The Lille 1 University of Science and Technology (French: Université Lille 1 : Sciences et Technologies, USTL) was a French university located on a dedicated main campus in Villeneuve d'Ascq, near Lille (Hauts-de-France - European Metropolis of Lille), with 20,000 full-time students plus 14,500 students in continuing education (2004). 1,310 permanent faculty members plus 1,200 staff and around 140 CNRS researchers work there in the different University Lille 1 institutes and 43 research labs. University Lille 1 was a member of the European Doctoral College Lille Nord de France, which produces 400 doctorate dissertations every year. The university is ranked in the world top 200 universities in mathematics by the Shanghai ranking.University Lille 1 was established as Faculty of Science in 1854 in Lille, although its academic roots extend back to 1559. It later moved to Villeneuve d'Ascq in 1967. The University focuses on science and technology. Law, business management and medical fields are taught in the independent campus of Université de Lille II, while literature and social sciences are taught as part of the independent campus of Université de Lille III. Altogether, the three university in Lille include more than 70,000 students and are the main parts of the Community of Universities and Institutions (COMUE) Lille Nord de France. At the beginning of 2018, the three universities (Lille 1, Lille 2, Lille 3) merged to form the University of Lille; the UFRs of Lille 1 become Departments of the new Faculty of Science and Technology.
Tidal dissipation in natural satellites plays a crucial role in shaping their thermal state, internal structure, and evolution; for example, sustaining subsurface oceans in Europa and Enceladus and driving Io’s volcanic activity. The amount of dissipation can be inferred from secular orbital drift and gravity variations induces by tides, which can be measured through astrometric observations and spacecraft radiometric data. We examine a discrepancy in the literature regarding the semimajor axis evolution due to tides in synchronously rotating satellites, whereby predictions from the variation of orbital elements approach (using direct tidal accelerations) differ by nearly a factor of three from the classical energetic method. This discrepancy may introduce systematic biases in dissipation estimates of the same order. We identify the source of this inconsistency as the effect of tidal dissipation on the moon’s rotation, which induces an offset of the prime meridian. In classical synchronous rotation models, the prime meridian always points to the empty focus of the orbit, but once this offset is properly accounted for we recover an agreement with the energetic method. We then extend our analysis to include the main physical libration at the orbital period. Additionally, we compare the time-lag and complex Love number tidal models from an orbital evolution perspective, finding unexpected differences and proposing a formula to reconcile the two models. Finally, we observe that a nonzero static S2,2 gravity coefficient of a moon, considering the classical synchronous rotation as mentioned above, produces a variation in energy and angular momentum, suggesting that it must be constrained with dissipation parameters to avoid biases.
Abstract Shale gas reservoirs represent important energy resources, and the injection of unseparated flue gas has been proposed as a potential strategy for coupling geological carbon storage with enhanced shale gas recovery. In this study, density functional theory (DFT) calculations were employed to systematically investigate facet-dependent adsorption behaviors and underlying surface chemistry of CO2, N2, H2O, and CH4 on two representative MnCO3 crystal facets, (1 0 4) and (1 1 0). The gas–solid interfacial interactions were elucidated through adsorption configurations, adsorption energies, charge density redistribution, and electronic structure characteristics. The results show that CO2 and N2 exhibit substantially stronger interactions with MnCO3 than CH4, particularly at exposed Mn sites on the (1 1 0) surface, accompanied by pronounced charge redistribution and orbital hybridization. The (1 1 0) surface also exhibits lower stability (smaller absolute cohesive energy: -1.09 vs -1.51) and correspondingly higher surface reactivity, consistent with its stronger gas–surface interactions. Thermodynamic analysis further indicates that CO2, N2, and H2O maintain more favorable adsorption tendencies over a wider temperature range than CH4. These results reveal a pronounced facet-dependent adsorption preference for flue-gas components over CH4 at the atomistic level. Based on independent single-molecule adsorption calculations, these findings establish a molecular-level framework for understanding the selective interaction of flue-gas constituents with MnCO3 surfaces and their potential role in CH4 displacement. They further underscore the critical role of surface structure in regulating gas–mineral interactions and provide a mechanistic foundation for future multicomponent and multiscale studies of flue-gas-assisted shale gas recovery under realistic reservoir conditions.
This paper addresses statistical inference for stochastic partial differential equations. We study the stochastic Allen-Cahn equation driven by space-time white noise and analyze its mild solution. Our main focus is the asymptotic behavior of the spatial quadratic variation of the solution, for which we establish the exact limiting value. As an application, we develop parameter estimation procedures based on these asymptotic results. We prove that the unique solution can be decomposed as u = X + Y where X denotes the solution of the linear stochastic heat equation and Y accounts for the nonlinear effects. Exploiting a detailed analysis of the heat kernel and its scaling behavior, we derive Hölder continuity properties of Y in both spatial and temporal variables, showing that Y exhibits substantially higher regularity than X. This decomposition and the resulting regularity estimates are key ingredients in the development of parameter estimation procedures based on the asymptotic behavior of quadratic variations of the solution.
Artificial intelligence (AI) is playing an increasingly prominent role in medicine, and nephrology is no exception. Yet, behind this generic term lie very different realities depending on the type of data being processed. This didactic article offers a structured account of how AI works across three main data families, illustrated with concrete examples drawn from nephrology practice. With tabular data (the kind found in everyday medical records), predictive models can already anticipate acute kidney injury, intradialytic hypotension, or graft loss. With histological images, neural networks learn to detect and quantify glomerular lesions with remarkable precision, without replacing the pathologist. With text, large language models excel at reformulation, summarization, and triage tasks, more so than at diagnostic reasoning in ambiguous settings. The common thread across all three domains is the same: AI learns statistical regularities from data. Understanding this is the prerequisite for informed use: neither reflexive distrust nor uncritical delegation.
The need to develop cost-effective and green adsorbents for dye removal from water is an area of concern for water treatment. In this paper, the green adsorbent Ceratonia siliqua leaves (CSL) was used to remove Congo Red (CR) dye. A series of physicochemical analyses were carried out to characterize the green adsorbent. First, the effects of various operational factors on the CR removal efficiency were investigated. Subsequently, the process was optimized using the Box-Behnken design (BBD) method. The optimal values for the parameters were found to be 2.55 mg for the amount of adsorbent used, a pH of 3.58, and a contact time of 45.13 min. Using these optimal values, the removal capacity of the adsorbent for CR dye was predicted to be 614.83 mg/g. However, the actual removal capacity of the adsorbent for CR dye was found to be 610.31 mg/g or 99.80% of the removal efficiency. The obtained results confirmed the effective adsorption performance of CSL toward CR removal, highlighting its potential as a low-cost and sustainable biosorbent for wastewater treatment applications. The adsorption kinetics and equilibrium data were best described by the pseudo-second-order and Langmuir models, respectively. The thermodynamic study showed that adsorption is spontaneous and endothermic. RSM and ANN models showed excellent predictive performance, with R2 values of 0.997 and 0.990, respectively. These findings demonstrate how statistical and machine learning methods can work in tandem. This study showed CSL as a promising adsorbent in the removal of dyes in water treatment, being eco-friendly and affordable.
