Nalanda Open University

ABSTRACT Collaborative spaces (CS) have begun to dot Europe’s countryside, yet their locational logic outside cities remains poorly measured. By geocoding every CS and every knowledge-intensive service (KIS) firm located in LEADER Local Action Groups (LAGs), this article measures their spatial behavior with three complementary statistics: the K-density function for basic proximity and the continuous-space M and m functions for the frequency and intensity of co-location. Anchored in territorial development theory that combines endogenous, place-based perspectives with the innovation-ecosystem view, the study interprets rural CS as both locally embedded initiatives and connectors within wider regional networks. The analysis confirms a clear intra-agglomeration of CS in both regions, but for very different reasons. In Rhineland-Palatinate, a dense lattice of market towns produces many short-distance connections. Catalonia’s CS concentrate in two dominant hubs, leaving most villages without a space or service partner. The study shows that rural CS geography is governed less by sector mix or distance than by the strength of local social ties and interregional networks.

The vibration, damping, and buckling behavior of graphene-reinforced sandwich beams are investigated in this work. The honeycomb sandwich beam is composed of graphene-reinforced with glass fiber-reinforced polymer (GFRP) composite skins and a GFRP strip-reinforced honeycomb core. The governing equations are developed by using the finite element formulation along with the higher-order beam theory (HOBT). A significant degree of agreement is found when comparing numerical results to accessible literature. Additionally, this work examines the effects of graphene reinforcement, GFRP honeycomb core thickness, graphene-reinforced GFRP skin layer thickness, and end conditions on the vibration, damping, and buckling behavior of honeycomb sandwich beams. The sandwich beam with graphene-reinforced skins outperforms the neat sandwich beam in terms of natural frequency, damping, and buckling, with improvements of 8.04%, 10.56%, and 18.29%, respectively. The lightweight GFRP honeycomb core, combined with the advanced graphene-reinforced composite face sheets, offers interesting applications and further research.

Sphaeranthus indicus is an important medicinal plant, widely used in traditional medicine for its therapeutic potential, particularly its antiviral, antimicrobial, anti-inflammatory and anticancer activities. Despite its medicinal importance, there is a need for developing efficient propagation methods to ensure its large-scale cultivation and conservation. This study aims to develop a protocol direct shoot organogenesis from nodal explants of Sphaeranthus indicus. In vitro regeneration through direct shoot organogenesis offers an efficient method for the propagation of medicinally valuable plants. Explants were subjected to surface sterilization followed by culture on MS medium (Murashige and Skoog medium) supplemented with different concentrations of BAP (6-Benzylaminopurine) for shoot induction. Root initiation was achieved on MS medium with α-naphthaleneacetic acid (NAA), and regenerated plantlets were successfully acclimatized and shifted to greenhouse and open field conditions. High shoot induction frequency of 91.6% with the lowest concentration of 1 mg/L BAP highlights the efficient regeneration protocol and initiate a pathway for micropropagation and cultivation of multipotential medicinal plant, S. indicus in large-scale. During the present investigation, an efficient in vitro regeneration protocol is developed

Introduction: Student academic performance is commonly measured using indicators like credit scores, class rankings, or passing thresholds, each offering insight into a student’s comprehension and progress. These measures give a numeric evaluation of a student's understanding of the course content, enabling educators and institutions to monitor growth over time. Moreover, the capacity to forecast academic performance proves highly beneficial for both students and teachers, as it enables early interventions and course corrections. This predictive ability can lead to tailored learning strategies, ultimately enhancing educational outcomes and supporting overall student success.Method: To carry out a statistical analysis on the dataset (kaggel), the first step will involve cleaning and preprocessing the data to address any missing or non-numeric entries. Once the dataset is refined, hypothesis testing and ANOVA will be employed to pinpoint the key factors that influence student performance. Result: Initial findings suggest that study hours and attendance are significant predictors of exam scores, with higher values generally leading to better performance. Additionally, motivational factors, parental involvement, and access to academic resources showed varying degrees of influence, highlighting their potential role in shaping educational outcomes. Conclusion: Through descriptive statistics, correlation analysis, and regression models, the dataset offers insights that can be used by educators and policymakers to enhance teaching strategies and student support systems. By identifying key factors that affect student outcomes, the dataset provides a foundation for future research and practical interventions aimed at improving academic performance in educational settings

Polyphenols are a diverse group of naturally occurring compounds in various plant-based foods with potential antiviral and anti-inflammatory properties. This chapter discusses the role of polyphenols in preventing and treating COVID-19, focusing on their potential mechanisms of action, preclinical and clinical evidence, ongoing and future clinical trials, safety concerns, and limitations. Although some in vitro and animal studies have demonstrated promising antiviral activities of polyphenols against SARS-CoV-2, clinical evidence remains limited. Several ongoing clinical trials are investigating the effects of polyphenols, like quercetin, epigallocatechin-3-gallate, resveratrol, and curcumin, on COVID-19 outcomes. However, low bioavailability, potential side effects, and drug interactions must be addressed to optimize their therapeutic potential. More well-designed clinical trials are required to establish the efficacy and safety of polyphenols in COVID-19 management. In the meantime, adherence to set public health guidelines remains essential for preventing and treating COVID-19.

Logic operations performed by semiconductor-based transistors are the basis of modern computing. There is considerable interest in creating autonomous materials systems endowed with the capability to make decisions. In this work, we introduce the concept of using topological defects in active matter to perform logic operations. When an extensile active stress in a nematic liquid crystal is turned on, +1/2 defects can self-propel, in analogy to electron transport under a voltage gradient. By relying on hydrodynamic simulations of active nematics, we demonstrate that patterns of activity, when combined with surfaces imparting certain orientations, can be used to control the formation and transport of +1/2 defects. We further show that asymmetric high- and low-activity patterns can be used to create effective defect gates, tunnels, and amplifiers. The proposed active systems offer the potential to perform computations and transmit information in active soft materials, including actin-, tubulin-, and cell-based systems.

In the present paper, a new one parameter lifetime distribution named, “Adya distribution’ has been proposed for modeling lifetime data from engineering. Its various statistical properties including moments and moments based measures, hazard rate function, mean residual life function, stochastic ordering, deviations from the mean and the median, Bonferroni and Lorenz curves, and stress-strength reliability have been studied. Both the method of moment and the maximum likelihood estimation have been discussed for estimating the parameter of the proposed distribution. A numerical example has been presented to test the goodness of fit of the proposed distribution over other one parameter lifetime distributions available in statistical literature.

In the present paper, a new one parameter lifetime distribution named, "Adya distribution' has been proposed for modeling lifetime data from engineering.Its various statistical properties including moments and moments based measures, hazard rate function, mean residual life function, stochastic ordering, deviations from the mean and the median, Bonferroni and Lorenz curves, and stress-strength reliability have been studied.Both the method of moment and the maximum likelihood estimation have been discussed for estimating the parameter of the proposed distribution.A numerical example has been presented to test the goodness of fit of the proposed distribution over other one parameter lifetime distributions available in statistical literature.