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This article presents the principles of the chemical vapor deposition method based on alternating precursor pulses, known as Atomic Layer Deposition (ALD). The discussion covers the range of materials that can be synthesized using this technique, its environmental impact, and its various fields of application together with their recent developments. Finally, a practical description of the steps involved in implementing an ALD process is provided.
H-embrittlement remains a problem for mechanical industries. Despite “degassing” heat treatment specifications and standards to avoid time-dependent rupture for quench/temper steel. The minimal heat treatment time depends only on the mechanical strength. The persistence of the problem, it means that the mechanism to avoid the H-embrittlement is not clear. The assumption that the degassing heat treatment might increase the hydrogen amount in the steel matrix is further explore. This article’s objective is to develop a new approach of the heat treatment function.
For more thantwenty years, Fibroline has been developing several patented technologies for electrostatic impregnation. These technologies allow to impregnate in a dry way, without water nor solvent, a great variety of powders in porous substrates such as nonwoven, textile, foam, paper, yarn or roving. This article begins with the presentation of the principle of Fibroline’s dry impregnation technology, the characteristics of the materials used as well as the various technological configurations developed around this principle. The transfer to industrial scale is then detailed considering the technical, economic and environmental benefits. Finally, examples of applications are presented for different industrial fields.
Artificial intelligence is reshaping industrial processes—predictive maintenance, quality control, energy optimisation—yet many projects fail due to poor organisational readiness. This article introduces an AI maturity audit—a simple method to assess a company’s ability to adopt such technologies. Built around six key levers—strategy, organization, people, offer, technology and environment—the audit reveals hidden barriers and supports a structured, sustainable approach before launching any AI project.
Our ability to manipulate individual particles over the past 20 years has enabled a new quantum revolution. This new revolution, known as the “second quantum revolution,” is characterized by the ability to exploit quantum effects for engineering purposes.
The widespread adoption of digital technologies is profoundly transforming urban governance. Long structured around the concept of the ‘smart city’, this transformation is now undergoing a significant shift towards the ‘wise city’ (or sober city), which places resilience, frugality, and collective intelligence at the heart of urban policies. This article offers a conceptual and operational analysis of this transition. It first examines the evolution of the smart city concept and its convergence with the notion of urban resilience. It then discusses the structuring role of digital twins and artificial intelligence (AI) as cognitive infrastructures of the contemporary city, and illustrates these dynamics with international applications.
This article clarifies what it takes, on an industrial shop floor, to design an artificial intelligence (AI) system that is truly useful. It contrasts the growing shortage of shop-floor staff with the strong appeal of so-called general-purpose AI and confronts this trend with the concrete needs of industrial operations. Three approaches are compared, data-driven, document-driven and hybrid, and their respective conditions for success are specified. The article proposes business-oriented KPIs, including source faithfulness, recall@k, P95 latency and MTTR, together with a calibrated abstention policy and a set of architectural and governance prerequisites required to move from a replicable pilot to large-scale deployment.
This paper presents an overview of the state of the art in natural language processing, exploring one specific computational architecture, the Transformer model, which plays a central role in a wide range of applications. This architecture condenses many advances in neural learning methods and can be exploited in many ways: to learn representations for linguistic entities; to generate coherent utterances and answer questions; to perform utterance transformations, an illustration being machine translation; to serve as backbone of versatile chatbots, able to answer queries and to perform, at will, a large variety of tasks. These facets of the architecture will be successively presented, which will also allow us to discuss its limitations.
There is currently an increasing need for fast and cost-effective analytical methods suitable for water pollutants monitoring and toxicological impact assessment. In this context, some techniques based on the specific properties of whole cells, enzymes, antibodies, DNA, biopolymers or materials of natural origin, appear as excellent alternatives or complementary techniques to classical chemical methods. This article will be more particularly focused on biosensors and biosorbents (principles, advantages and limitations, application to water quality assessment, new trends).
Operationalizing AI ethics is a major challenge for organizations. The Amazon, COMPAS and Obermeyer cases illustrate how AI systems can produce systemic discrimination despite the absence of malicious intent. This article proposes an operational framework for transforming ethical principles into concrete risk and impact management practices, based on the ISO/IEC 42001:2023 standard. We first show how this standard aligns with GDPR requirements in Europe and Law 25 in Quebec. We then detail the integration of risk and impact management at different stages of the AI systems lifecycle. Finally, we present operational tools (lifecycle × risks × controls matrix, indicators, organization) and a progressive roadmap for implementation.
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