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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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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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Computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

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Computers, Materials & Continua 2024年第11期81卷 2161-2194页

作者： Mujahid Ali Department of Transport Systems Traffic Engineering and LogisticsFaculty of Transport and Aviation EngineeringSilesian University of TechnologyKatowice40019Poland

Forecasting travel demand requires a grasp of individual decision-making ***,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to *** characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport *** statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex *** systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such *** in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present ***,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport *** two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous *** a total of 39 studies,our findings shed important light on the significance of considering factors that influence the *** adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML ***(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical *** sensitivity analysis indicates that the most sign

关键词： Machine learning techniques AI transport mode choice discrete choice model sustainable transportation

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Browsing History Data Analysis Based on a Modified Latent LSTM Allocation Model Considering Browsing Time and its Application to a Fresh Flower EC Site

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Journal of Japan Industrial Management Association 2024年第3期75卷 89-101页

作者： Zhang, Zhiying Yang, Tianxiang Goto, Masayuki Department of Industrial and Management Systems Engineering School of Creative Science and Engineering Waseda University Japan Department of Industrial and Systems Engineering Faculty of Science and Technology Keio University Japan

In recent years, it has become possible to accumulate a large amount of browsing history data from users on websites and it is desirable to make use of such data for marketing purposes. In particular, customer browsing history collected on EC sites is an important resource for analyzing purchasing behavior because it shows the differences in user preferences and their motivations behind purchasing products. In a previous study, the Latent LSTM Allocation model was proposed for grouping customers’ browsing history data. This is a model that combines the Long Short-Term Memory and the Latent Dirichlet Allocation models, but it does not consider the relationship between browsing behavior and purchase made. In addition, a customer’s browsing time on each page is not taken into consideration, and a page is treated the same regardless of whether a user has spent a long time carefully browsing it, or if they have quickly moved on to the next page. In this study, a new analysis model is proposed that can analyze a user’s willingness to purchase each product based on their relationship between their user’s browsing behavior data including their browsing time and the purchasing behavior, based on the understanding that their browsing time for each page is also an important factor in the customer’s browsing behavior. Specifically, the present paper proposes a new modified Latent LSTM Allocation model that is able to consider not only the information related to a user’s browsing and purchasing behaviors but also the time intervals for their browsing behaviors on individual pages on the target EC site. The proposed model enables to the extraction of groups based on purchasing tendencies considering users’ browsing times for each page. Finally, the proposed model is applied to a case study for an EC site that handles fresh flower products. The obtained findings are then analyzed by applying the proposed model to actual usage history data on the flesh flower EC site. © 2024 Japan

关键词： Sales

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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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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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Modelling customer demand for mobile value-added services: non-stationary time series models or neural networks time series analysis?

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International Journal of Industrial and systems engineering 2023年第4期43卷 555-581页

作者： Vaghefzadeh, Mohammad Hossein Karimi, Behrooz Ahmadi, Abbas Industrial Engineering and Management Systems Faculty Amirkabir University of Technology Tehran Iran Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Iran

The present research applies two different modelling approaches to evaluate the historical demand for a special mobile value-added service (VAS) that is offered and delivered to airline customers. The first method is deterministic and includes non-stationary time series models that cover both mean and variance fluctuation, as well as seasonality effect, in the dataset. The second method is a metaheuristic approach in the form of artificial neural network time series analysis (ANN-TSA). These methods are used to evaluate the power of each category and to choose the best model based on appropriate criteria. The results show that non-stationary time series models outperform ANN-TSA, as indicated by the smaller number of errors in the simulation of the demand dataset. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Neural networks

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Combatting energy poverty in eastern Turkey: innovative debt solutions and the power-cut index

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Sustainable Energy Research 2024年第1期11卷 1-15页

作者： Emre, Tamer Sözen, Adnan Istanbul Turkey Energy Systems Engineering Department Technology Faculty Gazi University Ankara Turkey

Energy poverty (EP), a pressing global concern, is uniquely manifested in regions like eastern Turkey due to intertwined socio-economic conditions and intricate energy consumption patterns. This study critically examines the electricity market dynamics, highlighting the direct impact on end-users, from households to entire communities facing challenges such as unauthorized consumption and waste. Our findings over 2 years period of 6 million customer invoices through 17 cities of 5 distribution companies underscore the limitations of traditional income-based measures in capturing the nuances of EP. In response, we introduce a novel metric—the power-cut index per consumer (PCPC)—spotlighting the prevalence of power interruptions due to non-payment as an actionable intervention metric. To address EP’s challenges, we present a mechanism encouraging consumers to reduce consumption, offering debt discounts as incentives. Our methodological approach, harnessing both the Monte Carlo simulation and optimization, promises flexible, actionable strategies tailored to diverse EP situations. Drawing parallels with the European Union’s energy transition efforts, this study proposes the adaptation of European frameworks to cater to Turkey’s unique landscape. By anchoring our insights in real stories of those affected by EP, we highlight the human dimension, emphasizing the urgency of stakeholder collaboration to ensure a future where energy facilitates prosperity rather than hindrance. The collective endeavors of infrastructure companies, governmental agencies, NGOs, and the public are pivotal in sculpting a brighter, equitable energy future. © The Author(s) 2024.

关键词： Energy efficiency

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Combustion characteristics of low-quality lignite for different bed material sphericities in a circulating fluidized bed boiler:A numerical study

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Particuology 2024年第7期90卷 364-382页

作者： Barış Gürel Gülşah Karaca Dolgun Osman İpek Ali Keçebaş Department of Mechanical Engineering Engineering FacultySüleyman Demirel UniversityÇünürIspartaTurkey Department of Energy Systems Engineering Technology FacultyMuğla SıtkıKoçman UniversityKötekliMenteşeMuğlaTurkey

This study delves into the combustion behavior of various lignite types within a circulating fluidized bed boiler(CFBB),with a primary focus on the impact of different bed material sphericity ratios(0.5,0.7,and 0.9).Utilizing bed material with a sphericity ratio of 0.9 sourced from theÇan power plant and verified through experimentation,the research reveals several key ***,furnace temperatures tended to rise with higher sphericity ratios,albeit with variations between lignite types,particularly highlighting the complexity of this relationship in the case of GLI-Tunçbilek *** levels in the combustion chamber remained consistent across different sphericity ratios,indicating minimal influence on pressure *** combustion efficiency,especially at the bottom of the boiler,was observed at lower sphericity levels(0.5 and 0.7)forÇan lignite,as reflected in CO_(2) mole *** NO_(x) emissions generally decreased with lower sphericity,the sensitivity to sphericity varied by lignite type,with Ilgın lignite showcasing low NO_(x) but high SO_(2) emissions,underscoring the intricate interplay between lignite properties,sphericity,and ***,this study advances our understanding of CFBB combustion dynamics,offering insights valuable for optimizing performance and emissions control,particularly in lignite-based power.

关键词： Circulating fluidized bed boiler Computational particle fluid dynamics method Particle sphericity ratios NO_(x)and SO_(2)emissions Energy production efficiency

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Continuous flow pyrolysis of virgin and waste polyolefins:a comparative study,process optimization and product characterization

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Frontiers of Chemical Science and engineering 2024年第6期18卷 133-149页

作者： Ecrin Ekici Güray Yildiz Magdalena Joka Yildiz Monika Kalinowska ErolŞeker Jiawei Wang Karlsruhe Institute of Technology Institute of Catalysis Research and TechnologyEggenstein-Leopoldshafen 76344Germany Izmir Institute of Technology Faculty of EngineeringDepartment of Energy Systems EngineeringIzmir 35430Türkiye Department of Materials Engineering and Production Faculty of Mechanical EngineeringBialystok University of TechnologyBialystok 15-351Poland Department of Agricultural and Food Engineering and Environmental Development Institute of Civil Engineering and EnergeticsFaculty of Civil Engineering and Environmental SciencesBialystok University of Technology15-351 BialystokPoland Department of Chemistry Biology and BiotechnologyInstitute of Civil Engineering and EnergeticsFaculty of Civil Engineering and Environmental SciencesBialystok University of TechnologyBialystok 15-351Poland Izmir Institute of Technology Faculty of EngineeringDepartment of Chemical EngineeringIzmir 35430Türkiye Energy and Bioproducts Research Institute(EBRI) Aston UniversityBirmingham B47ETUnited Kingdom

Under optimal process conditions,pyrolysis of polyolefins can yield ca.90 wt%of liquid product,i.e.,combination of light oil fraction and heavier *** this work,the experimental findings reported in a selected group of publications concerning the non-catalytic pyrolysis of polyolefins were collected,reviewed,and compared with the ones obtained in a continuously operated bench-scale pyrolysis *** process parameters were used for the pyrolysis of waste and virgin counterparts of high-density polyethylene,low-density polyethylene,polypropylene and a defined mixture of those(i.e.,25:25:50 wt%,respectively).To mitigate temperature drops and enhance heat transfer,an increased feed intake is employed to create a hot melt plastic *** 1.5 g·min^(-1) feed intake,1.1 L·min^(-1) nitrogen flow rate,and a moderate pyrolysis temperature of 450℃,the formation of light hydrocarbons was favored,while wax formation was limited for polypropylene-rich *** of virgin plastics yielded more liquid(maximum 73.3 wt%)than that of waste plastics(maximum 66 wt%).Blending polyethylenes with polypropylene favored the production of liquids and increased the formation of gasoline-range *** products were mainly composed of C3 hydrocarbons,and no hydrogen production was detected due to moderate pyrolysis temperature.

关键词： waste plastics polyolefins chemical recycling pyrolysis alternative fuels waste-to-energy

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