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Afflecto: A Web Server to Generate Conformational Ensembles of Flexible Proteins from Alphafold Models
No full textIntrinsically disordered proteins and regions (IDPs/IDRs) leverage their structural flexibility to fulfill essential cellular functions, with dysfunctions often linked to severe diseases. However, the relationships between their sequences, structural dynamics and functional roles remain poorly understood. Understading these complex relationships is crucial for therapeutic development, highlighting the need for methods that generate ensembles of plausible IDP/IDR conformers. While AlphaFold (AF) excels at modeling structured domains, it fails to accurately represent disordered regions, leaving a significant portion of proteomes inaccurately modeled. We present AFflecto, a user-friendly web server for generating large conformational ensembles of proteins that include both structured domains and IDRs from AF structural models. AFflecto identifies IDRs as tails, linkers or loops by analyzing their structural context. Additionally, it incorporates a method to identify conditionally folded IDRs that AF may incorrectly predict as natively folded elements. The conformational space is globally explored using efficient stochastic sampling algorithms. AFflecto's web interface allows users to customize the modeling, by modifying boundaries between ordered and disordered regions, and selecting among several sampling strategies. The web server is freely available at https://moma.laas.fr/applications/AFflecto/.</div
High-performance cooling of thermophotovoltaic cells uing microchannel-based front-surface techniques
No full textInternational audienc
High-resolution laser ablation of a copper layer for the fabrication of 3D-printed MEMS and microsensors
No full textThis work was supported by LAAS-CNRS microand nanotechnologies platform, a member of theRenatech french national networInternational audienceThis paper presents a novel method for structuring metallized layers on a polymer layer using laser ablation. The technique enables the fabrication of metallic tracks with a resolution of about 1 µm with minimal or no damage to the underlying polymer layer. It offers the flexibility required for complex 3Dprinted structures and overcomes the limitations of conventional approaches. It will provide an efficient alternative for MEMS fabrication
Superviz25-SQL: High-Quality Dataset to Empower Unsupervised SQL Injection Detection Systems
No full textInternational audienceThe digitalization of public and private services has led to more sophisticated and serious cybersecurity threats. Among them, SQL injection attacks leverage user inputs to remotely execute malicious actions on a database, such as data exfiltration and deletion, or privilege escalation. They are regularly classified as one of the most prominent threats to web services. Intrusion detection systems are widely used to detect such injection attacks and react to them, but it is difficult to assess their actual effectiveness and compare them because of a lack of high-quality datasets. Current SQL injection detection datasets lack diversity, are poorly documented, and the generated samples are not representative of real-world infrastructures. This article presents a new dataset Superviz25-SQ , whose design is structured around four quality dimensions: realism, diversity, benchmarking capabilities and the presence of good documentation. We examine the dataset diversity using lexical, syntactic and semantic metrics, and demonstrate that its size is sufficient to evaluate data-intensive detectors. Finally, we provide nine classical and state-of-the art SQL injection detection pipelines as baselines for future works
Towards Real-Time Blood Pressure Measurement Using BallistoCardioGraphy and Machine Learning
No full textInternational audienceBlood pressure (BP) is one of the most challenging vital parameters to measure continuously without disrupting the patient. Developing a method for non-intrusive, continuous BP monitoring would be a significant medical advancement. Ballistocardiography (BCG) offers a non-invasive way to monitor cardiac signals by detecting small body deformations. Machine Learning (ML) can then use these signals to estimate blood pressure through regression. This work demonstrates a correlation between BP and BCG patterns, though it also identifies several key factors that must be addressed for effective relative BP measurement using BCG. These factors are thoroughly examined and discussed
Using assurance cases to support type approval and regulatory compliance in the automotive sector
No full textInternational audienceThere is a wide body of literature and experience in the use of assurance cases in safety-critical industries such as nuclear and medical, but the use of assurance cases in the automotive sector is relatively new. In this position paper, we argue that assurance cases are the best way to demonstrate compliance with increasingly complex regulations for type approval in the automotive sector because they provide a means for the manufacturer to present a clear and compelling argument to the regulator and communicate to the public that the risks associated with the use of new technology have been reduced to an acceptable level
Towards global stabilization of a hovercraft model using hybrid systems and discontinuous feedback laws
No full textIn this paper, we propose discontinuous control laws to globally stabilize a target position of a hovercraft. Equations of motion of the hovercraft are derived through a kinematic model approximation of a dynamic model taken from the literature. Discontinuous feedback laws for the kinematic and the dynamic model are derived and analyzed using a hybrid systems formulation of the closed-loop dynamics. Numerical simulations confirm the theoretical convergence guarantees and validate the kinematic model as a valid simplification of the dynamic model
Pyrrolidinium-based protic ionic liquid electrolytes for high performance RuO<sub>2</sub> micro-supercapacitors
No full textInternational audienceAdvances in smart technologies and Internet of Things (IoT) call for compact and efficient energy storage devices. Among microscale systems, RuO2-based micro-supercapacitors (MSCs) provide high power pulses, but the limited stability window of their aqueous electrolytes constrains their energy density. In this study, we investigated pyrrolidinium-based protic ionic liquids (PILs) with varying alkyl chain lengths and anion types, achieving pseudocapacitive charge storage in RuO2 MSCs with an extended cell voltage up to 1.5 V. Among the tested electrolytes, 1-propyl pyrrolidinium trifluoroacetate [Pyr3H]⁺[TFA]⁻ delivered excellent energy density and cycling performances, while 1-methyl pyrrolidinium tetrafluoroborate [Pyr1H]⁺[BF4]⁻ showed low equivalent series resistance and superior power retention. Experimental findings align with Reactive Force Field (ReaxFF) molecular dynamics simulations, revealing proton exchange and ion diffusion mechanisms. Ionogel-based MSCs demonstrated long-term cycling stability, retaining performance over 5000 charge/discharge cycles. These results underscore the potential of pyrrolidinium-based PILs for on-chip solid-state MSCs powering microelectronics
Improved S-variable results applied to the analysis of time-varying uncertain systems
No full textInternational audienceFinite horizon performance analysis is addressed for time-varying uncertain linear systems. Results apply to state-space systems with matrices rational in both time and a scalar uncertainty. Performances include stability and L2 induced gain like criteria over a given time interval and extend to usual stability and L2 induced norm as the upper bound goes to infinity. Results are formalized in terms of linear matrix inequalities by applying the S-variable approach. Two improvements to this approach are proposed to reduce conservatism and deal with positive unbounded indeterminates such as time. Descriptor modeling is adopted and a lifting based methodology allows to build tractable results aiming at decreasing conservatism