Miracle-dz/UniFAR-FTUs huggingface dataset:
A super-grid approach for LES combustion closure using the Linear Eddy Model

LES–LEM is a simulation approach for turbulent combustion in which the stochastic Linear Eddy Model (LEM) is used for sub-grid mixing and combustion closure in Large-Eddy Simulation (LES). LEM resolves, along a one-dimensional line, all spatial and temporal scales, provides on-the-fly local turbulent flame statistics, captures finite rate chemistry effects and directly incorporates turbulence-chemistry interaction. However, the approach is computationally expensive as it requires advancing an LEM-line in each LES cell. This paper introduces a novel turbulent combustion closure model for LES using LEM to address this issue. It involves coarse-graining the LES mesh to generate a coarse- level ‘super-grid’ comprised of cell-clusters. Each cell-cluster, instead of each LES cell, then contains a single LEM domain. This domain advances the combined advection–reaction–diffusion solution and also provides suitably conditioned statistics for thermochemical scalars such as species mass fractions. Local LES-filtered thermochemical states are then obtained by probability-density-function (PDF) weighted integration of binned conditionally averaged scalars, akin to standard presumed PDF approaches for reactive LES but with physics-based determination of the full thermochemical state for particular values of the conditioning variables. The proposed method is termed ‘super-grid LEM’ or ‘SG-LEM’. The paper describes LEM reaction–diffusion advancement, the LEM representation of turbulent advection, a novel splicing algorithm (a key feature of LES–LEM) formulated for the super-grid approach, a wall treatment, and a thermochemical LES closure procedure. To validate the proposed model, a pressure-based solver was developed using the OpenFOAM library and tested on a premixed ethylene flame stabilised over a backward facing step, a setup for which some DNS data is available. SG-LEM provides high resolution flame structures, temperature and mass fractions suitable for LES thermochemical closure. Additionally, it provides reaction-rate data at the coarse level, a unique feature compared to other mapping-type closure methods. Quantitative comparisons are made between the proposed model and time-averaged DNS data, focussing on velocity, temperature and species mass fraction. Results show good agreement downstream of the step. Furthermore, comparison with an equivalent Partially-Stirred Reactor (PaSR) simulation demonstrates the superior predictive capability of SG-LEM. Additionally, the paper briefly examines the sensitivity of the model to coarse-graining parameters and finally, explores computational efficiency highlighting the substantial speedup achieved when compared to the standard LES–LEM approach with potentially significant speedup relative to PaSR closure for the intensely turbulent regimes of principal interest.
Effects of Filtering and Current-Angle Adjustment on the Multi-Material Topology Optimization of a Three-Phase Stator

This article investigates the multi-material topology optimization (MMTO) of the three-phase stator of an electrical machine. The density-based optimization framework uses a gradient descent on the physical properties of the candidate materials (magnetic polarization and current density). The results indicate that a smoothing procedure is necessary to avoid non-manufacturable designs and promote a broader exploration of solutions. They also highlight a current angle shifting due to the presence of permanent magnets from the beginning of the optimization. The current angle is, therefore, added to the optimization variables to address this issue. An efficient optimization procedure that couples both techniques is proposed to improve the performance of the algorithm. The following hybrid method returns symmetrical results that perform better than the asymmetrical structures obtained with the other techniques applied independently, indicating that the asymmetry represents a local optimum.
Influence of Catalyst Content and Epoxy/Carboxylate Ratio on Isothermal Creep of Epoxy Vitrimers

In the present work, a commercial epoxy based on epoxy anhydride and tertiary amine was modified by a metallic catalyst (Zn2+) to induce vitrimeric behavior by promoting the transesterification reaction. The effect of two different epoxy/acid ratios (1 and 0.6) at two different zinc acetate amounts (Zn(Ac)2) on the thermomechanical and viscoelastic performances of the epoxy vitrimers were investigated. Creep experiments showed an increase in molecular mobility above the critical “Vitrimeric” temperature (Tv) of 170 °C proportionally to the amount of Zn(Ac)2. A procedure based on Burger’s model was set up to investigate the effect of catalyst content on the vitrimer ability to flow as the effect of the dynamic exchange reaction. The analysis showed that in the case of a balanced epoxy/acid formulation, the amount of catalyst needed for promoting molecular mobility is 5%. This system showed a value of elastic modulus and dynamic viscosity at 170 °C of 9.50 MPa and 2.23 GPas, respectively. The material was easily thermoformed in compression molding, paving the way for the recyclability and weldability of the thermoset system.
One mask to rule them all: Writing arbitrary intensity distributions by scanning a single illuminated spatially-random screen

Arbitrary intensity distributions may be written by transversely scanning a single spatially-random screen that is normally illuminated by spatially but not necessarily temporally uniform radiation or matter wave fields. The arbitrariness, of the written pattern, holds up to both (i) a spatial resolution that is dictated by the characteristic transverse length scale of the illuminated spatially random screen, and (ii) an additive constant. Two forms of the method are developed. The former assumes the distance between the illuminated random mask and the target plane to be sufficiently small that the effects of diffraction may be neglected. The latter accounts for the effects of Fresnel diffraction in the regime of large Fresnel number. Numerical simulations are provided for both variants of the method. The method may be parallelized, and is also suited to both magnifying and de-magnifying geometries. Possible applications include spatial light modulators and intensity projectors for those matter and radiation fields for which such devices do not exist, printing/micro-fabrication in both two and three spatial dimensions, and lithography.
An optimized deep learning model for human activity recognition using inertial measurement units

Human activity recognition (HAR) has recently gained popularity due to its applications in healthcare, surveillance, human‐robot interaction, and various other fields. Deep learning (DL)‐based models have been successfully applied to the raw data captured through inertial measurement unit (IMU) sensors to recognize multiple human activities. Despite the success of DL‐based models in human activity recognition, feature extraction remains challenging due to class imbalance and noisy data. Additionally, selecting optimal hyperparameter values for DL models is essential since they affect model performance. The hyperparameter values of some of the existing DL‐based HAR models are chosen randomly or through the trial‐and‐error method. The random selection of these significant hyperparameters may be suitable for some applications, but sometimes it may worsen the model's performance in others. Hence, to address the above‐mentioned issues, this research aims to develop an optimized DL model capable of recognizing various human activities captured through IMU sensors. The proposed DL‐based HAR model combines convolutional neural network (CNN) layers and bidirectional long short‐term memory (Bi‐LSTM) units to simultaneously extract spatial and temporal sequence features from raw sensor data. The Rao‐3 metaheuristic optimization algorithm has been adopted to identify the ideal hyperparameter values for the proposed DL model in order to enhance its recognition performance. The proposed DL model's performance is validated on PAMAP2, UCI‐HAR, and MHEALTH datasets and achieved 94.91%, 97.16%, and 99.25% accuracies, respectively. The results reveal that the proposed DL model performs better than the existing state‐of‐the‐art (SoTA) models.
Explaining a prediction in some nonlinear models

In this article we will analyse how to compute the contribution of each input value to its aggregate output in some nonlinear models. Regression and classification applications, together with related algorithms for deep neural networks are presented. The proposed approach merges two methods currently present in the literature: integrated gradient and deep Taylor decomposition. Compared to DeepLIFT and Deep SHAP, it provides a natural choice of the reference point peculiar to the model at use.
Relationship between β-Cell Autoantibodies and Their Combination with Anthropometric and Metabolic Components and Microvascular Complications in Latent Autoimmune Diabetes in Adults

Aims: Our study aimed to investigate the relationship between three autoantibodies and their combination with anthropometric and metabolic components and microvascular complications in patients with latent autoimmune diabetes in adults (LADA). Methods: Our study included 189 LADA patients divided into four subgroups according to the autoantibodies present: glutamic acid decarboxylase autoantibodies (GADA) only; zinc transporter-8 autoantibodies (ZnT8A)+GADA; insulinoma-associated-2 autoantibodies (IA-2)+GADA; and ZnT8+IA-2+GADA. Results: Compared to GADA positivity only, patients with ZnT8+GADA positivity and ZnT8+IA-2+GADA positivity had a shorter diabetes duration and lower body mass index (BMI); patients with ZnT8+GADA positivity were younger and showed an increase in glomerular filtration rate, while those with ZnT8+IA-2+GADA positivity had lower C-peptide and lower insulin resistance measured with HOMA2-IR. In a multiple regression analysis, ZnT8 positivity was associated with lower BMI (p = 0.0024), female sex (p = 0.0005), and shorter duration of disease (p = 0.0034), while IA-2 positivity was associated with lower C-peptide levels (p = 0.0034) and shorter diabetes duration (p = 0.02). No association between antibody positivity and microvascular complications of diabetes, including retinopathy, neuropathy, and microalbuminuria, as well as with variables of glucose control and β-cell function were found. Conclusion: The results of our study suggest that ZnT8 and IA-2 autoantibodies are present in a significant number of LADA patients and associated with clinical and metabolic characteristics resembling classic type 1 diabetes. Due to increased LADA prevalence, earlier identification of patients requiring frequent monitoring with the earlier intensification of insulin therapy might be of special clinical interest.
Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things

Deep learning has undergone significant progress for machinery fault diagnosis in the Industrial Internet of Things; however, it requires a substantial amount of labeled data. The lack of sufficient fault samples in practical applications remains a challenge. One feasible approach is to leverage prior knowledge from similar source domains to enhance fault diagnosis with limited samples in the target domain. Nevertheless, complex operating conditions and fault types can give rise to domain shift issues between different domains, therefore hindering direct data-sharing due to data privacy concerns. To address these challenges, this article introduces a novel federated few-shot fault-diagnosis method called FedCDAE-MN. FedCDAE-MN employs a convolutional denoising auto-encoder and feature-space metric learning to enhance the model’s generalization across domains for improving the adaptability to varying working conditions, new fault types, and noisy data. Moreover, our approach ensures privacy preservation by avoiding the need to share sensitive data with other participants. Through extensive experiments on real-world datasets, FedCDAE-MN surpasses existing methods and significantly improves the accuracy of fault diagnosis.
Modulation of intestinal IL-37 expression and its impact on the epithelial innate immune response and barrier integrity

Background and Aims Intestinal epithelial cells separate the luminal flora from lamina propria immune cells and regulate innate immune responses in the gut. An imbalance of the mucosal immune response and disrupted intestinal barrier integrity contribute to the evolution of inflammatory bowel diseases. Interleukin (IL)-37 has broad anti- inflammatory activity and is expressed by the human intestinal epithelium. Mice ectopically expressing human IL-37 show reduced epithelial damage and inflammation after DSS-induced colitis. Here, we investigated the impact of IL-37 on the innate immune response and tight junction protein expression of mouse intestinal organoids and the modulation of IL37 expression in human intestinal organoids. Methods Murine intestinal organoids were generated from IL-37tg and wildtype mice. Human ileal organoids were generated from healthy young donors. Results Expression of transgene IL-37 or recombinant IL-37 protein did not significantly reduce overall proinflammatory cytokine mRNA expression in murine intestinal organoids. However, higher IL37 expression correlated with a reduced proinflammatory cytokine response in murine colonic organoids. IL37 mRNA expression in human ileal organoids was modulated by proinflammatory cytokines showing an increased expression upon TNF-α-stimulation and decreased expression upon IFN-gamma stimulation. Transgene IL-37 expression did not rescue TNF-α-induced changes in morphology as well as ZO-1, occludin, claudin-2, and E-cadherin expression patterns of murine jejunal organoids. Conclusions We speculate that the anti-inflammatory activity of IL-37 in the intestine is mainly mediated by lamina propria immune cells protecting intestinal epithelial integrity.
The association of folds and fractures and the link between folding, fracturing and fluid flow during the evolution of a fold–thrust belt: a brief review

Abstract Because of the importance of understanding the association of folds and fractures in the hydrocarbon and mining industry, a considerable amount of work has been undertaken to establish the geometric relationship between these structures. The structures are linked in a variety of ways. Sometimes, as for example in the formation of fractures in the inner and outer arcs of the hinge zones of single-layer folds or of accommodation thrusts in the hinge regions of multilayer folds, it is the process of folding that generates the fractures. At other times, as in the formation of a fault-bend fold and other types of forced folds, the reverse is true. In this paper an attempt is made to look briefly at the various types of fold-fracture associations found in nature and, by considering the evolution of a typical fold–thrust belt, obtain an insight into the controls on the temporal and spatial organization of the different types of folds and their associated fractures that form in this tectonic regime. The role of fluids in the initiation of both folds and thrusts is considered, as is the subsequent impact of these structures on fluid migration. It is shown that understanding the links between stress, fluid pressure, fracturing and folding provides a clear insight into the fluid mechanics operating in an active fold–thrust belt.
3D Soft-Landing Dynamic Theoretical Model of Legged Lander: Modeling and Analysis

In this paper, a novel 3D (three-dimensional) soft-landing dynamic theoretical model of a legged lander is developed in detail as well as its numerical solution process. The six degrees of freedom motion (6-DOF) of the base model of the lander with mass center offset setting is considered in the model as well as the spatial motion (3-DOF) of each landing gear. The characteristics of the buffering force, the footpad–ground contact, and the inter-structure friction are also taken into account during the motion of each landing gear. The direct constraint violation correction is used to control the constraint stabilization of the nonlinear dynamic equation. Comparative studies between the results from the proposed model and the simulated model (built in MSC Adams) under four classical load cases show the validity of the model. Additionally, the influences of different types of contact force models, friction force models, and a friction correction model used in the soft-landing dynamic model are further investigated as a step toward understanding the soft-landing dynamic performance and the feasibility of the dynamic model method of a legged lander. The results indicate that a precise lateral force model of the footpad–ground contact is necessary to obtain the soft-landing performance of one lander during soft landing.
Agro-Morphological and Molecular Characterization Reveal Deep Insights in Promising Genetic Diversity and Marker-Trait Associations in Fagopyrum esculentum and Fagopyrum tataricum

Characterisation of genetic diversity is critical to adequately exploit the potential of germplasm collections and identify important traits for breeding programs and sustainable crop improvement. Here, we characterised the phenotypic and genetic diversity of a global collection of the two cultivated buckwheat species Fagopyrum esculentum and Fagopyrum tataricum (190 and 51 accessions, respectively) using 37 agro-morphological traits and 24 SSR markers. A wide range of variation was observed in both species for most of the traits analysed. The two species differed significantly in most traits, with traits related to seeds and flowering contributing most to differentiation. The accessions of each species were divided into three major phenoclusters with no clear geographic clustering. At the molecular level, the polymorphic SSR markers were highly informative, with an average polymorphic information content (PIC) of over 0.65 in both species. Genetic diversity, as determined by Nei’s expected heterozygosity (He), was high (He = 0.77 and He = 0.66, respectively) and differed significantly between species (p = 0.03) but was homogeneously distributed between regions, confirming the lack of genetic structure as determined by clustering approaches. The weak genetic structure revealed by the phenotypic and SSR data and the low fixation indices in both species suggested frequent seed exchange and extensive cultivation and selection. In addition, 93 and 140 significant (p < 0.05) marker-trait associations (MTAs) were identified in both species using a general linear model and a mixed linear model, most of which explained >20% of the phenotypic variation in associated traits. Core collections of 23 and 13 phenotypically and genetically diverse accessions, respectively, were developed for F. esculentum and F. tataricum. Overall, the data analysed provided deep insights into the agro-morphological and genetic diversity and genetic relationships among F. esculentum and F. tataricum accessions and pointed to future directions for genomics-based breeding programs and germplasm management.
Predicting Volume of Distribution in Neonates: Performance of Physiologically Based Pharmacokinetic Modelling

The volume of distribution at steady state (Vss) in neonates is still often estimated through isometric scaling from adult values, disregarding developmental changes beyond body weight. This study aimed to compare the accuracy of two physiologically based pharmacokinetic (PBPK) Vss prediction methods in neonates (Poulin & Theil with Berezhkovskiy correction (P&T+) and Rodgers & Rowland (R&R)) with isometrical scaling. PBPK models were developed for 24 drugs using in-vitro and in-silico data. Simulations were done in Simcyp (V22) using predefined populations. Clinical data from 86 studies in neonates (including preterms) were used for comparison, and accuracy was assessed using (absolute) average fold errors ((A)AFEs). Isometric scaling resulted in underestimated Vss values in neonates (AFE: 0.61), and both PBPK methods reduced the magnitude of underprediction (AFE: 0.82–0.83). The P&T+ method demonstrated superior overall accuracy compared to isometric scaling (AAFE of 1.68 and 1.77, respectively), while the R&R method exhibited lower overall accuracy (AAFE: 2.03). Drug characteristics (LogP and ionization type) and inclusion of preterm neonates did not significantly impact the magnitude of error associated with isometric scaling or PBPK modeling. These results highlight both the limitations and the applicability of PBPK methods for the prediction of Vss in the absence of clinical data.
Graphene-Based TiO2 Cement Composites to Enhance the Antibacterial Effect of Self-Disinfecting Surfaces

This paper studies the photocatalytic performance of graphene-based titanium dioxide (TiO2) on cementitious composites for the decomposition of Escherichia coli (E. coli) under visible light. Graphene-based TiO2 was first synthesized through a hydrothermal process. The composites were then evaluated in terms of adsorption capability and degradation of methylene blue dyes. The adsorption test shows a remarkable increase in the amount of dye adsorbed into the composite surface. GO-P25 could adsorb around 60% of the initial dye, while less than 10% of the initial dye was adsorbed by pristine TiO2-P25. The synthesized graphene-based TiO2 significantly enhanced the dye degradation activity (94%) compared to pristine P25 (36%) and Krono (52%), even with the longer irradiation time for P25 and Krono. This led to an increase in reaction rate that was almost 20 times that of P25. Considering the good adsorption capabilities and high photodegradation of dye under visible light for GO-P25, cement-based surfaces containing GO-P25 are expected to be improved for the decomposition of Escherichia coli (E. coli) under visible light. Graphene-based TiO2 on a cement-based surface showed high antibacterial activity with a 77% reduction in number of bacteria compared to a cement-based surface containing pristine TiO2. This study confirms the effectiveness of the composites for disinfection of E. coli under visible light.
Response of the transcription factor BABY BOOM of Arabidopsis thaliana L. in the formation of embryogenic calluses of cocoa leaves (Theobroma cacao L.)

Cocoa (Theobroma cacao L.) is one of the most important economic crops worldwide. The propagation of elite varieties of cocoa has been achieved through somatic embryogenesis, but still one of the main limitations is the low rates of embryo formation, which is a genotype-dependent trait. Manipulation of transcription factors (TFs) such as BABY BOOM (BBM) promotes the transition of cocoa somatic cells from the vegetative to the embryonic state. This work validated the use of clonal cocoa leaves cv. IMC-67 to induce somatic embryogenesis, overcoming their recalcitrant limitation with the help of the introduction of TF-BBM from Arabidopsis thaliana (AtBBM). The vectors were constructed by the Gateway system using the donor vector pENTR/D-TOPO and the expression vector pk7WG2. The overexpression vector pk7WG2:AtBBM was obtained, allowing successful transformation into Agrobacterium tumefaciens GV3101. The AtBBM gene was characterized (1755 base pairs), and its expression was observed in the formation of embryogenic calluses in cocoa leaves. Overexpression of AtBBM allowed the obtainment of a 92% response in the formation of embryogenic callus in cocoa leaves with Agrobacterium-mediated vacuum infiltration and overexpression of the pk7WG2:AtBBM vector. This high transformation efficiency reached with the insertion of the overexpression vector provides validation of transient response of the TF AtBBM in the formation of embryogenic calluses in cocoa leaves of the IMC-67 clone. Through this methodology, it is possible to continue with studies of gene overexpression, insertion, silencing, and gene editing in Peruvian cocoa.