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Electrodeposited CrMnFeCoNi Oxy-carbide film and effect of selective dissolution of Cr on oxygen evolution reaction

Journal of Materials science & technology 2024年第33期200卷 176-184页

作者： Tian Xiao Chenghua Sun Rongguang Wang Graduate School of Science and Technology Hiroshima Institute of TechnologyHiroshimaJapan Faculty of Science Engineering and TechnologySwinburne University of TechnologyHawthornAustralia Department of Mechanical Systems Engineering Hiroshima Institute of TechnologyHiroshimaJapan

High entropy compounds were proven to exhibit excellent catalytic ***,a series of amorphous CrMnFeCoNi Oxy-carbide films were successfully synthesized by one-step *** demonstrated,the film presented superior electrocatalytic activity for oxygen evolution reaction(OER)with an overpotential of 295 mV at a current density of 10 mA/cm^(2).Uniquely,selective dissolution of Chromium(Cr)was observed,which increased the catalytic activity and showed high stability under a large current density of up to 400 mA/cm^(2).Cr dissolution not only increased the surface area but also improved the conductivity due to newly formed metal-metal bonding,promoting electron transfer and improving OER *** revealed by density functional theory(DFT)calculations,Cr-dissolution mediates the bonding of OER intermediates over surface active sites and ultimately reduces OER *** one-step electrodeposition method and the micro-dissolution mechanism provided a potential way to design and prepare high entropy compound electrodes,aiming to achieve efficient water electrolysis.

关键词： High entropy compound Electrodeposition Selective dissolution Oxygen evolution reaction Density functional theory

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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Computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Preparation and dielectric properties of Al2O3-based ceramic composites with Sm2O3 and ZrO2 additives for terahertz applications

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Ceramics International 2025年第13期51卷 17744-17754页

作者： Purlu, Kagan Murat Dalgac, Sekip Isik, Elif Yildiz, Betul Kafkaslioglu Elmabruk, Kholoud Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Electrical and Electronics Engineering Department Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Metallurgical and Materials Engineering Department Sivas Turkey

As Terahertz (THz) electromagnetic waves become significant for industrial applications, the choice and electrical characterization of materials play important roles in improving system efficiency. Due to their unique properties, ceramics have recently become increasingly explored for THz applications. To this end, the samples of Al2O3-based ceramic composites with Sm2O3 and ZrO2 additives are prepared. The prepared ceramic composites are characterized utilizing THz time-domain spectroscopy (THz-TDS), a powerful analytical technique employing THz radiation. First, the impact of sintering temperature on the dielectric properties of the pure Al2O3 is examined. Thus, the sintering temperature is set at 1550 °C to fabricate the other composites. Moreover, the impact of ZrO2, Sm2O3 and ZrO2-Sm2O3 additives on the dielectric properties of Al2O3-based ceramic composites is examined. In this context, the refractive index, permittivity, absorption coefficient and loss tangent of Al2O3-based ceramic composites are studied. Accordingly, it is observed that the dielectric constant and refractive indices of composites containing ZrO2, Sm2O3 and ZrO2-Sm2O3 additives increase compared to the pure-Al2O3. Moreover, while the loss tangent decreases with Sm2O3 additives, ZrO2, and ZrO2-Sm2O3 additives cause a rise in the loss tangent. The presented results enable us not only to choose the proper composite but also to enhance the dielectric properties of Al2O3-based ceramics. Consequently, they pave the way for the use of Al2O3-based ceramic composites with Sm2O3 and ZrO2 additives in a wide range of THz applications. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Sintering

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An Innovative Bispectral Deep Learning Method for Protein Family Classification

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Computers, Materials & Continua 2023年第5期75卷 3971-3991页

作者： Isam Abu-Qasmieh Amjed Al Fahoum Hiam Alquran Ala’a Zyout Biomedical Systems and Informatics Engineering Department Hijjawi Faculty for Engineering TechnologyYarmouk UniversityIrbid21163Jordan

Proteins are essential for many biological *** example,folding amino acid chains reveals their functionalities by maintaining tissue structure,physiology,and *** that quantifiable protein characteristics are vital for improving therapies and precision *** automatic inference of a protein’s properties from its amino acid sequence is called“basic structure”.Nevertheless,it remains a critical unsolved challenge in bioinformatics,although with recent technological advances and the investigation of protein sequence *** protein function from amino acid sequences is crucial in *** study considers using raw sequencing to explain biological facts using a large corpus of protein sequences and the Globin-like superfamily to generate a vector *** power of two representations was used to identify each amino acid,and a coding technique was established for each sequence ***,the encoded protein numerical sequences are transformed into an image using bispectral analysis to identify essential characteristics for discriminating between protein sequences and their families.A deep Convolutional Neural Network(CNN)classifies the resulting images and developed non-normalized and normalized encoding ***,the dataset was split 70/30 for training and ***,the dataset was utilized for 70%training,15%validation,and 15%*** suggested methods are evaluated using accuracy,precision,and *** non-normalized method had 70%accuracy,72%precision,and 71%recall.68%accuracy,67%precision,and 67%recall after ***,the normalized approach without validation had 92.4%accuracy,94.3%precision,and 91.1%*** showed 90%accuracy,91.2%precision,and 89.7%*** that both algorithms outperform the *** paper presents that bispectrum-based nonlinear analysis using deep learning models outperforms standard machine learning methods and other deep learning

关键词： Globin-like superfamily numerical encoding bispectral analysis classification model deep convolutional neural network(CNN)

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A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

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Computers, Materials & Continua 2023年第7期76卷 1173-1193页

作者： Amjed Al Fahoum Ala’a Zyout Hiam Alquran Isam Abu-Qasmieh Biomedical Systems and Informatics Engineering Department Hijjawi Faculty for Engineering TechnologyYarmouk UniversityIrbid21163Jordan

Complex proteins are needed for many biological *** amino acid chains reveals their properties and *** support healthy tissue structure,physiology,and *** medicine and treatments require quantitative protein identification and *** technical advances and protein sequence data exploration,bioinformatics’“basic structure”problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains *** function inference from amino acid sequences is the main biological data *** study analyzes whether raw sequencing can characterize biological facts.A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.A coding technique for each sequence in each family was devised using two representations to identify each amino acid precisely.A bispectral analysis converts encoded protein numerical sequences into images for better protein sequence and family *** and validation employed 70%of the dataset,while 30%was used for *** paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by a higher spectral representation image(Bispectrum).Cascading minimized false positive and negative cases in all *** initial stage focused on two classes(six groups and ten groups).The subsequent stages focused on the few classes almost accurately separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning *** single-stage technique had 64.2%+/-22.8%accuracy,63.3%+/-17.1%precision,and a 63.2%+/19.4%*** two-stage technique yielded 92.2%+/-4.9%accuracy,92.7%+/-7.0%precision,and a 92.3%+/-5.0%*** work provides balanced,reliable,and precise forecasts for all families in all measures.

关键词： Globin-like superfamily numerical encoding bispectral analysis classification model deep convolutional neural network

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Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

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Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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Rice spikelet’s disease detection using hybrid optimization model and optimized CNN

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Soft Computing 2024年第21期28卷 12787-12806页

作者： Patel, Bharati Department of Computer Science and Engineering Faculty of Science and Technology ICFAI University Raipur India

Rice fields all across the world are affected by spikelet sterility, often known as rice spikelet's disease. It is characterized by the improper development of spikelet’s, which lowers grain output and quality. For optimal management and the avoidance of yield losses, this disease must be discovered early. In this study, a deep learning-based approach utilizing optimization techniques was proposed for accurate segmentation and disease detection in rice crops. The research aimed to address the vulnerability of rice crops to disease attacks from seed germination to mature spikelets. The proposed model consisted of three major phases: pre-processing, segmentation, feature extraction and disease detection. Gaussian filtering was employed to preprocess the raw rice crop images, while a new hybrid Whale Customized Gravitational Optimization Algorithm was utilized for segmentation. Disease detection was performed using a hybrid deep learning model called HybridNet, which combines convolutional neural networks (CNN) with an optimized recurrent neural network (RNN) model. The dataset intended for this proposed project is sourced from the CABI PlantwisePlus Knowledge Bank. The proposed model for rice spikelet disease detection achieved high accuracy with Sensitivity (0.9269), Specificity (0.9756), and Accuracy (0.9634). This indicates that the model effectively identifies and detects diseases in rice spikelets, demonstrating its reliable performance in disease management and crop protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Food ingredient recognition model via image and textual feature extraction and hybrid classification strategy

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International Journal of Reasoning-based Intelligent Systems 2024年第1期16卷 74-90页

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Department of Computer Science and Engineering Faculty of Engineering and Technology Sharnbasva University India Department of Electronic and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Karanataka Kalburgi India

This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initially, the image features and textual features will be extracted, where image features like SIFT and improved CNN-based deep features, textural features are extracted. Then, the hybrid classifier is used for the identification of food ingredients that combines the models like neural network (NN) as well as long short-term memory (LSTM). In order to make the accurate results, the weights of NN and LSTM are fine-tuned via the Chebyshev map evaluated teamwork optimisation (CME-TWO) algorithm. At the final stage, the primacy of the offered scheme is proven concerning varied metrics. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Feature extraction

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Effect of sodium zeolite mixed metal oxide catalysts on catalytic conversion of mixed-density plastic into carbon nanotubes and hydrogen fuel

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Chinese Journal of Chemical engineering 2025年第2期78卷 196-204页

作者： Farzin Sheikh Hammad Hussain Muhammad Yasin Naz Bilal Shoukat Yasin Khan Muhammad Shoaib Department of Physics University of Agriculture Faisalabad Department of Agricultural Engineering Faculty of Agricultural Engineering & Technology University of Agriculture Faisalabad Department of Electrical Engineering College of Engineering King Saud University Center for Green Innovation University of Science and Technology Beijing

A combination of experimental and statistical analysis presents a comprehensive understanding of the microwave pyrolysis technique for catalytic deconstruction of mixed-density plastics. By optimizing the process parameters and catalyst selection, it is possible to maximize the production of valuable solid and energy products, contributing to sustainable waste management. In this work, different mixed-density plastics were pyrolyzed with different catalysts and residence times to yield liquid fuel, syngas, and structured carbon residue. The effect of inputs on the product type, yield and composition was statistically evaluated using ANOVA, which showed an F value of 4.108 and a p-value of 0.098(>1.00). FTIR and GC-MS revealed that the oil product consisted of C13+fractions in the form of alkanes, alkenes, and aromatics. The microscopic analysis of the residue confirmed the formation of carbon nanotubes along with other amorphous products. The presence of impurities in the solid product was further analyzed through XRD analysis. The pyrolytic liquid fuel revealed the presence of conjugated aromatic structure and carbonyl group in their concentration. This research demonstrated that converting mixed-density plastics using sodium zeolite, aluminum oxide, and nickel oxide catalysts yields 84% valuable products, confirming wasted plastics as a lucrative energy feedstock for producing hydrogen and high-value carbon compounds.

关键词： Hydrocarbons Hydrogen production Mixed-density plastics Pyrolysis Zeolite

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Semi-supervised learning for on-street parking violation prediction using graph convolutional networks

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Neural Computing and Applications 2024年第31期36卷 19643-19652页

作者： Karantaglis, Nikolaos Passalis, Nikolaos Tefas, Anastasios Group AIIA Lab Department of Informatics Faculty of Science Aristotle University of Thessaloniki Thessaloniki Greece Department of Chemical Engineering Faculty of Engineering Aristotle University of Thessaloniki Thessaloniki Greece

Controlled parking systems in cities provide designated parking zones and allow citizens to easily find parking spaces increasing comfort and potentially reducing traffic and pollution. However, illegally occupied parking spaces can negatively affect the accuracy of these systems, diminishing their benefits. This work presents a semi-supervised deep learning approach for fine-grained parking violation rate prediction using graph neural networks, which can capture both the spatial and temporal dynamics of parking systems and driver behavior, providing a valuable tool for controlled parking systems. The proposed method addresses several challenges faced in this task, including the need to appropriately construct temporal graph-based training datasets and design a model that can handle missing values. Additionally, the method includes a novel semi-supervised data augmentation and smoothing technique to mitigate the effect of the small amount of annotated data and/or missing data in the training dataset. The proposed method was evaluated using a large-scale dataset from Thessaloniki’s on-street public parking system and found to significantly improve the accuracy of parking violation prediction compared to state-of-the-art approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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