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Gold nanoclusters supported Molybdenum diselenide-porous carbon composite as an efficient electrocatalyst for selective ultrafast probing of chlorpyrifos-pesticide

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Chemical engineering Journal 2023年 472卷

作者： Ezhil Vilian, A.T. Mohammadi, Ali Han, Soobin Tiwari, Jitendra N. Kumar, Krishan Senthil Kumar, Annamalai Saravanan, Adhimoorthy Huh, Yun Suk Han, Young-Kyu Department of Energy and Materials Engineering Dongguk University-Seoul Seoul100-715 Korea Republic of Department of Biological Sciences and Bioengineering Incheon402-751 Korea Republic of Nano Bioelectrochemistry Research Laboratory CO2 Research and Green Technologies Centre Vellore Institute of Technology University Vellore632014 India Graduate Institute of Electro-Optical Engineering and Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Chlorpyrifos (CPS) is an organophosphorus pesticide widely utilized in agricultural production. Much like other commonly used highly toxic and hazardous substances, is harmful to humans, plants, and animals. Thus, the development of highly efficient electrocatalysts that can monitor and detect levels of CPS in environmental samples is urgently required. This research describes a simple means of synthesizing Au nanoclusters (AuNCs) incorporated into MoSe2-Porous carbons (PCs) via a single-step hydrothermal reaction followed by chemical reduction. AuNC-MoSe2-PC coated Glassy carbon electrodes (GCEs) exhibited excellent electrocatalytic activity, interfacial charge transfer ability (96 Ω), and cathodic peak intensities at a low reduction wave potential (∼ −0.74 V) for sensing CPS. The developed sensor exhibited a wide linear range from 5 to 185 nM, a rapid amperometric response of −1 cm−2) for CPS at −0.74 V vs. Ag|AgCl than other reported modified electrodes. Furthermore, the sensor had excellent reproducibility, repeatability, reusability, and long-time stability (88 % activity retention after 1 month) with a relative standard deviation (RSD) of © 2023 Elsevier B.V.

关键词： Selenium compounds

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Spatial and Temporal Variation of Particulate Matter (PM10 and PM2.5) and Its Health Effects during the Haze Event in Malaysia

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Journal of Atmospheric Science research 2023年第4期6卷 26-47页

作者： Afiqah Ma’amor Norazian Mohamed Noor Izzati Amani Mohd Jafri Nur Alis Addiena Ahmad Zia Ul Saufie Nor Azrita Amin Madalina Boboc Gyorgy Deak Faculty of Civil Engineering&Technology Universiti Malaysia PerlisJejawiArauPerlis02600Malaysia Sustainable Environment Research Group(SERG) Centre of Excellence Geopolymer and Technology(CEGeoGTech)JejawiArauPerlis02600Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi Mara(UiTM)Shah Alam40450Malaysia Institute of Engineering Mathematics Universiti Malaysia PerlisPauh Putra CampusArauPerlis02600Malaysia National Institute for Research and Development in Environmental Protection INCDPM Splaiul Independentei 294Bucharest060031Romania

This study aims to assess and compare levels of particulate matter(PM10 and PM2.5)in urban and industrial areas in Malaysia during haze episodes,which typically occur in the south west monsoon *** high concentrations of atmospheric particles are mainly due to pollution from neighbouring *** PM concentrations were analysed for urban and industrial areas including Alor Setar,Tasek,Shah Alam,Klang,Bandaraya Melaka,Larkin,Balok Baru,and Kuala Terengganu in 2018 and *** analysis employed spatiotemporal to examine how PM levels were *** data summary revealed that PM levels in all study areas were right-skewed,indicating the occurrence of high particulate *** peaks in PM concentrations during haze events were consistently observed between June and October,encompassing the south west monsoon and inter-monsoon *** study on acute respiratory illnesses primarily focused on *** revealed that Klang had the highest mean number of inpatient cases for acute exacerbation of bronchial asthma(AEBA)and acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with values of 260.500 and 185.170,***,for outpatient cases of AEBA and AECOPD,Klang had the highest average values of 41.67 and 14.00,*** Alam and Sungai Buloh did not show a significant increase in cases during periods of biomass *** statistical analysis concluded that higher concentrations of PM were associated with increased hospital admissions,particularly from June to September,as shown in the bar *** episodes were associated with more healthcare utilization due to haze-related respiratory illnesses,seen in higher inpatient and outpatient visits(p<0.05).However,seasonal variability had minimal impact on healthcare *** findings offer a comprehensive assessment of PM levels during historic haze episodes,providing valuable insights for authorities to develop policies and guidelines fo

关键词： Haze Particulate matter(PM10 and PM2.5) AEBA and AECOPD Spatial variability

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Investigating Multi-Feature Selection and Ensembling for Audio Classification

arXiv

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arXiv 2022年

作者： Turab, Muhammad Kumar, Teerath Bendechache, Malika Saber, Takfarinas Mehran University of Engineering and Technology Jamshoro Pakistan ADAPT – Science Foundation Ireland Research Centre Ireland CRT AI School of Computing Dublin City University Dublin Ireland Lero – the Irish Software Research Centre Ireland School of Computer Science National University of Ireland Galway Ireland

Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns–particularly in classification of audio data. For better performance of audio classification, feature selection and combination play a key role as they have the potential to make or break the performance of any DL model. To investigate this role, we conduct an extensive evaluation of the performance of several cutting-edge DL models (i.e., Convolutional Neural Network, EfficientNet, MobileNet, Supper Vector Machine and Multi-Perceptron) with various state-of-the-art audio features (i.e., Mel Spectrogram, Mel Frequency Cepstral Coefficients, and Zero Crossing Rate) either independently or as a combination (i.e., through ensembling) on three different datasets (i.e., Free Spoken Digits Dataset, Audio Urdu Digits Dataset, and Audio Gujarati Digits Dataset). Overall, results suggest feature selection depends on both the dataset and the model. However, feature combinations should be restricted to the only features that already achieve good performances when used individually (i.e., mostly Mel Spectrogram, Mel Frequency Cepstral Coefficients). Such feature combination/ensembling enabled us to outperform the previous state-of-the-art results irrespective of our choice of DL model. © 2022, CC BY.

关键词： Classification (of information)

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Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network

arXiv

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arXiv 2022年

作者： Liu, Haofeng Li, Heng Fu, Huazhu Xiao, Ruoxiu Gao, Yunshu Hu, Yan Liu, Jiang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China IHPC A*STAR Singapore The School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Eye Research Institute Singapore National Eye Centre Singapore

As an economical and efficient fundus imaging modality, retinal fundus images have been widely adopted in clinical fundus examination. Unfortunately, fundus images often suffer from quality degradation caused by imaging interferences, leading to misdiagnosis. Despite impressive enhancement performances that state-of-the-art methods have achieved, challenges remain in clinical scenarios. For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data. Firstly, high-quality images are randomly degraded to form sequences of low-quality ones sharing the same content (SeqLCs). Then individual low-quality images are decomposed to Laplacian pyramid features (LPF) as the multi-level input for the enhancement. Subsequently, a feature pyramid constraint (FPC) for the sequence is introduced to enforce the PCE-Net to learn a degradation-invariant model. Extensive experiments have been conducted under the evaluation metrics of enhancement and segmentation. The effectiveness of the PCE-Net was demonstrated in comparison with state-of-the-art methods and the ablation study. The source code of this study is publicly available at https://***/HeverLaw/PCENet-Image-Enhancement. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Enhanced fractional prediction scheme for effective matrix factorization in chaotic feedback recommender systems

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Chaos, Solitons and Fractals 2023年 176卷

作者： Khan, Zeshan Aslam Chaudhary, Naveed Ishtiaq Khan, Taimoor Ali Farooq, Umair Pinto, Carla M.A. Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering International Islamic University Islamabad Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Islamabad Model College for Boys F-7/3 Islamabad Pakistan Polytechnic of Porto and Centre for Mathematics University of Porto Porto Portugal Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

The biggest wish of the e-commerce industry is to forecast and estimate the taste and repugnance of users by utilizing user ratings for various products. Chaotic users' feedback drives the e-commerce industry to develop efficient, robust, and smart recommendation systems with the capability to deal with the inconsistencies in users' rating patterns and provide extremely related, appropriate, and timely recommendations. A few fractional order-based solutions for an effective matrix factorization procedure are suggested to boost recommender systems' performance regarding recommendations speed and precision. We further investigate a newly proposed enhanced fractional stochastic gradient descent (EFSGD) technique for accelerating recommendations speed through efficient matrix factorization. The Faa di Bruno fractional derivative used in EFSGD exploits the users' historical feedback for providing accurate predictions. As a result of the growing computational complexity of recommendation models, it will a challenge for EFSGD to extract group of prominent features and ignore the cluster of redundant users' and items' latent attributes for increasing the recommendation speed and reducing the computational complexity respectively. Therefore, (a) to resolve chaotic users' ratings patterns problem and (b) reduce the complexity by identifying and selecting groups of highly correlated latent features, an innovative elastic net regularized enhanced fractional adaptive model is developed for efficient matrix factorization. The proposed model for recommender systems outperforms baseline and state-of-the-art in terms of convergence speed, computational cost, and prediction accuracy. The substantial performance of the suggested strategy is verified through various latent factors, ratings prediction-based valuation measures, learning-rates, and fractional order values. Whereas the authenticity is verified through Movie-Lens and FilmTrust datasets. © 2023 Elsevier Ltd

关键词： Recommender systems

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FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech

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computers in Biology and Medicine 2024年 173卷 108280-108280页

作者： Tuncer, Turker Dogan, Sengul Baygin, Mehmet Barua, Prabal Datta Palmer, Elizabeth Emma March, Sonja Ciaccio, Edward J. Tan, Ru-San Acharya, U. Rajendra Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey Department of Computer Engineering Faculty of Engineering and Architecture Erzurum Technical University Erzurum Turkey University of Southern Queensland Australia Centre of Clinical Genetics Sydney Children's Hospitals Network Randwick2031 Australia School of Women's and Children's Health University of New South Wales Randwick2031 Australia School of Psychology and Counselling and Centre for Health Research University of Southern Queensland Springfield Australia Department of Medicine Columbia University Irving Medical Center United States Department of Cardiology National Heart Centre Singapore Singapore Duke-NUS Medical School Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Centre for Health Research University of Southern Queensland Australia

Background: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. Materials and methods: In this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor;(2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector;(3) classification via support vector machine and k-nearest neighbors;and (4) outcome determination using combinational majority voting to select the most favorable results. Results: To validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79%

关键词： Feature extraction

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RETRACTED: Energy efficient multi-tasking for edge computing using federated learning

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International Journal of Pervasive Computing and Communications 2022年第3期20卷 18-32页

作者： Mukesh Soni Nihar Ranjan Nayak Ashima Kalra Sheshang Degadwala Nikhil Kumar Singh Shweta Singh Department of CSE University Centre for Research and Development Chandigarh University Mohali India School of Information Science Presidency University Bengaluru India ECE Department Chandigarh Engineering College Landran India Department of Computer Engineering Sigma Institute of Engineering Vadodara India Department of CSE Maulana Azad National Institute of Technology Bhopal India Electronics and Communication Department IES College of Technology Bhopal India

Purpose The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage. Design/methodology/approach The new greedy algorithm is proposed to balance the energy consumption in edge computing. Findings The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent. Originality/value The results are shown in this paper which are better as compared to existing algorithms.

关键词： Edge computing Federated learning Energy balancing Greedy algorithm Multi-tasking

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Evaluation of the Montenegrin Academic Digital Innovation Hub 27

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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27th International Conference on Information technology, IT 2023

作者： Lakovic, Luka Kara, Peter A. Ognjanovic, Ivana Bokor, Laszlo Sendelj, Ramo Reich, Christoph Roganovic, Milovan Zoulias, Emmanouil Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada Simon, Aniko University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Faculty of Science Engineering and Computing Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro Sigma Technology Budapest1093 Hungary

ISBN: (纸本)9798350397512

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro. © 2023 IEEE.

关键词： Life cycle

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Feature Subset Selection for Big Data Via parallel Chaotic Binary Differential Evolution Island Model Under Apache Spark

SSRN

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SSRN 2022年

作者： Yelleti, Vivek Vadlamani, Ravi Radha Krishna, P. Analytics Lab Centre for Artificial Intelligence Machine Learning Institute for Development and Research in Banking Technology Castle Hills Road #1 Masab Tank Hyderabad500076 India Department of Computer Science and Engineering National Institute of Technology Warangal506004 India

Feature subset selection (FSS) using a wrapper approach is essentially a combinatorial optimization problem having two objective functions viz., (i) the cardinality of the selected feature subset, and (ii) the area under the ROC curve (AUC) of a classifier built on this subset. They should be minimized and maximized respectively. In this paper, we propose a novel multiplicative single objective function involving cardinality and AUC for FSS in high-dimensional and large-scale datasets. Since the randomness involved in the Binary Differential Evolution (BDE) may yield less diverse solutions and get the algorithm entrapped in local minima, we embed Logistic and Tent Chaotic maps into the BDE, called Chaotic Binary Differential Evolution (CBDE). We (i) develop an adaptive version of CBDE based on SHADE (called CSHADE) and (ii) propose a scalable island (IS) based parallelization approach, where the data is divided into multiple partitions/islands thereby enabling the solutions evolve individually and get combined eventually in a migration strategy/policy. The results empirically show that the proposed parallel Chaotic BDE (P-CBDE-IS) and its corresponding adaptive version can find better quality feature subsets than the parallel BDE (P-BDE-IS). Overall, P-CBDE-IS performed best in most of the cases. Logistic Regression (LR) is used as a classifier owing to its simplicity and power. The speedup attained by the proposed parallel approach signifies its importance. © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Assistive Technical Communication of Montenegrin eServices: a Case Study 27

Assistive Technical Communication of Montenegrin eServices: ...

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27th International Conference on Information technology, IT 2023

作者： Simon, Aniko Lakovic, Luka Kovacevic, Pavle Kara, Peter A. Ognjanovic, Ivana Sendelj, Ramo Reich, Christoph Roganovic, Milovan Mantas, John Bokor, Laszlo Sigma Technology Budapest1093 Hungary University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece

ISBN: (纸本)9798350397512

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity. © 2023 IEEE.

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