École Supérieure Angevine d'Informatique et de Productique

The single-beam Z-scan thermal lens technique is applied to investigate absorption efficiencies of spherical gold nanoparticles or fluorescence quantum yield in various suspensions and solutions with high-moderate absorption. Gold NPs of different diameters were analyzed at three distinct wavelengths (blue, green, red). The method extends traditional Gaussian beam analysis to top-hat beams and allows for higher absorption conditions. The experimental data, validated against Mie theory, confirms that photothermal efficiency increases with smaller diameters and shorter wavelengths. Furthermore, the TL technique is leveraged to evaluate fluorescence quantum yield in solutions exhibiting either strong fluorescence or high thermal efficiency with weak fluorescence. Analytical calculations were performed to estimate random errors introduced by optical detectors, revealing significant uncertainty in low-fluorescence solutions, while stronger fluorescence helps minimize measurement errors. The study further discusses the challenges posed by random errors when multiple measurements are required to obtain precise fluorescence estimations. These combined findings contribute to refining optical measurement techniques and improving accuracy in photothermal and fluorescence studies.

The Internet of Things (IoT) and its industrial counterpart, the Industrial Internet of Things (IIoT), have transformed sectors such as home automation, healthcare, and manufacturing by enhancing data management through advanced networking. However, the rapid growth of IIoT has introduced significant cybersecurity challenges, necessitating a comprehensive approach to securing data across the TCP/IP model. This paper presents a novel cybersecurity investment strategy formulated as a bi-objective optimization problem, validated through genetic and iterative algorithms. The strategy effectively balances security and cost, achieving nearly 50% efficiency in solution effectiveness. By utilizing these optimization techniques, the approach provides a practical and cost-effective solution to improve IIoT security within budget constraints, offering valuable insights for cybersecurity professionals seeking robust and economically viable solutions.

As we move towards the era of 6G and IoE (Internet of Everything), there is more and more seamless interconnection and interaction among things, people, data, and processes in one intelligent network. In IoE, whether 5G or 6G, the ecosystem embodies a plethora of connected devices and people, user-context heterogeneity, and fully distributed interactions, adding up to a highly complex ecosystem. The 5G will increase cellular bandwidth by huge amounts, making it much easier for the IoE to network large numbers of devices together.Adaptation to the right situational context, especially when it comes to security and protection has become a key requirement for IoE and other complex ecosystems. Especially the Access Control (AC) part is required to be robust, scalable, and resilient (from a security point of view). A dynamic, context-aware security with a breakout from the traditional statically mapped roles and mechanisms is essential today.Robust authentication can be achieved for IoT and IoE using a technique called Device Fingerprinting (DF) that we thoroughly investigate in this paper. We look at the mechanism itself, where and how it applies, and how Access Control (AC) works and scales with DF. We also model the DF generation process and look at different cases with different devices, and the respective overhead incurred by DF compared to the payload as well as the security overhead incurred for different frame sizes and devices as a percentage of the overall traffic (DF and payload combined). All models and assessments provided serve as a basis for future publications based on the continuous ongoing work we are doing in this area.

Blockchain technology provides a promising solution for collaborative economy systems by offering a decentralized, transparent, and secure platform. This is mainly accomplished through smart contracts, which are self-executing computer programs that facilitate, verify, and enforce the negotiation or performance of a contract. Digital tokens, on the other hand, are used to represent assets or currencies in these systems. Despite the benefits of Blockchain-based collaborative economy systems, significant security concerns are associated with them. These include the possibility of fraud, risk assessment, bugs in smart contracts, and cyber-attacks. For instance, attackers can exploit vulnerabilities in smart contracts to perform reentrancy and infinite loop attacks, leading to significant financial losses. To address these security challenges, this paper proposes integrating artificial intelligence models to prevent vulnerabilities in smart contracts and detect anomalies. Specifically, Graph Neural Networks models can be utilized to safeguard Blockchain-based collaborative economy platforms from attacks such as reentrancy and infinite loop attacks. According to the findings, this approach can accurately identify both normal and abnormal traffic and classify specific types of attacks. The framework's performance is further evaluated using various metrics to ensure its effectiveness in detecting anomalies, thereby providing an additional layer of security for Blockchain-based collaborative economy systems.

ISHS XXXI International Horticultural Congress (IHC2022): III International Symposium on Mechanization, Precision Horticulture, and Robotics: Precision and Digital Horticulture in Field Environments Deep learning-based detection of seedling development from controlled environment to field

Abstract With the enormous amount of sensitive data generated by supply chains and with the accession of Industry 4.0, the development of airline supply chain management (SCM) strategies and the preservation of privacy have become a necessity. The implementation of robust privacy strategies allows supply chains to protect their personal data from being lost, used, or accessed in an unauthorized way. Blockchain technologies (BT) allow data to be traded transparently and to automate transactions through smart contracts. Indeed, blockchain technology is attracting more and more attention as it ensures integrity and non-repudiation. However, secure supply chain existing models neglect the complexity of the airline supply chain management (ASCM), data confidentiality, and advanced and persistent attacks (APT) which make them exposed to many vulnerabilities. In this article, we propose a blockchain-based framework suitable for the ASCM context. Thus, we will develop an efficient model based on blockchain and machine learning to authenticate, validate, and secure transactions between suppliers and legitimate users on ASCM environments. The proposed solution reduces transaction costs, enhances security level by detecting and preserving anomalies, and fosters transparency and validity of transactions.KeywordBlockchain technologiesSupply chain managementAirline supply chain managementMachine learningSecurity and privacy

Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.