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The utilization of natural fibers and fillers in composite materials has gained significant attention for their potential to enhance mechanical, thermal, and tribological properties while promoting sustainability. In this study, Prosopis juliflora fiber (PJF) reinforced epoxy composites, treated with silane and incorporating Syzygium cumini filler (SCF), were investigated to assess their performance across various parameters. The composites were subjected to comprehensive mechanical, tribological, thermo-gravimetric, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), 3D profilometer, and morphology studies to elucidate the effects of silane treatment and SCF content on composite properties. The results of the mechanical characterization revealed significant improvements in tensile strength, impact strength, and microhardness in silane-treated composites with higher SCF content compared to untreated samples and those with lower SCF content. Specifically, the ST/SCF3 composite exhibited the highest values, with a tensile strength of 118.65 ± 4.63 MPa, impact strength of 27.83 ± 1.96 kJ/m2, and microhardness of 91.63 ± 2.84 HV. Tribological analysis indicated enhanced wear resistance and reduced coefficient of friction in silane-treated composites with higher SCF content, with the ST/SCF3 composite demonstrating the lowest wear loss (62 μm) and frictional force (5.2 N). Thermal characterization revealed improved thermal stability in silane-treated composites, particularly in the ST/SCF1 and ST/SCF3 formulations, suggesting increased resistance to temperature-induced degradation.

Numerous studies of rotating detonation engines (RDEs) have noted the appearance of weaker secondary waves which travel with or counter to the primary wave system. These secondary waves can affect the speed, strength, multiplicity, and directional preference of the primary wave system, all of which have implications for engine performance and operability. As such, understanding the formation and stabilization of different wave modes is critical for developing practical RDE systems. To this end, the present work uses an adaptive mesh refinement framework to simulate two-dimensional unwrapped RDEs at high spatial resolution. Four injection configurations are considered, including a simplified continuous injection boundary, as well as three discrete injection setups with varying injector diameter and spacing. In the discrete injection cases, the effects of mass flow rate and near-wave grid resolution are also investigated. Continuous injection is found to produce a single wave, while discrete injection yields increasing numbers of co- and counter-propagating waves when the number of injectors or the reactant flow rate increases. The generation of secondary waves is linked to acoustic reflections associated with wave passage over the discrete injectors, as well as successive “micro-explosions” that occur when a reaction zone recouples to a shock wave traversing a reactant jet. These secondary waves can then coalesce in the presence of fresh reactants, providing a mechanism for new primary waves to form and the directional preference of the wave system to switch. The diameter and spacing of the injectors directly impact the sustained propagation of the primary waves, as well as the availability of reactants needed to form a strong counter-propagating wave system. The unsteadiness induced by the different injection schemes is manifested in conditional statistics for heat release and heat release rate, which show enhanced deflagrative combustion in discrete injection configurations.

We present the case of a 60-year-old male patient admitted to our intensive care unit after a high-velocity car accident. On-site clinical examination revealed normal consciousness with no focal deficits. Chest X–Ray was suggestive of right pneumothorax with pneumomediastinum and extensive subcutaneous emphysema. CT scan revealed right pneumothorax, pneumomediastinum, extensive subcutaneous emphysema and extradural pneumorachis. A right tube thoracostomy was done and the patient was placed on High flow nasal cannula at 40 lpm and 100% FiO2. There was a gradual resolution of pneumothorax and all the extra-alveolar air including pneumorachis by the 7th day. The patient was discharged in a clinically stable condition.

This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external vector database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. Furthermore, we present a targeted scaling study to quantify the algorithmic performance of the framework as the number of prompt tokens increases. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future improvements. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.

High-fidelity simulations of RDEs with H2-Air-NOx chemistry are employed to study NOx emissions in such devices. Discrete injection of gaseous hydrogen fuel and continuous injection of air oxidizer is used at various mass flow rate conditions in several 3D RDE simulations to understand resulting NOx production behaviors. Simulations are also performed for two different injector configurations, one in which air is injected axially into the detonation chamber [Axial Air Inlet (AAI)] and one in which air is injected radially [Radial Air Inlet (RAI)]. It is seen that the AAI RDE produces much less NOx than the RAI RDE, mainly due to the weaker waves seen in this system as a result of parasitic combustion losses from product gas recirculation. Parasitic combustion does lead to NOx formation in its own right, but the emissions levels from this process are negligible compared to emissions stemming directly from detonation processes. In regards to detonation strength in particular, it is generally seen that detonation strength increases with increasing mass flow rate, in turn increasing peak pressure, peak heat release and NOx emissions levels. Nevertheless, even the highest recorded NOx levels at the combustor exit in this study remain on the same order of magnitude as compared to gas turbine exhaust emissions levels, supporting the claim that significant differences between detonative and deflagrative combustion do not necessarily lead to significant differences in NOx levels. Overall, this study provides greater understanding into the behaviors of NOx formation in RDEs and how these behaviors are affected by changes in operating parameters.

A data-based reduced-order model (ROM) is developed to accelerate the time integration of stiff chemically reacting systems by effectively removing the stiffness arising from a wide spectrum of chemical time scales. Specifically, the objective of this work is to develop a ROM that acts as a non-stiff surrogate model for the time evolution of the thermochemical state vector (temperature and species mass fractions) during an otherwise highly stiff and nonlinear ignition process. The model follows an encode-forecast-decode strategy that combines a nonlinear autoencoder (AE) for dimensionality reduction (encode and decode steps) with a neural ordinary differential equation (NODE) for modeling the dynamical system in the AE-provided latent space (forecasting step). By means of detailed timescale analysis by leveraging the dynamical system Jacobians, this work shows how data-based projection operators provided by autoencoders can inherently construct the latent spaces by removing unnecessary fast timescales, even more effectively than physics-based counterparts based on an eigenvalue analysis. A key finding is that the most significant degree of stiffness reduction is achieved through an end-to-end training strategy, where both AE and neural ODE parameters are optimized simultaneously, allowing the discovered latent space to be dynamics-informed. In addition to end-to-end training, this work highlights the vital contribution of AE nonlinearity in the stiffness reduction task. For the prediction of homogeneous ignition phenomena for H2-air and C2H4-air mixtures, the proposed ROM achieves several orders-of-magnitude increase in the integration time step size when compared to (a) a baseline CVODE solver for the full-chemical system, (b) statistical technique – principal component analysis (PCA), and (c) computational singular perturbation (CSP), a vetted physics-based stiffness-reducing modeling framework.

The advent of data-based modeling has provided new methods and algorithms for analyzing the complex flow fields in high-speed combustion applications. These techniques can be used to study the fluid dynamics and reaction progress in different regions of the flow, which can provide insight into the underlying physics of the system while helping one identify avenues for the development and application of new models. The present work implements two such algorithms – the standard K-means clustering algorithm and a modified Jacobian-scaled K-means clustering algorithm – to analyze a high-speed reacting flow field. The results show that the Jacobian-scaled K-means algorithm allocates more clusters in the regions of the thermochemical state space where chemical source term is highest. In physical space, this concentrates the flow partitions in regions where heat release rate is higher and chemical timescales are lower. This is observed for different cluster numbers and data sets. When moving between data sets that occupy different portions of the state space, the cluster centroid locations are found to be less sensitive to the data set when the Jacobian-scaled K-means algorithm is used. Altogether, the modified K-means algorithm provides a new tool for identifying thermodynamic and thermochemical similarities in and across compressible reactive flow fields.

Modern diagnostic tools in turbulent combustion allow for highly-resolved measurements of reacting flows; however, they tend to generate massive data-sets, rendering conventional analysis intractable and inefficient. To alleviate this problem, machine learning tools may be used to, for example, discover features from the data for downstream modeling and prediction tasks. To this end, this work applies generative adversarial networks (GANs) to generate realistic flame images based on a time-resolved data set of hydroxide concentration snapshots obtained from planar laser induced fluorescence measurements of a model combustor. The generative model is able to generate flames in attached, lifted, and intermediate configurations dictated by the user. Using k-means clustering and proper orthogonal decomposition, the synthetic image set produced by the GAN is shown to be visually similar to the real image set, with recirculation zones and burned/unburned regions clearly present, indicating good GAN performance in capturing the experimental data statistical structure. Combined with techniques for controlling the configuration of generated flames, this work opens new avenues towards tractable statistical analysis and modeling of flame behavior, as well as rapid and inexpensive flame data generation.

High-fidelity numerical simulations of an experimental rotating detonation engine with discrete fuel/air injection were conducted. A series of configurations with different feed-plenum pressures but with constant equivalence ratio were studied. Detailed chemical kinetics for the hydrogen/air system is used. A resolution study for the full rotating detonation engine (RDE) system simulation is also conducted. Two kinds of boundary conditions, a total pressure boundary and a constant mass flow rate boundary, are used to assess the effects of the inlet boundary. As mass flow rate is increased, the total pressure boundary causes more error in the axial pressure distribution while the constant mass flow rate gives a better solution for all cases ran. The simulations confirm experimental findings, and reproduce qualitative as well as some of the quantitative trends. These results demonstrate that a) fuel-air mixing is highly non-uniform within the detonation chamber, leading to variations in local equivalence ratio, b) the fuel and oxidizer injectors experience significant backflow as the detonation wave passes over, but recover at different rates which further augments the inefficiencies in mixing, and c) parasitic combustion in the mixing region makes the detonation wave weak by extending the reaction zone across the wave.

A closure model for turbulent flows is developed based on a dynamical system theory. An appropriately discretized formulation of the governing equations is considered for this process. The key ingredient is an approximation of the system’s attractor, where all the trajectories in phase space are confined. This approximate inertial manifold based approach provides a path to track trajectories of the system in a lower-dimensional subspace. Unlike conventional coarse-graining approaches, the turbulent field is decomposed into resolved and unresolved dynamics using the properties of the governing equations. The novelty of the approach relies on the reconstruction of the unresolved field constrained by the governing equations. A posteriori tests for homogeneous isotropic turbulence and the Kuramoto–Sivashinsky equation show promising results for considerable dimension reduction with strong convergence properties. The proposed model outperforms the dynamic Smagorinsky model, and the computational overhead is competitive with similar approaches.

This study profiled various levels of engagement and related communication behaviours among 50 Asian Indian Tamil autistic children (AUT) and their mothers. The interaction was compared with two groups of mother-child dyads of non-autistic (NA) children, 50 in each group, matched for chronological age (CA), and for language level (LL). Results indicated that despite mother's efforts to engage with their children, autistic children were often 'engaged with objects' or remained 'unengaged' due to children's preference for solitary play, while NA children were often engaged in 'co-ordinated' and 'people engagement'. Across the three groups, mothers predominantly took the lead and dominated the interaction, irrespective of children's language levels. These initiations by the mothers were often to provide instructions and to ask 'What' questions. Autistic children initiated communication predominantly to ask for an object and responded often in the form of negations and protests with limited verbal output or non-verbally. Most of the communication behaviours of both children and mothers in AUT group was quantitatively and qualitatively different when compared to those in both the NA groups, indicating unique nature of interactions despite matching for CA or LL. The observations from the study highlights the need for considering adult's contingent behaviours also, while assessing communication skills of autistic children in order to provide effective intervention.

Objective. To assess the periodontal status among the leather factory workers in Dewas and to provide a baseline data for the oral health care and promotion programs. Materials and Methods. A total of 850 study population was considered for examination, and the age ranged from 20 to 60 years. All those factory workers with low socioeconomic status and poor background were considered. The data were collected by means modified World Health Organization (WHO) Oral Health Assessment 1997 Performa and clinical examination with the use of the Community Periodontal Index (CPI). Statistical analyses were used: chi-squared test, Mann–Whitney U test, and Kruskal–Wallis test. Results. The sociodemographic characteristics were found to be significantly associated with the periodontal status in the study population (p≤0.05). Mean number of loss of attachment (LOA) 0 sextants was 3.55 ± 2.35, LOA 1 was 0.935 ± 1.3, LOA 2 was 0.414 ± 0.8, LOA 3 was 0.529 ± 0.94, and LOA 4 was 1.24 ± 0.04; all the parameters showed statistically significant difference (p=0.001) except LOA 1 and LOA 2. Conclusion. The periodontal health status of the factory workers is poor. Factors associated with high prevalence of periodontal disease could be the following: a high rate of tobacco consumption and not cleaning their teeth at all along with other associated factors like stress, poor oral hygiene, etc.

When operating under lean fuel–air conditions, flame flashback is an operational safety issue in stationary gas turbines. In particular, with the increased use of hydrogen, the propagation of the flame through the boundary layers into the mixing section becomes feasible. Typically, these mixing regions are not designed to hold a high-temperature flame and can lead to catastrophic failure of the gas turbine. Flame flashback along the boundary layers is a competition between chemical reactions in a turbulent flow, where fuel and air are incompletely mixed, and heat loss to the wall that promotes flame quenching. The focus of this work is to develop a comprehensive simulation approach to model boundary layer flashback, accounting for fuel–air stratification and wall heat loss. A large eddy simulation (LES) based framework is used, along with a tabulation-based combustion model. Different approaches to tabulation and the effect of wall heat loss are studied. An experimental flashback configuration is used to understand the predictive accuracy of the models. It is shown that diffusion-flame-based tabulation methods are better suited due to the flashback occurring in relatively low-strain and lean fuel–air mixtures. Further, the flashback is promoted by the formation of features such as flame tongues, which induce negative velocity separated boundary layer flow that promotes upstream flame motion. The wall heat loss alters the strength of these separated flows, which in turn affects the flashback propensity. Comparisons with experimental data for both non-reacting cases that quantify fuel–air mixing and reacting flashback cases are used to demonstrate predictive accuracy.

High-fidelity simulations of turbulent flames are computationally expensive when using detailed chemical kinetics. For practical fuels and flow configurations, chemical kinetics can account for the vast majority of the computational time due to the highly non-linear nature of multi-step chemistry mechanisms and the inherent stiffness of combustion chemistry. While reducing this cost has been a key focus area in combustion modeling, the recent growth in graphics processing units (GPUs) that offer very fast arithmetic processing, combined with the development of highly optimized libraries for artificial neural networks used in machine learning, provides a unique pathway for acceleration. The goal of this paper is to recast Arrhenius kinetics as a neural network using matrix-based formulations. Unlike ANNs that rely on data, this formulation does not require training and exactly represents the chemistry mechanism. More specifically, connections between the exact matrix equations for kinetics and traditional artificial neural network layers are used to enable the usage of GPU-optimized linear algebra libraries without the need for modeling. Regarding GPU performance, speedup and saturation behaviors are assessed for several chemical mechanisms of varying complexity. The performance analysis is based on trends for absolute compute times and throughput for the various arithmetic operations encountered during the source term computation. The goals are ultimately to provide insights into how the source term calculations scale with the reaction mechanism complexity, which types of reactions benefit the GPU formulations most, and how to exploit the matrix-based formulations to provide optimal speedup for large mechanisms by using sparsity properties. Overall, the GPU performance for the species source term evaluations reveals many informative trends with regards to the effect of cell number on device saturation and speedup. Most importantly, it is shown that the matrix-based method enables highly efficient GPU performance across the board, achieving near-peak performance in saturated regimes.

We demonstrate a two-hybrid assay based on antibody micropatterns to study protein-protein interactions at the cell surface of major histocompatibility complex class I (MHC I) proteins. Anti-tag and conformation-specific antibodies are used for individual capture of specific forms of MHC I proteins that allow for location- and conformation-specific analysis by fluorescence microscopy. The assay is used to study the in cis interactions of MHC I proteins at the cell surface under controlled conditions and to define the involved protein conformations. Our results show that homotypic in cis interactions occur exclusively between MHC I free heavy chains, and we identify the dissociation of the light chain from the MHC I protein complex as a condition for MHC I in cis interactions. The functional role of these MHC I protein-protein interactions at the cell surface needs further investigation. We propose future technical developments of our two-hybrid assay for further analysis of MHC I protein-protein interactions.

The polyphagous insect-pest, Helicoverpa armigera, is a serious threat to a number of economically important crops. Chemical application and/or cultivation of Bt transgenic crops are the two strategies available now for insect-pest management. However, environmental pollution and long-term sustainability are major concerns against these two options. RNAi is now considered as a promising technology to complement Bt to tackle insect-pests menace. In this study, we report host-delivered silencing of HaAce1 gene, encoding the predominant isoform of H. armigera acetylcholinesterase, by an artificial microRNA, HaAce1-amiR1. Arabidopsis pre-miRNA164b was modified by replacing miR164b/miR164b* sequences with HaAce1-amiR1/HaAce1-amiR1* sequences. The recombinant HaAce1-preamiRNA1 was put under the control of CaMV 35S promoter and NOS terminator of plant binary vector pBI121, and the resultant vector cassette was used for tobacco transformation. Two transgenic tobacco lines expressing HaAce1-amiR1 was used for detached leaf insect feeding bioassays. Larval mortality of 25% and adult deformity of 20% were observed in transgenic treated insect group over that control tobacco treated insect group. The reduction in the steady-state level of HaAce1 mRNA was 70-80% in the defective adults compared to control. Our results demonstrate promise for host-delivered amiRNA-mediated silencing of HaAce1 gene for H. armigera management.

Major histocompatibility complex (MHC) class I molecules (proteins) bind peptides of eight to ten amino acids to present them at the cell surface to cytotoxic T cells. The class I binding groove binds the peptide via hydrogen bonds with the peptide termini and via diverse interactions with the anchor residue side chains of the peptide. To elucidate which of these interactions is most important for the thermodynamic and kinetic stability of the peptide-bound state, we have combined molecular dynamics simulations and experimental approaches in an investigation of the conformational dynamics and binding parameters of a murine class I molecule (H-2Kb) with optimal and truncated natural peptide epitopes. We show that the F pocket region dominates the conformational and thermodynamic properties of the binding groove, and that therefore the binding of the C terminus of the peptide to the F pocket region plays a crucial role in bringing about the peptide-bound state of MHC class I.

Understanding the phenotypic and transcriptional signature of immunoglobulin E (IgE)–producing cells is fundamental to plasma cell (PC) biology and development of therapeutic interventions for allergy. Here, using a mouse model of intranasal house dust mite (HDM) exposure, we showed that short-lived IgE PCs emerge in lung draining lymph nodes (dLNs) during early exposure (<3 weeks) and long-lived IgE PCs accumulate in the bone marrow (BM) with prolonged exposure (>7 weeks). IgE PCs had distinct surface and gene expression profiles in these different tissues compared with other Ig isotypes. IgE BMPCs up-regulated genes associated with prosurvival and BM homing, whereas IgE dLN PCs expressed genes associated with recent class switching and differentiation. IgE PCs also exhibited higher expression of endoplasmic reticulum (ER) stress and protein coding genes and higher antibody secretion rate when compared with IgG1. Overall, this study highlights the unique developmental path and transcriptional signature of short-lived and long-lived IgE PCs. Editor's summary: Immunoglobulin E (IgE)–mediated allergic diseases can be fatal, yet our understanding of IgE-producing plasma cells (PCs) is limited. Vecchione et al. used a transgenic mouse reporter strain to characterize IgE PCs during chronic house dust mite exposure. IgE PCs were transcriptionally and phenotypically unique compared with other immunoglobulin isotypes, with higher levels of endoplasmic reticulum stress and increased antibody production. Short-lived IgE PCs appeared early in the lung draining lymph nodes post–allergen exposure, whereas long-lived bone marrow (BM) IgE PCs were not detected until much later. IgE BMPCs from humans with allergy also had unique transcriptional signatures. These findings demonstrate that IgE PCs are developmentally, transcriptionally, and functionally unique. —Hannah Isles [ABSTRACT FROM AUTHOR]

This paper examines dance reviews of transnational performances of Indian male dancers Uday Shankar and Ram Gopal from the 1930s to the 1950s. Throughout, this research seeks to contribute to methods for decolonizing dance criticism by advocating for embodied spectatorship as well as evaluation that challenges hegemony by validating classical traditions and innovative artistry. Western and Indian critics savored the rasa, meaning the flavor of the performances, and criticism reactivated the felt or sensorial experience in what I term post-performance performative. In India, anti-Orientalist criticism reclaimed traditions that critics in Europe and the United States had dismissed as irrational and unprogressive. [ABSTRACT FROM AUTHOR]