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Controlled diesel-mixed soils for roadway embankments: laboratory and ultrasonic characterization

Arabian Journal of Geosciences 2023年第5期16卷 1-12页

作者： AbdelSalam, Sherif S. Hasan, Ahmed M. M. Civil and Infrastructure Engineering and Management Program School of Engineering and Applied Science Nile University Giza Egypt

Soils contaminated with hydrocarbons is a repetitive site condition that may occur at locations adjacent to underground fuel tanks and other facilities. The main purpose of this study is to characterize and assess possibilities of reusing contaminated soil in useful applications such as roadway embankments. Accordingly, controlled amounts of diesel were mixed with clays and sands using percentages ranging from 0 to 13.5%. Unconfined compression, direct shear, California bearing ratio, and other tests were conducted on the diesel-mixed soil, along with the ultrasonic test. The ultrasonic test provided measures for the pulse wave velocity and received frequency, which was correlated with other soil parameters such as elastic modulus, resilient modulus, and California bearing ratio. From the main outcomes, it was found that small amounts of diesel can help improved soil shear strength. For instance, adding 5% diesel increased the soil cohesion by 63% and the unconfined compressive strength from 0.14 to 0.21 N/mm2. In sands, the California bearing ratio increased by almost double by adding 5% diesel, which is essential for roadway embankments especially in remote locations. Damping coefficient of soil was determined at different levels of diesel using the ultrasonic wave decay envelops. The ultrasonic waves were analyzed using fast Fourier transform to determine useful correlations between the wave frequency and the diesel content starting from 0 to 13.5%, and it was realized that an uncomplicated ultrasonic test can be acceptably used to assess static and dynamic properties of clean as well as contaminated soils.

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Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks

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Mobile Networks and Applications 2024年第3期29卷 886-904页

作者： Lin, Chun-Cheng Peng, Yi-Chun Chen, Zhen-Yin Annie Fan, Yu-Hong Chin, Hui-Hsin Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Technology Overseas Chinese University Taichung407 Taiwan

Flexible job shop scheduling (FJSP) has garnered enormous attention within the realm of smart manufacturing, where, beyond job sequencing, the selection of machines holds considerable importance. As smart factories progress with the Internet of things (IoT) and cyber-physical systems (CPS), scheduling methodologies are advancing towards intelligent decentralization. However, with the expansion of factories, conventional cloud computing struggles to manage the substantial influx of data. To tackle this issue, this work incorporates a fog computing and edge computing framework into the distributed FJSP workstations. In this framework, the workstations each of which consists of multiple machines are categorized based on the different nature of the accommodated machines, and operate independently to reduce unnecessary information transmission, in which each machine is equipped with edge computing capacity. The fusion of fog computing and edge computing allows for the offloading of computational tasks from cloud computing, effectively reducing latency. While previous solutions for FJSP have predominantly relied on linear programming or metaheuristic algorithms, this work proposed a novel distributed approach based on a dual deep Q networks (dual DQN) architecture, integrating deep learning (DL) with reinforcement learning (RL). Within the cloud center, the initial neural network determines the machine selection rules for fog computing, while the secondary neural network decides the job dispatching rules for edge computing devices. Edge computing devices execute the schedule and provide feedback to the cloud, which refines the results through an iterative training process, so that to minimize the makespan. The experimental findings indicate that employing dual DQNs outperforms the methods of utilizing only one single machine selection rule. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Technology Complexity and Firm Performance: The Moderating Effect of Design Innovation 18

Technology Complexity and Firm Performance: The Moderating E...

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18th European Conference on Innovation and Entrepreneurship, ECIE 2023

作者： Hur, Jisoo Kim, Kyeungoui Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of School of Applied Artificial Intelligence Handong University Pohang Korea Republic of

ISBN: (纸本)9781914587863

In today's highly competitive markets, firms can no longer ensure competitive advantage through the development of highly complex technologies or differentiation based solely on technological and functional factors. Simultaneously, there is growing recognition that design and aesthetic factors can contribute to firms' competitive advantage and profitability. Hence, this study examines the effects of design innovation, captured by formal design registration, on firms' financial performance. We investigated the relationship between technology complexity, design innovation, and firm performance to unveil design innovation's role in exploiting complex technologies. Our main hypothesis is that design innovation positively moderates the negative effect that technology complexity yields on firm performance. We used a unique dataset that aggregates all patent data published by US-headquartered publicly-traded firms to the parent company level. We combined this with firms' financial information and design patent data from the Computat and PATSTAT databases based on their matching firm identifier. Focusing on 1,765 US firms from 1980 to 2015, we calculated firm-level technology complexity based on the structural diversity of technologies. The results confirmed our hypotheses. This study contributes to firms' resource-based view by recognizing design as a strategic resource that firms can utilize to gain a competitive advantage and by discovering design innovation's role in generating economic profits. © 2023 Academic Conferences and Publishing International Limited. All rights reserved.

关键词： Profitability

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Quantifying intragroup and intergroup connections in non-disjoint groups in social networks: A comprehensive analysis incorporating edges' and nodes' weights

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Information Sciences 2024年 670卷

作者： de Andrade, Ricardo Lopes Rêgo, Leandro Chaves Management Engineering Graduate Program Federal University of Pernambuco Brazil Statistics and Applied Math Department Universidade Federal do Ceará Brazil

In this paper, we propose generalizations of the EI index to quantify the distinction between intragroup and intergroup connections in social networks. These generalizations enable the analysis of interactions between non-disjoint groups, as large-scale disjoint groups are rarely found in many empirical networks. By expanding social network analysis, these measures facilitate the identification of actors with greater similarities or differences, generating previously untapped knowledge. Furthermore, we propose incorporating both edges' and nodes' weights in assessing the EI index. Intuitively, our approach classifies each network edge as internal or external, depending on the attribute groups that each node linked by the given edge belongs to. Since nodes can belong to more than one attribute group, three methods of classifying a network edge are proposed which depend on the number of shared groups by the pair of nodes. We evaluate the new measures in two distinct network contexts. The first context involves a co-authorship network, where researchers, acting as network actors, are categorized based on their respective areas of expertise in management engineering. The second network consists of trade relations among countries in the Americas, with countries grouped according to their participation in trade agreements. The analysis showed which area of management engineering is more independent by having more internal connections and made it possible to investigate the role of trade agreements in the actual trades made by countries. These proposals provide a more comprehensive and nuanced understanding of network dynamics, allowing for a more precise comprehension of interactions and patterns present in social systems. © 2024 Elsevier Inc.

关键词： Commerce

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Occurrence and risk assessment of glycidyl and 3-monochloropropanediol esters in infant formulas marketed in Taiwan

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Food Additives and Contaminants - Part A 2024年第4期41卷 352-364页

作者： Liu, Chen Wang, Shang-Ta Tan, Choon-Hui Lin, Zih-Ee Lee, Wei-Ju Master Program in Food Safety Taipei Medical University Taipei Taiwan Department of Food Science National Taiwan Ocean University Keelung Taiwan Institute of Food Safety and Risk Management National Taiwan Ocean University Keelung Taiwan Department of Food Science and Nutrition Faculty of Applied Sciences UCSI University Kuala Lumpur Malaysia Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan School of Food Safety Taipei Medical University Taipei Taiwan

Glycidyl esters (GEs) and 3-monochloropropanediol esters (3-MCPDEs) are process contaminants commonly found in refined edible oils which are often added to infant formulas. The Taiwan Food and Drug Administration (TFDA) launched regulations for GEs in infant formulas that went into effect on 1 July 2021. To investigate levels of GEs and 3-MCPDEs in infant formula powder, 45 products were sampled and analysed during 2020–2021. The contents of GEs and 3-MCPDEs in formulas of different brands significantly varied, but their concentrations in all of the formulas complied with European Union (EU) regulations. Infant formulas containing palm oil had significantly higher 3-MCPDE levels in both extracted oils and milk powder than those without palm oil. Concentrations of GEs and 3-MCPDEs in infant formula powder and extracted oils were significantly lower in products from Europe than those from Australia and New Zealand. Infants aged 0–1 years in Taiwan who consumed only infant formula showed a margin of exposure (MoE) exceeding 25,000. Mean consumer exposures to 3-MCPDEs stayed below the tolerable daily intake (TDI), while high exposures at the 95th percentile (P95) exceeded the TDI by 1.7-fold. Herein, we present the changing trends in the risk assessment results of infant formula across various countries in the decade. Implementation of regulations and mitigation strategy effectively reduced the risk of infants being exposed to GEs and 3-MCPDEs through infant formula. © 2024 Taipei Medical University.

关键词： Esters

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhandarkar, Vishakha D. Rongali, Aruna Sri Shamout, Mohamed Dawood Pund, Sachin Nama, Gigih Forda Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities India University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Doctoral Program of Environtmental Science University of Lampung Department of Informatics Bandar Lampung Indonesia Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

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A stochastic MPEC approach for grid tariff design with demand-side flexibility

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2023年第3期14卷 707-729页

作者： Askeland, Magnus Burandt, Thorsten Gabriel, Steven A. Energy Systems SINTEF Energy Research Trondheim Norway Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Workgroup for Infrastructure Policy Technical University of Berlin Berlin Germany The Energy Transportation Environment Department German Institute for Economic Research (DIW Berlin) Berlin Germany Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Trondheim Norway Department of Mechanical Engineering Applied Math and Statistics Scientific Computation Program University of Maryland College Park MD USA

As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs is one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results based on both deterministic and stochastic settings are presented and discussed. The findings suggest how electricity grid tariffs should be designed to provide an efficient price signal for reducing aggregate network peaks.

关键词： Bilevel problem Grid tariffs Mathematical program with equilibrium constraints (MPEC) Uncertainty

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Preparing future entrepreneurs: reflections about the COVID-19 impacts on the entrepreneurial potential of Brazilian students

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Journal of Work-applied management 2021年第2期13卷 277-283页

作者： Cazeri, Gustavo T. Anholon, Rosley Rampasso, Izabela Simon Quelhas, Osvaldo L.G. Leal Filho, Walter School of Mechanical Engineering State University of Campinas Campinas Brazil PNPD/CAPES Program Doctoral Program in Sustainable Management Systems Federal Fluminense University Niterói Brazil Master Program in Management Systems Doctoral Program in Sustainable Management Systems Federal Fluminense University Niterói Brazil Faculty of Life Sciences Hamburg University of Applied Sciences Hamburg Germany

Purpose: This viewpoint aims to present reflections on the coronavirus disease 2019 (COVID-19) pandemic scenario and the entrepreneurial potential of Brazilian students. Guidelines are proposed to support debates in the academic environment and better prepare future entrepreneurs to the labour market. Design/methodology/approach: Since this is a viewpoint article, some statements presented here are characterised by the authors' opinions. However, for a better foundation, bibliographic research related to the theme was carried out. Findings: In the authors' point of view, the COVID-19 pandemic scenario may negatively influence the decisions of Brazilian students that are characterised as potential future entrepreneurs. Educators can use the guidelines presented here to conduct debates with students in order to clarify aspects related to profile, behaviour and other issues. Originality/value: By understanding better entrepreneurial aspects in the COVID-19 pandemic scenario, Brazilian students can make better decisions. This initiative will contribute to future success in the Brazilian entrepreneurial activity and national economy. © 2021, Gustavo T. Cazeri, Rosley Anholon, Izabela Simon Rampasso, Osvaldo L.G. Quelhas and Walter Leal Filho.

关键词： Brazilian higher education institutions COVID-19 pandemic Entrepreneurial activity Labour market

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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Computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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