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Investigation of the mesmeric effect of substituents in DSSC based on thiophene and oxadiazole. Structural and optoelectronic study using the DFT approach

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Interactions 2025年第1期246卷 1-15页

作者： Agda, Fatima Nebbach, Diae Louazri, Latifa Ninis, Ouafae Merimi, Imane Ajana, Mohammed Aziz Bouachrine, Mohammed Laboratory Molecular and Computational Chemistry: Design Synthesis and Reactivity. Department of Chemistry Faculty of Science Moulay Ismail University Meknes Morocco Regional Center for Education and Training Professions Meknes Morocco Energy Materials and Computing Physics Research Group ENS Abdelmalek Essaadi University Tetouan Morocco Advanced Materials and Process Engineering Laboratory Faculty of Science Ibn Tofail University Kenitra Morocco National School of Architecture National Road N2 Compus de Savoir Technolopole Oujda Oujda60000 Morocco Faculty of Sciences Mohammed First University Oujda Morocco

In this work, we have developed a range of organic Donor- π -Acceptor;we are changing the π-spacers by adding novel substitutions (F, CH3, and CN) to the base molecule D-TTOxTT-A for dye-sensitized solar cells (DSSCs). Our aim is to improve the photovoltaic performance of these engineered organic dyes. We use the B3LYP (d,p) and TD/CAM-B3LYP (d,p) to study the impact of different types of the introduction of the substitution in the spacer, on the sensitizer characteristics, with the aim of highlighting the links between structure and property. Based on these results, it is possible to use these dyes as promising sensitizers for DSSCs. In addition, M4 could be an ideal compound for high throughput DSSC due to its planar geometry, low energy gap (1.73eV), higher absorption wavelength (449.3nm), longer lifetime (2.366 ns), lower ΔGreg (-0.64 eV), large hardness (-0.869 eV) and reasonable energy reorganization (0.488eV). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Thiophene

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Flower automata pattern-based discrimination of fibromyalgia from control subjects using fusion of sleep EEG and ECG signals

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IEEE Access 2025年 13卷 99032-99047页

作者： Barua, Prabal Datta Kobayashi, Makiko Dogan, Sengul Baygin, Mehmet Tuncer, Turker Paul, Jose Kunnel Iype, Thomas Acharya, U.R. Australia Kumamoto University Graduate School of Science and Technology Kumamoto860-8555 Japan Kumamoto860-8555 Japan Firat University Technology Faculty Department of Digital Forensics Engineering Elazig Turkey Erzurum Technical University Faculty of Engineering and Architecture Department of Computer Engineering Erzurum Turkey Government Medical College Department of Neurology Kerala Thiruvananthapuram India University of Southern Queensland School of Mathematics Physics and Computing Springfield Australia

Electroencephalogram (EEG) and electrocardiogram(ECG) signals provide vital insights into brain and heart activity and are widely used in automated medical diagnostics. This study introduces a novel, multimodal fibromyalgia detection system developed by the fusion of EEG and ECG signals recorded during sleep stages 2 and 3. The novelty of the model is the use of dynamic and interpretable feature engineering framework comprising of two innovations: (i) Flower Automata Pattern (FAP) for self-organized pattern-based feature extraction, and (ii) Attention-Driven Wavelet Transform and Absolute Maximum Pooling (ADWTAMP) method for signal decomposition and compression. Three feature selection strategies—Neighborhood Component Analysis (NCA), Chi2, and the intersection of NCA and Chi2 (NCA˄Chi2) —are employed to generate robust feature vectors, which are classified using k-nearest neighbors (kNN) and support vector machine (SVM) under the leave-one-record-out cross-validation (LORO CV) scheme. The final decision is derived through an iterative voting and greedy fusion approach. The proposed model achieved classification accuracies of 99.36% and 98.37% for sleep stages 2 and 3, respectively. Key advantages of the model include its high accuracy, low computational requirements (CPU-only execution), and explainable architecture. To the best of our knowledge, this is the first multimodal automata-based classification framework designed for fibromyalgia detection. © 2013 IEEE.

关键词： Electrocardiography

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Performance of Enhanced CNN-LSTM Prediction Model for Vehicle-Mounted Air Quality Monitoring

Performance of Enhanced CNN-LSTM Prediction Model for Vehicl...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Ceferino Llamelo Ruji Medina Arnel Fajardo Graduate Programs Technological Institute of the Philippines Quezon City Philippines and School of Architecture Computing and Engineering University of Saint Louis Tuguegarao City Philippines Graduate Programs Technological Institute of the Philippines Quezon City Philippines

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

CNN-LSTM has been established for predictive modeling in urban air pollution. However, while the method is accurate for time series forecasting, it struggles with accurately capturing spatial variability in air quality data. To address this limitation, we propose an Enhanced CNN-LSTM Prediction Model for vehicle-mounted air quality monitoring systems. This model integrates Convolutional Neural Networks (CNNs), spatial attention mechanisms, and Long Short-Term Memory (LSTM) networks to enhance the capture of both spatial and temporal dependencies in the data. Performance evaluation shows a Mean Absolute Error (MAE) of 5.2 µg/m 3 for PM2.5, 1.3 µg/m 3 for PM10, 0.1 ppm for CO, 0.1 ppm for VOCs, and 0.68°C for temperature, with Root Mean Squared Error (RMSE) values of 7.8µg/m 3 for PM2.5, 1.5µg/m 3 for PM10, 0.10 ppm for CO, 0.10 ppm for VOCs, and 0.75°C for temperature. Compared to traditional models like Linear Regression and standalone LSTM networks, the Enhanced CNN-LSTM model demonstrates substantial improvements in prediction accuracy. These findings highlight the model's superior performance in delivering accurate, real-time air quality predictions, offering a robust framework for pollution management and public health interventions.

关键词： Temperature sensors Accuracy Atmospheric modeling Predictive models Air quality Real-time systems Convolutional neural networks Forecasting Monitoring Long short term memory

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GRUvader: Sentiment-Informed Stock Market Prediction

arXiv

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arXiv 2024年

作者： Mamillapalli, Akhila Ogunleye, Bayode Inacio, Sonia Timoteo Shobayo, Olamilekan School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom School of Computing and Digital Technologies Sheffield Hallam University SheffieldS1 2NU United Kingdom

Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examined the influence of a sentiment analysis indicator on the prediction of stock prices. Our results were two-fold. Firstly, we used a lexicon-based sentiment analysis approach to identify sentiment features, thus evidencing the correlation between the sentiment indicator and stock price movement. Secondly, we proposed the use of GRUvader, an optimal gated recurrent unit network, for stock market prediction. Our findings suggest that stand-alone models struggled compared with AI-enhanced models. Thus, our paper makes further recommendations on latter systems. © 2024, CC BY.

关键词： Generative adversarial networks

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Progress and Achievements of Multi-sensor Fusion Navigation in China during 2019—2023

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Journal of Geodesy and Geoinformation Science 2023年第3期6卷 102-114页

作者： Xingxing LI Xiaohong ZHANG Xiaoji NIU Jian WANG Ling PEI Fangwen YU Hongjuan ZHANG Cheng YANG Zhouzheng GAO Quan ZHANG Feng ZHU Weisong WEN Tuan LI Jianchi LIAO Xin LI Shool of Geodesy and Gevmatics Wuhan UniversityWuhan 430074China School of Geomatics and Urban Spatial Infor-matics Beijing University of Civil Engineering and ArchitectureBeijing 100044China Shanghai Jiao Tong University Shanghai 200052China Center for Brain-inspired Computing Research Tsinghua UniversityBeijing 100084China School of Land Science and Technology China University of Geosciences(Beijing)Beijing 100084China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong Kong 900777China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Sci-ences Beijing Institute of TechnologyBeijing 100081China Hubei Luojia Laboratory Wuhan 430074China

Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other ***,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite ***-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex *** summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.

关键词： Simultaneous Localization And Mapping(SLAM) integrated navigation multi-sensor fusion

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Analysis of Machine Learning (ML) and Internet of Things (IoT): A Scientometric Review

Analysis of Machine Learning (ML) and Internet of Things (Io...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Samuel-Soma M. Ajibade Muhammed Basheer Jasser Rosıe Fe B. Legaspıno Anabella R. Gımeno Leomar A. Ypanto Jeem Clyde C. Baird Anthonia Oluwatosin Adediran Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Malaysia Cebu Technological University Moalboal Cebu Philippines Faculty of Architecture and Urban Design Federal University of Uberlandia Uberlândia Brazil

ISBN: (数字)9798350382310

ISBN: (纸本)9798350382327

The objective of this paper is to critically examine the publication’s trends and research landscape on the Internet of Things and Machine Learning (Io T-ML) research through bibliometric analysis (BA). The Elsevier Scopus database was selected to identify, screen, and analyze the published documents on IoT-ML. The publication trends and major stakeholders on the topic were subsequently. BA was performed to examine the co-authorships, keyword occurrences, and citations using VOSviewer software. Lastly, a systematic literature review was performed to summarize the scientific and technological advancements. Results revealed that the publications on IoT-ML largely comprise articles, conference proceedings, and reviews, whereas the publication trends showed an increase from 2 to 105 between 2015 and 2021. The top researchers on IoT-ML are S. Baskar, M. Guizani, and A. Souri, whereas the top institution is the Vellore Institute of Technology (India). However, the top country and research funder are; India, and National Natural Science Foundation (China), respectively. BA showed that the research landscape on IoT-ML comprises numerous networks of co-authors and countries working on related themes. The most impactful publication is Mahdavinejad et al. (2018) which has 479 citations, whereas the most prestigious journal is the IEEE Io T journal with 9 published documents in Scopus. Keyword analysis revealed IoT-ML has potential applications in health care, medicine, computer security and data analytics etc. Overall, IoT-ML has significant socio-economic and research impacts, and as such, the field will witness geometric techno-scientific growth and development in the future, particularly in the areas of network security and big data analytics.

关键词： Systematics Reviews Databases Bibliometrics Machine learning Network security Market research

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Defeating the Credit Card Scams through Machine Learning Algorithms

Defeating the Credit Card Scams through Machine Learning Alg...

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2021 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2021

作者： Bains, Kameron Fasanmade, Adebamigbe Morden, Jarrad Al-Bayatti, Ali H. Sharif, Mhd Saeed Alfakeeh, Ahmed S. De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom School of Architecture Computing and Engineering UEL Dockland Campus University Way LondonE16 2RD United Kingdom King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9781665440325

Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art. © 2021 IEEE.

关键词： Support vector machines

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A new non-perturbative approach in quantum mechanics for time-independent Schr?dinger equations

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Science China(Physics,Mechanics & Astronomy) 2020年第3期63卷 70-81页

作者： ShiJun Liao Center of Advanced Computing School of Naval ArchitectureOcean and Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China

A new non-perturbative approach is proposed to solve time-independent Schr?dinger equations in quantum *** is based on the homotopy analysis method(HAM)that was developed by the author in 1992 for highly nonlinear equations and has been widely applied in many *** perturbative methods,this HAM-based approach has nothing to do with small/large physical ***,convergent series solution can be obtained even if the disturbance is far from the known status.A nonlinear harmonic oscillator is used as an example to illustrate the validity of this approach for disturbances that might be one thousand times larger than the possible superior limit of the perturbative *** HAM-based approach could provide us rigorous theoretical results in quantum mechanics,which can be directly compared with experimental ***,this is of great benefit not only for improving the accuracy of experimental measurements but also for validating physical theories.

关键词： non-perturbative series homotopy analysis method quantum mechanics

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Fractional Fourier Transform Aided Computerized Framework for Alcoholism Identification in EEG 11th

Fractional Fourier Transform Aided Computerized Framework fo...

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11th International Conference on Health Information Science, HIS 2022

作者： Sadiq, Muhammad Tariq Akbari, Hesam Siuly, Siuly Li, Yan Wen, Paul School of Architecture Technology and Engineering University of Brighton BrightonBN2 4AT United Kingdom Department of Biomedical Engineering Islamic Azad University Tehran*** Iran Institute for Sustainable Industries & Liveable Cities Victoria University MelbourneVIC3011 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Campus Toowoomba4350 Australia School of Engineering University of Southern Queensland Toowoomba Campus Toowoomba4350 Australia

ISBN: (纸本)9783031206269

Alcoholism has a detrimental impact on brain functioning. Electroencephalogram (EEG) signals are commonly used by clinicians and researchers to quantify and document alcoholic brain activity. Despite widespread attention in these signals, the non-stationarity of physiological EEG signals has complications in alcoholism applications. Fourier Transform have been used to examine stationary signals in a straightforward manner. Non-stationary signal analysis, on the other hand, is unsatisfactory using such an approach because it cannot show the occurrence time of distinct frequency components. Furthermore, it is critical to capture both time and frequency characteristics. To overcome these aforementioned issues in alcoholism EEG signals, a computer-aided diagnosis (CAD) approach is proposed in this study to distinguish between normal and alcoholic subjects. The dataset is first split into multiple EEG signals, and the multiscale principal component analysis approach is used to remove noises. Second, as a novel and powerful feature extraction method for EEG signals, the Fractional Fourier Transform (FrFT) methodology with different coefficients is used. A generalization of the classical Fourier Transform, the FrFT, may reveal the fluctuating frequencies of non-stationary EEG signals. The t-test method is used to evaluate the FrFT derived coefficients as features. Finally, to categorize normal vs alcoholic signals, relevant features are tested on multiple machine learning classifiers accessible in the WEKA platform using a 10-fold cross-validation technique. The obtained results effectively support the usefulness of FrFT coefficients as features. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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Degradation analysis and optimization of temperature effect on MEMRISTOR-based Neural Network Accelerators by electro-thermal simulation

Degradation analysis and optimization of temperature effect ...

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2020 International Conference on Electronics, Communications and Information Technology, CECIT 2020

作者： Shang, Mengjun Yin, Longxiang Xu, Ning School of Information Engineering Wuhan University of Technology Wuhan China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Nowadays, memristor-based neural network accelerators have been widely studied due to their outstanding performance in massive parallel vector matrix multiplication. However, the memristor is sensitive to temperature and its on/off state operation window can be seriously degraded by the increasing temperature, which may lead to computation failures in memristor -based NN accelerators. In this work, we establish an electro-thermal simulation platform to evaluate the temperature impact on memristor -based NN accelerators. With this platform, we first investigate the impact on computation accuracy with the temperature increase in different NN layers in the accelerators. We then apply a temperature-aware NN weight mapping scheme to the most temperature-sensitive layer and achieve 28.89% improvement in computation accuracy, which only has 0.06% difference with the improvement achieved by applied the mapping scheme to the whole NN model. This finding can help to simplify the temperature-aware hardware optimization design in memristor-based neural network accelerators and reduce the power consumption. © Published under licence by IOP Publishing Ltd.

关键词： Memristors

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