ScholarSphere: IITJ Research Insights Hub

Human culinary traditions have been deeply rooted in the consumption of fermented food products, offering health-promoting benefits. Among these, soymilk fermentation by lactic acid bacteria emerges as a captivating opportunity to produce enhanced soy-based flavor profiles with extended nutritional value. Among the numerous advantages, components of soymilk, such as isoflavone aglycones and peptides, show a hypolipidemic effect, thus proving beneficial to humans. Of central interest is Lactobacillus, a well-studied probiotic genus that exerts a pivotal role in the maintenance of gut barrier and microbial diversity. The interplay between gut microbiome and host physiology underscores its role in health and disease. Gut dysbiosis results from the interaction of environmental cues with host metabolic state, triggering pathological consequences. These range from irritable bowel syndrome and gastric cancer to neurological disorders. This review attempts to evaluate the knowledge surrounding fermented soymilk to shed light on the vital role of Lactobacillus in restoring a dysbiotic state within the gut microbiome. Further, we elucidate multifaceted mechanisms underlying the therapeutic potential of Lactobacillus-fermented soymilk. By exploring this complex interplay of microbial metabolites and host immune responses, and incorporating recent advancements in probiotic therapeutics, we emphasize the utilization of Lactobacillus-fermented soymilk as a dietary intervention to promote gut health and alleviate disease states. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026.

The increasing scale of deep learning models in high-energy physics research has posed significant challenges for their deployment on low-power, latency-sensitive platforms, such as FPGAs and ASICs used in trigger systems, as well as CPUs and GPUs employed in offline data reconstruction and processing pipelines. In this context, top-quark tagging, a key task in precision measurements and searches for new physics at the Large Hadron Collider (LHC), provides a representative benchmark due to its stringent latency and accuracy requirements under extremely large data volumes. In this work, we introduce BitParT, a 1-bit Transformer-based architecture designed specifically for top-quark tagging. Building upon recent advances in ultra-low-bit large language models, we extend these ideas to the collider-physics domain by developing a binary-weight variant of the Particle Transformer (ParT) model. Our results indicate that this approach enables a substantial reduction in model size and computational complexity while maintaining high tagging performance. We benchmark BitParT on the public Top Quark Tagging Reference Dataset and show that it achieves performance competitive with its full-precision counterpart. This work demonstrates that extreme quantization can provide a practical route toward real-time inference in collider experiments with minimal and optimized resource usage. © The Author(s) 2026.

Monitoring dynamic pressure in sloshing liquids is critical for the safety of storage, transportation, and aerospace systems, as uncontrolled sloshing can increase structural loads and cause vehicle instability or containment failure. Conventionally, such measurements are done using rigid pressure transducers that are flush-mounted through drilled ports. This process disturbs the flow, limits the repositioning of sensors, and is unsuitable for curved or flexible tank surfaces. In this study, a hierarchically microstructured flexible pressure sensor (FlexiHMS) was employed to monitor slosh-induced wall pressures and benchmarked against a commercial piezoelectric (PZT) sensor. Controlled sloshing experiments were conducted in a lab-developed rectangular tank at 22%, 30%, and 70% fill volumes under base excitation from 0.8 to 1.2 times the first-mode natural frequency. Free-decay tests validated the test protocol, where dominant frequencies deviated from theoretical values by less than 5%. FlexiHMS successfully captured peak dynamic pressures ranging over (0.3–127) Pa and closely followed the expected resonant amplification behavior. Frequency-domain analysis using fast Fourier transforms and steady-state boxplots revealed pronounced nonlinear pressure characteristics near resonance, with increased dispersion and intermittency. While both sensors demonstrated repeatable cycle-to-cycle responses across multiple trials, FlexiHMS continued to exhibit a measurable pressure response under conditions where the PZT response diminished, especially at higher fill volumes. Our results highlight the potential of flexible pressure sensors like FlexiHMS for conformal, nonintrusive, dynamic pressure monitoring in fluid-structure interaction systems where conventional rigid sensor mounting is impractical. © 2026 The Authors. Published by American Chemical Society.

Industrial robots with higher performance measures are essential for several advanced manufacturing processes; for instance, geometric accuracy plays a crucial role in manufacturing tasks such as machining and forming. This research proposes an approach for combining existing geometric calibration methods with machine learning algorithms to improve the geometric accuracy of industrial robots. The methodology integrates kinematic calibration with advanced regression techniques to identify and compensate for kinematic errors, thereby enhancing the positioning performance of the robotic system. The implementation of a hierarchical machine learning pipeline consisting of decision trees, random forest, and gradient boosting, was evaluated across various train-test data splits to assess its performance. The hierarchical nature of the proposed approach ensures both robustness and adaptability in modelling complex kinematic deviations. Experimental validation conducted in the ABB IRB 7600 robot demonstrates the effectiveness of the proposed hybrid approach, achieving a 96.03% reduction in positioning error. The results suggest that integrating traditional calibration methods with machine learning not only enhances precision but also provides a robust solution for addressing kinematic deviations. © 2025 Copyright held by the owner/author(s).

Neurodegenerative disorders such as Parkinson's (PD) and Alzheimer's diseases (AD) are linked with the assembly and accumulation of proteins into structured scaffold called amyloids. These diseases pose significant challenges due to their complex and multifaceted nature. While the primary focus has been on endogenous amyloids, recent evidence suggests that bacterial amyloids may contribute to the development and exacerbation of such disorders. The gut-brain axis is emerging as a communication pathway between bacterial and human amyloids. This review delves into the novel role and potential mechanism of bacterial amyloids in modulating human amyloid formation and the progression of AD and PD.

In the MaxSAT with Cardinality Constraint problem (CC-MaxSAT), we are given a CNF-formula Φ, and a positive integer k, and the goal is to find an assignment β with at most k variables set to true (also called a weight k-assignment) such that the number of clauses satisfied by β is maximized. Maximum Coverage can be seen as a special case of CC-MaxSat, where the formula Φ is monotone, i.e., does not contain any negative literals. CC-MaxSat and Maximum Coverage are extremely well-studied problems in the approximation algorithms as well as the parameterized complexity literature. Our first conceptual contribution is that CC-MaxSat and Maximum Coverage are equivalent to each other in the context of FPT-Approximation parameterized by k (here, the approximation is in terms of the number of clauses satisfied/elements covered). In particular, we give a randomized reduction from CC-MaxSat to Maximum Coverage running in time O(1/ϵ)k · (m + n)O(1) that preserves the approximation guarantee up to a factor of (1 − ϵ). Furthermore, this reduction also works in the presence of “fairness” constraints on the satisfied clauses, as well as matroid constraints on the set of variables that are assigned true. Here, the “fairness” constraints are modeled by partitioning the clauses of the formula Φ into r different colors, and the goal is to find an assignment that satisfies at least tj clauses of each color 1 ≤ j ≤ r. Armed with this reduction, we focus on designing FPT-Approximation schemes (FPT-ASes) for Maximum Coverage and its generalizations. Our algorithms are based on a novel combination of a variety of ideas, including a carefully designed probability distribution that exploits sparse coverage functions. These algorithms substantially generalize the results in Jain et al. [SODA 2023] for CC-MaxSat and Maximum Coverage for Kd,d-free set systems (i.e., no d sets share d elements), as well as a recent FPT-AS for Matroid Constrained Maximum Coverage by Sellier [ESA 2023] for frequency-d set systems.

The technique of micro-exfoliation has gained prominence as a highly effective and adaptable method for exploiting two-dimensional (2D) materials, such as graphene Transition metal dichalcogenides (TMDCs), Borophene, Molybdenum disulfide (MoS2), among others. This paper presents an analysis of optical images and the micro exfoliation technique, focusing on the application to MoS2 and graphene. Additionally, the study investigates the exfoliated sheet of graphene, MoS2, and their hybrid on a (111) crystal plane of silicon wafer. The micro-exfoliation technique employed for MoS2 involves a mechanical process that gently disentangles the layers of MoS2 from the larger crystal structure, resulting in the formation of ultrathin two-dimensional nanosheets. This paper comprehensively analyses the exfoliation processes' mechanisms, emphasizing the intricate relationship between van der Waals forces, interlayer bonding, and external forces. The micro-mechanical exfoliation, a fundamental technique, entails the utilization of adhesive scotch tape to remove monolayers from a large MoS2 crystal delicately. The integration of MoS2 into various applications such as electronics, optoelectronics, sensors, and energy storage devices has been driven by its exceptional properties, including its distinctive electronic, optical, and mechanical characteristics. Furthermore, the ability to adjust the bandgap of MoS2 has created novel opportunities for potential applications in the field of semiconductors. This paper provides a succinct summary of recent studies, that have concentrated on the optical characterization of MoS2 monolayers. Optical and Raman spectroscopy was employed to characterize the 2D sheets of MoS2 and its hybrid materials.