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Electrochemical response of MoS_(2) nanosheets/SiO_(2)-NH_(2) nanoparticles modified glassy carbon electrode to phenylacetic acid

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Rare Metals 2023年第12期42卷 4142-4152页

作者： Tian-Yao Qi Zhen-Yu Yuan Fan-Li Meng College of Information Science and Engineering Northeastern UniversityShenyang 110819China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China

Phenylacetic acid(PAA)is a primary raw material for illegal Methamphetamine(MATM)synthesis under the strong precursor chemicals supervisions of safrole and ***,trace detection of PAA at ultra-low concentration is a strategic technique and an urgent issue in the field of drug *** this paper,trace determination of PAA at sub-nmol-L-1 concentration level is achieved by hydrogen bond adsorption and electrochemical catalysis through the prepared aminated SiO_(2)nanoparticles(SiO_(2)-NH_(2) NPs)and MoS_(2) nanosheets(NSs)modified glassy carbon electrode(GCE).The prepared MoS_(2) NS s/SiO_(2)-NH_(2) NPs modified electrode represents a detecting limit of 0.0989 nmol·L^(-1)and an obvious increasing linear range before the concentration increasement up to 60 nmol·L^(-1)in square wave voltammetry(SWV)responses of *** SWV response of the modified electrode to PAA in the concentration range within 100 nmol·L^(-1)is higher than phenol,acetic acid(HOAc)and benzoic Acid(BEN).This electrochemical method for trace detection of PAA in aqueous solution with desired performance provides a feasible scheme for the detection of other drugs and aromatic precursor chemicals.

关键词： Phenylacetic acid detection Modified electrode MoS_(2)nanosheet Square wave voltammetry

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Li, Qiuting Wang, Xiuqing Du, Wenxia Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China Hebei Normal University College of China Combustion Engineering Shijiazhuang050024 China

ISBN: (纸本)9798350337754

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual Network (ResNet), and Spiking Neural Networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings. © 2023 IEEE.

关键词： Failure analysis

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Exploring Classification Models for Video Source Device Identification: A Study of CNN-SVM and Softmax Classifier

Exploring Classification Models for Video Source Device Iden...

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Ottakath, Najmath Akbari, Younes Al-Maadeed, Somaya Bouridane, Ahmed Khelifi, Fouad Qatar University Department of Computer Science and Engineering Doha Qatar Center for Data Analytics and Cybernetics University of Sharjah Sharjah United Arab Emirates Northumbria University Department of Computer and Information Sciences Newcastle United Kingdom

ISBN: (纸本)9798350335590

Video Source device identification plays a crucial role in video forensics as the proliferation of video capturing devices has given rise to crimes with videos that are challenging to trace. Reliance on metadata extraction is insufficient as it can be corrupted or manipulated to conceal the source of the crime. Another technique employed for source identification is noise pattern extraction, which generates a unique identification for the video camera. However, this method is susceptible to capture faults and can produce diverse noise patterns for each video. In addressing these challenges, there is a need to identify distinctive features that are consistent across all videos captured by the same camera. This has led to the adoption of computer vision techniques utilizing machine learning and deep learning. Classifiers play a crucial role in machine learning and data analysis, as they are responsible for categorizing or predicting results based on input data. Our experiments show that the subject is sensitive to classifiers and developing a good classifier or classifier-level fusions can improve results in practice for all datasets. © 2023 IEEE.

关键词： Image classification

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A Harmful Disorder: Predictive and Comparative Analysis for fetal Anemia Disease by Using Different Machine Learning Approaches 11

A Harmful Disorder: Predictive and Comparative Analysis for ...

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11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Hasan, Mahadi Tahosin, Mst. Sazia Farjana, Afia Sheakh, Md. Alif Hasan, Md Maruf The University of Tennessee Ms in Data Analytics Chattanooga United States Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of South Dakota Department of Computer Science VermillionSD United States

ISBN: (纸本)9798350336986

Anemia is a major issue for public health with significant implications for national development, it remains a largely neglected health problem in many developing countries. Iron deficiency is responsible for at least 50% of all cases of anemia and kills nearly 1 million people each year. Africa and Southeast Asia account for three quarters of these deaths. Surprisingly, one of the top ten risk factors that contributes to the global burden of disease is iron deficiency anemia (IDA). This study investigated the use of machine learning models to predict anemia. The study compared the performance of five different machine learning models: K-Nearest Neighbors, Logistic Regression, Support Vector Machines, Gaussian Naive Bayes, and Light Gradient Boosting Machines. These models are combined using a voting classifier method to improve prediction accuracy. The study highlights the importance of accurately predicting diseases in the medical field. The ability to predict anemia at the right time is essential for effective prevention and treatment. This study demonstrates the potential of machine learning models to predict anemia and improve disease prevention and treatment. Using advanced algorithms and data processing techniques can help doctors make accurate predictions and make decisions, leading to better patient outcomes. This research result shows that the voting classifier achieved 99.95% accuracy. © 2023 IEEE.

关键词： Forecasting

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A Novel Framework for Detection of Digital Face Video Manipulation using Deep Learning 3

A Novel Framework for Detection of Digital Face Video Manipu...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Chandra, Sanskriti Saxena, Shivam Kumar, Santosh Chaube, Mithilesh Kumar Srinivas, K.G. Alsamhi, Saeed Hamood Curry, Edward Saif, Abdu IIIT-Naya Raipur Department of Data Science and Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India University of Galway Insight Sfi Research Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. Researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security. © 2023 IEEE.

关键词： Textures

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Effect of Wind on Electric Vehicle Energy Consumption: Sensitivity Analyses and Implications for Range Estimation and Optimal Routing

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ACM Journal on Autonomous Transportation Systems 2024年第2期1卷 1-31页

作者： Trung Bach Tran Ilya Kolmanovsky Erik Biberstein Omar Makke Marina Tharayil Oleg Gusikhin Department of Aerospace Engineering University of Michigan - Ann Arbor Ann Arbor MI USA Global Data Insight & Analytics Ford Motor Company Dearborn MI USA

The energy consumption of electric vehicles (EVs) depends on multiple factors. As it affects vehicle range, energy consumption must be accurately predicted. After a summary of the relevant literature, this article focuses on two sensitivity studies: one on the impact of wind on energy consumption, and the other on the identifiability of wind in the absence of vehicles’ speed and acceleration profiles. The studies show that wind has a significant impact on the energy consumption for a trip, and without high-resolution knowledge of the acceleration and instantaneous velocity, minor variations in the wind condition do not drastically alter the energy consumption distribution. After that, data sources for the information on the wind velocity and direction are discussed. A data-driven approach based on fuzzy set theory is proposed to incorporate wind into the energy prediction; the best model from this approach shows a notable improvement (3.62%) over the currently implemented production-level predictive model for energy consumption on a dataset of 35,139 real-world trips; the improvement is even more pronounced (~ 7%) for trips with more substantial headwind or tailwind level. Recognizing the interplay between range prediction and route selection, we consider a Markov Decision Process (MDP) framework for battery-charge- and travel-time-aware optimal route planning that accounts for the impact of the wind and includes stops at the charging stations. Finally, we propose a framework that includes wind in the operation of EVs, which consists of learning the impact of wind, incorporating wind forecasting into range and energy prediction, and using that prediction to perform optimal routing.

关键词： Electric vehicles range estimation wind modeling routing Markov decision process

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Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks

Distributed Combinatorial Optimization of Downlink User Assi...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Peng, Bile Guo, Bihan Besser, Karl-Ludwig Kunz, Luca Raghunath, Ramprasad Schmeink, Anke Jorswieck, Eduard A. Caire, Giuseppe Poor, H. Vincent Technische Universität Braunschweig Institute for Communications Technology Germany Princeton University Department of Electrical and Computer Engineering United States Rwth Aachen University Information Theory and Data Analytics Germany Technische Universität Berlin Communications and Information Theory Group Germany

ISBN: (纸本)9798350351255

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address this combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to an upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario. © 2024 IEEE.

关键词： Combinatorial optimization

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Hierarchical Time Series Forecasting of COVID-19 Cases Using County-Level Clustering data

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Operations Research Forum 2025年第1期6卷 1-28页

作者： Mohanty, Sonaxy Shimamura, Airi Nicholson, Charles D. González, Andrés D. Razzaghi, Talayeh School of Industrial and Systems Engineering University of Oklahoma 202 W. Boyd Norman 73019-1022 OK United States Data Science and Analytics Institute University of Oklahoma 865 Asp Ave. Norman 73019-1022 OK United States Faculty Fellow Data Institute for Societal Challenges (DISC) Five Partners Place 201 Stephenson Pkwy Ste 4600 Norman 73019 OK United States

In light of the far-reaching impact of the COVID-19 pandemic, the accurate estimation of infected cases, fatalities, and recoveries has become crucial. While much research has focused on national-level predictions, it has become evident that an exclusive focus on broader statistics may lead to inadequate preparedness in managing hospital resources in rural and smaller regions. Given the critical role local areas play in the spread of COVID-19 and the hierarchical structure of available data, this study proposes a novel modeling framework using hierarchical time series forecasting (HTSF) tailored to county-level clusters within the USA. This approach aims to provide short-term daily forecasts for every county in the USA, employing bottom-up, top-down, and minimum trace optimal reconciliation techniques. A major barrier to accurate short-term forecasting is the scarcity of COVID-19 hospitalization data in both rural and urban regions across the USA. To address this limitation, the study employs county-level clustering, a method that enables the generation of forecasts even for areas with limited or no available data. The primary aim is to improve the accuracy of COVID-19 case forecasts by combining autoregressive integrated moving average (ARIMA) models as base forecasts with the HTSF approach. The findings reveal that the bottom-up HTSF method offers comparable performance at the county level but significantly outperforms other approaches at the cluster and national levels for 3-week-ahead forecasting. This highlights the vital role of local regions in achieving more precise and effective pandemic prediction strategies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Clustering COVID-19 Hierarchical time series Time series forecasting

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Rethinking Node-wise Propagation for Large-scale Graph Learning

arXiv

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arXiv 2024年

作者： Li, Xunkai Su, Daohan Ma, Jingyuan Zhang, Wentao Wu, Zhengyu Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University National Engineering Laboratory for Big Data Analytics and Applications Beijing China

Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (i) Most scalable GNNs tend to treat all nodes with the same propagation rules, neglecting their topological uniqueness;(ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes’ local properties is required. Intuitively, different nodes in web-scale graphs possess distinct topological roles, and therefore propagating them indiscriminately or neglect local contexts may compromise the quality of node representations. To address the above issues, we propose Adaptive Topology-aware Propagation (ATP), which reduces potential high-bias propagation and extracts structural patterns of each node in a scalable manner to improve running efficiency and predictive performance. Remarkably, ATP is crafted to be a plug-and-play node-wise propagation optimization strategy, allowing for offline execution independent of the graph learning process in a new perspective. Therefore, this approach can be seamlessly integrated into most scalable GNNs while remain orthogonal to existing node-wise propagation optimization strategies. Extensive experiments on 12 datasets have demonstrated the effectiveness of ATP. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Smart DCU Digital Twin: Towards Autism-Friendly Universities

Smart DCU Digital Twin: Towards Autism-Friendly Universities

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Smart World Congress (SWC), IEEE

作者： Jaime B. Fernandez Iryna Osadcha Andrius Jurelionis Kieran Mahon Noel E. O’Connor Muhammad Intizar Ali Insight SFI Research Centre for Data Analytics Dublin City University Faculty of Civil Engineering and Architecture Kaunas University of Technology

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Digital Twins are one of the emerging technologies which facilitate the creation of a digital replica of physical assets and infrastructure. The majority of initial use-cases of digital twins for smart cities and smart universities has focused on creation of the 3D representation of the infrastructure. However, with the maturity of technology, digital twins are nowadays used to build use-cases for smart applications which involve multiple sources of information including geographical information, IoT sensors and use-case specific knowledge bases. Smart DCU Digital Twin is one of the initiative towards achieving a smart university status for Dublin City University (DCU). Across the globe and in Ireland many universities including DCU are working on providing additional support to students with ASD (Autism Spectrum Disorder). The primary focus of these initiatives is on improving social interactions, and reducing academic and sensory barriers for the students. In this paper, we present our work on using digital twin technology to assist DCU in achieving its objective of becoming a fully inclusive and an Autism-Friendly university, with a particular focus on the reduction of sensory barriers for its students.

关键词： Autism Three-dimensional displays Smart cities data visualization Predictive models Real-time systems Software Digital twins Noise measurement Intelligent sensors

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