail.cmu.edu.tw. Department of Cell Biology and Anatomy College of Medicine National Cheng Kung University Tainan Taiwan. Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan. TMU Research Center of Neuroscience Taipei Medical University Taipei Taiwan. TMU Research Center of Cancer Translational Medicine Taipei Taiwan. Cellular Pathobiology Section Integrative Neuroscience Research Branch Intramural Research Program National Institute on Drug Abuse NIH/DHHS Suite 3512 333 Cassell Drive Baltimore MD 21224 USA. Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan Taiwan. Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan Taiwan. yumingw@mail.ncku.edu.tw. Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei Taiwan. yumingw@mail.ncku.edu.tw. International Research Center for Wound Repair and Regeneration National Cheng Kung University Tainan Taiwan. yumingw@mail.ncku.edu.tw. Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan. yumingw@mail.ncku.edu.tw. Institute of Bioinformatics and Biosignal Transduction College of Bioscience and Biotechnology National Cheng Kung University Tainan 701 Taiwan. yumingw@mail.ncku.edu.tw.

Astroglial-fibrotic scars resulted from spinal cord injury affect motor and sensory function, leading to paralysis. In particular, the fibrotic scar is a main barrier that disrupts neuronal regeneration after spinal cord injury. However, the association between astrocytes and fibrotic scar formation is not yet understood. We have previously demonstrated that the transcriptional factor Cebpd contributes to astrogliosis, which promotes glial scar formation after spinal cord injury. Herein, we show that fibrotic scar formation was decreased in the epicenter region in Cebpd mice after contusive spinal cord injury and astrocytic Cebpd promoted fibroblast migration through secretion of Ptx3. Furthermore, the expression of Mmp3 was increased under recombinant protein Ptx3 treatment in fibroblasts by observing microarray data, resulting in fibroblast migration. In addition, regulation of Mmp3 occurs through the NFκB signaling pathway by using an irreversible inhibitor of IκBα phosphorylation in pretreated fibroblasts. Of note, we used the synthetic peptide RI37, which blocks fibroblast migration and decreases fibroblast Mmp3 expression in IL-1β-treated astrocyte conditioned media. Collectively, our data suggest that fibroblast migration can be affected by astrocytic Cebpd through the Ptx3/NFκB/Mmp3 axis pathway and that the RI37 peptide may act as a therapeutic medicine to inhibit fibrotic scar formation after spinal cord injury.

关键词： Astrocyte Cebpd Fibrotic scar Ptx3 Spinal cord injury

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Sex Differences of Co-Exposures to Melamine and Phthalates and Early Renal Injury in Children

SSRN

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SSRN 2024年

作者： Wu, Ming-Tsang Tsai, Hui-Ju Wu, Chia-Fang Li, Sih-Syuan Chen, Jia-Jen Hsieh, Chia-Jung Chen, Chu-Chih Wang, Shu-Li Chen, Mei-Lien Graduate Institute of Clinical Medicine Taiwan Department of Family Medicine Kaohsiung Municipal Ta-Tung Hospital Kaohsiung Medical University Kaohsiung Taiwan Department of Family Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Department of Family Medicine School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Research Center for Precision Environmental Medicine Kaohsiung Medical University Kaohsiung Taiwan Taipei Taiwan Ph.D. Program in Environmental and Occupational Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Public Health Tzu Chi University Hualien Taiwan Division of Biostatistics and Bioinformatics Institute of Population Health Sciences National Health Research Institutes Taiwan Institute of Environmental and Occupational Health Sciences School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan National Institute of Environmental Health Sciences National Health Research Institutes Miaoli Taiwan

Studies concerning the effect of co-exposure to melamine and phthalates, two common environmental chemicals and potential nephrotoxins, on kidney function in children are rare. Thus, this study aims to examine the above-mentioned relationship, and their sex-different effect is also explored. Study children (3-7 years old) were recruited from one cohort of the Taiwan Maternal and Infant Cohort Study (TMICS) between 2016 and 2020. One-spot urine specimens were collected to simultaneously measure melamine, 11 phthalate metabolites, and two markers of renal injury, microalbumin and N-acetyl-beta-D-glucosaminidas (NAG). Daily intakes of melamine and six phthalates, including DEHP (di-2-ethylhexylphthalate), DiBP (Dibutyl phthalate), DnBP (Di-n-butyl phthalate), BBzP (Butyl benzyl phthalate), and DEP (Diethyl phthalate), were estimated using a creatinine excretion-based model. A total of 552 children (319 boys and 213 girls) were studied, with a mean age of 4 years. Although the individual effect of study chemicals on early renal injury was not significant, we did find that boys in the highest quartile of estimated melamine intake (≥ 0.68 µg/kg/day) had significantly higher urine ACR levels and in the highest quartile of estimated phthalate intake of DEHP (≥ 5.36 µg/kg/day), DEP (≥ 0.89 µg/kg/day), and DiBP (≥ 1.19 µg/kg/day) had significantly higher urine NAG levels when compared to the combined three lowest quartile ones as comparison groups. Categorizing the highest quartile of the above four significant chemicals as "1" score and summed, the higher the score of the joint effect of melamine, DEHP, DEP, or DiBP, the higher the urine ACR and NAG levels noted in study children, particularly in boys. We conclude that children with high co-exposure of melamine and certain phthalate chemicals have increased markers of early kidney injury particularly in boys. © 2024, The Authors. All rights reserved.

关键词： Metabolites

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Two-Step Activated Carbon Cloth Enhances Microbial Interactions and Methane Production During Anaerobic Digestion of Municipal Sludge

SSRN

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SSRN 2022年

作者： Kumar, Vikas Kieft, Brandon Devi, Parmila Hallam, Steven J. Eskicioglu, Cigdem UBC Bioreactor Technology Group School of Engineering University of British Columbia Okanagan Campus KelownaBCV1V 1V7 Canada Department of Microbiology and Immunology University of British Columbia VancouverBCV6T 1Z4 Canada Department of Microbiology and Immunology University of British Columbia VancouverBC Canada Graduate Program in Bioinformatics University of British Columbia VancouverBCV6T 1Z4 Canada Genome Science and Technology Program University of British Columbia 2329 West Mall VancouverBCV6T 1Z4 Canada Life Sciences Institute University of British Columbia VancouverBCV6T 1Z3 Canada ECOSCOPE Training Program University of British Columbia VancouverBCV6T 1Z3 Canada

This study investigated the effects of two-step activated carbon cloth (CC) on the syntrophic microbial association during anaerobic digestion (AD) of municipal sludge. A simple two-step activation, acid pretreatment (H2SO4/HNO3) followed by air calcination (550°C), was used to convert commercial untreated CC (U-CC) into high performance activated CC (A-CC). Biochemical methane potential (BMP) assays conducted at different CC dosages indicated that A-CC, at a dose of 0.80 g A-CC {1.48 g A-CC/g-VS (volatile solids)substrate}, enhanced methane yield from by 3.7 times and reduced the lag phase up to 15.6-fold, compared to non-CC control within 3 days. Characterization of CC surfaces and kinetic modeling indicated a 435-times increased A-CC pore size area (Barrett, Joyner, and Halenda, BJH) with the potential to promote electron transfer and accelerate kinetic rates of methane production. Consistent with these results, microbial community structure analysis of CC surfaces indicated rapid adherence of methanogenic archaea and bacterial syntrophs to A-CC within 3 days. Multi-variate statistical analyses indicated that these populations were highly enriched on 0.80 g A-CC dosage compared to controls. Based on these results it can be concluded that A-CC provides a robust selective environment for syntrophic energy metabolism driving enhanced methane production from municipal sludge. © 2022, The Authors. All rights reserved.

关键词： Anaerobic digestion

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Employing best input SVR robust lost function with nature-inspired metaheuristics in wind speed energy forecasting

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2020年第3期47卷 572-584页

作者： Caraka, Rezzy Eko Chen, Rung Ching Bakar, Sakhinah Abu Tahmid, Muhammad Toharudin, Toni Pardamean, Bens Huang, Su-Wen College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan School of Mathematical Sciences The National University of Malaysia Bandar Baru Bangi Selangor43600 Malaysia Bioinformatics and Data Science Research Center Bina Nusantara University 11480 Indonesia Manado North Sulawesi91594 Indonesia Department of Statistics Padjadjaran University Jatinagor west java45363 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University and Computer Science Dept. BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Taichung Veterans General Hospital Taiwan

Wind power has been experiencing a quick improvement. Without a doubt, wind is a variable asset that is hard to forecast. For instance, traditionally time series, extra holds are distributed to deal with this uncertainty. This paper presents a comparison of the performance of various Support Vector Regression (SVR) applied to short-term wind power forecasting. The analogy with BORUTA and multivariate adaptive regression splines (MARS) as judge best input and employ genetic algorithm and particle swarm optimization to find best parameter in Support Vector Regression with robust lost function. We measure the accuracy of this models by Symmetric means absolute percentage error (sMAPE) and we get the best model BORUTA-SVR-PSO with sMAPE 2.07155%. Moreover, we measure the energy conversion using Feedback Linearization Control (FLC). © International Association of Engineers.

关键词： Genetic algorithms

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Expression of concern: Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy

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Nanoscale 2024年第9期16卷 4919页

作者： Michael A Casasanta G M Jonaid Liam Kaylor William Y Luqiu Maria J Solares Mariah L Schroen William J Dearnaley Jarad Wilson Madeline J Dukes Deborah F Kelly Department of Biomedical Engineering Pennsylvania State University University Park PA 16802 USA. Debkelly@psu.edu. Materials Research Institute Pennsylvania State University University Park PA 16802 USA. Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Department of Electrical and Computer Engineering Duke University Durham NC 27708 USA. RayBiotech Life Peachtree Corners GA 30092 USA. Applications Science Protochips Inc. Morrisville NC 27560 USA.

Expression of concern for 'Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy' by Michael A. Casasanta , , 2021, , 7285-7293, https://***/10.1039/D1NR00388G.

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