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Remote real-time wastewater surveillance by the optical sensor supported by machine learning

Sensors and Actuators Reports 2025年 10卷

作者： Małgorzata Szczerska Kacper Cierpiak Monika Kosowska Paweł Wityk Sebastián García-Galán Patryk Sokołowski Sylwia Fudala-Książek Michał T. Tomczak Bei Ye Aneta Łuczkiewicz Department of Metrology and Optoelectronics Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdansk Poland Faculty of Telecommunications Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Al. Prof. S. Kaliskiego 7 85-796 Bydgoszcz Poland Department of Biotechnology and Microbiology Faculty of Chemistry Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdansk Poland Department of Biopharmaceutics and Pharmacodynamics Medical University of Gdańsk Hallera 107 Pharmacy Faculty Gdańsk Poland Telecommunications Engineering Department Linares Higher Polytechnic School Jaén University Avenida de la Universidad S/N 23700 Linares Jaén Spain Department of Water and Wastewater Technology Faculty of Civil and Environmental Engineering Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdańsk Poland Faculty of Management and Economics Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdańsk Poland Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Environmental Protection Key Laboratory of Microorganism Application and Risk Control Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University Shenzhen 518055 PR China

In this study, a remote monitoring sewage system based on optical method is presented for the first time. The built-in wastewater surveillance system can perform autonomous monitoring with no requirement of sample collection and its transport to the laboratory. The measurement results can be obtained in real time, the work operation allows continuous or on-demand mode. The study includes design, development and application of a biofunctionalized fiber-optic sensor, as well as engaging machine learning algorithms for measured signals classification. The validated SARS-CoV-2 antibodies sensor has a measurement range of 10 −12 mg/mL to 10 −1 mg/mL. Known concentrations of Immunoglobulin G (IgG) from 10 −6 mg/mL to 10 −1 mg/mL were added to the tested wastewater samples and then detected by the prepared optical probe. The data obtained were then processed and classified by traditional method and selected machine learning algorithms; the results obtained for the KNeighbors algorithm are Balanced Accuracy of 92.97 % and F1-score of 94.19 %. This study is of significance to establish a system that can effectively monitor the outbreak of potential infections in society.

关键词： Remote wastewater surveillance Real-time monitoring SARS-CoV-2 Optical sensors Fiber optic sensor Machine learning

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

引用

arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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Scoreboard wireless universal based on microcontroller 2

Scoreboard wireless universal based on microcontroller

引用

2nd Nommensen International Conference on Technology and engineering, NICTE 2018

作者： Hutauruk, Sindak Siagian, Pandapotan Sianturi, Libianko Department of Electrical Engineering University of HKBP University 4 A Sutomo Street Medan Indonesia Institut Teknologi Del Faculty of Informatics and Electrical Engineering Computer Network Management Study Program Jl. Sisingamangaraja Sitoluama Toba Samosir Sumatera Utara22381 Indonesia

The scoreboard is designed and made for a universal use that can be used in matches in some sports such as basketball, futsal, ping pong, badminton, and others. The scoreboard is designed and made based on the needs of several sports in the Student Activity Unit at HKBP Nommensen University which can be easily moved. Scores displayed on the scoreboard is not only the result of the game but also displayed time in the countdown. This scoreboard can be operated by using a cable and can also without using a cable by using a mobile phone through communication using bluetooth. This scoreboard is based on a microcontroller and uses backup power through rechargeable batteries that can be automatically charged. In trials in several games, this tool works well with a few drawbacks in case of a match that takes more than 100 minutes because the time display can only display a maximum time of 99 minutes. © Published under licence by IOP Publishing Ltd.

关键词： Microcontrollers

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Developing self-adaptive microservice systems: Challenges and directions

arXiv

引用

arXiv 2019年

作者： Mendonça, Nabor C. Jamshidi, Pooyan Garlan, David Pahl, Claus Post Graduate Program in Applied Informatics University of Fortaleza FortalezaCE Brazil Computer Science and Engineering Department University of South Carolina ColumbiaSC United States School of Computer Science Carnegie Mellon University PittsburghPA United States Faculty of Computer Science Free University of Bozen-Bolzano Bozen-Bolzano Italy

A self-adaptive system can dynamically monitor and adapt its behavior to preserve or enhance its quality attributes under uncertain operating conditions. This article identifies key challenges for the development of microservice applications as self-adaptive systems, using a cloud-based intelligent video surveillance application as a motivating example. It also suggests potential new directions for addressing most of the identified challenges by leveraging existing microservice practices and technologies. Copyright © 2019, The Authors. All rights reserved.

关键词： Adaptive systems

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Observation of topological polaritons and photonic magic angles in twisted van der Waals bi-layers

arXiv

引用

arXiv 2020年

作者： Alù, Andrea Krasnok, Alex Dai, Zhigao Wu, Jing Qiu, Cheng-Wei Wu, Yingjie Bao, Qiaoliang Si, Guangyuan Ou, Qingdong Hu, Guangwei Mazor, Yarden Zhang, Qing Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Photonics Initiative Advanced Science Research Center City University of New York New YorkNY United States Monash University ClaytonVIC Australia Melbourne Centre for Nanofabrication Victorian Node of the Australian National Fabrication Facility ClaytonVIC Australia Singapore Singapore Faculty of Materials Science and Chemistry China University of Geosciences 388 Lumo Road Wuhan430074 China Department of Electrical and Computer Engineering University of Texas at Austin AustinTX United States Physics Program Graduate Center City University of New York New YorkNY United States

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Grasping and attached mode in human-computer interaction in the study of mouse substitution

Grasping and attached mode in human-computer interaction in ...

引用

International Conference on Information System, computer Science and engineering 2018, ICONISCSE 2018

作者： Widodo, R.B. Quita, R.M. Amrizal, S. Gunawan, R.S. Wada, C. Ardiansyah, A. Sutrisno, I. Rahmat, M.B. Arfianto, A.Z. Handoko, C.R. Santoso, A.D. Santosa, A.W.B. Ardhana, V.Y.P. Setiawan, E. Informatics Engineering Study Program Ma Chung University Malang65151 Indonesia Department of Mathematics Faculty of Science National Central University Taoyuan City32001 Taiwan Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Wakamatsu Fukuoka808-0196 Japan Politeknik Pelayaran Surabaya Gunung Anyar Surabaya60294 Indonesia Department of Marine Electrical Engineering Politeknik Perkapalan Negeri Surabaya Sukolilo Surabaya60111 Indonesia Departemen Teknik Perkapalan Fakultas Teknik Universitas Diponegoro Semarang50275 Indonesia Universitas Qamarul Huda Badaruddin Bagu Central Lombok Indonesia

Some disabilities caused by sensory malfunction, accident, or congenital disorder could cause people difficulty in grasping the computer mouse. A well-known substitute uses an inertial sensor to monitor the body's orientation. This paper evaluates the efficiency and effectiveness of different methods of holding the sensor. There are two modes of holding the sensor, the grasping mode and the attached mode;we also evaluate a mouse as a baseline. The attached mode works by placing the sensor on the back of user's hand. The quantitative and qualitative evaluation procedure is based on ISO/TS 9241 part 411: Ergonomics of human-system interaction standard. The test consists of four levels of difficulty and indicates that the throughput and task completion times were not statistically different between grasping and attached mode. We also found that grasping mode and attached mode did not show significantly statistical differences in comfort and fatigue based on the questionnaires. The results also suggest that the orientation of the hand used in grasping and attached modes is suitable only for lower-impact computer use. © Published under licence by IOP Publishing Ltd.

关键词： Ergonomics

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The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions

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The Journal of Data Education 2025年第1期65卷

作者： Keng-Boon Ooi Garry Wei-Han Tan Mostafa Al-Emran Mohammed A. Al-Sharafi Alexandru Capatina Amrita Chakraborty Yogesh K. Dwivedi Tzu-Ling Huang Arpan Kumar Kar Voon-Hsien Lee Xiu-Ming Loh Adrian Micu Patrick Mikalef Emmanuel Mogaji Neeraj Pandey Ramakrishnan Raman Nripendra P. Rana Prianka Sarker Anshuman Sharma Ching-I Teng Samuel Fosso Wamba Lai-Wan Wong a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiab FORE School of Management New Delhi Indiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysia a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysiad College of Business Administration Adamson University Manila Philippines e Faculty of Engineering & IT The British University in Dubai Dubai UAEf Department of Computer Techniques Engineering Dijlah University College Baghdad Iraq g Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Selangor Malaysia h Department of Business Administration “Dunarea de Jos” University of Galati Galati Romania i Tech Talk New Delhi India j Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Swansea UKk Department of Management Symbiosis International (Deemed University) Pune India l Department of Information Management National Central University Taoyuan Taiwan m Department of Management Studies Indian Institute of Technology Delhi New Delhi India n Faculty of Business and Finance Universiti Tunku Abdul Rahman Kampar Malaysia o Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norwayp Department of Technology Management SINTEF Digital Trondheim Norway q Keele Business School Keele University Keele UK r Indian Institute of Management Mumbai Mumbai India k Department of Management Symbiosis International (Deemed University) Pune India s College of Business and Economics Qatar University Doha Qatar t Business School Manchester Metropolitan University Manchester UK u Department of Marketing College of Business Administration Ajman university Ajman UAE v Graduate Institute of Management Chang Gung University Taoyuan Taiw

ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

关键词： Generative artificial intelligence machine learning large language model ChatGPT Bard

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Short-Term Effects of a Therapeutic Diet on Biochemical Parameters in Hemodialysis Patients: A Randomized Crossover Trial

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Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation 2023年第6期33卷 731-739页

作者： Wan-Chuan Tsai Shih-Ping Hsu Yen-Ling Chiu Hon-Yen Wu Chia-Chin Luan Ju-Yeh Yang Mei-Fen Pai Cheng-Jui Lin Wan-Yu Lin Wen-Huei Sun Yu-Sen Peng Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan. School of Life Science National Taiwan Normal University Taipei City Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan. Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan Faculty of Medicine School of Medicine National Yang-Ming University Taipei City Taiwan. Dietary Department Far Eastern Memorial Hospital New Taipei City Taiwan. Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Division of Nephrology Department of Internal Medicine Mackay Memorial Hospital Taipei City Taiwan Department of Medicine Mackay Medical College Taipei City Taiwan Mackay Junior College of Medicine Nursing and Management Taipei City Taiwan. Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan. Dietary Department Far Eastern Memorial Hospital New Taipei City Taiwan. Electronic address: 830636231@mail.femh.org.tw. Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan Department of Electrical Engineering Yuan Ze University Taoyuan City Taiwan. Electronic address: taan70@***.

OBJECTIVE:Although unhealthy diets exacerbate nutritional and metabolic derangements in patients with end-stage kidney disease (ESKD), how therapeutic diets that possess a variety of different dietary strategies acutely modify diverse biochemical parameters related to cardiovascular disease remains underexplored. METHODS:Thirty-three adults with end-stage kidney disease undergoing thrice-weekly hemodialysis participated in a randomized crossover trial comparing a therapeutic diet with their usual diets for 7 days, separated by a 4-week washout period. The therapeutic diet was characterized by adequate calorie and protein amounts, natural food ingredients with a low phosphorus-to-protein ratio, higher portions of plant-based food, and high fiber content. The primary outcome measure was the mean difference in the change-from-baseline intact fibroblast growth factor 23 (FGF23) level between the 2 diets. The other outcomes of interest included changes in mineral parameters, uremic toxins, and high-sensitivity C-reactive protein (hs-CRP) levels. RESULTS:Compared with the usual diet, the therapeutic diet lowered intact FGF23 levels (P = .001), decreased serum phosphate levels (P < .001), reduced intact parathyroid hormone (PTH) levels (P = .003), lowered C-terminal FGF23 levels (P = .03), increased serum calcium levels (P = .01), and tended to lower total indoxyl sulfate levels (P = .07) but had no significant effect on hs-CRP levels. Among these changes, reduction in serum phosphate level achieved in 2 days, modifications of intact PTH and calcium levels in 5 days, and reductions in intact and C-terminal FGF23 levels in 7 days of therapeutic diet intervention. CONCLUSION:Within the 1-week intervention period, the dialysis-specific therapeutic diet rapidly reversed mineral abnormalities and tended to decrease total indoxyl sulfate levels in patients undergoing hemodialysis but had no effect on inflammation. Future studies to assess the long-term effects of such therapeuti

关键词： FGF23 dialysis inflammation therapeutic diet uremic toxin

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting 3

Single Layer & Multi-layer Long Short-Term Memory (LSTM) Mod...

引用

3rd International Conference on computer Science and Computational Intelligence, ICCSCI 2018

作者： Salman, Afan Galih Heryadi, Yaya Abdurahman, Edi Suparta, Wayan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Science and Technology Sanata Dharma University Yogyakarta55282 Indonesia

Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled and the wide availability of massive weather observation data have motivated many researches to explore hidden hierarchical pattern in the large volume of weather dataset for weather forecasting. The purposes of this research are to build a robust and adaptive statistical model for forecasting univariate weather variable in Indonesian airport area and to explore the effect of intermediate weather variable related to accuracy prediction using single layer Long Short Memory Model (LSTM) model and multi layers LSTM model. The proposed forecasting model is an extension of LSTM model by adding intermediate variable signal into LSTM memory block. The premise is that two highly related patterns in input dataset will rectify the input patterns so make it easier for the model to learn and recognize the pattern from the training dataset. In an effort to achieve a robust model for learning and recognizing weather pattern, this research will also explore various architectures such as single layer LSTM and Multiple Layer LSTM (4 layers LSTM). The dataset is weather variable data collected by Weather Underground at Hang Nadim Indonesia airport. This research used visibility as predicted data and temperature, pressure, humidity, dew point as intermediates data. The best model of LSTM in this experiment is multiple layers LSTM and the best intermediate data is pressure variable. Using the pressure variable this model has gained the validation accuracy 0.8060 and RMSE 0.0775. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Long short-term memory

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