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Determinant factors for consumers?? intention in choosing an e-commerce Platform (online shop): An extended Theory of Planned Behavior (TPB) approach 24

Determinant factors for consumers?? intention in choosing an...

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Proceedings of the 10th International Conference on Industrial and Business engineering

作者： Ramon Francisco I. Jorge Maela Madel L. Cahigas Ierenne Novendah Reny Nadlifatin Rachkanok Sukhavalli Yogi Tri Prasetyo School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Industrial Engineering and Management Yuan Ze University Taoyuan Taiwan China Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia International Bachelor Program in Engineering Yuan Ze University Taoyuan Taiwan China

ISBN: (纸本)9798400710742

This study investigates the determinant factors influencing consumers' intention to choose an e-commerce platform (online shop) through the lens of an extended Theory of Planned Behavior (TPB) approach. By examining key elements such as service quality, user interface design, customer support, transparency, personalization, and security measures, the research provides a comprehensive understanding of how these factors contribute to consumer behavior in the online shopping environment. The findings underscore the critical role of service quality in shaping consumer intentions, highlighting the need for online shopping platforms to prioritize and continuously enhance their service offerings. Additionally, the study emphasizes the importance of a user-friendly interface, responsive customer support, transparency in communication, personalized experiences, and robust security measures in fostering consumer trust and satisfaction. These insights offer practical recommendations for e-commerce platforms to improve their service quality, ultimately leading to increased consumer engagement and loyalty. The research concludes that a focus on delivering exceptional service quality, coupled with informed consumer decision-making, can significantly enhance the overall online shopping experience, benefiting both consumers and platform providers.

关键词： e-commerce

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Millennials' social media-driven online entrepreneurship: Assessing their aspirations for multi-level marketing jobs 24

Millennials' social media-driven online entrepreneurship: As...

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Proceedings of the 10th International Conference on Industrial and Business engineering

作者： Ahmad Sattam Alhawamdeh Ierenne Novendah Yogi Tri Prasetyo Reny Nadlifatin Maela Madel L. Cahigas Department of Industrial Engineering and Management Yuan Ze University Taoyuan Taiwan China International Bachelor Program in Engineering Yuan Ze University Taoyuan Taiwan China Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia School of Industrial Engineering and Engineering Management Mapua University Manila Philippines

ISBN: (纸本)9798400710742

Abstract: With the development of Internet technology and the popularization of mobile devices and so on, e-commerce has entered a period of high-speed development, consumers are faced with a wealth of channel choices, and the competition for traffic in different channels is becoming more and more intense, which poses a great challenge to enterprises. From the perspective of PPM theoretical model to study the consumers' migration intention of different online channels, using SPSS software and Amos to construct structural equations for the influencing factors of consumers' migration intention of different online channels, the results show that the push force and pull force positively affect the consumers' migration intention, and the push force has the greatest influence on the consumers' migration intention. It provides a basis for the live e-commerce platform to propose measures to enhance user channel stickiness.

关键词： Consumers

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AdaMesh: Personalized Facial Expressions and Head Poses for Adaptive Speech-Driven 3D Facial Animation

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IEEE Transactions on Multimedia 2025年

作者： Chen, Liyang Bao, Weihong Lei, Shun Tang, Boshi Wu, Zhiyong Kang, Shiyin Huang, Haozhi Meng, Helen Tsinghua University Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Shenzhen International Graduate School Shenzhen China SenseTime Research Shenzhen China XVerse Technology Shenzhen China The Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong Hong Kong

Speech-driven 3D facial animation aims at generating facial movements that are synchronized with the driving speech, which has been widely explored recently. Existing works mostly neglect the person-specific talking style in generation, including facial expression and head pose styles. Several works intend to capture the personalities by fine-tuning modules. However, limited training data leads to the lack of vividness. In this work, we propose AdaMesh, a novel adaptive speech-driven facial animation approach, which learns the personalized talking style from a reference video of about 10 seconds and generates vivid facial expressions and head poses. Specifically, we propose mixture-of-low-rank adaptation (MoLoRA) to fine-tune the expression adapter, which efficiently captures the facial expression style. For the personalized pose style, we propose a pose adapter by building a discrete pose prior and retrieving the appropriate style embedding with a semantic-aware pose style matrix without fine-tuning. Extensive experimental results show that our approach outperforms state-of-the-art methods, preserves the talking style in the reference video, and generates vivid facial animation. The supplementary video and code will be available on the project page https://***/thuhcsi/AdaMesh. © 1999-2012 IEEE.

关键词： Animation

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Sodium cholesterol sulfate mediated mitoxantrone prodrug electrostatic nanocomplexes:achieving the therapeutic efficacy and safety of mitoxantrone

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Science China Chemistry 2025年第3期68卷 1185-1198页

作者： Erwei Zhao Lingxiao Li Jingyi Zhang Yaqiao Li Rong Chai Bowen Zhang Jialin Xing Minglong Huang Lurong Zhang Xiaohui Pu Zhonggui He Bingjun Sun Department of Pharmaceutics Wuya College of InnovationShenyang Pharmaceutical UniversityShenyang 110016China Joint International Research Laboratory of Intelligent Drug Delivery Systems Ministry of EducationShenyang Pharmaceutical UniversityShenyang 110016China Peking Union Pharmaceutical Factory Ltd. Beijing 102600China State Key Laboratory of Antiviral Drugs School of PharmacyHenan UniversityKaifeng 475004China Department of Bioengineering Graduate School of EngineeringThe University of TokyoTokyo 113-0033Japan

Forming electrostatic nanocomplexes(ENCs)with counter-ions can improve the delivery efficiency of chemotherapy ***,water-soluble chemotherapy drugs like mitoxantrone(MTO),have limited affinity for counter-ions,posing challenges in the creation of stable ***,MTO was connected to fatty alcohols of varying chain lengths(C8,C12,C16)via disulfide bonds,forming hydrophobic *** found that conjugating MTO to fatty alcohols significantly improved its affinity for the counter-ion sodium cholesterol sulfate(SCS).Among the designed prodrugs,conjugated to fatty alcohols with longer carbon chain lengths exhibited heightened affinity for SCS,resulting in the formation of more stable ***,extending the carbon chain also slowed the rate of drug ***,compared with MTO solution,these ENCs demonstrated comparable therapeutic efficacy while causing minimal damage to healthy tissues,especially for MTO-SS-C16 *** research provides new insights for constructing stable and safe ENCs for hydrophilic drugs like MTO.

关键词： mitoxantrone prodrug sodium cholesterol sulfate electrostatic nanocomplexes disulfide bond

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Relational analysis of route searches and transportation IC card use

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Transportation Research Procedia 2025年 82卷 957-970页

作者： Mio Hosoe Masashi Kuwano Taku Moriyama Department of Management of Social Systems and Civil Engineering Graduate School of Engineering Tottori University 4-101 Koyama-Minami Tottori 680-8552 Japan Department of Data Science Yokohama City University 22-2 Seto Kanazawa Ward Yokohama Kanagawa 236-0027 Japan

Route search data are saved in public transportation route search systems. These data record the potential travel plans of searchers and are expected to serve as a reliable source of information to predict travel demand. However, route search data merely represent latent demand and their relationship with actual traffic demand is not known. The purpose of this study was to identify the relationship between the number of route searches and the number of actual public transportation users to predict traffic demand using route search data. To achieve this, both route search data and transportation IC card data were analyzed for the Kotoden lines that operate in Kagawa Prefecture, Japan. By adopting an approach that combined bivariate state-space and weighted regression models, we extracted the varying hidden relationships between the two seemingly unrelated data types and modeled the behaviorally related parts. The results revealed a positive correlation between sudden surges in the number of route searches and transportation IC card use.

关键词： route search system transportation IC card bivariate state-space model weighted regression model

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Image segmentation via two-step deep variational priors

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Pattern Recognition Letters 2025年 195卷 44-50页

作者： Tan, Lu Tai, Xue-Cheng Li, Ling Liu, Wan-Quan Chan, Raymond H. Hong, Dan-Feng School of Computer Science and Information Engineering Bengbu University Bengbu Anhui China Bergen Norway Curtin University Perth Australia School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Department of Operations and Risk Management and School of Data Science Lingnan University Tuen Mun Hong Kong Hong Kong Centre for Cerebro-Cardiovascular Health Engineering Hong Kong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

This paper proposes an iterative deep variational approach for image segmentation in a fusion manner: it is not only able to realize selective segmentation, but can also alleviate the issue of parameter/initialization dependency. Moreover, it possesses a refinement process designed to handle challenging scenarios, such as images containing obscured, damaged, or absent objects, or those with complex backgrounds. Our proposed approach consists of two main procedures, i.e., selective segmentation and shape transformation. The first procedure works as a stem in a totally unsupervised way. A convolutional neural network (CNN) based architecture is properly incorporated into the selective weighting constrained variational segmentation model. The second procedure is to further refine the outputs. This part can be achieved in two ways: one direction is to establish a joint model with the semantic shape constraint. The other technical direction is to make the shape descriptor separated from the joint model and work as an individual unit. In the proposed approach, the minimization problem is transformed from iterative minimization for each variable to automatically minimizing the loss function by learning the generator network parameters. This also leads to a good inductive bias associated with classic variational methods. Extensive experiments have demonstrated the significant advantages. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

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A multimodal automatic generation and annotation framework for prohibited and restricted goods in online transactions

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Liu, Wenjin Zhang, Shudong Liu, Haoming Zou, Jingyu Hainan University No. 58 Renmin Avenue Hainan Haikou570228 China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin999077 Hong Kong

Transactions of prohibited and restricted goods on e-commerce platforms threaten consumer safety and hinder Internet economy growth. However, the lack of such datasets hinders intelligent identification and effective regulation. To address this issue, a large-scale multimodal dataset of prohibited and restricted goods is constructed, comprising 18,446 images and 36,892 texts. Nevertheless, this is insufficient due to the diverse forms and deep concealment of prohibited and restricted products in online transactions. Therefore, we propose a multimodal automatic generation and annotation framework for prohibited and restricted goods in online transactions. This framework consists of an image generation module, a text description module, and an image annotation module. The image generation module is utilized to generate more diverse images for prohibited and restricted goods. The text description module can generate text descriptions for the generated images. The image annotation module is employed to annotate the generated images for various visual tasks. This framework achieved a 24.71 user rating for image generation, a 92.72 % mean average precision for image annotation, and an 81.67 % semantic score for image description. Experimental results demonstrate the effectiveness and accuracy of the proposed automatic annotation framework, which can enhance the effective supervision of prohibited and restricted goods. © 2025 Elsevier Ltd

关键词： Electronic money

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Versatile Test Reactor Open Digital engineering Ecosystem

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Insight 2025年第1期28卷 64-68页

作者： Ritter, Christopher Browning, Jeren Suyderhoud, Peter Hays, Ross Marshall, AnnMarie Han, Kevin Ashbocker, Taylor Darrington, John Nelson, Lee Idaho National Laboratory Department of Digital Systems & Engineering Versatile Test Reactor Program Idaho FallsID83401 United States Pivotal Solutions 3 Grace Avenue Suite 162 Great NeckNY11021 United States North Carolina State University Department of Civil and Environmental Engineering United States Department of Computer Science University of Illinois at Urbana-Champaign 205 N. Mathews Ave. UrbanaIL61801 United States

Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors made early in design can introduce silent errors. These errors can cascade causing unknown risk of complex engineering programs. The Versatile Test Reactor (VTR) program uses digital-engineering principles for design, procurement, construction, and operation to reduce risk and improve efficiencies. Digital engineering is an integrated, model-based approach which connects proven digital tools such as building information management (BIM), project controls, and systems-engineering software tools into a cohesive environment. The VTR team hypothesizes using these principals can lead to similar risk and cost reductions and schedule efficiencies observed in other engineering industries. This research investigates the use of a digital engineering ecosystem in the design of a 300-MWt sodium-cooled fast reactor. This ecosystem was deployed to over 200 engineers and used to deliver the conceptual design of the VTR. We conclude that initial results show significant reductions in user latency (1000x at peak use), the possibility of direct finite-element-analysis (FEA) integrations to computer-aided design (CAD) tools, and nuclear reactor system design descriptions (SDDs) that we can fully link throughout design in data-driven requirements-management software. These early results led to the VTR maintaining milestone performance during the COVID-19 pandemic. Copyright © 2022 by Idaho National Laboratory. Published by INCOSE with permission.

关键词： Sodium-cooled fast reactors

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IDS: Robust Network Intrusion Detection via Temporal Contrastive Graph Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1475-1486页

作者： Wu, Cong Sun, Jianfei Chen, Jing Alazab, Mamoun Liu, Yang Xiang, Yang Wuhan University School of Cyber Science and Engineering Wuhan430072 China Singapore Management University School of Computing and Information Systems Bras Basah Singapore188065 Singapore Charles Darwin University College of Engineering IT and Environment DarwinNT0810 Australia Nanyang Technological University College of Computing and Data Science Jurong West Singapore639798 Singapore Swinburne University of Technology Department of Digital Research MelbourneVIC3122 Australia

In the era of zero trust security models and next-generation networks (NGN), the primary challenge is that network nodes may be untrusted, even if they have been verified, necessitating continuous validation and scrutiny. Effective intrusion detection systems (IDS) are crucial for continuously monitoring network traffic and identifying potential threats. However, traditional IDS approaches often struggle to keep pace with evolving threats, requiring extensive supervised training on labeled datasets. This limitation leads to high false positive rates, low detection accuracy, and a failure to provide real-time detection, thereby undermining the security of NGNs. This paper proposed the first self-supervised learning-based IDS, designed on temporal contrastive graph neural network (GNN), namely TCG-IDS. It innovatively integrates three contrastive learning strategies: temporal contrasting to capture temporal dependencies, asymmetric contrasting to account for the diverse interactions within network data, and masked contrasting to enhance the learning of node representations by masking parts of the data during training. Performance evaluation was conducted on two publicly available network traffic datasets, NF-CSE-CIC-IDS2018-V2 and NF-UNSW-NB15-V2. TCG-IDS achieved a balanced accuracy of 99.48% and 91.48% on two datasets respectively, significantly outperforming state-of-the-art graph learning models. In multi-class detection, TCG-IDS attained a mean false positive rate of 4.15% and 3.34% on the two datasets respectively. Besides, it exhibits high efficiency with its running time of 0.37s and 0.51s on the two datasets to predict per batch of 100 samples. Results highlight the effectiveness and efficiency of TCG-IDS in accurately detecting various types of network intrusions. This work significantly advances the field of network intrusion detection via self-supervised temporal graph learning, offering a promising solution for future network security systems. © 2005-2012

关键词： Network intrusion

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LEARNING DISCRIMINATIVE FEATURES FROM SPECTROGRAMS USING CENTER LOSS FOR SPEECH EMOTION RECOGNITION

arXiv

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arXiv 2025年

作者： Dai, Dongyang Wu, Zhiyong Li, Runnan Wu, Xixin Jia, Jia Meng, Helen Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Graduate School at Shenzhen Tsinghua University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin Hong Kong

Identifying the emotional state from speech is essential for the natural interaction of the machine with the speaker. However, extracting effective features for emotion recognition is difficult, as emotions are ambiguous. We propose a novel approach to learn discriminative features from variable length spectrograms for emotion recognition by cooperating softmax cross-entropy loss and center loss together. The softmax cross-entropy loss enables features from different emotion categories separable, and center loss efficiently pulls the features belonging to the same emotion category to their center. By combining the two losses together, the discriminative power will be highly enhanced, which leads to network learning more effective features for emotion recognition. As demonstrated by the experimental results, after introducing center loss, both the unweighted accuracy and weighted accuracy are improved by over 3% on Mel-spectrogram input, and more than 4% on Short Time Fourier Transform spectrogram input. Copyright © 2025, The Authors. All rights reserved.

关键词： Spectrographs

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