Nouveaux Matériaux, Systèmes Intelligents et Entreprises Innovantes

@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_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{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{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{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},

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 = {online},

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 = {http://dx.doi.org/10.1016/j.ejor.2025.03.009},

year  = {2025},

date = {2025-03-01},

journal = {European Journal Of Operational Research},

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},

keywords = {},

pubstate = {online},

tppubtype = {article}

}

@article{nguyen_3413,

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

author = {Thuy Nguyen},

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

year  = {2025},

date = {2025-02-01},

journal = {Mrs Communications},

note = {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},

keywords = {},

pubstate = {online},

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{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{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 = {2025-02-01},

journal = {Mrs Communications},

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 = {online},

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{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{aloosh_2899,

title = {Direct Evidence of Bitcoin Wash Trading},

author = {Arash Aloosh and Jiasun Li},

url = {https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2021.01448?af=R},

year  = {2024},

date = {2024-12-01},

journal = {Management Science},

volume = {70},

number = {12},

pages = {8875-8921},

abstract = {We use the internal trading records of a major Bitcoin exchange leaked by hackers to detect

and characterize wash trading ? a type of market manipulation in which a single trader clears

her own limit orders to ?cook? transaction records. Our finding provides direct evidence for

the widely-suspected ?fake volume? allegation against cryptocurrency exchanges, which has

so far only been backed by indirect estimation. We then use our direct evidence to evaluate

various indirect techniques for detecting the presence of wash trades and find measures based on

Benford's law, trade size clustering, lognormal distributions, and structural breaks to be useful,

while ones based on power law tail distributions to give opposite conclusions. We also provide

suggestions to effectively apply various indirect estimation techniques.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ben_eleuchi_2941,

title = {Effect of friction coefficients and extrapolation laws on springback prediction of HSLA420 Steel},

author = {Slim Ben Eleuchi and Seddik Shiri and Mohamed Guerich},

url = {http://www.jomac.it/index.php?id=2024b_05},

year  = {2024},

date = {2024-12-01},

journal = {International Journal of Mechanics and Control},

volume = {25},

number = {2},

pages = {39-45},

abstract = {The high strength steels are widely used especially for the metal stamping. This process could have important differences with the desired geometry due to the springback phenomenon. The numerical simulation of the stamping and springback is widely adopted in automotive industry. The accurateness is highly dependent on numerical techniques as well as process parameters. Among the parameters that affect the precision, we can mention: the bending radius-thickness ratio, the friction coeffcients, the yield loci, the finite element formulation, the damage ... In this work, the effects of friction coefficients and the extrapolation laws on springback numerical prediction are investigated. The accuracy of this work is demonstrated through the industrial stamping process of an automotive part with High-Strength low-alloy steel HSLA420. The numerical results are obtained with the Pam-stamp 2G. It was shown that the extrapolations of the laws have the same order of influence as the friction coefficients},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ahmed_3081,

title = {Digital leadership and innovative work behavior: impact of LMX, learning orientation and innovation capabilities},

author = {Farooq Ahmed and M. Muzamil Naqshbandi and Mehwish Waheed and Noor Ul-Ain},

url = {https://www.emerald.com/insight/content/doi/10.1108/MD-04-2023-0654/full/html},

year  = {2024},

date = {2024-12-01},

journal = {Management Decision},

volume = {62},

number = {11},

pages = {3607-3632},

abstract = {Purpose - Grounded in the Social Exchange theory, this study focuses on the perception of digital leadership

and innovative work behaviors. It also examines the complex effects of leader-member exchange, learning

orientation, and innovation capabilities.

Design/methodology/approach - We undertake a quantitative approach using a two-wave longitudinal

field survey of 440 employees and managers from various backgrounds working in the automobile industry in

France.

Findings - The findings based on Structural Equation Modeling reveal that the perception of digital

leadership leads to innovative work behavior while leader-member exchange moderates between the

perception of digital leadership and learning orientation. The findings also support the mediating roles of

learning orientation and innovation capabilities.

Originality/value - The study contributes important policy suggestions, raises queries for additional

investigation, and suggests theoretical and practical implications for leadership and organizational

environmental factors to foster innovative work behaviors in organizations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zhang_3170,

title = {Robotic warehouse systems considering dynamic priority},

author = {Zhengmin Zhang and Yeming Gong and Zhe Yuan and Wanying Chen},

url = {https://www.sciencedirect.com/science/article/pii/S1366554524003703?via%3Dihub},

year  = {2024},

date = {2024-12-01},

journal = {Transportation Research Part E-Logistics And Transportation Review},

volume = {192},

pages = {103779},

abstract = {The research proposes a new methodological framework based on dynamic priority to handle different order classes in robotic warehouse systems. Traditional static priority methods in facility logistics may cause low-priority orders to experience excessive delays and fail to ensure fairness. Our dynamic priority approach addresses this fairness issue by adjusting priorities over time to fulfill orders within promised times, ensuring both high-priority orders and long-waiting low-priority orders receive timely attention. We present stochastic models of dynamic priority queueing networks to describe warehouse systems and estimate throughput times. Experiments validate the analytical stochastic models, and experimental results indicate that the dynamic priority model achieves shorter delay times than the static priority model and the FCFS model. We propose design insights based on experimental results and provide an approach to select the optimal robot number. Furthermore, by employing a fairness index, we develop a new decision support tool for determining warehouse configurations with requested performance objectives. Experimental results demonstrate that dynamic priority can ensure fairness across a wider range of scenarios. Additionally, with insufficient pickers, the system performs better with the put wall than without it.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chaabene_3194,

title = {Experimental Investigation of the Influence of Process Parameters on the Mechanical Properties of Maraging Steel Produced by Electron Beam Melting},

author = {Ameni Chaabene and Slim Ben Elechi and Sami Chatti and Mohamed Guerich and Ated Ben Khalifa},

url = {https://link.springer.com/article/10.1007/s00170-024-14731-0#citeas},

year  = {2024},

date = {2024-12-01},

journal = {International Journal Of Advanced Manufacturing Technology},

volume = {135},

pages = {3865-3882},

abstract = {Maraging steels are used in industries that require high precision due to their exceptional mechanical properties. This paper

presents the impact of ten process parameters on the mechanical properties of maraging steel produced via electron beam

melting (EBM). The parameters studied here include printing orientation, scanning speed, beam power, beam diameter,

line offset, layer thickness, scan strategy, number of contours, preheating temperature, and post-processing. The mechanical

properties evaluated are tensile strength, strain at fracture, elongation at failure, energy density, material hardness, porosity,

and surface morphology. The results showed a significant correlation between these mechanical properties and the EBM

parameters. The highest ultimate tensile stress, determined from the tensile test, was recorded at 1491.51 MPa, corresponding

to an energy density of 64.28 J/mm3. The optimal parameters were identified as a line offset of 0.1 mm, a layer thickness

of 50 ?m, a scanning speed of 2800 mm/s, and a beam power of 15 mA. The energy density during EBM in turn affected

microstructure, porosity, and mechanical properties. Furthermore, the study revealed that aging heat treatment had a considerable

impact on the mechanical properties, with the highest tensile strength observed for a sample that had undergone

aging at 460 °C for 8 h. The results show the critical role of process parameters and aging heat treatment in achieving optimal

mechanical properties in EBM-produced maraging steel M789. These findings contribute to the optimization of this

manufacturing technique for industrial applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kadri_3203,

title = {Virtual reality in medical education: Effectiveness of Immersive Virtual Anatomy Laboratory (IVAL) Compared to traditional learning approaches},

author = {Mohammed Kadri and Fatima Ezzahra Boubakri and Timothy Teo and Fatima Zahra Kaghat and Ahmed Azough and Khalid Alaoui Zidani},

url = {https://www.sciencedirect.com/science/article/pii/S0141938224002348?via%3Dihub},

year  = {2024},

date = {2024-12-01},

journal = {Displays},

volume = {85},

pages = {102870},

abstract = {Immersive Virtual Anatomy Laboratory (IVAL) is an innovative learning tool that combines virtual reality and serious games elements to enhance anatomy education. This experimental study compares IVAL with traditional learning methods in terms of educational effectiveness and user acceptance. An experimental design was implemented with 120 undergraduate health-science students, randomly assigned to two groups: an experimental group using IVAL, and a control group following traditional learning methods. Data collection focused on quantitative measures such as pretest and posttest vocabulary assessment scores and task completion times, alongside qualitative measures obtained through a user experience questionnaire. This study utilizes the Technology Acceptance Model (TAM), incorporating variables such as Perceived Usefulness and Perceived Ease of Use. Results revealed significant improvements in the experimental group, with a 55.95% increase in vocabulary scores and an 18.75% reduction in task completion times compared to the control group. Qualitative data indicated that IVAL users reported greater Perceived Usefulness of the technology, improved Perceived Ease of Use, a more positive Attitude Toward Using IVAL, and stronger Behavioral Intention to continue using IVAL for anatomy learning. This study demonstrates that the integration of immersive virtual reality in the IVAL approach offers a promising method to enhance anatomy education. The findings provide insights into the effectiveness of immersive learning environments in improving learning outcomes and user acceptance. While further research is needed to explore long-term effects, this innovative approach not only enhances the effectiveness and enjoyment of anatomy learning but also provides valuable data on optimizing educational technology for improved learning outcomes.},

keywords = {},

pubstate = {published},

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  = {2024},

date = {2024-12-01},

journal = {Ieee Access},

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 = {online},

tppubtype = {article}

}

@article{navazhylava_3216,

title = {The Impact of Legal Uncertainties on Innovating HR Practices in Developing Countries: A Case Study of Remote Work in Kazakhstan's Technical Gas Industry during the healthcare crisis},

author = {Kseniya Navazhylava and Meruyert Ibraimova},

url = {https://www.tandfonline.com/doi/abs/10.1080/09585192.2024.2428356},

year  = {2024},

date = {2024-11-01},

journal = {International Journal Of Human Resource Management},

volume = {35},

number = {21},

pages = {3715-3743},

abstract = {This study explores the influence of legal uncertainties on the process of innovating human resources (HR) practices in developing countries. Through a case study focused on introducing remote work within Kazakhstan's Technical Gas Industry during a healthcare crisis, we examine the multifaceted challenges and opportunities that emerge when navigating a complex legal landscape. Our findings reveal that legal uncertainties, stemming from inadequacies in legislation and the tightness of norms, significantly impede the ability to adapt and modernize HR practices during crises. Furthermore, the criticality of the company's position within the industry, combined with a low degree of legal enforcement, underscores the concept of "responsibilization" among HR professionals. This phenomenon compels HR practitioners to assume greater responsibility and make strategic decisions that occasionally push the boundaries of existing laws and regulations. In this context, we propose a novel conceptualization of responsibilization, distinct from empowerment, as it involves embracing negative legal consequences associated with proactive decision-making during crises. This study contributes significantly to our understanding of how legal uncertainties influence the process of HR innovation in developing countries, highlighting the intricate interplay between regulatory frameworks, crisis management, and organizational transformation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{peticca-harris_3217,

title = {Neoliberal healthism and women's entrepreneurial subjectivities in yoga},

author = {Amanda Peticca-Harris and Sara R.S.T.A Elias and Kseniya Navazhylava and M.N. Ravishankar},

url = {https://journals.sagepub.com/doi/abs/10.1177/13505084241295736},

year  = {2024},

date = {2024-11-01},

journal = {Organization},

abstract = {Women entrepreneurs have long encountered the neoliberal expectation to perform as ideal subjects by exhibiting an enterprising femininity. However, existing literature on critical women's entrepreneurship has largely overlooked how health and wellbeing discourses permeate enterprising ideals and impact one's subjectivity. This qualitative study explores how 41 women entrepreneurs construct an enterprising subjectivity through neoliberal healthism, and by doing so, provides a deeper understanding of alternative logics within and beyond the neoliberal logic of the enterprise. Our findings detail who they understand themselves to be by internalizing and at times challenging neoliberal ideals through themes of self-responsibilization, self-optimization, and reflective health.  Our main contribution to critical women's entrepreneurship is that we conceptualize how an enterprising subjectivity is constructed through the personal optimization of health and wellbeing, fueled by shared-self-care rather than only the logic of market enterprise.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}

@article{josserand_3255,

title = {Australia's right to disconnect from work: Beyond rhetoric and towards implementation},

author = {Emmanuel Josserand and Martijn Boersma},

url = {https://journals.sagepub.com/doi/epub/10.1177/00221856241290625},

year  = {2024},

date = {2024-11-01},

journal = {Journal Of Industrial Relations},

volume = {66},

number = {5},

pages = {703-720},

abstract = {Amendments to the Fair Work Act now allow workers in Australia a right to disconnect. The implementation of this right precipitated a polarised public debate that was not consistently evidence-based, encompassing the often-contradictory perspectives of unions, employers, business lobbies and politicians. This study offers a more nuanced and evidence-based understanding of the right to disconnect, its benefits and challenges and the possible impact on Australian employment practices and relations. It provides an international comparative analysis; it explores the literature on related topics such as work-life balance, occupational stress, management practices and productivity; and it proposes a model of the consequences of technology-enabled flexible work. The comparative analysis and literature review are supplemented with themes identified in Australian media coverage through a Leximancer analysis. The findings discuss the advantages and limitations of diverse top-down legislative or self-regulatory pathways experienced overseas by early adopters. While the right to disconnect can improve work-life balance, health and well-being and productivity, its implementation requires careful consideration of industry-specific contexts, clear policies and cultural shifts in workplaces to mitigate the risks associated with hyperconnectivity. These insights are useful for Australia as it enters the implementation phase of the right to disconnect.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aggarwal_3278,

title = {Inside story of impact investing in emerging market: A systematic review to measure the responsible and sustainable investing pattern using the ADO framework},

author = {Shalini AGGARWAL and Prerna RATHEE and Vikas ARYA and Hiran ROY},

year  = {2024},

date = {2024-11-01},

journal = {Journal of Economic Surveys},

abstract = {Impact investing has emerged as a significant global phenomenon as it provides a valuable avenue for investors to shape their cognitive decision-making ability to have a societal impact. The present study aims to review the existing literature on impact investing systematically. It tries to understand the major motivational factors that impact the investor in impact investing using the ADO framework by linking it with McClelland's Theory of Motivation, geographical areas, journal of publication, and type of research articles for impact investing, significant research gaps in impact investing, theoretical and managerial implications and future research of impact investing. PRISMA framework has been used to finalize the articles from the Scopus database. As a result, 154 articles have been identified from the year 2011 to 2024. The result identifies three motivational factors that drive the investor to invest in impact investing. It includes financial, social, and self-actualization. The study will guide the policymaker in introducing comprehensive regulatory policies in the area of impact investing. Accordingly, tax incentives and subsidies should be granted for promoting investment in impact investing. The development of proper infrastructure for trading in impact investing needs attention.},

keywords = {},

pubstate = {online},

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  = {2024},

date = {2024-11-01},

journal = {Journal Of Consumer Behaviour},

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 = {online},

tppubtype = {article}

}

@article{meng_3010,

title = {Consensus adjustment for multi-attribute group decision making based on cross-allocation},

author = {Fan-Yong Meng and Deng-Yu Zhao and Zai-Wu Gong and Jun-Fei Chu and Witold Pedrycz and Zhe Yuan},

url = {https://www.sciencedirect.com/science/article/pii/S0377221724003400},

year  = {2024},

date = {2024-10-01},

journal = {European Journal Of Operational Research},

volume = {318},

number = {1},

pages = {200-216},

abstract = {Consensus adjustment is crucial in group decision making (GDM), aiming to reduce discrepancies in decision makers' (DMs') judgments for final decision results. The consensus adjustments made by DMs are interactive rather than independent. However, few studies investigate the consensus adjustment problem from the perspective of interactions among DMs. This study first constructs a model to determine the total minimum consensus adjustment under the requirements of consensus threshold and individual-collective judgment bias. Then, a consensus adjustment approach based on cross-allocation scheme is proposed to address the inconsistency between individual and collective minimum adjustments, which concerns all DMs' individual favorable allocation results under the total minimum consensus adjustment. The cross-allocation scheme ensures unanimous support for the same allocation result among all rational DMs, thereby eliminating the discrepancies among them. Subsequently, the optimization model-based minimum consensus judgment penalty is formulated to handle the non-cooperative behaviors of DMs. Furthermore, we show how to determine the unique consensus adjustment result for the non-cooperative DMs using the cross-allocation scheme. Therefore, our study contributes by developing a new GDM method that conducts consensus adjustment allocation by considering interactions among DMs. Finally, a case study is employed to demonstrate the specific application of the new method and discuss its efficiency. Meanwhile, comparison analysis and simulation experiments are provided.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sun_3024,

title = {LCAMix: Local-and-contour aware grid mixing based data augmentation for medical image segmentation},

author = {Danyang Sun and Fadi Dornaika and Jinan Charafeddine},

url = {https://pdf.sciencedirectassets.com/272144/1-s2.0-S1566253524X00068/1-s2.0-S1566253524002628/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBcaCXVzLWVhc3QtMSJHMEUCIB%2Bm2yVgKP%2BTa1w1tSg%2FydrF%2Fvb0NtGUi2OtE0dCfm5ZAiEAvS5zznXIlJGGqU0uDk6urwemR%2FjBXI8flOEVsv%2Bqi7MqvAUIj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAFGgwwNTkwMDM1NDY4NjUiDGlNeMR%2BKpLBIx%2FJkiqQBUxi37rUay01aPHdPyUjlEmZScIOm09oNOKMm2j4kAJ%2BBXu4xZ6ts%2FYVWeKloZ9bKO688mVLpZYW96XCoW8DS9E%2FGvhCoLNFgFEV35VB1ai8O7M21%2BlU%2Br8%2FcPbj77ypLNC%2BKDWJV0NPrB1DKOvao6ZpbspbrdK4kPspEAzZ%2FVkFT%2FKiopgMXf6iniq2tANtPaizXkk%2BZaNSAGIFt9OmCXMk6H7VYJALKEF4ZJm9kBslRj5nE5Y6edtx2JSSptvlujKMXhJbwjIiUiX7Ui1B4pUWDVtzmLpVcElBM78VwS0H8TKlHXBl5pyVDCym%2Fp2r%2BZbZdJWW6ZWgrqb26Dqc%2F22RSCtts8Lm%2Fs8vSHEc2cfI2mYpPSj5gL51yaDqoZyA9JcEBsQRAecvv%2F8RkbvtzMzuNBRjn4V8HoQR137Q2pYM%2FJgAly5tkgk5KS8JB2jb%2BaTA88ZJLx6YSDeqJdX2PXwyB1XvrrRy8oUgMmff8s2zVgT1f1dX5iPmZ1jtYEVu40cdvJ88ac8j4zAfWQPmBb%2FvfXWd%2BDVOp7h8ftVwQnI7bLDlFBXbhH3oVb%2FFpJ3Bw3DFakM7j%2FSOVug7DP8dC9h8Dj2oIOW8C4QAgdCeHaDRMfh2F7VZjw3FZf3i631%2BAdBp8r4d4XkYeREmbmiovEoVWBBHoYD6RTIK9lp37AmDnhFSAMr2LEmzG2NypUQ3Hwb7zLu4thOxdGz5MBApBAh44ukY8YuN1ufa38eyKRtCUO%2Fxd2kuIIJ2LVXa3bQYug5Lx%2B9KIKhgqxCrwptvf8OaxtQ5FoYhuqNU0pL1Chohou2d1vld8RViOconvLl0VWMsZ5YyP9ZxaM0xtzdz9jZQUUyYOV92u8ehS7ie1qwrMNDAwrIGOrEBxpUgPvpVhdMKearFfabKLjWh5dtniMHIFTE5cSfjDp3cUuAHaEMlV2m22S1E7%2B%2BqLhJRHUxrx0Ahj9AgijOS6Ej7rGG3xGTKGOnhYGjvrjhMzhpYH7bBR2UDVY%2Ff5JnU3IqbM8%2BmzPkwAtPDE4f74N3zJ3CUErPd4nF0UOXyQABK0JE3XGOZmeqIIDshevKKQuAQWWCmCYyGaPWgjtPkrFNgDEqhkhXN4QWxs%2FJfC2Tb&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240524T152432Z&X-Amz-SignedHeaders=host&X-Amz-Expires=299&X-Amz-Credential=ASIAQ3PHCVTY4RJ4ZEGT%2F20240524%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=49fff6e8e4aa2160d63ba58a605230103a570a99a899c33049a2447724567bf6&hash=cf866cff1fcd93c461170f4149fdcc586b3e8c5bd13fbef0361db16227b04cc0&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1566253524002628&tid=spdf-53f47d50-21b1-4fa1-a93b-92addbddb736&sid=5b8e3010830f534aec5849b2797ace9af2f9gxrqb&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=001156565a0352015207&rr=888e4b0cfc530203&cc=fr},

year  = {2024},

date = {2024-10-01},

journal = {Information Fusion},

volume = {110},

pages = {102484},

abstract = {Medical image segmentation often faces challenges related to overfitting, primarily due to the limited and complex training samples. This challenge often prompts the use of self-supervised learning and data augmentation. However, self-supervised learning requires well-defined hand-crafted tasks and multiple training stages. On the other hand, basic image augmentation techniques like cropping, rotation, and flipping, effective for natural scene images, have limited efficacy for medical images due to their isotropic nature.



While regional dropout regularization data augmentation methods have proven effective in image recognition tasks, their application in image segmentation is not as extensively studied. Additionally, existing augmentation methods often operate on square regions, leading to the loss of crucial contour information. This is particularly problematic for medical image segmentation tasks dealing with regions of interest characterized by intricate shapes. In this work, we introduce LCAMix, a novel data augmentation approach designed for medical image segmentation. LCAMix operates by blending two images and their segmentation masks based on their superpixels, incorporating a local-and-contour-aware strategy. The training process on augmented images adopts two auxiliary pretext tasks: firstly, classifying local superpixels in augmented images using an adaptive focal margin, leveraging segmentation ground truth masks as prior knowledge; secondly, reconstructing the two source images using mixed superpixels as mutual masks, emphasizing spatial sensitivity. Our method stands out as a simple, one-stage, model-agnostic, and plug-and-play data augmentation solution applicable to various segmentation tasks. Notably, it requires no external data or additional models. Extensive experiments validate its superior performance across diverse medical segmentation datasets and tasks. The source codes are available at https://github.com/DanielaPlusPlus/DataAug4Medical.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chu_3041,

title = {An improved equilibrium efficient frontier data envelopment analysis approach for evaluating decision-making units with fixed-sum outputs},

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

url = {https://www.sciencedirect.com/science/article/pii/S0377221724004314},

year  = {2024},

date = {2024-10-01},

journal = {European Journal Of Operational Research},

volume = {318},

number = {2},

pages = {592-604},

abstract = {In recent years, significant development has been made in efficiency evaluation for Decision-Making Units (DMUs) with fixed-sum outputs. The Generalized Equilibrium Efficient Frontier Data Envelopment Analysis (GEEFDEA) approach introduced by Yang et al. (2015) is one of the most representative methods. In the GEEFDEA approach, all DMUs are adjusted to become efficient under the same set of weights, indicating that the Equilibrium Efficient Frontier (EEF) constructed consists of only one hyperplane. However, in practical scenarios under the Variable Return to Scale (VRS) assumption, the production frontier always consists of multiple hyperplanes, presenting a more complex shape. To fill this research gap, we propose an improved Equilibrium Efficient Frontier Data Envelopment Analysis approach. Our approach allows DMUs to have different weights for inputs, variable-sum outputs, and fixed-sum outputs, resulting in an EEF with multiple hyperplanes. It is noted that our new approach uses a non-linear model to obtain the EEF. We show that the model can be linearized in the case of a single fixed-sum output. In situations involving multiple fixed-sum outputs, we propose an algorithm based on the Expectation Maximization (EM) mechanism to solve the model to obtain an EEF. Finally, we illustrate the advantages of our new approach through a numerical example and a case study in the global motor vehicle industry.},

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  = {2024},

date = {2024-10-01},

journal = {Thunderbird International Business Review},

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 = {online},

tppubtype = {article}

}

@article{alirezazadeh_3188,

title = {Mises-Fisher similarity-based boosted additive angular margin loss for breast cancer classification},

author = {Pandar Alirezazadeh and Fadi Dornaika and Jinan Charafeddine},

url = {https://link.springer.com/article/10.1007/s10462-024-10963-4#citeas},

year  = {2024},

date = {2024-10-01},

journal = {Artificial Intelligence Review},

volume = {57},

pages = {326},

abstract = {To enhance the accuracy of breast cancer diagnosis, current practices rely on biopsies and microscopic examinations. However, this approach is known for being time-consuming, tedious, and costly. While convolutional neural networks (CNNs) have shown promise for their efficiency and high accuracy, training them effectively becomes challenging in real-world learning scenarios such as class imbalance, small-scale datasets, and label noises. Angular margin-based softmax losses, which concentrate on the angle between features and classifiers embedded in cosine similarity at the classification layer, aim to regulate feature representation learning. Nevertheless, the cosine similarity's lack of a heavy tail impedes its ability to compactly regulate intra-class feature distribution, limiting generalization performance. Moreover, these losses are constrained to target classes when margin penalties are applied, which may not always optimize effectiveness. Addressing these hurdles, we introduce an innovative approach termed MF-BAM (Mises-Fisher Similarity-based Boosted Additive Angular Margin Loss), which extends beyond traditional cosine similarity and is anchored in the von Mises-Fisher distribution. MF-BAM not only penalizes the angle between deep features and their corresponding target class weights but also considers angles between deep features and weights associated with non-target classes. Through extensive experimentation on the BreaKHis dataset, MF-BAM achieves outstanding accuracies of 99.92%, 99.96%, 100.00%, and 98.05% for magnification levels of ×40, ×100, ×200, and ×400, respectively. Furthermore, additional experiments conducted on the BACH dataset for breast cancer classification, as well as on the LFW and YTF datasets for face recognition, affirm the generalization capability of our proposed loss function.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nguyen_3192,

title = {Size-dependency and lattice-discreetness effect on fracture toughness in 2D crystals under antiplanar loading},

author = {Thuy Nguyen and Daniel Bonamy},

url = {https://link.springer.com/article/10.1007/s10704-024-00812-4?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20241017&utm_content=10.1007%2Fs10704-024-00812-4},

year  = {2024},

date = {2024-10-01},

journal = {International Journal Of Fracture},

volume = {248},

pages = {257-273},

abstract = {Fracture toughness is the material property

characterizing resistance to failure. Predicting its value from the solid structure at the atomistic scale remains elusive, even in the simplest situations of brittle fracture. We report here numerical simulations of crack propagation in two-dimensional fuse networks of different periodic geometries, which are electrical analogs of bidimensional brittle crystals under antiplanar loading. Fracture energy is determined from Griffith's analysis of energy balance during crack propagation, and fracture toughness is determined from fits of the displacement fields with Williams' asymptotic solutions. Significant size dependencies are evidenced in small lattices, with fracture energy and fracture toughness both converging algebraically with system size toward well-defined material-constant values in the limit of infinite system size. The convergence speed depends on the loading conditions and is faster when the symmetry of the considered lattice increases. The material

constants at infinity obey Irwin's relation and properly define the material resistance to failure. Their values are approached up to ? 15% using the recent analytical method proposed in Nguyen and Bonamy (Phys

Rev Lett 123:205503, 2019). Nevertheless, the deviation remains finite and does not vanish when the system size goes to infinity. We finally show that this deviation is a consequence of the lattice discreetness and

decreases when the super-singular terms of Williams' solutions (absent in a continuum medium but present here due to lattice discreetness) are taken into account.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{huang_3211,

title = {Catch-up in Complex Products and Systems: A Fuzzy-Set Qualitative Comparative Analysis of China's Equipment Manufacturing Industry},

author = {Han Huang and Jie Xiong and Lu XU and Zhe Yuan and Chun Liu},

url = {https://ieeexplore.ieee.org/document/10738479},

year  = {2024},

date = {2024-10-01},

journal = {Ieee Transactions On Engineering Management},

volume = {71},

pages = {15375 - 15389},

abstract = {The rapid advancement of Chinese Complex Products and Systems (CoPS) enterprises marks their transition into a post-catch-up phase, challenging conventional theories of catch-up. This study employs a configurational approach to explore the intricate relationships between catch-up environments and strategies, specifically focusing on the distinct paths of State-Owned Enterprises (SOEs) and non-State-Owned Enterprises (non-SOEs) within the CoPS sector. Utilizing Fuzzy-Set Qualitative Comparative Analysis (fsQCA) with data sourced from the EU Industrial R&D Investment Scoreboard (2017-2020) and corresponding Chinese-listed companies, our research identifies diverse catch-up configurations for SOEs, characterized by ?complexity adoption? and ?complexity decipher? models. In contrast, non-SOEs encounter challenges in strategically adapting to environmental shifts, which affects their catch-up strategies. Our findings emphasize the critical role of strategic alignment with external conditions, technological learning, and resource utilization in achieving successful catch-up in CoPS. These configurations enable SOEs to effectively align internal resources with external opportunities, resulting in superior catch-up performance. In contrast, non-SOEs encounter significant obstacles in adapting to environmental changes and optimizing resource utilization, which hinders their ability to attain similar successes. Moreover, our study sheds light on specific challenges faced by non-SOEs in responding to environmental shifts. This enriched understanding provides valuable theoretical insights into the catch-up of latecomer CoPS enterprises and has practical implications for both policymakers and business practitioners.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}