From graph theory and geometric probabilities to a representative width for three-dimensional detonation cells

We present a model for predicting a representative width for the three-dimensional cells observed on detonation fronts in reactive gases. Its physical premise is that the dynamics of the transverse waves of irregular cells obeys a stochastic process both stationary and ergodic and produces the same burnt mass per unit of time as the average planar steady ZND process. Graph theory then defines an ideal cell whose grouping is equivalent to the actual 3D cellular front, geometric probabilities determine the mean burned fraction that parameterizes the model, and ZND calculations close the problem with the time-position relationship of a fluid element in the ZND reaction zone. The model is limited to detonation reaction zones whose sole ignition mechanism is adiabatic shock compression, such as those of the mixtures with H2, C3H8 or C2H4 as fuels considered in this work. Indeed, the comparison of their measured and calculated widths shows an agreement better than or within the accepted experimental uncertainties, depending on the quality of the chemical kinetic scheme used for the ZND calculations. However, the comparison for CH4:O2 mixtures shows high overestimates, indirectly confirming that the detonation reaction zones in these mixtures certainly include other ignition mechanisms contributing to the combustion process, such as turbulent diffusion. In these situations, the cell mean width derived from longitudinal soot recordings shows a very large scatter and may thus not be a relevant detonation characteristic length. The model is easily implementable as a post-process of ZND profiles and provides fast estimates of the cell width, length and reaction time.Comment: Extended versio

MACARONS: Mapping And Coverage Anticipation with RGB Online Self-Supervision

We introduce a method that simultaneously learns to explore new large environments and to reconstruct them in 3D from color images only. This is closely related to the Next Best View problem (NBV), where one has to identify where to move the camera next to improve the coverage of an unknown scene. However, most of the current NBV methods rely on depth sensors, need 3D supervision and/or do not scale to large scenes. Our method requires only a color camera and no 3D supervision. It simultaneously learns in a self-supervised fashion to predict a "volume occupancy field" from color images and, from this field, to predict the NBV. Thanks to this approach, our method performs well on new scenes as it is not biased towards any training 3D data. We demonstrate this on a recent dataset made of various 3D scenes and show it performs even better than recent methods requiring a depth sensor, which is not a realistic assumption for outdoor scenes captured with a flying drone.Comment: To appear at CVPR 2023. Project Webpage: https://imagine.enpc.fr/~guedona/MACARONS

ESG investments: Filtering versus machine learning approaches

We designed a machine learning algorithm that identifies patterns between ESG profiles and financial performances for companies in a large investment universe. The algorithm consists of regularly updated sets of rules that map regions into the high-dimensional space of ESG features to excess return predictions. The final aggregated predictions are transformed into scores which allow us to design simple strategies that screen the investment universe for stocks with positive scores. By linking the ESG features with financial performances in a non-linear way, our strategy based upon our machine learning algorithm turns out to be an efficient stock picking tool, which outperforms classic strategies that screen stocks according to their ESG ratings, as the popular best-in-class approach. Our paper brings new ideas in the growing field of financial literature that investigates the links between ESG behavior and the economy. We show indeed that there is clearly some form of alpha in the ESG profile of a company, but that this alpha can be accessed only with powerful, non-linear techniques such as machine learning

ESG Investments: Filtering versus Machine Learning Approaches

We designed a machine learning algorithm that identifies patterns between ESG profiles and financial performances for companies in a large investment universe. The algorithm consists of regularly updated sets of rules that map regions into the high-dimensional space of ESG features to excess return predictions. The final aggregated predictions are transformed into scores which allow us to design simple strategies that screen the investment universe for stocks with positive scores. By linking the ESG features with financial performances in a non-linear way, our strategy based upon our machine learning algorithm turns out to be an efficient stock picking tool, which outperforms classic strategies that screen stocks according to their ESG ratings, as the popular best-in-class approach. Our paper brings new ideas in the growing field of financial literature that investigates the links between ESG behavior and the economy. We show indeed that there is clearly some form of alpha in the ESG profile of a company, but that this alpha can be accessed only with powerful, non-linear techniques such as machine learning

Monocyte/macrophage response to β2-microglobulin modified with advanced glycation end products

Monocyte/macrophage response to β2-microglobulin modified with advanced glycation end products. We recently found that acidic β2-microglobulin (β2m), a major isoform of β2m in amyloid fibrils of patients with dialysis-related amyloidosis (DRA), contained early Amadori products and advanced glycation end products (AGEs) formed nonenzymatically between sugar and protein. Further analysis revealed that acidic β2m induces monocyte chemotaxis and macrophage secretion of bone-resorbing cytokines, suggesting the involvement of acidic β2m in the pathogenesis of DRA. Acidic β2m, however, is a mixture of heterogeneous molecular adducts due to various types of modification. In the present study, we investigated the modification responsible for the biological activity of acidic β2m toward monocytes/macrophages. The presence of a fair amount of β2m species with deamidation was detected in acidic β2m isolated from urine of non-diabetic long-term hemodialysis patients, but deamidated β2m had no biological activity. In contrast, normal β2m acquired the activity upon incubation with glucose in vitro. Among the glycated β2m, the pigmented and fluorescent β2m that formed after a long incubation period, that is, AGE-modified β2m, exhibited biological activity, whereas β2m modified with Amadori products, major Maillard products in acidic β2m, had no such activity. These findings suggest that AGEs, although only a minor constituent of acidic β2m, are responsible for monocyte chemotaxis and macrophage secretion of cytokines, implicating the contribution of AGEs to bone and joint destruction in DRA

Small- and large-fiber neuropathy after 40 years of type 1 diabetes associations with glycemic control and advanced protein glycation: the Oslo Study

OBJECTIVE To study large- and small-nerve fiber function in type 1 diabetes of long duration and associations with HbA1c and the advanced glycation end products (AGEs) N-&#949-(carboxymethyl)lysine (CML) and methylglyoxal-derived hydroimidazolone. RESEARCH DESIGN AND METHODS In a long-term follow-up study, 27 persons with type 1 diabetes of 40 &#177 3 years duration underwent large-nerve fiber examinations, with nerve conduction studies at baseline and years 8, 17, and 27. Small-fiber functions were assessed by quantitative sensory thresholds (QST) and intraepidermal nerve fiber density (IENFD) at year 27. HbA1cwas measured prospectively through 27 years. Serum CML was measured at year 17 by immunoassay. Serum hydroimidazolone was measured at year 27 with liquid chromatography– mass spectrometry. RESULTS Sixteen patients (59%) had large-fiber neuropathy. Twenty-two (81%) had smallfiber dysfunction by QST. Heat pain thresholds in the foot were associated with hydroimidazolone and HbA1c. IENFD was abnormal in 19 (70%) and significantly lower in diabetic patients than in age-matched control subjects (4.3 &#177 2.3 vs. 11.2 &#177 3.5 mm, P , 0.001). IENFD correlated negatively with HbA1c over 27 years (r = 20.4, P = 0.04) and CML (r = 20.5, P = 0.01). After adjustment for age, height, and BMI in a multiple linear regression model, CML was still independently associated with IENFD. CONCLUSIONS Small-fiber sensory neuropathy is a major manifestation in type 1 diabetes of 40 years duration and more prevalent than large-fiber neuropathy. HbA1c and the AGEs CML and hydroimidazolone are important risk factors in the development of large- and small-fiber dysfunction in long-term type 1 diabetes

Skin collagen advanced glycation endproducts (AGEs) and the long-term progression of sub-clinical cardiovascular disease in type 1 diabetes

BACKGROUND: We recently reported strong associations between eight skin collagen AGEs and two solubility markers from skin biopsies obtained at DCCT study closeout and the long-term progression of microvascular disease in EDIC, despite adjustment for mean glycemia. Herein we investigated the hypothesis that some of these AGEs (fluorescence to be reported elsewhere) correlate with long-term subclinical cardiovascular disease (CVD) measurements, i.e. coronary artery calcium score (CAC) at EDIC year 7-9 (n = 187), change of carotid intima-media thickness (IMT) from EDIC year 1 to year 6 and 12 (n = 127), and cardiac MRI outcomes at EDIC year 15-16 (n = 142). METHODS: Skin collagen AGE measurements obtained from stored specimens were related to clinical data from the DCCT/EDIC using Spearman correlations and multivariable logistic regression analyses. RESULTS: Spearman correlations showed furosine (early glycation) was associated with future mean CAC (p \u3c 0.05) and CAC \u3e0 (p = 0.39), but not with CAC score100. Glucosepane and pentosidine crosslinks, methylglyoxal hydroimidazolones (MG-H1) and pepsin solubility (inversely) correlated with IMT change from year 1 to 6(all P \u3c 0.05). Left ventricular (LV) mass (cMRI) correlated with MG-H1, and inversely with pepsin solubility (both p \u3c 0.05), while the ratio LV mass/end diastolic volume correlated with furosine and MG-H1 (both p \u3c 0.05), and highly with CML (p \u3c 0.01). In multivariate analysis only furosine (p = 0.01) was associated with CAC. In contrast IMT was inversely associated with lower collagen pepsin solubility and positively with glucosepane, CONCLUSIONS: In type 1 diabetes, multiple AGEs are associated with IMT progression in spite of adjustment for A1c implying a likely participatory role of glycation and AGE mediated crosslinking on matrix accumulation in coronary arteries. This may also apply to functional cardiac MRI outcomes, especially left ventricular mass. In contrast, early glycation measured by furosine, but not AGEs, was associated with CAC score, implying hyperglycemia as a risk factor in calcium deposition perhaps via processes independent of glycation. TRIAL REGISTRATION: Registered at Clinical trial reg. nos. NCT00360815 and NCT00360893, http://www.clinicaltrials.gov