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Effects of sodium alginate-poly(acrylic acid) cross-linked hydrogel beads on soil conditioner in the absence and presence of phosphate and carbonate ions

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Case Studies in Chemical and Environmental engineering 2024年 9卷

作者： Hidayat, Endar Sarbani, Nur Maisarah Mohamad Lahiri, Sudip Kumar Samitsu, Sadaki Yonemura, Seiichiro Mitoma, Yoshiharu Harada, Hiroyuki Graduate School of Comprehensive Scientific Research Program in Biological System Sciences Prefectural University of Hiroshima Shobara 727-0023 Japan Department of Life System Science Faculty of Bioresources Science Prefectural University of Hiroshima Shobara 727-0023 Japan Data-driven Polymer Design Group Research Center for Macromolecules and Biomaterials National Institute for Materials Science 1−2−1 Sengen Tsukuba 305-0047 Japan Department of Mechanical & Industrial Engineering University of Toronto Toronto M5S 3G8 ON Canada Faculty of Allied Health Sciences Chettinad Hospital and Research Institute Chettinad Academy of Research and Education Tamil Nadu Kelambakkam 603103 India

Hydrogel beads, composed of polymers, possess the ability to absorb substantial quantities of water simultaneously and gradually release it in dry conditions. In this work, hydrogel beads were synthesized using sodium alginate (S-Alg) and poly (acrylic acid) (P-Acc), either in the absence or presence of phosphate (PO₄ ³⁻) (S-Alg/P-Acc@PO₄ ³⁻) and carbonate (CO₃ ²⁻) anionic ions (S-Alg/P-Acc@CO₃ ²⁻). Subsequently, these beads were crosslinked with Ca²⁺ ions. The scanning electron microscopy (SEM) images demonstrated that the presence of anionic ions has an impact on the structure of the hydrogel beads and enhances porosity expansion while concurrently reducing the concentration of carboxyl (COOH) groups. The hydrogel beads exhibited a great swelling behavior, with S-Alg/P-Acc@CO₃ ²⁻ capable of absorbing up to 73.5 % of water in acidic conditions, but decreased under neutral and basic conditions. Soil water loss (SWL) experiments confirmed the ability of hydrogel beads to retain water in the soil. Additionally, observations of soil characteristics after a 14-day treatment revealed no significant difference in pH and C/N ratio (P-value <0.05). However, soil's available Fe and N–NO₃ ^- significantly reduced (P-value <0.05) while substantially promoting soil N–NH₄ ⁺, available phosphorus (P), cation exchange capacity (CEC), soil organic carbon (SOC), and exchangeable cation (Ca²⁺, K⁺ and Mg²⁺). The presence of hydroxyl (-OH) and –COOH groups was found to be crucial for enhancing soil properties. © 2024 The Authors

关键词： Anionic ions Hydrogel beads Poly (acrylic acid) Sodium alginate Soil characteristics Soil water retention Swelling

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Continuous black-box optimization with an Ising machine and random subspace coding

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Physical Review Research 2022年第2期4卷 023062-023062页

作者： Syun Izawa Koki Kitai Shu Tanaka Ryo Tamura Koji Tsuda Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa 277-8561 Japan Department of Mechanical Engineering The University of Tokyo Tokyo 113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama 223-8522 Japan Green Computing System Research Organization Waseda University Tokyo 162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds the extremum of an unknown function by alternating the inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply Ising machines to overcome the difficulty in the continuous black-box optimization. As an Ising machine specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution by Ising machines has to be translated back. Our method has the following three parts: (1) random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector, (2) a quadratic unconstrained binary optimization (QUBO) defined by the acquisition function based on the nonnegative-weighted linear regression model, which is solved by Ising machines, and (3) a penalization scheme to ensure that the solution can be translated back. It is shown with benchmark tests that its performance using the D-Wave Advantage quantum annealer and simulated annealing is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up the possibility of Ising machines and other QUBO solvers, including a quantum approximate optimization algorithm using gated-quantum computers, and may expand its range of application to continuous-valued problems.

关键词： Adiabatic quantum optimization Machine learning Quantum algorithms Quantum computation

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Droughts Monitoring Under Climate Change Scenarios in the Source Region of the Indus River

SSRN

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SSRN 2023年

作者： Rizwan, Muhammad Li, Xin Anjum, Lubna Chen, Yingying Gulakhmadov, Aminjon Jamal, Kashif Department of Civil Engineering Swedish College of Engineering and Technology Rahim Yar Khan Pakistan National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System Resources and Environment Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Faculty of Agricultural Engineering and Technology University of Agriculture Faisalabad Pakistan Research Center for Ecology and Environment of Central Asia Chinese Academy of Sciences Urumqi830011 China Key Laboratory of Remote Sensing and Geospatial Science Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou730000 China University of Chinese Academy of Sciences Beijing100049 China

Droughts are caused by water scarcity because of no or less precipitation during a longer period of time. They are natural phenomena that significantly affect the economy of any country. Different complexities are involved with them, as they are considered one of the most dangerous climatic events. Drought can badly affect a large number of people and cause economic losses due to its impacts. Drought investigations are important in the study area as the whole Indus basin derives its water from there and it is experiencing precipitation insufficiencies because of climate change. Historical and future droughts are monitored in the study area using the Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI). According to the results, two drought periods (1976-1985 & 1998-2006) were identified in the historical data and mostly categorized as mild droughts while three periods of extreme drought are expected during 2020-2059 and during 2060-2099 under RCP 2.6, 4.5 & 8.5 respectively. Due to increasing temperature, the SPEI drought index identified more months of extreme drought than the SPI drought index, which are expected to be 150-188% more than that of the historical period. About 100% of the area will be under extreme drought during 2032 under RCP 2.6 and during 2046 under RCP 4.5 while about 50% of the area will be under extreme drought during 2034 under RCP 8.5. © 2023, The Authors. All rights reserved.

关键词： Drought

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EdgeDis: Enabling Fast, Economical, and Reliable data Dissemination for Mobile Edge Computing

arXiv

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arXiv 2023年

作者： Li, Bo He, Qiang Chen, Feifei Lyu, Lingjuan Bouguettaya, Athman Yang, Yun The College of Arts Business Law Education and Information Technology Victoria University MelbourneVIC3122 Australia The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia The School of Information Technology Deakin University Geelong Australia SONY AI Inc. Tokyo108-0075 Japan The School of Computer Science University of Sydney CamperdownNSW2006 Australia

Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis’s validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster;2) reduces dissemination costs by 48.21% to 91.87%;and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs. © 2023, CC BY-NC-SA.

关键词： Efficiency

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第6期42卷 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of St.Thomas School of EngineeringMinnesota 55105USA NSF National Center for Atmospheric Research BoulderColorado 80307USA University of Auckland Auckland 0630New Zealand National Oceanic and Atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA National Oceanic and Atmospheric Administration National Centers for Environmental InformationAshevilleNC 28801USA National Research Council(CNR)Institute of Marine Sciences(ISMAR) Rome 00133Italy Mercator Ocean International Toulouse 31400France Department of Earth and Environmental Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA Institute of Oceanology Chinese Academy of SciencesQingdao 266071China National Marine Environmental Forecasting Center Ministry of Natural Resources of ChinaBeijing 100081China North Carolina Institute for Climate Studies(NCICS) North Carolina State UniversityAshevilleNC 28804USA Oceanographic Data Center Chinese Academy of SciencesQingdao 266071China College of Oceanography Hohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South China Sea Institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMD 20740USA Computer Network Information Center Chinese Academy of SciencesBeijing 100083China Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment AdministrationMinistry of Ecology and EnvironmentGuangzhou 510611China National Meteorological Information Center China Meteorological AdministrationBeijing 100081China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Xiamen Universit

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

关键词： ocean heat content sea surface temperature ocean temperature global warming climate

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Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies

Novel Truncated-rank Graph-structured and Tree-guided Sparse...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Yongyi Xie Kang Lai Xiao Geng Yijun Song Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine Tongji University School of Mechanical & Aerospace Engineering Nanyang Technological University Orthopaedic Department Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Dongfang College Zhejiang University of Finance & Economics Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nal̈ively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential.

关键词： Adaptation models Input variables Biological system modeling High performance computing Noise Genomics Medical services data models Robustness Bioinformatics

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Retraction notice to “Automation industry in cyber physical systems based on 4G IoT networks for operation controlling and monitoring management” [Optik 272 (2023) 170308]

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Optik 2025年 333卷

作者： K.V. Mahesan Anupong Wongchai C. Tamizhselvan S. Manoj Kumar P. Vijayakumar Bharat Singh Dr. Ambedkar Institute of Technology Bangalore India Department of Agricultural Economy and Development Faculty of Agriculture Chiang Mai University Thailand Department of Data Science and Business System College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore 641407 India School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Mechanical Engineering GLA University Mathura UP India

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

arXiv

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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Navigating the Noise: Bringing Clarity to ML Parameterization Design with O(100) Ensembles

arXiv

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arXiv 2023年

作者： Lin, Jerry Yu, Sungduk Peng, Liran Beucler, Tom Wong-Toi, Eliot Hu, Zeyuan Gentine, Pierre Geleta, Margarita Pritchard, Mike Department of Earth System Sciences University of California at Irvine IrvineCA United States Multimodal Cognitive AI Intel Labs Santa ClaraCA95054 United States Faculty of Geosciences and Environment University of Lausanne Lausanne Switzerland Expertise Center for Climate Extremes University of Lausanne Lausanne Switzerland Department of Statistics University of California at Irvine IrvineCA United States Department of Earth and Planetary Sciences Harvard University United States NVIDIA Research United States LEAP Science and Technology Center School of Engineering and Applied Sciences Climate School Columbia University United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA United States Department of Biomedical Data Science Stanford University School of Medicine Palo AltoCA United States

Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large-scale general circulation model (GCM)) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi-automated, end-to-end pipeline for O(100) size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error (MSE) to a Mean Absolute Error (MAE) loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of O(100) may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise. © 2023, CC BY.

关键词： Parameterization

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Causally-informed Deep Learning to Improve Climate Models and Projections

arXiv

引用

arXiv 2023年

作者： Iglesias-Suarez, Fernando Gentine, Pierre Solino-Fernandez, Breixo Beucler, Tom Pritchard, Michael Runge, Jakob Eyring, Veronika Institute of Atmospheric Physics Oberpfaffenhofen Germany Columbia University New York United States Science and Technology Center Columbia University New York United States University of Lausanne Institute of Earth Surface Dynamics Lausanne Switzerland University of California Department of Earth System Science Irvine United States NVIDIA Corporation Santa Clara United States Institute of Data Science Jena Germany Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany Bremen Germany

Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly clouds and convection. Deep learning can learn these subgrid-scale processes from computationally expensive storm-resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious nonphysical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid-scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally-informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally-informed neural network parameterizations retain many convection-related properties and accurately generate the climate of the original high-resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non-causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way towards more physically-based causal deep learning approaches also in other scientific disciplines. © 2023, CC BY-NC-ND.

关键词： Climate models

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