Dirigisme, politique industrielle et rhétorique industrialiste

Le dirigisme économique français relève de ces évidences peu discutées aussi bien par les acteurs que dans la littérature. Pourtant, une étude empirique systématique des politiques publiques industrielles montre que, hors le cas des « grands projets » où sont mobilisés par un Etat de passe-droit la commande, la recherche, le financement et l'entreprise publics au service d'objectifs de souveraineté, les politiques industrielles relèvent en fait de l'accompagnement de stratégies d'entreprises ou de la recherche de la paix civile. La mise en perspective historique n'infirme pas ce résultat, elle révèle des configurations contrastées de relations Etat-Industrie où l'administration économique agit davantage sur l'environnement de l'entreprise qu'elle ne pèse sur les orientations sectorielles des acteurs. Dès lors, la logique partiellement autonome de la rhétorique industrialiste devient objet d'étude

CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption

In the era of Industrial IoT (IIoT) and Industry 4.0, ensuring secure data transmission has become a critical concern. Among other data types, images are widely transmitted and utilized across various IIoT applications, ranging from sensor-generated visual data and real-time remote monitoring to quality control in production lines. The encryption of these images is essential for maintaining operational integrity, data confidentiality, and seamless integration with analytics platforms. This paper addresses these critical concerns by proposing a robust image encryption algorithm tailored for IIoT and Cyber-Physical Systems (CPS). The algorithm combines Rule-30 cellular automata with chaotic scrambling and substitution. The Rule 30 cellular automata serves as an efficient mechanism for generating pseudo-random sequences that enable fast encryption and decryption cycles suitable for real-time sensor data in industrial settings. Most importantly, it induces non-linearity in the encryption algorithm. Furthermore, to increase the chaotic range and keyspace of the algorithm, which is vital for security in distributed industrial networks, a hybrid chaotic map, i.e., logistic-sine map is utilized. Extensive security analysis has been carried out to validate the efficacy of the proposed algorithm. Results indicate that our algorithm achieves close-to-ideal values, with an entropy of 7.99 and a correlation of 0.002. This enhances the algorithm's resilience against potential cyber-attacks in the industrial domain

Integration of TTF, UTAUT, and ITM for mobile Banking Adoption

The introduction of mobile banking facility has enabled customers to carry out banking transactionswith the use of smartphones and other handheld devices from anywhere. It has become a luxurious and exclusive method of online payments. The recent growth of telecommunication sector and a tremendous increase in mobile USAge has opened new doors for sparking future of banking sector industry. The following research is aimed to find out the mobile banking adoption attitudes with the integration of TTF, UTAUT,and ITM models

Stark Tuning and Electrical Charge State Control of Single Divacancies in Silicon Carbide

Neutrally charged divacancies in silicon carbide (SiC) are paramagnetic color centers whose long coherence times and near-telecom operating wavelengths make them promising for scalable quantum communication technologies compatible with existing fiber optic networks. However, local strain inhomogeneity can randomly perturb their optical transition frequencies, which degrades the indistinguishability of photons emitted from separate defects, and hinders their coupling to optical cavities. Here we show that electric fields can be used to tune the optical transition frequencies of single neutral divacancy defects in 4H-SiC over a range of several GHz via the DC Stark effect. The same technique can also control the charge state of the defect on microsecond timescales, which we use to stabilize unstable or non-neutral divacancies into their neutral charge state. Using fluorescence-based charge state detection, we show both 975 nm and 1130 nm excitation can prepare its neutral charge state with near unity efficiency.Comment: 12 pages, 4 figure

The Nrf2 Activator (DMF) and Covid-19: Is there a Possible Role?

COVID-19 is a new viral illness that can affect the lungs and airways with lethal consequences leading to the death of the patients. The ACE2 receptors were widely disturbed among body tissues such as lung, kidney, small intestine, heart, and others in different percent and considered a target for the nCOVID-19 virus. S-protein of the virus was binding to ACE2 receptors caused downregulation of endogenous anti-viral mediators, upregulation of NF-κB pathway, ROS and pro-apoptotic protein. Nrf2 was a transcription factor that's play a role in generation of anti-oxidant enzymes. To describe and establish role of Nrf2 activators for treatment COVID-19 positive patients. We used method of analysis of the published papers with described studies about COVID-19 connected with pharmacological issues and aspects which are included in global fighting against COVID-19 infection, and how using DMF (Nrf2 activator) in clinical trial for nCOVID-19 produce positive effects in patients for reduce lung alveolar cells damage. we are found that Nrf2 activators an important medication that's have a role in reduce viral pathogenesis via inhibit virus entry through induce SPLI gene expression as well as inhibit TRMPSS2, upregulation of ACE2 that's make a competition with the virus on binding site, induce gene expression of anti-viral mediators such as RIG-1 and INFs, induce anti-oxidant enzymes, also they have a role in inhibit NF-κB pathway, inhibit both apoptosis proteins and gene expression of TLRs. We are concluded that use DMF (Nrf2 activator) in clinical trial for nCOVID-19 positive patients to reduce lung alveolar cells damage. [Abstract copyright: © 2020 Saif M Hassan, Mahmood J Jawad, Salam W. Ahjel, Ram B. Singh, Jaipaul Singh4, Samir Mohamed Awad, Najah R Hadi.

Isolated spin qubits in SiC with a high-fidelity infrared spin-to-photon interface

The divacancies in SiC are a family of paramagnetic defects that show promise for quantum communication technologies due to their long-lived electron spin coherence and their optical addressability at near-telecom wavelengths. Nonetheless, a mechanism for high-fidelity spin-to-photon conversion, which is a crucial prerequisite for such technologies, has not yet been demonstrated. Here we demonstrate a high-fidelity spin-to-photon interface in isolated divacancies in epitaxial films of 3C-SiC and 4H-SiC. Our data show that divacancies in 4H-SiC have minimal undesirable spin-mixing, and that the optical linewidths in our current sample are already similar to those of recent remote entanglement demonstrations in other systems. Moreover, we find that 3C-SiC divacancies have millisecond Hahn-echo spin coherence time, which is among the longest measured in a naturally isotopic solid. The presence of defects with these properties in a commercial semiconductor that can be heteroepitaxially grown as a thin film on shows promise for future quantum networks based on SiC defects.Comment: 26 pages, 4 figure

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework