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IEEE Transactions on technology and society 2024年第2期5卷 183-190页

作者： Birch, Kean Marquis, Sarah Cavalcante Silva, Guilherme York University Institute for Technoscience and Society TorontoONM3J 1P3 Canada University of Ottawa Centre for Law Technology and Society OttawaONK1H 8M5 Canada York University Graduate Program in Science and Technology Studies TorontoONM3J 1P3 Canada

Digital personal data are increasingly understood as a key asset in our digital economies. But how should we value such data? Numerous policymakers, regulators, and stakeholders are trying to work out how to manage the collection, use, and valuation of data in order to balance the advantages and disadvantages of its collection and use. The negative implications of data practices may include privacy loss, data breaches, or declining market competition, while social and economic benefits include improved service delivery, more efficient welfare systems, or better products. Increasingly, data are conceptualized as an asset. To understand the value of data as an asset means understanding how data are configured as an asset;data value does not reflect ownership and property rights per se, but rather diverse modes of access and use restrictions (usually delineated by opaque contractual agreements). Data are increasingly controlled by a few, large digital technology firms, especially so-called 'Big Tech' firms. In this paper, we use Google as a case study of how Big Tech firms configure and value digital data as an asset. We analyse how Google understands, frames, values, and monetizes the data they collect from users. We qualitatively analyse an extensive dataset of financial documentary materials produced by and about Google to identify the different modes of access and use restrictions that Google deploys to turn digital data into a valuable asset. We conclude that, despite being highly ambiguous, Google's approach to data value focuses on monetizing users. © 2024 The Authors.

关键词： Data privacy

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Synergistic sodium alginate- and biochar-immobilized cells for enhancing fermentative hydrogen production from food waste

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Environmental science and Pollution Research 2024年第56期31卷 64524-64533页

作者： Ngamnurak, Phonsini Plangklang, Pensri Pomdaeng, Prakaidao Ko, Ting-Wu Reungsang, Alissara Chu, Chen-Yeon Graduate School Khon Kaen University Khon Kaen40002 Thailand Department of Biology Technology Faculty of Technology Khon Kaen University Khon Kaen40002 Thailand Research Group for Development of Microbial Hydrogen Production Process From Biomass Khon Kaen University Khon Kaen40002 Thailand Institute of Green Products Feng Chia University Taichung Taiwan Ph.D. Program of Mechanical and Aeronautical Engineering Feng Chia University Taichung Taiwan Master’s Program of Green Energy Science and Technology Feng Chia University Taichung Taiwan Academy of Science Royal Society of Thailand Bangkok10300 Thailand

An immobilized hydrogen-producing consortium investigated biohydrogen production from food waste using a combination of sodium alginate and cassava rhizome biochar. We investigated the effect of varying the biochar concentration from 0 to 3% and the size of immobilized cells from 1 to 7 mm. Immobilized cells were prepared using 50% (v/v) enriched hydrogen-producing consortium, 2% (w/v) sodium alginate, and 0 to 3% (w/v) cassava rhizome biochar. The optimal conditions for achieving the highest hydrogen production in the batch fermentation reactor were identified as a biochar concentration of 2% (w/v) and an immobilized cell size of 2 mm. The highest hydrogen yield, maximum hydrogen production rate, and lag time recorded were 0.69 mmol H2/g-COD, 0.02 mmol H2/g-COD.h, and 41.51 h, respectively. This research highlights the potential of cassava biochar technology for efficient biohydrogen production from food waste, contributing to renewable energy generation and sustainable waste management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Anaerobic digestion

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Traveling with Einstein

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The European Legacy 2020年第7-8期25卷 845-851页

作者： Oren Harman Graduate Program in Science Technology and Society Bar-Ilan University Ramat Gan Israel

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Comparing LSTM and BLSTM Deep Neural Networks for Power Consumption Prediction: Preliminary studies

arXiv

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arXiv 2023年

作者： da Silva, Davi Guimarães de Moura Meneses, Anderson Alvarenga Federal University of Western Pará – Graduate Program in Society Nature and Development R. Vera Paz s/n Salé PA SantarémCEP 68.035-110 Brazil Federal Institute of Education Science and Technology of Pará Brazil Federal University of Western Pará Institute of Geosciences and Engineering Laboratory of Computational Intelligence Brazil

Electric consumption prediction methods are investigated for many reasons such as decision-making related to energy efficiency as well as for anticipating demand in the energy market dynamics. The objective of the present work is the comparison between two Deep Learning models, namely the Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast. The Data Sets (DSs) were selected for their different contexts and scales, aiming the assessment of the models’ robustness. Four DSs were used, related to the power consumption of: (a) a household in France;(b) a university building in Santarém, Brazil;(c) the Tétouan city zones, in Morocco;and (c) the Singapore aggregated electric demand. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. The Friedman’s test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating improves significantly the LSTM performance concerning different scales of electric power consumption. © 2023, CC BY-NC-ND.

关键词： Long short-term memory

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Mental Health, Loneliness, and Social Support Among Undergraduate Students: A Multinational Study in Asia

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Asia-Pacific Journal of Public Health 2023年第4期35卷 244-250页

作者： Backhaus, Insa Fitri, Mustika Esfahani, Mahdi Ngo, Huyen Thi Lin, Li-Jung Yamanaka, Akira Alhumaid, Majed M. Qin, Ling Khan, Azizuddin Fadzullah, Nur ’Ashikin Khoo, Selina Institute of Medical Sociology Centre for Health and Society Medical Faculty Heinrich Heine University Düsseldorf Düsseldorf Germany Sport Science Study Program Faculty of Sport and Health Education Universitas Pendidikan Indonesia Bandung Indonesia Department of Sport Science Imam Reza International University Mashhad Iran Faculty of Library and Information Science University of Social Sciences and Humanities Vietnam National University Ho Chi Minh City Ho Chi Minh City Viet Nam Graduate Institute of Sport Leisure & Hospitality Management National Taiwan Normal University Taipei Taiwan Faculty of Collaborative Regional Innovation Ehime University Ehime Japan Department of Physical Education College of Education King Faisal University Al-Ahsa Saudi Arabia Faculty of Physical Education and Health Sciences Chongqing Normal University Chongqing China Psychophysiology Laboratory Department of Humanities and Social Sciences Indian Institute of Technology Bombay Mumbai India Faculty of Sports and Exercise Science Universiti Malaya Kuala Lumpur Malaysia

In this study, we aimed to investigate the prevalence of poor mental health and its association with loneliness and social support among 3531 undergraduate students in nine Asian countries. Mental health was assessed using the Self-Reporting Questionnaire, which was developed by the World Health Organization. Across the entire sample, we detected that nearly half of the students reported poor mental health according to the Self-Reporting Questionnaire and nearly one out of seven students felt lonely. While feeling lonely increased the odds of experiencing poor mental health (odds ratio [OR]), moderate (OR: 0.35) and strong social support (OR: 0.18) decreases the odds of experiencing poor mental health. The high prevalence of poor mental health calls for further in-depth investigations and implementation of mental health support interventions. © 2023 APJPH.

关键词： loneliness mental health social support university students

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Performance evaluation of LSTM neural networks for consumption prediction

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2022年 2卷

作者： da Silva, Davi Guimarães Geller, Marla Teresinha Barbosa Moura, Mauro Sérgio dos Santos Meneses, Anderson Alvarenga de Moura Federal University of Western Pará – Graduate Program in Society Nature and Development R. Vera Paz s/n Salé CEP 68.035-110 PA Santarém Brazil Federal Institute of Education Science and Technology of Pará Brazil Laboratory of Computational Intelligence Federal University of Western Pará Institute of Geosciences and Engineering Brazil

Energy consumption and energy efficiency are topics that have attracted the attention of researchers in recent years, in order to seek sustainable solutions for energy production and reduction of costs, aiming to provide a balance between development and protection of natural resources. One of the alternatives that have obtained satisfactory results is the use of technologies based on Internet of Things (IoT) and Deep Learning systems. Based on this, we assessed the performance of Long Short-Term Memory (LSTM) neural networks in time series electric energy consumption prediction, for a forecasting module of an IoT system. Three time series were used and we compared LSTM to the algorithms Extreme Boost Gradient and Random Forest. Computational results indicate that the LSTM model showed a tendency of better RMSE performance in the first data set, and statistically significant better results in other two data sets, according to the Kruskal-Wallis test (p < 0.0001 in both cases). Thus, the proposed model was implemented and validated from experiments, presenting accurate prediction for monitoring and estimating power consumption, being applicable to Energy Efficiency and decision making. © 2022 The Authors

关键词： Deep learning Energy efficiency Internet of things Long short-term memory Monitoring and prediction Neural networks Prediction module

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Entropic Herding

arXiv

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arXiv 2021年

作者： Yamashita, Hiroshi Suzuki, Hideyuki Aihara, Kazuyuki Intelligent Mobility Society Design Social Cooperation Program Graduate School of Information Science and Technology the University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Graduate School of Information Science and Technology Osaka University 1-5 Yamadaoka Suita-shi Osaka Japan International Research Center for Neurointelligence the University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan

Herding is a deterministic algorithm used to generate data points that can be regarded as random samples satisfying input moment conditions. The algorithm is based on the complex behavior of a high-dimensional dynamical system and is inspired by the maximum entropy principle of statistical inference. In this paper, we propose an extension of the herding algorithm, called entropic herding, which generates a sequence of distributions instead of points. Entropic herding is derived as the optimization of the target function obtained from the maximum entropy principle. Using the proposed entropic herding algorithm as a framework, we discuss a closer connection between herding and the maximum entropy principle. Specifically, we interpret the original herding algorithm as a tractable version of entropic herding, the ideal output distribution of which is mathematically represented. We further discuss how the complex behavior of the herding algorithm contributes to optimization. We argue that the proposed entropic herding algorithm extends the application of herding to probabilistic modeling. In contrast to original herding, entropic herding can generate a smooth distribution such that both efficient probability density calculation and sample generation become possible. To demonstrate the viability of these arguments in this study, numerical experiments were conducted, including a comparison with other conventional methods, on both synthetic and real data. © 2021, CC BY.

关键词： Dynamical systems

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One City, Two Tales: Using Mobility Networks to Understand Neighborhood Resilience and Fragility during the COVID-19 Pandemic

arXiv

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arXiv 2022年

作者： Boz, Hasan Alp Bahrami, Mohsen Balcisoy, Selim Bozkaya, Burcin Mazar, Nina Nichols, Aaron Pentland, Alex Sabanci University Faculty of Engineering and Natural Sciences Istanbul34956 Turkey Massachusetts Institute of Technology Institute for Data Systems and Society CambridgeMA02139 United States New College of Florida The Graduate Program in Applied Data Science 5800 Bay Shore Rd SarasotaFL34243 United States Boston University Questrom School of Business BostonMA02215 United States

What predicts a neighborhood’s resilience and adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, in this paper we present a novel application of human mobility patterns and human behavior in a network setting. We analyze mobility data in New York City over two years, from January 2019 to December 2020, and create weekly mobility networks between Census Block Groups by aggregating Point of Interest level visit patterns. Our results suggest that both the socioeconomic and geographic attributes of neighborhoods significantly predict neighborhood adaptability to the shelter-in-place policies active at that time. That is, our findings and simulation results reveal that in addition to factors such as race, education, and income, geographical attributes such as access to amenities in a neighborhood that satisfy community needs were equally important factors for predicting neighborhood adaptability and the spread of COVID-19. The results of our study provide insights that can enhance urban planning strategies that contribute to pandemic alleviation efforts, which in turn may help urban areas become more resilient to exogenous shocks such as the COVID-19 pandemic. © 2022, CC BY.

关键词： COVID-19

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The evaluation of an on-site monitoring program for activity meter quality assurance with exemption-level sources

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Journal of Radiological Protection 2023年第2期43卷 021508-021508页

作者： Feng, Chen-Ju Wu, Chin-Hui Lin, Chin-Hsiung Yuan, Ming-Chen Lee, Shen-Hao Huang, David YC Lin, Jo-Yu Chen, Ying-Fang Hsu, Shih-Ming Medical Physics and Radiation Measurements Laboratory National Yang Ming Chiao Tung University Taipei Taiwan Department of Biomedical Imaging and Radiological Sciences National Yang Ming Chiao Tung University Taipei Taiwan Department of Medical Imaging and Radiological Sciences Tzu-Chi University of Science and Technology Hualien Taiwan Department of Medical Physics Koo Foundation Sun Yat-Sen Cancer Center Taipei Taiwan Health Physics Division Institute of Nuclear Energy Research Taoyuan City Taiwan Department of Radiation Oncology Chang Gung Memorial Hospital Taoyuan City Taiwan Chinese Society of Medical Physics Taipei Taiwan Medical Physics Graduate Program Duke Kunshan University Jiangsu Kunshan China

This paper discusses the feasibility of a monitoring program for the quality assurance status of activity meters. We sent a questionnaire to clinical nuclear medicine departments of medical institutions, requesting information on their activity meters and quality assurance practices. On-site visits were conducted with exemption-level standard sources (Co-57, Cs-137 and Ba-133) for dose calibrators in nuclear medicine departments including physical inspection, accuracy and reproducibility. A method offering a quick check on the detection efficiency of the space dimension inside the activity meters was also introduced. For dose calibrator quality assurance, the daily checks had the highest implementation. However, annual checks and upon acceptance/after a repair check were reduced to 50% and 44%, respectively. The accuracy results of dose calibrators showed that all models exceeded the ±10% criteria with Co-57 and Cs-137 sources. The reproducibility results showed that some models exceeded the ±5% criteria with Co-57 and Cs-137 sources. The appropriate application of exemption-level standard sources considering the uncertainty that affects the measurement is discussed. © 2023 society for Radiological Protection. Published on behalf of SRP by IOP Publishing Limited. All rights reserved.

关键词： Quality assurance

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The unseen invaders: Tracking phylogeographic dynamics and genetic diversity of cryptic Pomacea canaliculata and P. maculata (Golden apple snails) across Taiwan

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Ecology and Evolution 2024年第4期14卷 e11268页

作者： Banerjee, Pritam Dey, Gobinda Maity, Jyoti Prakash Stewart, Kathryn A. Sharma, Raju Kumar Chan, Michael W. Y. Lee, Kuanhsuen Chen, Chien-Yen Department of Environmental Science Policy and Management University of California Berkeley CA United States Department of Biomedical Sciences Graduate Institute of Molecular Biology National Chung Cheng University Chiayi County Min-Hsiung Taiwan Department of Agricultural Chemistry National Taiwan University Taipei Taiwan Environmental Science Laboratory Department of Chemistry Biological Laboratory School of Applied Sciences KIIT Deemed to be University Odisha Bhubaneswar India Institute of Environmental Sciences Leiden University Leiden Netherlands Doctoral Program in Science Technology Environment and Mathematics Department of Earth and Environmental Sciences National Chung Cheng University Chiayi County Min-Hsiung Taiwan Department of Earth and Environmental Sciences National Chung Cheng University Chiayi County Min-Hsiung Taiwan Department of Emergency Medicine Ditmanson Medical Foundation Chia-Yi Christian Hospital Chiayi Taiwan Center for Nano Bio-Detection Center for Innovative Research on Aging Society AIM-HI National Chung Cheng University Chiayi Taiwan

The cryptic invasion of golden apple snails (Pomacea canaliculata and P. maculata) in Taiwan has caused significant ecological and economical damage over the last few decades, however, their management remains difficult due to inadequate taxonomic identification, complex phylogeny, and limited population genetic information. We aim to understand the current distribution, putative population of origin, genetic diversity, and potential path of cryptic invasion of Pomacea canaliculata and P. maculata across Taiwan to aid in improved mitigation approaches. The present investigation conducted a nationwide survey with 254 samples collected from 41 locations in 14 counties or cities across Taiwan. We identified P. canaliculata and P. maculata based on mitochondrial COI and compared their genetic diversity across Taiwan, as well as other introduced and native countries (based on publicly available COI data) to understand the possible paths of invasion to Taiwan. Based on mitochondrial COI barcoding, sympatric and heterogeneous distributions of invasive P. canaliculata and P. maculata were noted. Our haplotype analysis and mismatch distribution results suggested multiple introductions of P. canaliculata in Taiwan was likely originated directly from Argentina, whereas P. maculata was probably introduced from a single, or a few, introduction event(s) from Argentina and Brazil. Our population genetic data further demonstrated a higher haplotype and genetic diversity for P. canaliculata and P. maculata in Taiwan compared to other introduced regions. Based on our current understanding, the establishment of P. canaliculata and P. maculata is alarming and widespread beyond geopolitical borders, requiring a concerted and expedited national and international invasive species mitigation program. © 2024 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

关键词： agricultural pest biological invasion DNA barcoding golden apple snails mitochondrial COI Pomacea population genetics

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