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Increasing accuracy of classification physical activity based on smartphone using ensemble logistic regression with boosting method

引用

Journal of Physics: Conference Series 2019年第4期1341卷

作者： A Lawi F Aziz S L Wungo Department of Computer Science Universitas Hasanuddin Makassar Indonesia. 90245 Faculty of Mathematics and Natural Sciences Universitas Pancasakti Makassar Indonesia. 90132 Program Study Informatics Engineering STMIK Kreatindo Monokwari Indonesia. 98300

At present, the smartphone is equipped with several sensors such as Accelerometer, Gravity sensor, and Gyroscope which can be used to recognize human physical activities such as walking upstair and walking downstairs etc. Machine learning is needed to group data and get information. Statistical methods have poor performance in classifying because procedures must be met. To overcome this, an ensemble technique was used. This study proposes the application of the gradientboost ensemble method to classify walking upstair and walking downstairs. The *** system is designed for data retrieval using a smartphone. Then, the dataset will be partitioned into 70% training data and 30% test data. The results show that the performance of the ensemble boosting method produces 81.82% accuracy, 86.11% sensitivity and 77.50% specificity.

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Preparation of Nanocellulose-Alginate Nanocomposites for Chlorhexidine Digluconate Drug Carrier

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IOP Conference Series: Materials science and engineering 2019年第1期547卷

作者： Angela Evelyna Tiara Khumaira Astifanni Imelda Ruth Lia Asri Bambang Sunendar Purwasasmita Faculty of Dentistry Maranatha Christian University Indonesia Materials Science and Engineering Bandung Institute of Technology Indonesia Advanced Materials Processing Group Engineering Physics Study Program Bandung Institute of Technology Indonesia Research Center for Nanosciences and Nanotechnology Bandung Institute of Technology Indonesia.

Root canal treatment failures generally caused by microorganisms that have ability to invade dentinal tubules. Chlorhexidine is widely known to effectively eradicate broad spectrum bacteria that infected dentinal tubules. The delivery of chlorhexidine to the infected site of root canal still becomes an issue. In order to increase the stability and to regulate the release of chlorhexidine, encapsulation of chlorhexidine digluconate was conducted by employing nanocellulose and alginate nanocomposites. Nanocellulose was prepared from palm kernel cake using acid hydrolysis method. Transmission electron microscopy images showed the formation of whisker and fiber nanocellulose with the average diameter of 20 nm. Resulting nanocellulose was combined with alginate in the presence of Ca2+ crosslinker. Scanning electron microscopy images displayed sphere and oval morphologies of microcapsules with diameter of 500 nm. The microcapsules were further loaded with chlorhexidine digluconate 2% (w/v). In vitro drug release properties were evaluated in PBS at pH 7.4 (normal tooth environment) and at pH 5.5 (infected tooth environment). The UV-VIS results showed that chlorhexidine digluconate has higher release rate at pH 5.5 than at pH 7.4, suggesting that the microcapsule is a good candidate for the delivery of chlorhexidine digluconate to the infected root canal tooth environment.

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Monitoring electrical energy in electronic energy audits through internet of things technology

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Journal of Physics: Conference Series 2019年第7期1402卷

作者： M P T Sulistyanto K B Pranata A N Afandi S Sendari I Sulistiyowati Informatics Study Program Faculty of Science and Technology Universitas Kanjuruhan Malang Jl. S. Supriadi No. 48 Malang Jawa Timur Indonesia Physics Education Study Program Faculty of Science and Technology Universitas Kanjuruhan Malang Jl. S. Supriadi No. 48 Malang Jawa Timur Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Negeri Malang Teknik Elektro Universitas Muhammadiyah Sidoarjo Jl. Raya Gelam 250 Candi Sidoarjo Jawa Timur Indonesia

Energy conservation, especially electricity, can be done by conducting an energy audit. Energy audit activities can analyse and find energy-saving opportunities from energy use. This study developed a prototype of a wireless electrical energy monitoring system on a laboratory scale to monitor the use of electrical energy from both electrical equipment using a microcontroller as a sensor node. These nodes have installed several sensors, namely the current sensor, humidity sensor, temperature sensor and light sensor. The protocol used in communication between nodes and servers is the HTTP protocol in the Internet of Things design that can communicate using internet network intermediaries. Data on the server can be monitored in real time using the application on the client side.

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Identified solution management in the landfill with analytic network process

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IOP Conference Series: Earth and Environmental science 2019年第1期399卷

作者： B Ratnawati M Yani Suprihatin H Hardjomijojo Graduate School of IPB University (Bogor Agriculture University) The Study Program of Natural Resources & Environmental Management Campus IPB Barangsiang Bogor 16144 Indonesia IPB University Vocational of Science IPB University (Bogor Agriculture University) The Study Program of Environmental Engineering and Management JL Kumbang No.14 Bogor Faculty of Agricultural Technology of IPB University Department of Agroindustrial Technics Campus IPB Dramaga Bogor

The solution to solve these challenges is the waste management. The waste management is needed to solve starting from upstream to downstream. The waste management in downstream is in the landfill. The management Landfill not only manages solid waste but also waste water treatment. One form of waste water treatments in a landfill is the leachate treatment. The leachate treatment is a priority for management in landfill because it can reduce water, soil, air pollution in the landfill and the surrounding environment. In order to determine the best option among several alternatives to manage the leachate treatment, this research used Analytic Network Process (ANP) with three levels (actors, criteria, and alternatives). Secondary and primary data collection was carried out from April until June 2019 in Indonesia. Data were collected through a survey using questionnaire instrument. Participants of questionnaire amount of 20 people. The participants from lecturers, government, and society. The finding of this research showed that the leachate treatment using coagulation-flocculation was easier to be used in Indonesia.

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Data Mining: The Classification Method to Predict the Types of Motorcycle Spare Parts to be Restocked

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IOP Conference Series: Materials science and engineering 2019年第2期662卷

作者： S L B Ginting Y R Ginting Sutono A Rakhman Computer System Study Program Faculty of Engineering and Computer Science Universitas Komputer Indonesia Jl. Dipati Ukur 112-116 Bandung 40132 Indonesia Department of Mechanical Engineering Universitas Riau Kampus Bina Widya KM 12 5 Simpang Baru Pekanbaru 28293 Indonesia

The research intends to create an application which is able to analyse sales data in a motorcycle company to predict the types of spare parts which should be stocked. This prediction is crucial since problems are often encountered while restocking. For instance, when there have been some imprecisions occurring in deciding regarding the types of spare parts to restock, the spare parts accumulate. It can cause inefficiency in terms of storage, the products quality deteriorates due to having been stored for too long, and sometimes the best-selling products are not available in the warehouse. This application is developed with Naïve Bayes Classifier (NBC) method which has a high accuracy in predicting future occurrences. This method works by calculating the probability value in each attribute class and determining the optimal probability value. From the test results, 4500 training data with 200 sample test data has 90% similarity with the results of the restock decision without application. For 500 test data, the similarity was 96%. It is proven that this method has a high accuracy so that it can help the decision makers solved the company problem in predicting the types of motorcycle parts to be restocked.

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Commentaries on Viewpoint: Using V̇o2max as a marker of training status in athletes - can we do better?

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Journal of applied physiology (Bethesda, Md. : 1985) 2022年第1期133卷 148-164页

作者： Pedro L Valenzuela Manuel Mateo-March Xabier Muriel Mikel Zabala Alejandro Lucia David Barranco-Gil Grégoire P Millet Franck Brocherie Johannes Burtscher Martin Burtscher Benjamin J Ryan Rachel A Gioscia-Ryan Stephane Perrey Víctor Rodrigo-Carranza Fernando González-Mohíno José María González-Ravé Jordan Santos-Concejero Benedito S Denadai Camila C Greco Arturo Casado Carl Foster Raffaele Mazzolari Giulia Nicole Baldrighi Elisa Pastorio Davide Malatesta Aurélien Patoz Fabio Borrani Stephen J Ives Justin A DeBlauw Ricardo Dantas de Lucas Fernando Klitzke Borszcz Eduardo Marcel Fernandes Nascimento Luiz Guilherme Antonacci Guglielmo Tiago Turnes Richard T Jaspers Stephan van der Zwaard Romuald Lepers Julien Louis Anderson Meireles Hiago L. R. de Souza Géssyca T de Oliveira Marcelo P dos Santos Rhaí A Arriel Moacir Marocolo B Hunter S Meyler D Muniz-Pumares Renato M Ferreira Abigail S Sogard Stephen J Carter Timothy D Mickleborough Guilherme Pereira Saborosa Raphael Dinalli de Oliveira Freitas Paula Souza Alves dos Santos João Pedro de Souza Ferreira Francisco de Assis Manoel Sandro Fernandes da Silva Christoph Triska Bettina Karsten Dajo Sanders Elliot S Lipksi David J Spindler Matthijs K. C. Hesselink Rodrigo Zacca Márcio Fagundes Goethel David Bruce Pyne Brayden M Wood Peyton E Allen Jaden L Gabelhausen Alexandra M Keller Mast T Lige Alicia S Oumsang Greg L Smart Hunter L Paris Arthur H Dewolf Guillaume Toffoli Borja Martinez-Gonzalez Samuele M Marcora Daniela Terson de Paleville Ricardo J Fernandes Susana M Soares J. Arturo Abraldes Guilherme Matta Arthur Henrique Bossi D G McCarthy W Bostad J Gibala Mary Vagula Grupo de Investigación en Actividad física y Salud (PaHerg) Instituto de Investigación Hospital 12 de Octubre (imas12) Madrid Spain Faculty of Sport Sciences Universidad Europea de Madrid Madrid Spain Sport Science Department. Universidad Miguel Hernández Elche Spain Human Performance and Sports Science Laboratory Faculty of Sport Sciences University of Murcia Murcia Spain Department of Physical Education & Sport Faculty of Sport Sciences University of Granada Granada Spain Institute of Sport Sciences University of Lausanne Lausanne Switzerland Laboratory Sport Expertise and Performance (EA 7370) French Institute of Sport (INSEP) Paris France Department of Sport Science University of Innsbruck Innsbruck Austria Thermal and Mountain Medicine Division US Army Research Institute of Environmental Medicine Natick Massachusetts Department of Anesthesiology University of Michigan Ann Arbor Michigan EuroMov Digital Health in Motion University of Montpellier Montpellier France Sport Training Lab University of Castilla-La Mancha Toledo Spain Facultad de Ciencias de la Vida y de la Naturaleza Universidad Nebrija Madrid Spain Department of Physical Education and Sport University of the Basque Country UPV/EHU Vitoria-Gasteiz Spain Human Performance Laboratory São Paulo State University Rio Claro Brazil Center for Sport Studies Rey Juan Carlos University Madrid Spain University of Wisconsin-La Crosse La Crosse Wisconsin Department of Physical Education and Sport University of the Basque Country (UPV/EHU) Vitoria-Gasteiz Spain Department of Molecular Medicine University of Pavia Pavia Italy Department of Brain and Behavioural Sciences − Medical and Genomic Statistics Unit University of Pavia Pavia Italy Department of Biomedical Sciences for Health University of Milan Milan Italy Institute of Sport Sciences of University of Lausanne (ISSUL) University of Lausanne Lausanne Switzerland Health and Human Physiological Sciences Skidmore College Saratoga Springs New

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The structure of evolved representations across different substrates for artificial intelligence

arXiv

引用

arXiv 2018年

作者： Hintze, Arend Kirkpatrick, Douglas Adami, Christoph Department of Integrative Biology Michigan State University Department of Computer Science and Engineering Michigan State University BEACON Center for the Study of Evolution in Action Michigan State University Department of Microbiology and Molecular Genetics Michigan State University Program in Ecology Evolution and Behavior Biology Michigan State University

Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection. Small amounts of noise can significantly affect their ability to correctly complete a task. Instead of generalizing concepts, ANNs seem to focus on surface statistical regularities in a given task. Here we compare how recurrent artificial neural networks, long short-term memory units, and Markov Brains sense and remember their environments. We show that information in Markov Brains is localized and sparsely distributed, while the other neural network substrates "smear" information about the environment across all nodes, which makes them vulnerable to noise. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Pyrolysis: An effective technique for degradation of COVID-19 medical wastes

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Chemosphere 2021年 275卷 130092页

作者： Selvakumar Dharmaraj Veeramuthu Ashokkumar Rajesh Pandiyan Heli Siti Halimatul Munawaroh Kit Wayne Chew Wei-Hsin Chen Chawalit Ngamcharussrivichai (Deemed to be University) Chennai 603112 Tamil Nadu India. Center of Excellence in Catalysis for Bioenergy and Renewable Chemicals (CBRC) Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Electronic address: rvashok2008@***. Department of Biochemistry Karpagam Academy of Higher Education (formerly Karpagam University) Pollachi Main Road Eachanari Post Coimbatore Tamil Nadu India. Chemistry Study Program Department of Chemistry Education Faculty of Mathematics and Science Education Universitas Pendidikan Indonesia Jl. Dr. Setiabudhi 229 Bandung 40154 Indonesia. School of Energy and Chemical Engineering Xiamen University Malaysia Jalan Sunsuria Bandar Sunsuria 43900 Sepang Selangor Malaysia College of Chemistry and Chemical Engineering Xiamen University Xiamen 361005 Fujian China. Department of Aeronautics and Astronautics National Cheng Kung University Tainan 701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung 407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung 411 Taiwan. Center of Excellence in Catalysis for Bioenergy and Renewable Chemicals (CBRC) Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Center of Excellence on Petrochemical and Materials Technology (PETROMAT) Chulalongkorn University Pathumwan Bangkok 10330 Thailand. Electronic address: Chawalit.Ng@chula.ac.th.

COVID-19 has led to the enormous rise of medical wastes throughout the world, and these have mainly been generated from hospitals, clinics, and other healthcare establishments. This creates an additional challenge in medical waste management, particularly in developing countries. Improper managing of medical waste may have serious public health issues and a significant impact on the environment. There are currently three disinfection technologies, namely incineration, chemical and physical processes, that are available to treat COVID-19 medical waste (CMW). This study focuses on thermochemical process, particularly pyrolysis process to treat the medical waste. Pyrolysis is a process that utilizes the thermal instability of organic components in medical waste to convert them into valuable products. Besides, the technique is environmentally friendly, more efficient and cost-effective, requires less landfill capacity, and causes lower pollution. The current pandemic situation generates a large amount of plastic medical wastes, which mainly consists of polyethylene, polypropylene, polystyrene, polyethylene terephthalate, and nylon. These plastic wastes can be converted into valuable energy products like oil, gas and char through pyrolysis process. This review provides detailed information about CMW handling, treatment, valuable product generation, and proper discharge into the open environment.

关键词： COVID-19 medical waste Disinfection techniques Energy conversion Medical waste management Pyrolysis

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Load balancing factor using greedy algorithm in the routing protocol for improving internet access

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IOP Conference Series: Materials science and engineering 2018年第1期420卷

作者： Ady Satria Poltak Sihombing Sawaluddin Informatics Engineering Study Program Magister Faculty of Computer Science and Information Technology University of Sumatera Utara

Load Balancing is very well applied to the distribution of internet access at any point of the Wi-Fi area so that the use of limited devices can be optimal and on target. The proper use of the Greedy Algorithms when applied to an access point device is very capable in resolving excessive loading in a single resource to take the best choice at every stage in an optimum process. Access point device is also a success factor in running the optimization of the distribution of Wi-Fi access on the Access Point is strongly influenced by the parameters set. In this research, user access factor in one very high Wi-Fi area also influences the possibility of incoming access failure. For the application of the results of this study can be utilized on all access point devices that have limits, but for speed internet access remains at the capacity provided bandwidth.

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