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Unraveling the Photocatalytic Performance of La2O3 Nanoparticles for the Degradation of Six Organic Dyes

Langmuir : the ACS journal of surfaces and colloids 2025年第25期41卷 2025 Jun 13页

作者： Rahul S Amal George Suresh Babu R Dhayal Raj A Jayakumar G Adarsh Rag S Department of Physics Sacred Heart College (Autonomous) Tirupattur Affiliated to Thiruvalluvar University Serkadu Vellore 632115 Tamil Nadu India. Department of Mechanical Engineering University of Saskatchewan Saskatoon S7N-5A9 Canada. Department of Physics Muthurangam Government Arts and Science College (Autonomous) Vellore Affiliated to Thiruvalluvar University Serkadu Vellore 632115 Tamil Nadu India. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 Karnataka India.

Lanthanum oxide (LaO) nanoparticles stand out as promising photocatalysts due to their remarkable stability and photocatalytic properties. In this study, LaO nanoparticles were synthesized via a hydrothermal method and explored how varying calcination time (3 and 5 h) influences their structural, morphological, optical, and catalytic properties. X-ray diffraction (XRD) confirmed stable hexagonal structure, with crystallite sizes increasing from 32.79 to 45.49 nm, while UV-vis absorption studies revealed that increasing calcination time led to a gradual decrease in bandgap energy from 4.6 to 4.4 eV, making the material more effective at utilizing light for pollutant degradation. When tested against a range of organic dyes, LaO nanoparticles calcinated for 5 h exhibited the highest degradation efficiencies, due to their improved crystallinity and enhanced charge carrier movement. The photocatalytic process followed first-order kinetics, and recyclability tests showed that the nanoparticles retained their efficiency over multiple cycles. Radical scavenger tests confirmed that hydroxyl radicals (OH) and superoxide radicals (O) were the dominant reactive species involved in dye degradation, affirming the key mechanism behind the observed photocatalytic performance. These results highlight how fine-tuning calcination time can significantly enhance LaO's potential, making it an eco-friendly solution for wastewater treatment.

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Machine learning approach for segmenting glands in colon histology images using local intensity and texture features

arXiv

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arXiv 2019年

作者： Khatun, Rupali Chatterjee, Soumick Dept of Computer Applications Amity University Kolkata India Biomedical Magnetic Resonance Faculty of Natural Science Data & Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany

— Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patients. The principal objective of this project is to assist the pathologist to accurate detection of colon cancer. In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dataset images are cropped into patches with different window sizes and taken the intensity of those patches, and also calculated texture-based features. Random forest classifier has been used to classify this patch into different labels. A multilevel random forest technique in a hierarchical way is proposed. This solution is fast, accurate and it is very much applicable in a clinical setup. Copyright © 2019, The Authors. All rights reserved.

关键词： Histology

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RFWNet: A Lightweight Remote Sensing Object Detector Integrating Multiscale Receptive Fields and Foreground Focus Mechanism

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IEEE Geoscience and Remote Sensing Letters 2025年 22卷

作者： Yujie Lei Wenjie Sun Sen Jia Qingquan Li Jie Zhang College of Information and Electrical Engineering the Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs and the National Innovation Center for Digital Agricultural Products Circulation China Agricultural University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Urban Informatics Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services and Research Institute of Smart Cities Shenzhen University Shenzhen China

Challenges in remote sensing object detection (RSOD), such as high interclass similarity, imbalanced foreground–background distribution, and the small size of objects in remote sensing images, significantly hinder detection accuracy. Moreover, the tradeoff between model accuracy and computational complexity poses additional constraints on the application of RSOD algorithms. To address these issues, this study proposes an efficient and lightweight RSOD algorithm integrating multiscale receptive fields and foreground focus mechanism, named robust foreground weighted network (RFWNet). Specifically, we proposed a lightweight backbone network receptive field adaptive selection network (RFASNet), leveraging the rich context information of remote sensing images to enhance class separability. Additionally, we developed a foreground–background separation module (FBSM) consisting of a background redundant information filtering module (BRIFM) and a foreground information enhancement module (FIEM) to emphasize critical regions within images while filtering redundant background information. Finally, we designed a loss function, the weighted CIoU-Wasserstein loss ( $L_{\text {WCW}}$ ), which weights the IoU-based loss by using the normalized Wasserstein distance to mitigate model sensitivity to small object position deviations. The comprehensive experimental results demonstrate that RFWNet achieved 95.3% and 73.2% mean average precision (mAP) with 6.0 M parameters on the DOTA V1.0 and NWPU VHR-10 datasets, respectively, with an inference speed of 52 FPS.

关键词： Remote sensing Accuracy Feature extraction Kernel Computational modeling Biological system modeling Tensors Object detection data mining Sensitivity

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A novel aircraft and missile accurate positioning and tracking system for military and intelligence using global satellite networks

A novel aircraft and missile accurate positioning and tracki...

引用

AIAA Information Systems-AIAA Infotech at Aerospace, 2018

作者： Chyad, Hayder Jabbar Al-Saedi, Firas Abdullah Thweny Data and Applications Security Branch Ministry of Science and Technology Baghdad Iraq Computer Engineering Department Al-Nahrain University Baghdad Iraq

ISBN: (数字)9781624105272

ISBN: (纸本)9781624105272

Our novel real time system is based on gathering aircrafts and missiles information from various Radar stations all over the world to a single or multi operation centers. Also, the data gathered by each Radar station can be sent to all other Radar stations, and this will result in extending the range from where information of targets can be obtained even that they are not detected by that specific Radar station itself. These data are tracked by the military and intelligence parties to make the analysis and take the proper decisions on this information. Then all/some of these data are retransmitted to a large and far areas all over the world that does not have a nearby Radar systems, so that the special forces troops on these areas, and by using any commercial Direct Broadcast Satellite TV receiver, can have all the important targets information in their areas shown on their anti-aircraft/anti-missile system’s display, so that they can track or even destroy these targets without being vulnerable till they react on that target. These data are transmitted using commercial satellites using their audio channel of the TV broadcasted channel. The transmitted data will contain all the targets information like speed, range, altitude, geolocation and type after being encrypted. The system was tested in reality and its performance was excellent. © 2018 American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Real time systems

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Time series forecast of sales volume based on XGBoost

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Journal of Physics: Conference Series 2021年第1期1873卷

作者： Lingyu Zhang Wenjie Bian Wenyi Qu Liheng Tuo Yunhai Wang School of Computer Science and Technology Shandong University Qingdao China Didi AI Labs Didi Chuxing Beijing China Smart Transportation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing China

Some problems such as the decline of new labor force, the increase of retired labor force emerge because of the complex and changeable market environment, consequently exacerbating the staffing problem in the retail industry. Also, the unreasonable distribution of personnel, there are few people in busy hours and too many people in idle hours, which causes waste of labor. To address this issue, we analyzed the time series of sales volume in the retail industry in detail, and processed the data with feature engineering for predicting the in-store sales volume in the future. At the same time, other features such as weather and temperature are added to improve the accuracy of the model. Considering the characteristics of the data, we choose XGBoost as the prediction model. The experiments on real-world datasets verified better performance of proposed model compared with other state-of-art models.

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A study on the uncertainty of convolutional layers in deep neural networks

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University ShenzhenGuangdong518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min-Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min-Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution. © 2020, CC-BY.

关键词： Convolution

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Incorporating Hidden Layer representation into Adversarial Attacks and Defences

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

In this paper, we propose a defence strategy to improves adversarial robustness incorporating hidden layer representation. The key of this defence strategy aims to compress or filter input’s information including adversarial perturbation. And this defence strategy can be regarded as an activation function which can be applied any kind of neural networks. We also prove theoretically the effectiveness of this defense strategy under certain conditions. Besides, incorporating hidden layer representation we propose three types of adversarial attacks to generate three types of adversarial examples, respectively. The experiments show that our defence method can significantly improve the adversarial robustness of deep neural networks which achieves the state-of-the-art performance even though we do not adopt adversarial training. © 2020, CC BY.

关键词： Deep neural networks

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RETRACTED ARTICLE: An enhanced pairing-based authentication scheme for smart grid communications

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Journal of Ambient Intelligence and Humanized Computing 2021年第1期15卷 165-165页

作者： Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Tian, Yuan Al-Nabhan, Najla Abdulrahman College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Computer Engineering Nanjing Institute of Technology Nanjing China Computer Science Department College of Computer and Information Sciences King Saud University Riyadh Kingdom of Saudi Arabia

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FAIR for AI: An interdisciplinary and international community building perspective

arXiv

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arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

arXiv

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arXiv 2020年

作者： Li, Wenhao Jin, Bo Wang, Xiangfeng Yan, Junchi Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai200062 China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a flexible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multiagent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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