New Materials, Intelligent Systems, and Innovative Companies

@article{chu_3793,

title = {A general framework and an individual dominance realization model for efficient fixed cost allocation based on DEA},

author = {Junfei Chu and Yanhua Dong and Zhe Yuan and Weijiao Wang},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0305048325001082?via%3Dihub},

year  = {2026},

date = {2026-01-01},

journal = {Omega-International Journal Of Management Science},

volume = {138},

pages = {103382},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{xiao_3803,

title = {Metric learning-enhanced semi-supervised Graph Convolutional Network for multi-view learning},

author = {Huaiyuan Xiao and Fadi Dornaika and Jinan Charafeddine and Bi Jingjun},

url = {https://www.sciencedirect.com/science/article/pii/S1566253525004932?via%3Dihub},

year  = {2025},

date = {2025-12-01},

journal = {Information Fusion},

volume = {124},

pages = {103420},

abstract = {Multi-view learning utilizes data from diverse perspectives or modalities, integrating complementary infor

mation from various sources. It plays a crucial role in intelligent systems and finds extensive applications in 

fields such as computer vision, recommender systems, and natural language processing. With the increasing 

complexity and heterogeneity of real-world data, the integration of Graph Convolutional Networks (GCNs) in 

multi-view learning scenarios is becoming increasingly important. Despite the advances in GCNs, it remains a 

major challenge to effectively generalize models and improve their stability across different data views. In this 

paper, we present a novel framework, the Enhanced Triplet Loss Based Semi-Supervised Graph Convolutional 

Network for Multi-View Learning (MV-TriGCN), which addresses these challenges through three primary 

innovations. First, we propose an enhanced triplet loss for deep metric learning tailored to the hidden features 

of GCNs based on semi-hard negative sample selection. Second, view graphs are constructed using the classical 

KNN scheme and a semi-supervised flexible method to improve the diversity of data structure representation 

resulting in a more stable hypothesis space. Moreover, we learn an end-to-end multi-view GCN by merging all 

available graphs and utilizing the aggregated cross-entropy losses and deep metric losses. Finally, we introduce 

a stepwise training strategy that allows the model to adapt to the losses during different optimization phases. 

Extensive experiments show that our method outperforms existing state-of-the-art approaches in terms of  accuracy and stability},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chu_3501,

title = {Data envelopment analysis fixed cost allocation based on dynamic bargaining game and the Nash equilibrium},

author = {Junfei Chu and Yanhua Dong and Weijiao Wang and Yuting RUI and Zhe Yuan},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0377221725002061},

year  = {2025},

date = {2025-09-01},

journal = {European Journal Of Operational Research},

volume = {325},

number = {2},

pages = {317-328},

abstract = {Fixed cost allocation (FCA) poses a significant challenge for decision-making units (DMUs) contributing to a shared cost. Each DMU aims to allocate the minimum possible cost to itself. As a result, different DMUs hold varying and often conflicting allocation proposals and preferences, which prevents them from reaching a consensus on the FCA outcome. This paper develops a dynamic bargaining game-based fixed cost allocation (DBG-FCA) approach within the data envelopment analysis (DEA) framework to address this issue. The DBG-FCA approach employs an iterative process where each DMU proposes its preferred allocation during each iteration. All DMUs gradually converge on a consensus FCA outcome through dynamic negotiations and gradual compromise. Notably, this consensus upholds the individual rationality of each DMU, allowing them to align their proposals with their specific interests. Furthermore, the analysis establishes that the resulting FCA solution constitutes a Nash equilibrium, guaranteeing stability and universal acceptance among the DMUs. The effectiveness of the proposed approach is further illustrated through a numerical example and a case study involving FCA across 14 bank branches, along with a comparison to existing FCA methods.},

note = {In press JOURNAL PRE PROOF. 04/06/2025 : demande pdf final par ACA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mhiri_3672,

title = {Optimization of a Macrofiber Piezoelectric Energy Harvester Using Artificial Neural Networks},

author = {Mohamed Taha Mhiri and Walid Larbi and Mnaouar Chouchane and Mohamed Guerich},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0263822325004349},

year  = {2025},

date = {2025-09-01},

journal = {Composite Structures},

volume = {367},

pages = {119269},

abstract = {This paper addresses the challenge of finding the optimal design parameters for a

piezoelectric energy harvester. It presents an advanced simulation-driven optimization

approach to determine the optimal geometric and circuit configuration for maximizing energy

harvesting efficiency. Additionally, it enhances power output at an excitation frequency within

the 10-100 Hz range, which is commonly found in the environment. The harvester consists of

a cantilever beam partially coated with a macrofibre composite (MFC) piezoelectric patch,

connected to a resistance load and subjected to base excitation. The optimization platform is

built upon both analytical and finite element (FE) models of the energy harvesting system. For

beams with a large aspect ratio (length/width), the analytical model based on Euler-Bernoulli

beam theory is used, while for those with a small aspect ratio, a 3D FE model is employed to

simulate the entire energy harvesting process. This approach enhances the accuracy of

piezoelectric energy prediction. Due to the high computational cost and the significant time and

memory required for running numerous simulations to evaluate the objective function (OF)

used in the optimization, a more efficient solution is implemented based on a Neural Networks

(NNs) model. Initially, the NNs is trained using a dataset derived from simulations and its

performance and accuracy are rigorously assessed through various statistical methods. Once

trained, the NNs serves as a surrogate model for OF evaluation, allowing for more efficient

black-box optimization via a Genetic Algorithm (GA). Finally, a thorough analysis of the

optimal design parameters obtained from the optimization process is conducted.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vanaei_3797,

title = {3D Printing Process and Local Preheating Technique Study Using a Laser?Based Method},

author = {Hamidreza Vanaei and Kaddour Raissi and Frédéric Coste and Alain Guinault and Abbas Tcharkhtchi and Sofiane Khelladi},

url = {http://dx.doi.org/10.1002/pen.70000},

year  = {2025},

date = {2025-08-01},

journal = {Polymer Engineering And Science},

volume = {65},

number = {8},

pages = {4335-4346},

abstract = {This study addresses the challenge of enhancing inter-filament

bonding in Fused Filament Fabrication (FFF) 3D printing, where

conventional methods often result in suboptimal mechanical strength or structural distortions. A novel local preheating technique

was introduced using a laser-based

approach to precisely control the temperature of previously deposited filaments. An

experimental setup integrating a 3D printer with a 30 W diode laser and an optical system was developed to evaluate key parameters,

including laser power, PLA color effects on laser absorptivity, and the relationship between preheating temperature and

bonding strength. Results demonstrate that preheating to 180°C increases mechanical bonding strength by up to 35% without

distortion, highlighting the technique's potential to improve FFF part quality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{su_3659,

title = {Bridging uncertainty: A data-driven DRO approach for correcting censored demand in newsvendor problems},

author = {Xiaoli SU and Zhe Yuan and Chenghu Yang and Evren Sahin and Jie Xiong},

url = {http://dx.doi.org/10.1016/j.ijpe.2025.109626},

year  = {2025},

date = {2025-07-01},

journal = {International Journal Of Production Economics},

volume = {285},

pages = {109626},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{peng_3810,

title = {Multi-view learning with graph convolution networks adopting diverse graphs and genuine deep feature fusion},

author = {Guowen Peng and Fadi Dornaika and Jinan Charafeddine},

url = {http://dx.doi.org/10.1007/s10462-025-11301-y},

year  = {2025},

date = {2025-07-01},

journal = {Artificial Intelligence Review},

volume = {58},

pages = {290},

abstract = {Multi-view data significantly enhances the accuracy of machine learning algorithms by providing a comprehensive representation of object features. Despite their potential, research on the use of Graph Convolutional Networks (GCNs) for processing node connectivity and data features remains limited. Existing methods mainly focus on weighted summation of graph matrices, with only a few approaches effectively integrating the feature information into the graph structures. To overcome these limitations, this paper proposes a novel deep learning architecture: the Feature Fusion and Multi-Graph Fusion Learning Framework (MGCN-FN). The framework consists of two core modules: Feature Fusion Network (FFN): Designed to extract and consolidate key features from multiple views. Multi-Graph Fusion Network (MGFN): Constructs multiple graphs for each view and jointly optimizes both the graph weights and the GCN model. Extensive experiments on various multi-view datasets show that MGCN-FN achieves superior performance compared to state-of-the-art methods, especially on semi-supervised multi-view classification tasks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chevalier_3811,

title = {On security notions for encryption in a quantum world},

author = {Céline Chevalier and Ehsan Ebrahimi and Quoc Huy Vu},

url = {http://dx.doi.org/10.1007/s10623-025-01664-2},

year  = {2025},

date = {2025-07-01},

journal = {Designs Codes And Cryptography},

abstract = {Indistinguishability against adaptive chosen-ciphertext attacks (IND-CCA2) is usually considered the most desirable security notion for classical encryption. In this work, we investigate its adaptation in the quantum world, when an adversary can perform superposition queries. The security of quantum-secure classical encryption has first been studied by Boneh and Zhandry (CRYPTO'13), but they restricted the adversary to classical challenge queries, which makes the indistinguishability only hold for classical messages (IND-qCCA2). We extend their work by giving the first security notions for fully quantum indistinguishability under quantum adaptive chosen-ciphertext attacks, where the indistinguishability holds for superposition of plaintexts (qIND-qCCA2).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jingjun_3817,

title = {Linear projection fused graph-based semi-supervised learning on multi-view data},

author = {Bi Jingjun and Fadi Dornaika and Jinan Charafeddine},

url = {http://dx.doi.org/10.1007/s10462-025-11313-8},

year  = {2025},

date = {2025-07-01},

journal = {Artificial Intelligence Review},

volume = {58},

pages = {309},

abstract = {In recent years, the surge in data-driven applications across various domains has spurred heightened interest in semi-supervised learning applied to graphs. This surge is attributed to the ubiquitous presence of graph data structures in real-world contexts, such as social networks' interpersonal relationships, recommender systems' user behavior graphs, and bioinformatics' molecular interaction networks. However, for certain data types like images, not only is there a dearth of explicit graph structure, but also the existence of multiple view description methods complicates matters further. The intricacies of multi-view data pose challenges in directly applying traditional semi-supervised learning techniques to graphs. Consequently, researchers have begun exploring the fusion of semi-supervised learning with deep learning to leverage its wealth of information and enhance model efficacy. Effectively amalgamating graph structures with multi-view data remains a challenging problem necessitating further research. This paper introduces the Linear projection Fused Graph-based Semi-supervised Classification (LFGSC) method tailored for multi-view data, building upon the Graph Convolutional Network (GCN) architecture. Firstly, for each view, we leverage a semi-supervised approach that provides the concurrent estimation of the corresponding graph and the flexible linear data representations in a low-dimensional feature space. Subsequently, an adaptive and unified graph is generated, followed by the utilization of a fully connected network to fuse the projected features further and reduce dimensionality. Finally, the fused features and graph are inputted into a GCN to conduct semi-supervised classification. During training, the model incorporates cross-entropy loss, manifold regularization loss, graph auto-encoder loss, and supervised contrastive loss. Leveraging linear transformation significantly diminishes the input feature dimensions for GCN, thereby achieving high accuracy while substantially reducing computational overhead. Furthermore, experimental results conducted on various bench-marked multi-view image datasets demonstrate the superiority of LFGSC over existing semi-supervised learning methods for multi-view scenarios. (Source code: https://github.com/BiJingjun/LFGSC.)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{peng_3611,

title = {CGCN-FMF:1D convolutional neural network based feature fusion and multi graph fusion for semi-supervised learning},

author = {Guowe Peng and Fadi Dornaika and Jinan Charafeddine},

url = {http://dx.doi.org/10.1016/j.eswa.2025.127194},

year  = {2025},

date = {2025-06-01},

journal = {Expert Systems With Applications},

volume = {277},

pages = {127194},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dornaika_3790,

title = {Deep Feature Disentanglement for Supervised Contrastive Learning: Application to Image Classification},

author = {Fadi Dornaika and B Wang and Jinan Charafeddine},

url = {https://link.springer.com/article/10.1007/s12559-025-10430-4},

year  = {2025},

date = {2025-06-01},

journal = {Cognitive Computation},

volume = {17},

pages = {117},

abstract = {In machine learning, deep metric learning from original data is essential, with supervised contrastive learning being a notable approach. This method aims to form a deep feature space where similar samples from the same class are clustered together, while dissimilar samples from different classes are separated. However, a common limitation of contrastive learning methods is that they utilize the entire feature space for data embedding and often neglect the within-class variability. To overcome this limitation, we propose a novel supervised contrastive learning method that decomposes deep features into two distinct components: common features, which encapsulate the essential, class-defining characteristics, and style features, which capture the within-class variability and nuanced differences. Additionally, we enhance this approach by introducing an overlapping field that synergistically integrates elements from both feature spaces, enabling a more comprehensive and robust feature representation. Our experiments with different image datasets and deep encoders, including CNNs and transformers, show that our approach outperforms traditional single-feature contrastive methods. On the CIFAR100 and PASCAL VOC databases, traditional supervised contrastive learning achieved accuracy rates of 75.5% and 51.41%, respectively, while our method improved them to 77.81% and 59.38%, respectively. We present an algorithm for deep contrastive learning that utilizes two feature spaces: one for encoding common class features and another for capturing within-class variability. This is achieved by partitioning the features of the last layer of the encoder into (i) a common field and (ii) a style field. Our loss function contrasts the common features while summarizing the style features within the same class so that the style field can capture the intra-class variability.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jabbar_al-allaq_3796,

title = {A Comprehensive Review of Cutting-Edge Disaster Response: UAVs Equipped with FSO-Based Communications},

author = {Zaid Jabbar Al-Allaq and Wafaa Shakir and Jinan Charafeddine},

url = {https://link.springer.com/article/10.1007/s11277-025-11782-8#citeas},

year  = {2025},

date = {2025-06-01},

journal = {Wireless Personal Communications},

volume = {141},

pages = {29-394},

abstract = {Unmanned aerial vehicles (UAVs) equipped with free-space optical (FSO) communications have emerged as pivotal technological assets in disaster management. Traditional communication infrastructures often fail during severe disaster scenarios, resulting in delayed or ineffective emergency response efforts. Consequently, the integration of unmanned aerial vehicles with free-space optical communication technologies has emerged as an essential innovation in disaster response management. UAVs provide agile and rapidly deployable platforms, while FSO technology offers high-bandwidth, secure, and interference-resistant communication links. This comprehensive review systematically explores the current state and advanced integration strategies of UAVs equipped with FSO communication in disaster scenarios. Initially, the paper establishes the research context, identifying challenges associated with conventional disaster communications. Subsequently, the necessity for this review is articulated through a critical examination of the limitations faced by existing terrestrial systems. The paper clearly delineates various aspects of UAV-FSO integration by providing essential background information on disasters and conventional emergency communication systems, elaborating on the roles, benefits, and applications of UAVs specifically in disaster management contexts, and introducing FSO technology with its operational principles and advantages. Furthermore, it critically examines existing challenges and technical limitations of UAV-based FSO systems, including atmospheric disturbances and alignment issues. Recent technological advancements and mitigation strategies, such as intelligent reflecting surfaces, quantum key distribution, and machine learning integration, are discussed to enhance system reliability and performance. Detailed performance metrics such as bit error rate (BER) and outage probability are analyzed for evaluating UAV-FSO systems. Finally, the review addresses the latest contributions and innovations in the field, emphasizing future research directions and open challenges, and concludes by summarizing the transformative potential of integrating UAVs with FSO communications, advocating for continued research and practical application to improve disaster response capabilities globally.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dornaika_3401,

title = {Semi-supervised learning for multi-view and non-graph data using Graph Convolutional Networks},

author = {Fadi Dornaika and Jinan Charafeddine and Huaiyuan Xiao and Jingjun Bi},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0893608025000978?via%3Dihub},

year  = {2025},

date = {2025-05-01},

journal = {Neural Networks},

volume = {185},

pages = {107218},

abstract = {Semi-supervised learning with a graph-based approach has become increasingly popular in machine learning, particularly when dealing with situations where labeling data is a costly process. Graph Convolution Networks (GCNs) have been widely employed in semi-supervised learning, primarily on graph-structured data like citations and social networks. However, there exists a significant gap in applying these methods to non-graph multi-view data, such as collections of images. To bridge this gap, we introduce a novel deep semi-supervised multi-view classification model tailored specifically for non-graph data. This model independently reconstructs individual graphs using a powerful semi-supervised approach and subsequently merges them adaptively into a unified consensus graph. The consensus graph feeds into a unified GCN framework incorporating a label smoothing constraint.

To assess the efficacy of the proposed model, experiments were conducted across seven multi-view image datasets. Results demonstrate that this model excels in both the graph generation and semi-supervised classification phases, consistently outperforming classical GCNs and other existing semi-supervised multi-view classification approaches. 1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dornaika_3653,

title = {One-phase multi-view clustering with unified graph and data representation convolution},

author = {Fadi Dornaika and Jinan Charafeddine},

url = {http://dx.doi.org/10.1007/s00500-025-10533-y},

year  = {2025},

date = {2025-05-01},

journal = {Soft Computing},

volume = {29},

pages = {4335-4356},

abstract = {The goal of multi-view clustering is to partition unlabeled objects into disjoint clusters or groups using consistent and complementary information provided by the different features of the same object. Most existing methods perform this clustering task sequentially: Computation of the individual or consistent graph matrices, spectral embedding, and clustering. In this work, we present an approach that can overcome some of the limitations of previous multiview clustering methods. We introduce a single objective function whose minimization can jointly determine the consistent graph matrix for all views, the unified spectral data representation, the soft cluster indices, and the view weights. We present a constraint term that relates the cluster indices to the convolution of the consistent spectral data representations over the consistent graph. The method we present has two interesting features that are not simultaneously present in recent work. First, the cluster indices can be estimated directly without the need for an additional clustering step, which depends heavily on initialization. Second, the soft cluster indices are directly linked to the kernel representation of the features of the views. Moreover, our proposed method automatically determines the weights of each view, thus requiring fewer hyperparameters. A series of experiments have been conducted on real datasets. These demonstrate the efficiency of the proposed method, which compares favorably to many multi-view clustering methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arya_3724,

title = {AI-Enhanced Competency Transfer Hubs: A Conceptual Framework for University-Industry Engagement and Knowledge Sharing},

author = {Vikas ARYA and Ankita Saraf and Nikolai Chichkanov and Armando Papa and Marco Romano},

url = {https://link.springer.com/article/10.1007/s10961-025-10233-7},

year  = {2025},

date = {2025-05-01},

journal = {Journal Of Technology Transfer},

abstract = {This paper introduces a framework for AI-driven competency transfer hubs, designed to facilitate effective knowledge exchange and collaboration between universities and industries. These hubs leverage artificial intelligence technologies like machine learning and natural language processing to enhance the efficiency and effectiveness of information flows between academic institutions and industry partners, optimizing the whole knowledge-sharing process. Using the TCM-ADO framework the paper consolidates existing perspectives and offers practical suggestions on how to incorporate AI technologies into competency hubs. The discussion further delves into outlining key layers of such hubs including AI-powered knowledge extraction and enrichment, knowledge customization, adaptive project management as well as collaboration outcome enhancement and feedback optimization. A set of key elements for AI-enhanced competency transfer hubs was also developed and presented including the issues of technical alignment, advanced AI integration as well as value aspects. The study wraps up by exploring key areas of application in the establishment of AI-enhanced competency transfer hubs and their wider societal significance.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}

@article{zhu_3394,

title = {CarNet: A generative convolutional neural network-based line-of-sight/non-line-of-sight classifier for global navigation satellite systems by transforming multivariate time-series data into images},

author = {Ni ZHU and Ruiwen He and Zhiqiang Wang},

url = {https://www.sciencedirect.com/science/article/pii/S0952197625001605?via%3Dihub},

year  = {2025},

date = {2025-04-01},

journal = {Engineering Applications Of Artificial Intelligence},

volume = {145},

pages = {110160},

abstract = {Urban environments present significant challenges to commercial Global Navigation Satellite Systems (GNSS) receivers due to degraded satellite visibility and Non-line-of-sight (NLOS) receptions. Mitigating NLOS receptions for GNSS is essential, especially for safety-critical and reliability-critical location-based applications. Traditional physical error channel propagation modeling encountered bottlenecks since the NLOS and multipath errors cannot be modeled accurately in complex urban environments. Data-driven methods show significant potential for effectively classifying GNSS Line-of-sight (LOS) and NLOS. This paper proposes the CarNet - a generative Convolutional Neural Network (CNN)-based GNSS LOS/NLOS classifier by transforming multivariate time-series data into images. CarNet comprises two modules: an image generator and an image classifier. The image generator enriches and augments the original 1-dimension feature vector into 2-dimension feature maps and the image classifier uses an inception-based CNN to realize multi-scale feature extraction and classification. The proposed architecture is trained and tested on more than 6 h of real vehicle data collected in different challenging environments (about 1.6 million samples). A thorough benchmark is conducted, comparing CarNet against the existing mainstream Artificial Intelligence (AI) methods. The results with cross-validation on unseen data indicate that CarNet achieves the highest accuracy, i.e., 81.47% while maintaining the optimal balance between precision for both classes: 83.3% for LOS and 70.99% for NLOS. Finally, positioning accuracy is assessed using a reweighting strategy based on the LOS/NLOS information predicted by CarNet. The assessment of total datasets shows that CarNet weighting can achieve the best accuracy compared to the traditional weighting schemes based on signal-to-noise ratio or satellite elevation. CarNet shows strong potential for embedding into GNSS receivers to enhance positioning accuracy in complex urban environments, benefiting a wide range of location-based applications such as autonomous driving, emergency response, and urban logistics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{schittecatte_3413,

title = {Dynamic mechanical analysis (DMA) of photopolymers: A new protocol for 3D-printed materials},

author = {Laura Schittecatte and Daniel Bonamy and Patrick Guenoun and Antoine Liénard and Thuy Nguyen and Valérie Geertsen},

url = {http://dx.doi.org/10.1557/s43579-025-00694-0},

year  = {2025},

date = {2025-04-01},

journal = {Mrs Communications},

volume = {15},

pages = {197-204},

abstract = {This work provides a complete methodology to measure the storage modulus of vat 3D?printed polymers via Dynamic Mechanical Analysis (DMA) which could enable real intercomparison of printing materials mechanical performance. We propose an 8?step methodology that includes the determination

of the linear viscoelastic region (LVER) and the glass transition temperature (T?), as well as a stress relief annealing post?treatment that is proven to

significantly reduce the dispersion of 3D?printed samples. The methodology applied to various 3D?printed resin samples, achieving repeatability and reproducibility values comparable to those of a reference molded PMMA},

note = {of the linear viscoelastic region (LVER) and the glass transition temperature (T?), as well as a stress relief annealing post?treatment that is proven to

significantly reduce the dispersion of 3D?printed samples. The methodology applied to various 3D?printed resin samples, achieving repeatability and reproducibility values comparable to those of a reference molded PMMA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sebban_3516,

title = {Improving visual perception through technology: a comparative analysis of real-time visual aid systems},

author = {Othmane Sebban and Ahmed Azough and Mohamed Lamrini},

url = {https://telkomnika.uad.ac.id/index.php/TELKOMNIKA/article/view/26455},

year  = {2025},

date = {2025-04-01},

journal = {TELKOMNIKA Telecommunication, Computing, Electronics and Control},

volume = {23},

number = {2},

pages = {349-370},

abstract = {Visually impaired individuals continue to face barriers in accessing reading and listening resources. To address these challenges, we present a comparative analysis of cutting-edge technological solutions designed to assist people with visual impairments by providing relevant feedback and effective support. Our study examines various models leveraging InceptionV3 and V4 architectures, long short-term memory (LSTM) and gated recurrent unit (GRU) decoders, and datasets such as Microsoft Common Objects in Context (MSCOCO) 2017. Additionally, we explore the integration of optical character recognition (OCR), translation tools, and image detection techniques, including scale-invariant feature transform (SIFT), speeded-up robust features (SURF), oriented FAST and rotated BRIEF (ORB), and binary robust invariant scalable keypoints (BRISK). Through this analysis, we highlight the advancements and potential of assistive technologies. To assess these solutions, we have implemented a rigorous benchmarking framework evaluating accuracy, usability, response time, robustness, and generalizability. Furthermore, we investigate mobile integration strategies for real-time practical applications. As part of this effort, we have developed a mobile application incorporating features such as automatic captioning, OCR based text recognition, translation, and text-to-audio conversion, enhancing the daily experiences of visually impaired users. Our research focuses on system efficiency, user accessibility, and potential improvements, paving the way for future innovations in assistive technology.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{boubakri_3013,

title = {Virtual Reality Classrooms vs. Video Conferencing Platforms: Initial Design and Evaluation Study for Collaborative Distance Learning},

author = {Fatima Ezzahra Boubakri and Mohammed Kadri and Fatima Zahra Kaghat and Ahmed Azough},

url = {https://link.springer.com/article/10.1007/s11042-024-19309-2},

year  = {2025},

date = {2025-03-01},

journal = {Multimedia Tools And Applications},

volume = {84},

pages = {9505-9536},

abstract = {In recent years, remote work and communication via videoconferencing tools have become increasingly prevalent across various sectors, including education, where students and teachers use these platforms to attend virtual classes. Although videoconferencing offers advantages such as convenience and flexibility, it also has limitations. For instance, remote group work and practical activities may pose challenges, and students may experience decreased motivation to learn due to a lack of social interaction and support provided in traditional classrooms. To address these limitations, this study proposes ?V-Class,? an immersive multi-user virtual reality learning system that combines distance learning with traditional learning by immersing students in a virtual classroom and facilitating collaboration and hands-on activities. This study evaluates the effectiveness of our system ?V-Class? using both subjective and objective measurements to examine variables like presence, performance, collaboration, and engagement. It is compared to Zoom, a popular videoconferencing tool, with 50 participants. V-Class outperformed Zoom in all dimensions, indicating its potential as a superior solution for enhancing distance learning over videoconferencing tools.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{latukha_3185,

title = {Adaptive Leadership for Multilevel Resilience in the Context of Disruptions},

author = {Marina Latukha and Polina Yakovleva and Kaifeng Yan},

url = {https://onlinelibrary.wiley.com/doi/10.1002/tie.22416},

year  = {2025},

date = {2025-03-01},

journal = {Thunderbird International Business Review},

volume = {67},

number = {2},

pages = {149-167},

abstract = {This paper examines the relationship between adaptive leadership and three levels of resilience: individual, team, and organizational. Developing multilevel resilience enables organizations to better anticipate conflicts and respond to environmental disruptions. Through correlation and regression analysis based on 148 respondents from companies in Russia, we build the relationship between adaptive leadership and three levels of resilience, unpacking the new role of leadership in shaping managerial responses to contextual change. The results suggest that adaptive leadership can positively influence all three levels of resilience, with adaptive leadership having the most significant impact on team resilience, then organizational resilience, leaving employee resilience behind. Such findings contribute to a deeper understanding of how exactly managerial mechanisms should be redesigned to overcome disruptions and develop leadership capabilities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arya_3279,

title = {Using Augmented Reality to Strengthen Consumer/Brand Relationships: The Case of Luxury Brands},

author = {Vikas ARYA and Deepa SETHI and Linda D. HOLLEBEEK},

url = {https://onlinelibrary.wiley.com/doi/10.1002/cb.2419},

year  = {2025},

date = {2025-03-01},

journal = {Journal Of Consumer Behaviour},

volume = {24},

number = {2},

pages = {545-561},

abstract = {Though augmented reality (AR) is increasingly adopted in marketing, its capacity to foster consumers' engagement and attachment remain tenuous, exposing an important literature-based gap. Addressing this gap, we deploy social presence theory and luxury consumption theory to develop and test a model that proposes that consumers' engagement with AR-deploying luxury brands drives the development of their perceived brand warmth, social value, and brand competence, in turn impacting their brand attachment. To explore these issues, we draw on survey data from a sample of 537 luxury apparel and automotive consumers. The results using structural equation modelling (SEM) show that first, luxury consumers who exhibit high engagement with the AR-deploying brand perceive higher levels of brand warmth, -competence, and social value, in turn raising their attachment to the AR-deploying luxury brand. Overall, the findings highlight AR's strategic capacity to engage consumers and raise the brand's perceived brand warmth, competence, and social value, in turn boosting individuals' attachment to the AR-deploying brand.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dornaika_3466,

title = {Leveraging Graph Convolutional Networks for Semi-supervised Learning in Multi-view Non-graph Data},

author = {Fadi Dornaika and Jingjun Bi and Jinan Charafeddine},

url = {http://dx.doi.org/10.1007/s12559-025-10428-y},

year  = {2025},

date = {2025-03-01},

journal = {Cognitive Computation},

volume = {17},

pages = {73},

abstract = {Semi-supervised learning with a graph-based approach has gained prominence in machine learning, particularly in scenarios

where labeling data involves substantial costs. Graph convolution networks (GCNs) have found widespread application in

semi-supervised learning, predominantly on graph-structured data such as citation and social networks. However, a noticeable

gap exists in the application of these methods to non-graph multi-view data, such as collections of images. In an effort to

address this gap, we introduce two innovative deep semi-supervised multi-view classification models specifically tailored for

non-graph data. Both models share a common architecture, leveraging GCNs and integrating a label smoothing constraint.

The primary distinction lies in the construction of the consensus similarity graph. The first model directly reconstructs the

consensus graph from different views using a specialized objective function designed for flexible graph-based semi-supervised

classification. In contrast, the second model independently reconstructs individual graphs and subsequently adaptively merges

them into a unified consensus graph. Our experiments encompass various multiple-view image datasets. The results consis-

tently demonstrate the superior performance of our proposed approach compared to traditional fusion methods with GCNs. In

this research, we present two approaches for tackling semi-supervised classification challenges involving multiple views. One

method is named Semi-supervised Classification with a Unified Graph (SCUG), and the other is referred to as Semi-supervised

Classification with a Fused Graph (SC-Fused). Both methods share a common semi-supervised classification process, utilizing

the GCN framework and incorporating label smoothing. However, the key distinction lies in the construction of the similarity

graph. Unlike traditional ad hoc graph construction approaches, our proposed methods, SCUG and SC-Fused, estimate the

unified graph or individual graphs, respectively, alongside the labels. This results in more optimized graphs that benefit from

data smoothing and the semi-supervised context.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zhang_3500,

title = {The Evolution of Complex Global Innovation Collaboration Network: A Multilevel Analysis of a CoPS Industry 2001-2020},

author = {Beibei ZHANG and Ximing Yin and Jie Xiong and Zhe Yuan},

url = {https://ieeexplore.ieee.org/document/10916686/authors},

year  = {2025},

date = {2025-03-01},

journal = {Ieee Transactions On Engineering Management},

volume = {72},

pages = {1039-1051},

abstract = {Global innovation collaboration networks (GICNs) constitute a fundamental component of the innovation ecosystem and significantly contribute to the advancement of complex products and systems (CoPS) in the domain of engineering management. Despite their significance, existing research has not adequately captured the intricate dynamics of GICNs, particularly from an evolutionary perspective. This study seeks to fill this gap by employing a holistic approach to analyze the complexity and evolutionary characteristics of a representative CoPS industry, across macro-network, meso-module, and micro-motif levels. Utilizing global patent data from 2001 to 2020 pertaining to chip manufacturing, our analysis identifies distinct collaborative patterns across the macro, meso, and micro levels within GICNs. At the macro level, there is a discernible shift towards a distributed structure, epitomized by a ?big center, multi-centers, decentralization? trend. The meso level exhibits a progression towards a ?loosely coupled? configuration of technical sub-modules, reflecting a specialized division of labor. Notably, the micro level demonstrates a significant centralization in collaborative innovation, with enterprises playing a pivotal role. This investigation provides an exhaustive empirical examination of GICNs within the CoPS industry and offers novel insights into the evolution of such networks. Furthermore, it furnishes actionable recommendations for policymakers and engineering managers seeking to navigate the complexities of CoPS innovation, thus providing a strategic roadmap for collaboration and innovation management.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{shakir_3613,

title = {Empowering MIMO-FSO Systems: RIS Technology for Enhanced Performance in Challenging Conditions},

author = {Wafaa Shakir and Jinan Charafeddine},

url = {https://ieeexplore.ieee.org/document/10937202},

year  = {2025},

date = {2025-03-01},

volume = {28},

number = {6},

pages = {1 - 1},

abstract = {This paper presents a novel analytical framework to enhance the performance of reconfigurable intelligent surfaces (RIS)-integrated multiple-input-multiple-output (MIMO) free-space optical (FSO) communication systems. The study addresses critical challenges such as atmospheric turbulence, misalignment, and signal attenuation. It introduces a series-based approach to model the combined effects of Gamma-Gamma turbulence, generalized Rician pointing errors, and RIS size-related constraints. In contrast to previous studies, which often rely on oversimplified or idealized channel models, this framework provides closed-form expressions for the first time for the probability density function and cumulative distribution function of the end-to-end channel specifically designed for RIS-empowered (RIS-E) MIMO-FSO systems. These expressions capture the complex interactions between channel impairments and system parameters, enabling accurate performance evaluation in real-world deployments. The derived formulations provide key performance metrics, including outage probability, average bit error rate, ergodic capacity, data rate, and energy efficiency, for a variety of system configurations. Practical diversity combining techniques such as equal gain combining, maximal ratio combining, and selection combining are rigorously analyzed. In addition, asymptotic analyses at high signal-to-noise ratios offer simplified expressions that provide valuable insights into coding gain, diversity order, and system behavior under extreme conditions. A key contribution of this work is the investigation of the optimization of RIS placement, which improves signal alignment and reduces the outage probability, even under challenging atmospheric conditions. In addition, the study highlights the computational efficiency of the proposed framework through a detailed complexity analysis that confirms its feasibility for practical, large-scale applications. Monte Carlo simulations validate the theoretical findings, demonstrating strong agreement with the analytical results. These results confirm the transformative potential of RIS technology in mitigating turbulence-induced fading and misalignment. This research establishes RIS-E MIMO-FSO systems as a robust, energy-efficient solution for next-generation, high-bandwidth optical networks. Additionally, it provides practical deployment guidelines, ensuring the effective integration of RIS technology into real-world wireless communication infrastructures, thereby advancing the development of resilient and high-performance optical communication systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{moradi_3630,

title = {Optimized bioactive glass/PLA nanocomposites for bone tissue engineering: balancing mechanical strength and biodegradability},

author = {Razie Moradi and Majid Vaseghi and Majid Sohrabian and Hamidreza Vanaei},

url = {https://doi.org/10.1080/00914037.2025.2477164},

year  = {2025},

date = {2025-03-01},

journal = {International Journal Of Polymeric Materials And Polymeric Biomaterials},

volume = {1},

number = {1},

pages = {1-14},

abstract = {Bioactive glass (BG)/polylactic acid (PLA) nanocomposites have gained significant attention for bone repair applications due to their mechanical and biological properties. In this study, sol-gel-derived bioactive glass nanoparticles (nBG) with a mean diameter of 53.38?nm were surface-modified with (3-Aminopropyl) triethoxysilane (APTES) to ensure uniform dispersion within the PLA matrix. Composite materials containing 0, 2, 8, and 16?wt% of surface-modified bioactive glass nanoparticles (m-nBG) were synthesized using a solvent-evaporation method. The composites' mechanical properties (tensile and flexural), degradation rate, hydrophilicity, and cellular responses were evaluated. The addition of 2?wt% m-nBG significantly improved the mechanical performance, achieving a tensile strength of 37.14?MPa and a flexural strength of 72.2?MPa. However, increasing the filler content beyond this threshold resulted in agglomeration, leading to a reduction in mechanical strength, though the tensile modulus continued to rise with higher nanoparticle loading. Biodegradation tests showed that m-nBG fillers accelerated the degradation process, with higher filler concentrations further promoting matrix degradation and increasing water absorption. The 2?wt% m-nBG nanocomposite exhibited optimal cell viability, proliferation, and attachment, highlighting its potential for bone tissue engineering applications. These findings suggest that a balanced nanoparticle content enhances both mechanical integrity and biological activity in PLA-based composites.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}

@article{ishii_3675,

title = {TeleAbsence: A Vision of Past and Afterlife Telepresence},

author = {Hiroshi Ishii and Daniel Pillis and Pat Pataranutaporn and Xiao Xiao and Hayoun Noh and Lucy Li and Alaa Algargoosh and Jean-Baptiste Labrune},

url = {https://doi.org/10.1162/PRES_a_00441},

year  = {2025},

date = {2025-03-01},

journal = {Presence-Virtual And Augmented Reality},

volume = {34},

pages = {65-95},

abstract = {This paper presents our vision of TeleAbsence, extending the concept of telepresence to the past and the afterlife to address the vast emotional and temporal distance caused by the memory of loved ones who drifted apart and faded away. Instead of explicit and literal representations of loved ones, TeleAbsence describes poetic encounters with digital and physical traces left by the absence of others. TeleAbsence fosters illusory communications to conjure the feeling of being there with those no longer with us without using synthetic or generative representations and utterances. Our vision is deeply inspired by the Portuguese concept ?Saudade??the ?desire for the beloved thing, people, place, and moment, made painful by its absence.? We present our vision through five design principles: presence of absence, illusory communication, the materiality of memory, traces of reflection, and remote time, grounded in historical and cultural contexts. We present exploratory narratives to illustrate these principles and the concept of ambient co-presence using poetry, phone, piano, and pen as mediums. We discuss challenges and opportunities for future work, including representational strategies to depict lost loved ones, ethical issues, and the possible extension of TeleAbsence to historical public figures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sebban_3242,

title = {SeeAround: an offline mobile live support system for the visually impaired},

author = {Othmane Sebban and Ahmed Azough and Mohamed Lamrini},

url = {https://www.beei.org/index.php/EEI/article/view/7904},

year  = {2025},

date = {2025-02-01},

journal = {Bulletin of Electrical Engineering and Informatics},

volume = {14},

number = {1},

pages = {485-504},

abstract = {The inability of blind or partially-sighted people to understand visual content and real-life situations reduces their standard of living, especially in a world mainly tailored for sighted individuals. Despite the progress made by certain devices to assist them in using touch, sound, or other senses, these solutions often fall short of bridging the comprehension gap. Our work proposes an intuitive, user-friendly mobile-based framework named "SeeAround" that is capable of automatically providing real-time audio descriptions of the user's immediate visual surroundings. Our solution addresses this challenge by leveraging key point detection, image captioning, text-to-speech (TTS), optical character recognition (OCR), and translation algorithms to offer comprehensive support for visually impaired individuals. Our system architecture relies on convolutional neural networks (CNNs) such as Inception-V3, Inception-V4, and ResNet152-V2 to extract detailed features from images and employs a multi-gated recurrent unit (GRU) decoder to generate word-by-word natural language descriptions. Our framework was integrated into mobile applications and optimized with TensorFlow lite pre-trained models for easy integration on the Android platform.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{khachai_3298,

title = {Discrete cutting path problems: a general solution framework and industrial applications},

author = {Daniil KHACHAI and Olga Battaia and Alexander Petunin and Michael Khachay},

url = {https://www.tandfonline.com/doi/full/10.1080/00207543.2024.2365360},

year  = {2025},

date = {2025-02-01},

journal = {International Journal Of Production Research},

volume = {63},

number = {3},

pages = {949-969},

abstract = {The optimal tool routing for cutting machines, also known as cutting path optimisation is an important problem in production research. This problem is relevant in various manufacturing environments such as aeronautic, automotive, garment and semiconductor industries. In this paper, we introduce a general solution framework for the discrete Cutting Path Problem which includes: (i) the universal approach to reduce numerous settings of this problem to the appropriate auxiliary instances of the well-known Precedence Constrained Generalized Traveling Salesman Problem; (ii) the proposition of efficient solution methods for finding (sub-) optimal solutions. We carry out extensive computational experiments in order to evaluate performance of the proposed framework and the obtained results demonstrate its efficiency for real-life industrial instances.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lebon_3330,

title = {Metrics for prosthetic cervical margin integrity assessment after dental CAD/CAM milling: a critical analysis from engineering viewpoint},

author = {Nicolas Lebon and Laurent Tapie},

url = {https://link.springer.com/journal/10266},

year  = {2025},

date = {2025-02-01},

journal = {Odontology},

pages = {1-12},

abstract = {Dental prostheses have significantly evolved due to advances in Computer Aided Design and Manufacturing (CAD/CAM) technology. CAD/CAM systems provide a variety of biomaterials like ceramics, Polymer-Infiltrated Ceramic Network (PICN), and composites, which are preferred for their mechanical and aesthetic properties. However, ceramics, despite their popularity, are brittle and prone to chipping during the machining process, impacting the prosthesis's clinical functions, aesthetics, biological integrity, and mechanical performance.

Chipping, especially at thin cervical margin, can cause visible defects, poor sealing, and bacterial growth, reducing prosthesis lifespan. Milling factors influence cervical margin integrity.

Chipping assessment involves understanding biomaterial mechanical and machinability characteristics regarding dimensional characterization of milled prosthesis shape. Thus, different type of metrics, based on biomaterial properties or dimensional measurement can be used to assess chipping phenomenon for milled dental ceramics. These metrics are both, based on experimental studies found in literature, and proposed by this paper authors to fill the existing lacks.

The brittleness index, based on the ratio between hardness and fracture toughness, predicts susceptibility to chipping after milling. Unidirectional dimensional metrics like the Chipping Factor and weighted Chipping Factor characterize the chipping ratio of the cervical margin. Advanced 2D and 3D metrics, including chip projected area, total weighted chip projected area, and Surface Aspect Ratio, offer more detailed assessments. 3D analysis involves comparing scanned files with CAD models to compute chipped volumes. The aim of this paper is to propose a critical analysis from an engineering viewpoint on metrics used to assess cervical margin integrity for milled dental prosthesis.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}

@article{tharrault_3598,

title = {Optical Absorption in Indirect Semiconductor to Semimetal PtSe2},

author = {Marin Tharrault and Sabrine AYARI and Mehdi Arfaoui and EVA Desgue and Romaric Le Goff and Pascal Morfin and José Palomo and Michael Rosticher and Sihem Jaziri and Bernard Plaçais and Pierre Legagneux and Francesca Carosella and Christophe Voisin and Robson Ferreira and Emmanuel Baudin},

url = {https://doi.org/10.1103/PhysRevLett.134.066901},

year  = {2025},

date = {2025-02-01},

journal = {Physical Review Letters},

volume = {134},

number = {6},

pages = {066901},

abstract = {PtSe2 is a van der Waals material transitioning from an indirect band gap semiconductor to a semimetal with increasing thickness. Its absorption threshold has been conjectured to originate from interband indirect transitions. By quantitative comparison between broadband (0.8-3.0 eV) optical absorption of high-quality exfoliated crystals and DFT ab initio simulations, we prove instead that the optical absorption arises only from direct transitions. This understanding allows us to shed light on the semiconductor-to-semimetal transition in an emblematic strongly thickness-dependent 2D material, and to explore the effect of stacking and excitons on the optical absorption.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{wang_3132,

title = {Determining the equilibrium efficient frontier by proportional frontier shifting for data envelopment analysis with fixed-sum outputs},

author = {Weijiao Wang and Fei Xu and Junfei Chu and Yanhua Dong and Zhe Yuan},

url = {https://www.sciencedirect.com/science/article/pii/S0305048324001397?via%3Dihub},

year  = {2025},

date = {2025-01-01},

journal = {Omega-International Journal Of Management Science},

volume = {130},

pages = {103174},

abstract = {The equilibrium efficient frontier data envelopment analysis (EEFDEA) has been extensively used to evaluate efficiencies of the decision-making units (DMUs) with fixed-sum outputs. This study develops a new EEFDEA approach based on a proportional frontier-shifting strategy. Our approach applies an iterative procedure to find the equilibrium efficient frontier (EEF). Each iteration uses a proportional frontier-shifting model to improve an inefficient DMU to the efficient frontier by increasing its fixed-sum outputs. Meanwhile, the DMUs on the efficient frontier decrease fixed-sum outputs proportionally to ensure the total fixed-sum outputs are unchanged. Our theoretical developments show that the proportional frontier-shifting strategy is feasible and can finally obtain a unique EEF. The new approach allows DMUs to use their preferred input and output weights when determining the EEF. This generates an EEF that better aligns with real-world practices and avoids the need to construct it as a single hyperplane, as required by conventional EEFDEA methods. It also avoids unfair adjustments in fixed-sum outputs among the DMUs and eliminates the problem of peculiar efficiency evaluation results (i.e., some DMUs obtain extremely high, or infinity, efficiencies). Finally, we apply our approach to a case study of Chinese vehicle industry companies to demonstrate its usefulness and compare it with the previous representative approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{can_3151,

title = {Institutional pressures and greenwashing in social responsibility: reversing the link with hybridization capability},

author = {Ozge Can and Duygu Türker},

url = {https://www.emerald.com/insight/content/doi/10.1108/MD-10-2023-1790/full/html},

year  = {2025},

date = {2025-01-01},

journal = {Management Decision},

volume = {63},

number = {1},

pages = {187-216.},

abstract = {Purpose



Despite the ongoing scholarly interest in greenwashing, it is not well known the impact of multiple institutional pressures on greenwashing in corporate social responsibility (CSR). Following the institutional logics perspective, this study investigates how three distinct logics - commercial, public, and social welfare - drive greenwashing and whether organizational capability for blending diverse CSR expectations reverses this link.

Design/methodology/approach



The current study conceptualized and tested an original model on how three institutional logics influence greenwashing in CSR, with the mediation effect of hybridization capability as a response to logic plurality. Partial least squares structural equation modeling was performed on a survey data, which was collected from 150 middle managers in Turkey.

Findings



The results show that while commercial logic has no direct or indirect impact on greenwashing, public and social welfare logics drive greenwashing in CSR. However, these effects are reversed when the CSR hybridization capability increases.

Practical implications



This study contributes to the understanding of what predicts CSR greenwashing by integrating a comprehensive theoretical framework involving multiple institutional logics, conflicting stakeholder demands, and organizational hybridity.

Originality/value



To the best of our knowledge, this is the first study that theoretically and empirically analyzed how the exposure of multiple external pressures affects the CSR greenwashing and how it can be reversed by CSR hybridization capability. This capability mitigates the threats and challenges of multiple logics and turns them into an opportunity to gain legitimacy in the eyes of stakeholders by preventing greenwashing.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dornaika_3269,

title = {Unified Multi-view Data Clustering: Simultaneous Learning of Consensus Coefficient Matrix and Similarity Graph},

author = {Fadi Dornaika and Sally El Hajjar and Jinan Charafeddine and Nagore Barrena},

url = {https://link.springer.com/article/10.1007/s12559-024-10392-z},

year  = {2025},

date = {2025-01-01},

journal = {Cognitive Computation},

volume = {17},

number = {38},

abstract = {Integrating data from multiple sources or views has become increasingly common in data analysis, particularly in fields

like healthcare, finance, and social sciences. However, clustering such multi-view data poses unique challenges due to the

heterogeneity and complexity of the data sources. Traditional clustering methods are often unable to effectively leverage the

information from different views, leading to suboptimal clustering results. To address this challenge, multi-view clustering

techniques have been developed, aiming to integrate information from multiple views to improve clustering performance.

These techniques typically involve learning a similarity matrix for each view and then combining these matrices to form

a consensus similarity matrix, which is subsequently used for clustering. However, existing approaches often suffer from

limitations such as the need for manual tuning of parameters and the inability to effectively capture the underlying structure

of the data. In this paper, we propose a novel approach for multi-view clustering that addresses these limitations by jointly

learning the consensus coefficient matrix and similarity graph. Unlike existing methods that follow a sequential approach

of first learning the coefficient matrix and then constructing the similarity graph, our approach simultaneously learns both

matrices, ensuring a more regularized consensus graph. Additionally, our method automatically adjusts the weight of each

view, eliminating the need for manual parameter tuning. Our approach involves several key steps. First, we formulate an

optimization problem that jointly optimizes the consensus coefficient matrix, unified spectral projection matrix, coefficient

matrix, and soft cluster assignment matrix. We then propose an efficient algorithm to solve this optimization problem, which

involves iteratively updating the matrices until convergence. To learn the consensus coefficient matrix and similarity graph, we

leverage techniques from matrix factorization and graph-based learning. Specifically, we use a self-representation technique

to learn the coefficient matrix (regularization graPh) and a graph regularization technique to learn the similarity graph. By

jointly optimizing these matrices, we ensure that the resulting consensus graph is more regularized and better captures the

underlying structure of the data. We evaluate our approach on several public image datasets, comparing it against state-of-the-

art multi-view clustering methods. Our experimental results demonstrate that our approach consistently outperforms existing

methods in terms of clustering accuracy and robustness. Additionally, we conduct sensitivity analysis to evaluate the impact

of different hyperparameters on the clustering performance. We present a novel approach for multi-view data clustering

that jointly learns the consensus coefficient matrix and similarity graph. By simultaneously optimizing these matrices, our

approach achieves better clustering performance compared to existing methods. Our results demonstrate the effectiveness and

robustness of our approach across different datasets, highlighting its potential for real-world applications in various domain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{abou_ali_3271,

title = {Enhancing Generalization and Mitigating Overfitting in Deep Learning for Brain Cancer Diagnosis from MRI},

author = {Mohamad Abou Ali and Jinan Charafeddine and Fadi Dornaika and Ignacio Arganda?Carreras},

url = {https://link.springer.com/article/10.1007/s00723-024-01743-y},

year  = {2025},

date = {2025-01-01},

journal = {Applied Magnetic Resonance},

abstract = {Brain cancer represents a significant global health challenge with increasing inci-

dence and mortality rates. Magnetic Resonance Imaging (MRI) plays a pivotal role

in early detection and treatment planning. This study adopts a systematic approach

across four phases: (1) Optimal Model Selection using the Adam optimizer, empha-

sizing accuracy metrics, weight computation, early stopping, and ReduceLROn-

Plateau techniques. (2) Real-world Scenario Simulation through synthetic per-

turbed datasets created by applying noise, blur (to simulate various magnetic field

strengths: 1T, 1.5T, 3T), and patient motion artifacts (mimicking MRI scanning

motion effects) to the testing data from the BT-MRI dataset, an online published

brain tumor MRI dataset. (3) Optimization involving a range of optimizers (Adam,

Adagrad, Nadam, RMSprop, SGD) and online augmentation techniques (AutoMix,

CutMix, LGCOAMix, PatchUp). (4) Solution Exploration integrating Gaussian

Noise and Blur as augmentation strategies during training to enhance model gener-

alization under diverse conditions. Initial evaluations achieved strong performance,

consistently reaching 99.45% accuracy on the BT-MRI dataset. However, testing

against synthetic perturbed datasets mimicking real-world conditions revealed chal-

lenges in maintaining robust model performance. Despite employing diverse opti-

mization methods and advanced augmentation techniques, this study identifies per-

sistent challenges in ensuring model robustness with synthetic perturbed datasets.

Notably, the integration of Gaussian Noise and Blur during training significantly

improved model resilience. This research underscores the critical role of method-

ological rigor and innovative augmentation strategies in advancing deep learning

applications for precise brain cancer diagnosis using MRI.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}

@article{kadri_3276,

title = {C-IVAL: A Longitudinal Study of Knowledge Retention and Technology Acceptance in Collaborative Virtual Reality-Based Medical Education},

author = {Mohammed Kadri and Fatima Ezzahra Boubakri and GWO-JEN HWANG and Fatima Zahra Kaghat and Ahmed Azough and Khalid Alaoui Zidani},

url = {https://ieeexplore.ieee.org/document/10817604},

year  = {2025},

date = {2025-01-01},

journal = {Ieee Access},

volume = {13},

pages = {16055-16071},

abstract = {Anatomy education faces challenges in providing engaging, interactive, and collaborative learning experiences, particularly in understanding complex three-dimensional structures and maintaining long-term knowledge retention. Although virtual reality (VR) has shown promise in addressing spatial comprehension challenges, questions remain regarding its effectiveness in supporting collaborative learning and sustained knowledge retention. This longitudinal study examined Collaborative Immersive Virtual Anatomy Laboratory (C-IVAL), an innovative VR platform designed to enhance traditional anatomy learning through integrated collaborative features, immersive technology, and serious game elements. We conducted an experimental study with 65 medical students to evaluate their knowledge acquisition and technology acceptance compared to its non-collaborative predecessor, the Immersive Virtual Anatomy Laboratory (IVAL). Our evaluation framework combined quantitative assessments (knowledge tests, comprising pre-test, immediate post-test, and delayed post-test) with Technology Acceptance Model (TAM) analysis. Knowledge assessment revealed significant cognitive improvements, with mean knowledge scores increasing from 2.48 to 3.94 in the immediate post-tests, while long-term retention of anatomy knowledge showed sustained engagement for over two months. Importantly, C-IVAL demonstrated significantly higher scores across all TAM dimensions than the non-collaborative IVAL system, particularly for perceived usefulness and intention to use. Post-session feedback analysis showed 73.8% positive responses, highlighting enhanced social presence, immersive engagement, and effective collaboration, with 26.2% of constructive feedback focusing on system refinement and feature enhancement. This study contributes to the understanding of the effectiveness of collaborative features in virtual reality education by offering insights into designing and implementing virtual learning environments that e...},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hentati_3277,

title = {Experimental study on the temperature effect and constitutive modeling of the tensile response of PC/ABS blend},

author = {FATMA HENTATI and RIDHA MNIF and Naila Hfaiedh and Johann Petit},

url = {http://dx.doi.org/10.1007/s00289-024-05631-0},

year  = {2025},

date = {2025-01-01},

journal = {Polymer Bulletin},

abstract = {This study investigates the tensile properties of the PC/ABS blend under both small

and large strains using experimental analysis and predictive analytical models. The

influence of temperature and strain rate on the tensile response were evaluated, with

strain rates reaching from 1.25 × 10?

4 to 1.25 × 10?

1 s?

1 and temperatures ranging

from 20 to 150 °C. The experimental results indicate that the tensile behavior of

the material blend exhibits sensitivity to both strain rate and temperature. As the

temperature rises, the yield strength and strain at failure decrease significantly;

while, the young's modulus only slightly decreases. Among several constitutive

models studied, including the G'sell and Jonas and Duan-Saigal-Greif-Zimmerman

(DSGZ) models, a modified version of the DSGZ model, referred to as the

?Zhu et al.? model. However, this model struggled to accurately represent material

behavior at elevated temperatures. To address this limitation, a new model named

Hentati-Mnif-Hfaiedh-Petit (HMHP) was developed by introducing a temperature

dependence into two key parameters of the ?Zhu et al.? model. This improvement

enabled the HMHP model to more accurately predict tensile behavior across a wide

temperature range. The results confirm that the new developed model, HMHP provides

a reliable prediction of tensile properties at different temperatures.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}

@article{chu_3285,

title = {Balancing contributions and rewards: a DEA approach for fair carbon emission abatement allocation},

author = {Junfei Chu and Yanhua Dong and Zhe Yuan and Fangqing Wei},

url = {http://dx.doi.org/10.1080/01605682.2024.2449470},

year  = {2025},

date = {2025-01-01},

journal = {Journal Of The Operational Research Society},

pages = {1-16},

abstract = {Fairness is imperative in implementing carbon emission abatement (CEA) allocation schemes. This study introduces a new data envelopment analysis (DEA) methodology for the fair distribution of CEA among decision-making units (DMUs), taking into account their individual fairness. First, we establish a model to determine the maximum CEA potential for each DMU. Subsequently, an environmental efficiency evaluation model is presented to estimate a DMU's maximum potential desirable output increment (referred to as individual reward) based on its CEA level (defined as individual contribution). The individual fairness index is then defined as the ratio of individual reward to individual contribution. A convergence of individual fairness indexes among DMUs indicates higher perceived fairness in the CEA allocation. To promote fairness, we propose a centralized CEA allocation model that maximizes the minimum individual fairness index among DMUs, aiming to minimize disparities. Our contribution lies in formulating the concept of individual fairness within the DEA-based CEA allocation paradigm and introducing an approach to generate a CEA allocation result that embodies fairness. Lastly, the proposed approach is applied to a case study involving 38 OECD countries, demonstrating its superiority in achieving equitable CEA allocation results.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}

@article{abdolmaleki_3315,

title = {On Quantum Simulation-Soundness},

author = {Behzad Abdolmaleki and Céline Chevalier and Ehsan Ebrahimi and Giulio Malavolta and Quoc Huy Vu},

url = {http://dx.doi.org/10.62056/a66ce0iuc},

year  = {2025},

date = {2025-01-01},

journal = {IACR Communications in Cryptology},

volume = {1},

number = {4},

abstract = {Non-interactive zero-knowledge (NIZK) proof systems are a cornerstone of modern cryptography, but their security has received little attention in the quantum settings. Motivated by improving our understanding of this fundamental primitive against quantum adversaries, we propose a new definition of security against quantum adversary. Specifically, we define the notion of quantum simulation soundness (SS-NIZK), that allows the adversary to access the simulator in superposition.



We show a separation between post-quantum and quantum security of SS-NIZK, and prove that Sahai's construction for SS-NIZK (in the CRS model) can be made quantumly-simulation-sound. As an immediate application of our new notion, we prove the security of the Naor-Yung paradigm in the quantum settings, with respect to a strong quantum IND-CCA security notion. This provides the quantum analogue of the classical dual key approach to prove the security of encryption schemes. Along the way, we introduce a new notion of quantum-query advantage functions, which may be used as a general framework to show classical/quantum separation for other cryptographic primitives, and it may be of independent interest.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bueno_merino_3411,

title = {Observatoire de la Recherche en Sciences de Gestion et du Management},

author = {Pascale Bueno Merino and Jean-Luc HERRMANN},

url = {https://www.calameo.com/read/00193017146eb5a63494d},

year  = {2025},

date = {2025-01-01},

journal = {Publication FNEGE},

abstract = {Les sciences de gestion et du management sont largement reconnues pour la qualité de leurs formations et l'insertion professionnelle des très nombreux étudiants concernés, puisque près de 20% de l'ensemble des étudiants inscrits dans l'enseignement supérieur français suivent des formations dont la gestion constitue la discipline principale, une réalité encore trop souvent méconnue des responsables économiques et politiques. Peut-être plus méconnue encore est la recherche en sciences de gestion et du management. Et pourtant, au regard des nombres impressionnants d'étudiants formés et des nombreuses entreprises et organisations qui les accueillent chaque année aux termes de leur formation pour répondre aux besoins de l'économie et de la société française, il est paradoxal que les connaissances dispensées dans les différents champs disciplinaires des sciences de gestion et du management ne soient pas perçues comme les résultats de continuelles activités de Recherche & Développement.

La FNEGE a décidé en 2022 de lancer l'Observatoire de la recherche en sciences de gestion et du management avec le parrainage de la Conférence des Directeurs d'Ecoles Françaises de Management (CDEFM) et du réseau des Instituts d'Administration des Entreprises, IAE FRANCE.

Cet observatoire a pour objectif de dresser un panorama quantitatif et qualitatif de la recherche en sciences de gestion et du management dans les établissements d'enseignement supérieur français. Cela permet de mesurer l'ampleur de la production de connaissances de la communauté académique en sciences de gestion et du management, et de prendre conscience de ses impacts et contributions pour l'ensemble de ses parties prenantes, qu'ils s'agissent des chercheurs et enseignants-chercheurs, des étudiants et doctorants, des managers ?uvrant dans les organisations du tissu économique et social, ou de la société plus généralement.

A l'automne 2023, une enquête en ligne a été lancée avec le concours de Sphinx auprès des directions de 66 laboratoires français afin de collecter des données sur les effectifs leurs productions scientifiques, leurs impacts national et international sur la communauté académique, leurs contributions pour les étudiants et les doctorants, pour les praticiens, ainsi que pour leurs territoires et la société. A l'issue de cette enquête, Pascale BUENO MERINO, Directrice de la Recherche à l'EMLV Business School (De Vinci Higher Education), Professeur de Management stratégique, Habilitée à Diriger des Recherches, et Jean-Luc HERRMANN, Professeur Agrégé des Universités (Sciences de gestion et du management) à l'Université de Lorraine ont rédigé un rapport d'analyse des principaux enseignements de cet observatoire.

La première partie s'attache à apporter un éclairage sur l'impact académique de la recherche en sciences de gestion et du management en France. Plus spécifiquement, cette partie met en exergue tout d'abord les contributions des laboratoires français de gestion à la production de connaissances, puis le rôle des laboratoires français dans l'écosystème de la production de connaissances, et enfin l'impact de la recherche comme pilier de la formation académique en sciences de gestion et du management.

La seconde partie porte sur l'impact de la recherche française en sciences de gestion et du management sur les praticiens et la société. De façon plus précise, elle met tout d'abord en avant les efforts consacrés par les enseignants-chercheurs aux activités de dissémination scientifique et de recherche partenariale. Elle permet ensuite d'insister sur les liens étroits qu'entretiennent de nombreux laboratoires avec leur(s) territoire(s) d'implantation, avant de mettre en lumière l'engagement de plus en plus important des laboratoires dans la réalisation de travaux de recherche dédiés à la transition environnementale et sociétale.

Lien internet : https://www.calameo.com/read/00193017146eb5a63494d},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{baddar_al_hussan_3807,

title = {Cultivating green growth through innovative city pilot policies: Evidence from China},

author = {Fawaz Baddar Al Hussan and Ge Bai and Zhiyang Shen and Jian Wu},

url = {https://www.tandfonline.com/doi/full/10.1080/14631377.2024.2439722},

year  = {2025},

date = {2025-01-01},

journal = {Post-Communist Economies},

volume = {37},

number = {1-2},

pages = {133-159},

abstract = {This study utilizes panel data encompassing 282 prefectural-level cities in China spanning the years 2007 to 2019. Its primary objectives are to assess the direct and indirect consequences of the National Innovative City Pilot Policy (NICPP) on Green Total Factor Productivity (GTFP) through the application of the Propensity Score Matching (PSM) and Difference-in-Difference (DID) models. Subsequently, the research delves into whether NICPP can enhance the GTFP of cities by means of fiscal investments in science and technology, the degree of green innovations, and the level of financial development. Additionally, robustness tests and heterogeneity analyses are conducted to strengthen the validity of the findings. The results indicate a positive influence of NICPP on increasing the GTFP of cities. The fiscal allocation towards science and technology, the extent of green innovation, and the degree of financial development represent the three primary mechanisms through which NICPP impacts the productivity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}