Nouveaux Matériaux, Systèmes Intelligents et Entreprises Innovantes

@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://doi.org/10.1177/13505084241295736},

year  = {2025},

date = {2025-11-01},

journal = {Organization},

volume = {32},

number = {8},

pages = {1223-1244},

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.},

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}

@article{dornaika_3847,

title = {Towards Dynamic Self-Training for Scalable Semi-Supervised Learning on Graphs},

author = {Fadi Dornaika and Zoulfakar IBRAHIM and Jinan Charafeddine and Alireza Bosaghzadeh},

url = {http://dx.doi.org/10.1016/j.neucom.2025.131223},

year  = {2025},

date = {2025-11-01},

journal = {Neurocomputing},

volume = {654},

pages = {131223},

abstract = {In the realm of graph-based semi-supervised learning (GSSL), traditional methodologies often struggle to effectively handle labeled samples and scale to accommodate large datasets. To increase supervision information in semi-supervised learning, the self-training paradigm is often used, mainly in datasets with moderate sizes. On the other hand, the use of anchors was adopted with large datasets. In this research endeavor, we propose a novel framework for GSSL that leverages a novel self-training principle tailored for very large datasets, and introduces an advanced method for automatic graph construction using anchors. Our approach focuses on utilizing generated labels of random batches of unlabeled samples, subsequently incorporating these predictions into the training set to enhance the model's accuracy. Pseudo-labeling, a specific instance of self-training, assigns pseudo-labels to the most confidently predicted unlabeled examples, treating them as ground truth during the training phase. By constructing anchor-to-anchor affinity graphs that incorporate both feature and label information, our method facilitates robust learning on large-scale datasets. Through comprehensive experimentation across diverse large datasets, our approach demonstrates its efficacy in achieving scalable and reliable semi-supervised learning outcomes. These findings represent a significant advancement in the field of GSSL, with wide-ranging implications for various applications across different domains. Our method not only addresses the scalability issue but also ensures the effective integration of both labeled and pseudo labeled data, thereby enhancing the overall learning process.},

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@article{latukha_3937,

title = {Why do they come back? Returnees' presence, talent management and firms' innovative capabilities},

author = {Marina Latukha and Mariia Shagalkina and Vyacheslav Telov and Liana Rysakova},

url = {https://doi.org/10.1108/EBR-10-2024-0313},

year  = {2025},

date = {2025-11-01},

journal = {European Business Review},

volume = {37},

number = {6},

pages = {940-963},

abstract = {Purpose



Investigation of returnees receives much attention in management as due to obtaining international human and social capital, returnees contribute to economic and non-economic performance, internationalization, corporate social responsibility activities, social change, innovativeness, and competitive advantage of companies where those returnees work. Returnees' effect on innovative capabilities, i.e. antecedents of innovativeness, is dependent on the extent to which these individuals can share their knowledge and experience. The paper aims to investigate the role of talent management (TM) practices for returnees in shaping firms' innovative capabilities.

Design/methodology/approach



Drawing from the knowledge-based view of the firm and dynamic capabilities theory, the authors use a sample of 80 companies with returnee employees to empirically test the hypotheses.

Findings



The authors show that attraction, development and retention practices aimed at returnees are positively associated with innovative capabilities and innovativeness of companies. Moreover, TM for returnees moderates the relationship between innovative capabilities and innovativeness in firms with returnee employees.

Originality/value



The authors provide statistical evidence of TM practices as a mechanism through which highly skilled returnees impact organizational outcomes in the context of emerging economies. As highly skilled returnees may be considered talents, a TM system may facilitate knowledge sharing and eliminate possible issues in cross-cultural adaptation and integration.},

note = {13/11/2025 : demande du PDF à Marina},

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pubstate = {published},

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}

@article{abuzerr_4085,

title = {A qualitative study on gendered wash insecurity and health outcomes among internally displaced populations in conflict-affected context},

author = {Samer Abuzerr and Hani Hamdan and Jinan Charafeddine},

url = {https://doi.org/10.1186/s12889-025-25777-4},

year  = {2025},

date = {2025-11-01},

journal = {Bmc Public Health},

abstract = {Background

Internally displaced populations (IDPs) in conflict-affected settings face severe challenges related to water, sanitation, and hygiene (WASH) insecurity. Gender plays a critical role in shaping these experiences and their consequent health outcomes. This study explores the gendered dimensions of WASH insecurity among IDPs living in makeshift shelters in the Gaza Strip and examines associated health impacts.



Methods

A qualitative study was conducted between March 28 and May 8, 2025, involving in-depth interviews with 26 female participants, including adult women and adolescent girls, residing in Gaza's displacement shelters. Descriptive themes were developed to explore: (1) access to clean water and sanitation facilities; (2) menstrual hygiene practices and challenges; (3) personal safety and dignity in WASH settings; (4) mental and physical health impacts; (5) gender roles and responsibilities related to WASH; and (6) coping mechanisms and support networks. Data were analyzed thematically to capture the lived experiences, coping strategies, and health consequences of WASH insecurity through a gender-sensitive lens.



Results

Findings reveal that WASH insecurity disproportionately affects women and girls by exacerbating risks to physical and mental health, including recurrent infections, menstrual hygiene challenges, and psychosocial distress. Social norms and displacement conditions restrict women's access to safe, private sanitation, increasing vulnerability to harassment and undermining dignity. Despite these hardships, participants demonstrated resilience through adaptive coping strategies, though these often reinforced gendered burdens.



Conclusions

Addressing WASH insecurity in Gaza's displacement settings requires gender-responsive humanitarian interventions that prioritize privacy, safety, and menstrual hygiene management. Integrating WASH services with protection and health programs is vital to improving health outcomes and upholding the dignity of displaced women and girls. Enhanced participation of women in WASH planning and decision-making is crucial to achieving equitable and sustainable solutions.},

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@article{abou_ali_4204,

title = {Agentic AI: a comprehensive survey of architectures, applications, and future directions},

author = {Mohamad Abou Ali and Fadi Dornaika and Jinan Charafeddine},

url = {https://doi.org/10.1007/s10462-025-11422-4},

year  = {2025},

date = {2025-11-01},

journal = {Artificial Intelligence Review},

volume = {59},

number = {11},

abstract = {Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbolic models?a practice known as conceptual retrofitting. This survey cuts through this confusion by introducing a novel dual-paradigm framework that categorizes agentic systems into two distinct lineages: the symbolic/classical (relying on algorithmic planning and persistent state) and the neural/generative (leveraging stochastic generation and prompt-driven orchestration). Through a systematic PRISMA-based review of 90 studies (2018-2025), we provide a comprehensive analysis structured around this framework across three dimensions: (1) the theoretical foundations and architectural principles defining each paradigm; (2) domain-specific implementations in healthcare, finance, and robotics, demonstrating how application constraints dictate paradigm selection; and (3) paradigm-specific ethical and governance challenges, revealing divergent risks and mitigation strategies. Our analysis reveals that the choice of paradigm is strategic: symbolic systems dominate safety-critical domains (e.g., healthcare), while neural systems prevail in adaptive, data-rich environments (e.g., finance). Furthermore, we identify critical research gaps, including a significant deficit in governance models for symbolic systems and a pressing need for hybrid neuro-symbolic architectures. The findings culminate in a strategic roadmap arguing that the future of Agentic AI lies not in the dominance of one paradigm, but in their intentional integration to create systems that are both adaptable and reliable. This work provides the essential conceptual toolkit to guide future research, development, and policy toward robust and trustworthy hybrid intelligent systems.},

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@article{elhadri_3893,

title = {Comparison of Self?Heating Behavior Between Continuous and Interrupted Control Modes},

author = {Hamza Elhadri and Johann Petit and Naila Hfaiedh and Danièle Wagner and Olivier Polit},

url = {http://dx.doi.org/10.1111/ffe.70038},

year  = {2025},

date = {2025-10-01},

journal = {Fatigue & Fracture Of Engineering Materials & Structures},

volume = {48},

number = {10},

pages = {4517-4527},

abstract = {In gigacyclic fatigue testing, reaching 109 cycles within a reasonable duration is challenging, leading to the use of ultrasonic

fatigue machines operating at 20 kHz (UFT). However, the high testing frequency causes significant temperature rises, potentially

enhancing microplasticity and leading to premature failure. This study compares two control modes in gigacyclic fatigue:

continuous and interrupted (pulse-pause)

during self-heating

tests on UFT. Two materials with distinct thermal properties, steel

42CrMo4 and aluminum alloy AA2024-T351,

were tested. The temperature evolution was measured using an infrared camera.

Then, a local expression of the heat equation is used to estimate the intrinsic dissipation, denoted as d1. Results showed that both

modes led to temperature increases with higher displacement, but pulse-pause

caused a smaller temperature rise. Reducing the

pulse time relative to the pause time decreased temperature variation. For 42CrMo4 steel, self-heating

was more pronounced

than for AA2024-T351,

and the d1 value increased with displacement amplitude.},

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@article{mammeri_3919,

title = {Aeroacoustic and aerodynamic evaluation of SLA-printed cross-flow fans: desktop vs. industrial-grade fabrication},

author = {Amrid Mammeri and Hamidreza Vanaei and Ouiza Saddaoui and Issiaka Traore and Messipssa Aoudjit and Ivan Dobrev and Kamel Azzouz and Sofiane Khelladi},

url = {https://doi.org/10.1108/RPJ-06-2025-0226},

year  = {2025},

date = {2025-10-01},

journal = {Rapid Prototyping Journal},

abstract = {The application of stereolithography (SLA) for functional turbomachinery components remains a developing area of interest, particularly regarding the performance impact of fabrication scale. This study aims to evaluate the aerodynamic and aeroacoustic performance of cross-flow fan (CFF) rotors fabricated using desktop and industrial-grade SLA machines, highlighting how geometric fidelity and mechanical behavior influence fan efficiency and noise.

Design/methodology/approach - CFF rotors were 3D printed using both desktop and industrial SLA printers. Mechanical properties were evaluated based on layer orientation and post curing temperature. Aerodynamic performance was assessed experimentally via static efficiency and pressure coefficient, while aeroacoustic behavior was characterized using sound pressure level (SPL) measurements across various flow coefficients and rotational speeds. Geometry distortions were linked to observed performance discrepancies using theoretical modeling grounded in Euler's equation and blade angle analysis. The results show that post curing at 80 °C significantly improved tensile strength, with up to 110% gain at 0° orientation. The desktop printed rotor exhibited a 10-15% reduction in peak aerodynamic efficiency and a ?1 dB increase in SPL, primarily due to blade deformation and altered inlet/outlet angles. Theoretical analysis confirmed the sensitivity of CFF performance to blade alignment, validating the observed effects. Despite minor drawbacks, desktop SLA was found viable for rapid prototyping in fan design. This work offers a novel experimental-theoretical framework for evaluating desktop SLA in aerodynamic and acoustic applications. It provides insights into the relationship between manufacturing fidelity, mechanical properties and functional performance of CFFs. These findings can inform the design and validation of 3D-printed turbomachinery components using accessible laboratory-scale equipment.},

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@article{vanaei_3920,

title = {A hybrid genetic algorithm-neural network model for optimizing thermal and mechanical characteristics of 3D-printed parts},

author = {Hamidreza Vanaei and Mohammad Moezzibadi and Abbas Tcharkhtchi and Sofiane Khelladi},

url = {http://dx.doi.org/10.1108/RPJ-02-2025-0085},

year  = {2025},

date = {2025-10-01},

journal = {Rapid Prototyping Journal},

abstract = {Fused filament fabrication (FFF) optimization remains a critical challenge due to the complex interplay of process parameters affecting the physicochemical and mechanical properties of printed parts. This study aims to optimize the FFF process by analyzing the effects of liquefier temperature, platform temperature and print speed using artificial neural networks (ANNs) and genetic algorithms (GAs).

A hybrid approach combining ANNs and GAs was employed to model and optimize the process parameters. The GA systematically explored the parameter space to identify optimal settings that maximize material properties such as degree of crystallinity, tensile strength, Young's modulus and elongation at break. The ANN, trained on an extensive experimental dataset, provided predictive insights into these properties, enabling a more efficient and precise optimization. A heat transfer model was utilized to predict the temperature profile of the filaments during deposition, validating the optimized conditions and offering insights into the thermal history of the printed parts.

The results demonstrate that the optimized process parameters significantly enhance material properties and improve inter-layer bonding. The study identifies the critical influence of liquefier temperature and print speed on mechanical performance, while also revealing limitations in traditional optimization methods. Furthermore, the thermal model predictions align well with experimental findings, reinforcing the validity of the proposed optimization strategy.

This study provides a novel hybrid approach to optimizing FFF process parameters by integrating machine learning techniques with traditional experimental and analytical models. The findings offer valuable insights into improving 3D printing efficiency and part quality, with potential applications in various industrial and research domains. Future work may explore the generalizability of the findings to different materials and incorporate real-time process monitoring for enhanced adaptability and precision.},

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@article{aajal_3928,

title = {Autonomous On-Street Parking Slot Detection: A Comprehensive Systematic Review of Vision-Based Approaches},

author = {Chaimae AAJAL and Othmane Sebban and Nada FAQIR and Ahmed Azough and Khalid Alaoui Zidani},

url = {https://ieeexplore.ieee.org/document/11207589},

year  = {2025},

date = {2025-10-01},

journal = {Ieee Access},

volume = {13},

pages = {183734-183754},

abstract = {Finding an available parking space remains a significant challenge, particularly in major international cities, where rapid population growth and increasing traffic congestion exacerbate this problem. To address this issue, numerous sensor-based systems have been developed to manage parking infrastructures. However, these systems are often expensive and impractical because their sensors are susceptible to damage from adverse weather conditions. Consequently, in-vehicle camera-based parking-slot detection methods have emerged as more effective and scalable alternatives. Rapid advances in artificial intelligence in recent decades have facilitated the development of diverse methods and tools for autonomous parking detection. This systematic review examines state-of-the-art parking detection techniques, including image processing, machine learning, deep learning, and reinforcement learning approaches. It considers various imaging perspectives such as the driver's point of view, bird's-eye view, and around-view monitoring (AVM). The reviewed methods are evaluated using widely adopted benchmark datasets, with a focus on their performance in identifying parking slots and determining the occupancy status. Key challenges, including real-time implementation constraints and performance robustness across diverse environmental conditions, were critically analyzed. The findings suggest that hybrid models that integrate reinforcement learning with supervised learning hold substantial promise for future research and applications. This study offers a comprehensive assessment of current methodologies, identifies persistent research gaps, and proposes actionable recommendations to advance the development of robust autonomous parking detection systems.},

note = {22/10/2025TELECHARGER LA VERSION AVEC MENTION DATE PUBLISHED ASAP},

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@article{baroni_3939,

title = {Quantum bounds for compiled XOR games and $d$-outcome CHSH games},

author = {Matilde Baroni and Quoc Huy Vu and Boris Bourdoncle and Eleni Diamanti and Damian Markham and Ivan ?upi?},

url = {https://doi.org/10.22331/q-2025-10-28-1894},

year  = {2025},

date = {2025-10-01},

journal = {Quantum},

volume = {9},

pages = {1894},

abstract = {Nonlocal games play a crucial role in quantum information theory and have numerous applications in certification and cryptographic protocols. Kalai et al. (STOC 2023) introduced a procedure to compile a nonlocal game into a single-prover interactive proof, using a quantum homomorphic encryption scheme, and showed that their compilation method preserves the classical bound of the game. Natarajan and Zhang (FOCS 2023) then showed that the quantum bound is preserved for the specific case of the CHSH game. Extending the proof techniques of Natarajan and Zhang, we show that the compilation procedure of Kalai et al. preserves the quantum bound for two classes of games: bipartite XOR games and a higher dimensional generalization of CHSH (the SATWAP inequality), that we refer to as d-outcome CHSH games. We also establish that, for any pair of qubit measurements, there exists a compiled XOR game such that its near-optimal winning probability serves as a robust selftest for that particular pair of measurements. Finally, we derive computational self-testing of three anticommuting qubit observables, based on the compilation of the nonlocal game corresponding to the so-called elegant Bell inequality.},

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@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},

url = {https://onlinelibrary.wiley.com/doi/10.1111/joes.12671},

year  = {2025},

date = {2025-09-01},

journal = {Journal of Economic Surveys},

volume = {39},

number = {4},

pages = {1695-1726},

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.},

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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 = {https://doi.org/10.1080/01605682.2024.2449470},

year  = {2025},

date = {2025-09-01},

journal = {Journal Of The Operational Research Society},

volume = {76},

number = {9},

pages = {1946-1961},

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.},

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@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://doi.org/10.1016/j.ejor.2025.03.009},

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},

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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://doi.org/10.1016/j.compstruct.2025.119269},

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.},

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tppubtype = {article}

}

@article{venkateswaran_3867,

title = {Special Issue; Robotics and Parallel Kinematic Machines},

author = {Swaminath Venkateswaran},

url = {https://www.mdpi.com/2218-6581/14/9/125},

year  = {2025},

date = {2025-09-01},

journal = {Robotics},

volume = {14},

number = {9},

pages = {1-2},

abstract = {Parallel kinematic machines (PKMs) are a class of robots that have long been recognized for their higher stiffness and high payload-to-weight ratio and precision compared to their serial counterparts [1,2]. Their applications are suitable in various areas such as high-speed machining, medical robotics, and space. Despite their advantages and widespread applications, PKMs suffer from various inherent challenges in design and control owing to complexities in kinematics, limited workspaces, and intricate singularity conditions [3]. Recent industrial developments and research work have focused on improving modeling techniques, the analysis of singular configurations, and reconfigurable architecture. Significant attention has been paid to domains such as the optimization of workspaces [4], the singularity avoidance approach [5], robust design procedures [6], and the integration of compliant components for PKM structures [7] in order to meet evolving application demands. However, several theoretical and practical aspects remain underexplored. For example, cuspidal configurations whereby a robot can transition between multiple inverse kinematic solutions without encountering singularities remain less documented [8,9]. Another interesting phenomenon includes self-motion conditions where the end-effector exhibits mobility even when all actuators are locked, thereby posing design challenges and difficulties in control [10,11]. Furthermore, emerging topics such as modular PKM architectures [12], dynamic performance evaluation [13], and control-aware design optimization [14] continue to attract the attention of the research community, especially for high-precision applications under uncertain or varying load conditions. The following Special Issue features eight articles in the field of PKMs and their potential applications to meet the present era's growing industrial demands.

In paper [15], the authors propose an approach for the 3D modeling of spatial manipulators using Maple 2023 software. This approach enables the creation of three-dimensional computer models of manipulators with clear visual representation of links, cross sections, kinematic pairs, and loads, differing in structure and degrees of freedom, while ensuring a comprehensive view from spatial perspectives. Using the Denavit-Hartenberg method for motion control of 3D models and the recursive Newton-Euler algorithm for calculating distributed dynamic loads, algorithms were developed for generating distribution diagrams of all dynamic loads in each link of a moving manipulator. This approach proves vital, especially during the design of new, innovative manipulators.

In paper [16], the author proposes a computationally efficient approach for dimensional synthesis using multi-objective particle swarm optimization with hierarchical constraints. The approach demonstrates the broad applicability of combined structural and dimensional synthesis for symmetric parallel robots with rigid links and serial-kinematic leg chains. The approach also allows for extension as an open-source MATLAB toolbox 2024b.

In paper [17], the authors present the design and analysis of a novel three-degrees-of-freedom parallel robot with self-sensing Nitinol actuators. The method is a continuation of a previous work and was extended to a 20 mm actuator length. The manipulator was realized, and the control strategy was validated experimentally. The work highlights the importance of self-sensing Nitinol actuators for the design of small parallel manipulators.

In paper [18], the authors propose a flexible surgical stapler mechanism for laparoscopic rectal cancer surgery. By performing workspace analysis and synthesizing kinematic equations, precise control of the mechanism was possible during surgical procedures in the rectal region. The singularities were also examined, considering the influential eyelet friction parameter. A preliminary design was presented, which facilitates the identification of the friction parameter.

In paper [19], the authors propose a neural network model to approximate the task time function of a generic multi-DOF robot. The method proposed a uniform interface that can be adapted to generic robots and tasks. The results presented an accurate model with evaluation times compatible with real-time process optimization.

In paper [20], the authors present the analysis of a spherical parallel mechanism of type 3-SPS-U. The usual singularity approach involves using Euler angles. However, this method is computationally expensive, especially when working with stacked models. Using the Tilt and Torsion approach, the singularity analysis was carried out successfully, which also enabled the work to be extended for stacked models. An experimental platform also aided in validating the approach.

In paper [21], the authors present a novel walking hybrid-kinematics robot that can be reconfigured to have three, five, or six degrees of freedom. A symmetric 3PRPR/3PRRR parallel manipulator with three translational DOFs was used to perform machining tasks. A serial module with two rotational DOFs was added to the 3T parallel mechanism to provide five DOFs. Similarly, a parallel 3SPR or 3SU mechanism can also be combined with the 3T parallel mechanism to provide six DOFs. The mobility, pose, kinematics, differential kinematics, singularities, and workspace of the parallel configuration are discussed in detail. The analysis showed that by making the moving platform of 3SPR or 3SU smaller than the base, the singularities can be easily avoided.

Lastly, in paper [22], the authors present a detailed review covering the fields of rehabilitation, assistive technologies, and humanoid robots. The authors focus on the study of parallel robots designed for human body joints with three degrees of freedom, particularly the neck, shoulder, wrist, hip, and ankle. The review authors discuss the timeline and advancements of parallel robots, focusing on technology readiness levels (TRLs), degrees of freedom, kinematic structure, workspace analysis, performance evaluation methods, and material selection for the design.

The Guest Editor would like to sincerely thank all of the contributors for taking a keen interest in our Special Issue. All of the articles included herein were subjected to rigorous peer review to ensure the production of a high-quality publication. We also thank all of the reviewers for their valuable comments and revisions. In addition, we would like to thank the editors from MDPI for their support in the organization and publication of this Special Issue.

It is our belief that this Special Issue will help fellow researchers and industrial experts to gain an in-depth understanding of the analysis of parallel kinematic machines and their applications based on their architecture.},

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@article{vanaei_3797,

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

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

url = {https://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{abuzerr_3846,

title = {Paediatric meningitis outbreak in Gaza amid health system collapse},

author = {Samer Abuzerr and Hani Hamdan and Jinan Charafeddine},

url = {http://dx.doi.org/10.1016/S0140-6736(25)01520-X},

year  = {2025},

date = {2025-08-01},

journal = {Lancet},

volume = {412},

number = {10504},

pages = {689-690},

abstract = {The Gaza Strip is facing a surge in paediatric meningitis cases amid total health system collapse. Since October, 2023, prolonged military assault and blockade have destroyed health, water, and sanitation infrastructure. As of July, 2025, the UN reports that at least 1·9 million people?approximately 90% of the population in the Gaza Strip?have been displaced by the ongoing war and are now living in overcrowded and unsanitary shelters.1 Many have been forced to flee multiple times, with some displaced ten times or more. The repeated displacement orders issued by Israeli forces following the collapse of the ceasefire have driven even more people to flee in search of safety.1 This environment has enabled the rapid spread of meningitis, especially among children.

At Nasser Medical Complex in Khan Younis (Gaza Strip, occupied Palestinian territory), more than 40 children have been admitted with suspected or confirmed meningitis.2 Physicians report severe shortages of antibiotics and cerebrospinal fluid kits, forcing empirical treatment and unsanitary procedures. Children present with high fevers, neck stiffness, vomiting, and seizures, yet are treated on floors, often without electricity or sterile equipment.2

Underlying this outbreak is a broader public health emergency. Displaced infants have scarce access to clean water, nutrition, and maternal care.3 Many mothers are malnourished and unable to breastfeed; formula and nappies are largely unavailable. In one case, a 1-month-old baby was diagnosed with meningitis after living for weeks in unsanitary conditions, without proper nutrition or hygiene supplies.3

The spread of meningitis in Gaza reflects the collapse of core public health protections. Hospitals are non-functional, staff are overwhelmed, and basic supplies are cut off.4 This outbreak, similar to those seen in conflict-affected settings such as South Sudan5 or Syria6, underscores the devastating impact of displacement and water, sanitation, and hygiene failures; however, Gaza's isolation and long-term siege make the crisis even more acute.7

Immediate international action is required. Priorities include reaching an immediate ceasefire; provision of antibiotics, vaccines, and diagnostic tools; restoration of water, sanitation, and hygiene services; establishment of humanitarian corridors; protection of health facilities; and support for disease surveillance. Without urgent intervention, more children will die from preventable illness. We call on the global health community to act now?to protect Gaza's children from further suffering and irreversible harm.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pairolero_3853,

title = {Closing the Gender Gap in Patenting: Evidence from a Randomized Control Trial at the USPTO},

author = {A. Nicholas Pairolero and Andrew Toole and Peter-Anthony Pappas and Charles de Grazia and Mike Teodorescu},

url = {https://www.aeaweb.org/articles?id=10.1257/pol.20230253},

year  = {2025},

date = {2025-08-01},

journal = {American Economic Journal-Economic Policy},

volume = {17},

number = {3},

pages = {281-310},

abstract = {Analyzing a randomized control trial at the United States Patent and Trademark Office that was designed to provide additional help to applicants who do not have legal representation, we find heterogeneous causal impacts across inventor gender, driven primarily by an increase in successful negotiations by women inventor teams via the use of examiner's amendments. While both men and women applicants benefited, the probability of obtaining a patent was over 12 percentage points greater for women. Our results suggest that a portion of the gender gap in patenting could be eliminated through additional assistance during patent examination.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ul-ain_3865,

title = {Measuring the success of business intelligence and analytics systems: A literature review},

author = {Noor Ul-Ain and William H. DeLone and Giovanni Vaia},

url = {http://dx.doi.org/10.1016/j.technovation.2025.103277},

year  = {2025},

date = {2025-08-01},

journal = {Technovation},

volume = {146},

pages = {103277},

abstract = {Technology-based solutions such as business intelligence and analytics (BI&A) systems have become indispensable for organizations due to their ability to support decision-making. Recent developments in big data availability and more powerful analytical tools have increased the potential value of BI&A systems. However, academic and practitioner-oriented research suggests that the potential success of BI&A systems has not yet been fully realized by most organizations. Existing studies have attempted to evaluate the effectiveness and success of BI&A systems by proposing various success measures. However, these studies have generated inconsistent results, limiting the ability to compare and generalize the findings. Therefore, this study takes a step forward by proposing an updated, comprehensive, and consolidated set of success measures for this unique class of information systems. Using a systematic literature review approach, this study examined and synthesized BI&A systems success measures across 173 past studies using the DeLone & McLean IS success framework. Findings revealed success measures such as ease of use, information accuracy, and financial performance that are consistently applied to the measurement of BI&A systems, and importantly, other recommended measures such as system features, presentation format, and decision-making performance that are uniquely important to BI&A systems but infrequently applied. Finally, a comprehensive set of BI&A success measures is proposed for future empirical research studies and practitioner use.},

note = {demande du PDF le 05/09/2025. Relance le 16/10/2025},

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{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://doi.org/10.1007/s10266-025-01066-9},

year  = {2025},

date = {2025-07-01},

journal = {Odontology},

volume = {113},

number = {1},

pages = {1220-1231},

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 = {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 = {https://doi.org/10.1016/j.ijpe.2025.109626},

year  = {2025},

date = {2025-07-01},

journal = {International Journal Of Production Economics},

volume = {285},

pages = {109626},

abstract = {When dealing with short-life cycle products, small and medium-sized enterprises (SMEs) commonly confront challenges stemming from limited local censored demand data. This often leads to a lack of comprehensive understanding of demand distribution and can result in suboptimal order decisions. To address this issue, we introduce a data-driven newsvendor framework that combines a novel cost-driven data correction procedure with distributionally robust optimization (CDDC-DRO). With cost minimization objectives, the proposed procedure integrates local censored demand data and external demand information to adaptively generate high-value improved censored datasets, while circumventing reliance on static correlations. Furthermore, we consider the granularities of external demand information and propose three DRO-based data correction strategies to effectively reduce demand censoring. Tests on both simulated and actual data indicate that the CDDC-DRO procedure adaptively corrects censored data based on demand characteristics and cost structures, thereby eliminating significant errors induced by demand censoring and improving the precision and robustness of order decisions. The correction degree of the improved censored datasets dynamically depends on cost structure. A high degree of data correction is employed under high critical ratios, whereas a minimal correction degree is applied under low critical ratios. In response to the significant negative impacts of demand censoring, SMEs prefer to implement the DRO-based data correction strategy with finer-grained external demand information. This strategy enhances correction capabilities while minimizing variations in decision accuracy. Even when finer-grained external demand information is unavailable, SMEs are able to make well-informed order decisions using the DRO-based data correction strategy with local censored demand data.},

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 = {https://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}

}