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Direct and Indirect Effects of Prolonged Exposure to Long Working Hours on Risk Stroke Subtypes in the CONSTANCES Cohort
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Integrating Social Dimensions into Urban Digital Twins: A Review and Proposed Framework for Social Digital Twins
No full textInternational audienceThe rapid evolution of smart city technologies has expanded digital twin (DT) applications from industrial to urban contexts. However, current urban digital twins (UDTs) remain predominantly focused on the physical aspects of urban environments (“spaces”), often overlooking the interwoven social dimensions that shape the concept of “place”. This limitation restricts their ability to fully represent the complex interplay between physical and social systems in urban settings. To address this gap, this paper introduces the concept of the social digital twin (SDT), which integrates social dimensions into UDTs to bridge the divide between technological systems and the lived urban experience. Drawing on an extensive literature review, the study defines key components for transitioning from UDTs to SDTs, including conceptualization and modeling of human interactions (geo-individuals and geo-socials), social applications, participatory governance, and community engagement. Additionally, it identifies essential technologies and analytical tools for implementing SDTs, outlines research gaps and practical challenges, and proposes a framework for integrating social dynamics within UDTs. This framework emphasizes the importance of active community participation through a governance model and offers a comprehensive methodology to support researchers, technology developers, and policymakers in advancing SDT research and practical applications
Neurotoxins Acting on TRPV1—Building a Molecular Template for the Study of Pain and Thermal Dysfunctions
No full textInternational audienceTransient Receptor Potential (TRP) channels are ubiquitous proteins involved in a wide range of physiological functions. Some of them are expressed in nociceptors and play a major role in the transduction of painful stimuli of mechanical, thermal, or chemical origin. They have been described in both human and rodent systems. Among them, TRPV1 is a polymodal channel permeable to cations, with a highly conserved sequence throughout species and a homotetrameric structure. It is sensitive to temperature above 43 °C and to pH below 6 and involved in various functions such as thermoregulation, metabolism, and inflammatory pain. Several TRPV1 mutations have been associated with human channelopathies related to pain sensitivity or thermoregulation. TRPV1 is expressed in a large part of the peripheral and central nervous system, most notably in sensory C and Aδ fibers innervating the skin and internal organs. In this review, we discuss how the transduction of nociceptive messages is activated or impaired by natural compounds and peptides targeting TRPV1. From a pharmacological point of view, capsaicin—the spicy ingredient of chilli pepper—was the first agonist described to activate TRPV1, followed by numerous other natural molecules such as neurotoxins present in plants, microorganisms, and venomous animals. Paralleling their adaptive protective benefit and allowing venomous species to cause acute pain to repel or neutralize opponents, these toxins are very useful for characterizing sensory functions. They also provide crucial tools for understanding TRPV1 functions from a structural and pharmacological point of view as this channel has emerged as a potential therapeutic target in pain management. Therefore, the pharmacological characterization of TRPV1 using natural toxins is of key importance in the field of pain physiology and thermal regulation
The Impact of Predictive Maintenance on the Performance of Industrial Enterprises
No full textInternational audienceOptimizing maintenance and enhancing the performance of businesses are significant concerns in the modern industrial world. Predictive maintenance is emerging as an innovative approach to address these challenges, allowing companies to shift from corrective maintenance to preventive maintenance. Predictive maintenance relies on the use of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to collect and analyse real-time data from equipment. Through this predictive analysis, it becomes possible to identify early warning signals of failures, enabling the anticipation of potential issues and the proactive planning of maintenance interventions. In this study, we will thoroughly examine the impact of predictive maintenance on the performance of businesses. We will explore the benefits and opportunities it offers in terms of reducing downtime, optimizing maintenance costs, and enhancing productivity. We will also investigate the various technologies and methods used in the implementation of predictive maintenance, along with potential challenges and best practices for successful adoption. This research focuses on studying the application of predictive maintenance within a company using data science and machine learning methods. Predictive maintenance represents an innovative approach aimed at anticipating equipment failures by leveraging real-time collected data. Through the analysis of this data using sophisticated algorithms, it becomes possible to identify early signals of potential problems and implement preventive maintenance actions before breakdowns occur. This approach not only reduces unexpected downtime but also optimizes maintenance operations by avoiding unnecessary interventions and maximizing resource utilization. The ultimate goal is to improve equipment availability, optimize operational performance, and maximize the overall yield of the company
Improving measurement system fidelity through the optimization of preventive maintenance and operating conditions: a comparative study of measurement system analysis approaches
No full textInternational audienceThis study focused on measurement system analysis MSA through gage of Repeatability and Reproducibility R and R methods (Average and Range Xbar/R, Analysis Of Variance ANOVA, Evaluating Measurement Process EMP III) to verify the accuracy and precision of a laser diffraction particle size analyser used to monitor particles’ size in wet grinding process, this device gained great faith in many researches without checking its fidelity. The initial evaluation demonstrates the measurement system’s unacceptability, driving to establish an improvement plan including preventive maintenance scheduling for the instrument to maintain it in good working conditions and anticipate futures anomalies that could affect the measurement system fidelity, standardization of measuring process by the instrument to reduce repeatability, which was found to be the largest source of variation in the gage R and R analysis affecting measurement accuracy and precision, along with unifying the method of calculating solid rate percentage of samples before measurements because it is a significant factor that can affect the measurement results, finally, operators training to conduct measurements in the same way and following unified procedures in the purpose of minimizing reproducibility and increasing the measurement system fidelity, which is proven by the final results. Along this evaluation, a critical comparison between these techniques is conducted in terms of calculations, result’s interpretation and acceptability requirements to evaluate the efficiency of each method. Xbar/R and ANOVA demonstrate a similarities in interpretation and acceptability criteria but ANOVA surpassed Xbar/R by adding new factors in the calculations, while EMP III, which is not widely addressed in the literature, shows a great difference with a distinguished methodology, distinct metrics and acceptability guidelines. Therefore, a combination of ANOVA and EMP III in measurement system analysis would be very effective by extracting comprehensive informations about the studied instrument
Methodological expectations for demonstration of health product effectiveness by observational studies
No full textInternational audienceThe issue of assessing the effectiveness of health technologies (drugs, devices, etc.) through observational studies is becoming increasingly important as registration and market access agencies consider them in their evaluation process. In this context, observational studies must be able to provide real demonstrations of a level of reliability comparable to those produced by the conventional randomized controlled trial (RCT) approach. The objective of the roundtable was to establish the acceptability criteria for an observational study (non-randomized, non-interventional study) to be able to provide these demonstrations, and possibly serve as a confirmatory study for registration and market access authorities, the construction of therapeutic strategies or the development of recommendations. In order to do this, the study must be a real confirmatory study respecting the hypothetical-deductive approach and guaranteeing the absence of HARKing and p-hacking by attesting to the establishment of a protocol and a statistical analysis plan, recorded before any inferential analysis. It must also be part of a formalized approach to causal inference and demonstrate that it correctly identifies the causal estimand sought. The study should ensure that there is no residual confusion bias by taking into account all confounding factors affecting the comparison, which should be determined by a formal approach (such as a graphical causality approach, DAGs). Residual confusion bias diagnoses by forgery and nullification analysis should be non-existent. The study shall be at low risk of bias, in particular selection bias, among others by using a target test emulation design. Overall type I error risk should be strictly controlled. The absence of selective publication of results and selection bias should be ensured
Le système de santé français face à ses défis : la réponse des soins primaires
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Data management and sharing
No full textInternational audienceGuided by the FAIR principles (Findable, Accessible, Interoperable, Reusable), responsible data sharing requires well-organized, high-quality datasets. However, researchers often struggle with implementing Data Management and Sharing Plans (DMSPs) due to lack of knowledge on how to do this, time constraints, legal, technical and financial challenges, particularly concerning data ownership and privacy. While patients support data sharing, researchers and funders may hesitate, fearing the loss of intellectual property or competitive advantage. Although some journals and institutions encourage or mandate data sharing, further progress is needed. Additionally, global solutions are vital to ensure equitable participation from low- and middle-income countries. Ultimately, responsible data sharing requires strategic planning, cultural shifts in research, and coordinated efforts from all stakeholders to become standard practice in biomedical research
In Tune With the Times? When Neo‐Peasants Choose Animal Traction to be Part of a More Sustainable Production Model
No full textInternational audienceAs regards alternative agricultural practices today, it is worth noticing the increase in the use of animal traction (AT) in France. Yet, the reasons for this reappropriation remain unclear. The life paths and motivations of users are the focus of this article. The study is based on 33 qualitative interviews with French farmers. It uses Gasson's theoretical framework and a life course analysis. Findings show that farmers who have chosen AT first tend to question the agricultural model. They can be described as neo-rurals motivated by their political opinions, a desire for greater autonomy and their passion for horses. These neo-peasants have opted for animal-drawn cultivation because they reject the main agricultural system and wish to create a new connection with nature. This study is also addressed to policymakers and people advocating more sustainable agricultural practices in general.All authors have participated in (a) conception and design or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.</p
Density of Uranus moons: Evidence for ice/rock fractionation during planetary accretion
No full textInternational audienceCurrent models suggest the five regular moons of Uranus formed in a single stage from a primary planetary disk or a secondary impact disk. Using latest estimates of moon masses (Jacobson, 2014), we find a power-law relationship between size and density of the moons due to varying rock/ice ratios caused by fractionation processes. This relationship is better explained by mild enrichment of rock with respect to ice in the solids that aggregate to form the moons following Rayleigh law for distillation (Rayleigh, 1896) than by differential diffusion in the disk, although the two mechanisms are not exclusive. Rayleigh fractionation requires that moon composition and density reflect their order of formation in a closed-system circumplanetary disk. For Uranus, the largest and densest moons Titania and Oberon (R ∼ 788 and 761 km, respectively) first formed, then the mid-sized Umbriel and Ariel (585 and 579 km), satellites in each pair forming simultaneously with similar composition, and finally the small rock-depleted Miranda (236 km). Fractionation likely occurred through impact vaporization during planetesimal accretion. This mechanism would add to those affecting the composition of accreting planets and moons in disks such as temporal/spatial variation of disk composition due to temperature gradients, advection, and large impacts. In the outer solar nebula, Rayleigh fractionation may account for the separation of a rock-dominated reservoir, and an ice-dominated reservoir, currently represented by CI carbonaceous chondrite/type-C asteroids and comets, respectively. Potential consequences for Uranus moons' composition are discusse