Carroll University

Topology change in Lorentzian quantum gravity demands geometric regulators that control curvature, spin structure, and chirality during nontrivial interpolations. We develop a framework for regulated topology change based on smooth Lorentzian spin cobordisms with interpolating metrics, allowing a transient failure of global hyperbolicity while preserving smoothness, Lorentz signature, and spin compatibility. Within this framework we introduce the Chiral Weyl Curvature Diagnostic, a curvature-based functional that weights topology-changing geometries by conformal curvature, spin admissibility, and topological complexity. The diagnostic functional is built from Weyl curvature invariants and includes a parity-odd dual Weyl term that is sensitive to geometric chirality. Spin consistency is enforced via a Stiefel-Whitney constraint, ensuring that only physically admissible cobordisms contribute. As an example, we construct a smooth spin cobordism between a Morris-Thorne wormhole and asymptotically flat Minkowski spacetime. In the throat region the curvature response is shown to be Weyl-dominated, and the parity-odd Weyl contribution sharply distinguishes chiral knotted embeddings while vanishing for amphichiral configurations. We then show that braids provide a natural language of throat dynamics: evolving wormhole throats trace time-dependent braid movies, with elementary braid generators representing the fundamental topological operations of the cobordism. Replacing crossing number by a braid-based complexity refines the diagnostic functional to operate at the level of these elementary exchanges and extends it naturally to multithroat and networked configurations.

PurposeThis study aims to investigate the impact of a firm's social network on the quality of the firm's information environment and whether the information benefit obtained through the social network (i.e., improved information quality)- is ultimately reflected in firm value.Design/methodology/approachThe study employs multiple linear regression including Weighted Least Squares (WLS) regression, economic significance test, Heckman two-step selection model, propensity score-matched (PSM), entropy balancing and two-stage least squares (2SLS) instrumental variable method.FindingsThe study provides evidence that a firm's social network size positively impacts the quality of its information environment and that the firm complexity moderates the relationship. Additionally, social network size positively influences firm value, mediated by improved information quality.Research limitations/implicationsThe study responds to the call by Engelberg et al. (2013) to identify the mechanism by which the social connections of the employees in a firm create value for the firm. This study will be of interest to managers, directors, policymakers and other stakeholders.Practical implicationsOur study highlights that stronger firm social networks are linked to reduced information opacity, offering practical insights for managers, investors, regulators, and policymakers. Managers and boards can enhance information quality by promoting executive networking through targeted initiatives. Investors may use a firm's connectedness as a signal of information transparency. Regulators and standard setters may find these findings relevant as they emphasize non-financial information. Policymakers can support this effort by encouraging networking skill development, ultimately improving firms' information environments.Social implicationsOur study contributes to this body of work by highlighting the social implications of firm-level connections. Specifically, we provide evidence that employee relationships, formed through shared educational backgrounds, professional experiences, and participation in social clubs, serve as effective channels for information exchange. These connections facilitate the flow of knowledge, ideas and insights within organizations, enhancing communication and decision-making processes.Originality/valueThe study fills the gap in the literature by investigating whether the presence of a firm's professional, educational and other social connections with employees of outside companies affects the quality of the firm's information environment and whether the information benefit obtained through the social network (i.e., improved information quality) is ultimately reflected in firm value.

The rules of prokaryotic cell design remain elusive. Here, a theory is presented for interpreting growth rate, overflow metabolism, respiration efficiency, and maintenance energy flux based on cell dimensions, membrane protein crowding, and metabolism. The theory employs biophysical properties and systems analysis to successfully interpret phenotypes of Escherichia coli K-12 strains MG1655 and NCM3722. These strains are genetically similar but differ in surface area-to-volume (SA : V) ratios (~ 30%), growth rate on glucose (~ 40%), and overflow-inducing growth rates (~ 80%). Six predictions were tested and validated using experimental phenomics, proteomics, and mutant data. Analyses did not require assumptions regarding cytosolic macromolecular crowding, highlighting the distinct properties of the theory. Cell geometry and membrane protein crowding are significant biophysical constraints of cell biology.

PurposeThis study aims to examine how institutional and individual capabilities shape ethical artificial intelligence (AI) readiness, psychological well-being (PWB) and inclusivity (INC) among students as higher education adopts AI tools. Drawing on a socio-technical systems framework, the authors investigate how organizational conditions and student perceptions jointly influence inclusive outcomes in AI-enabled learning environments.Design/methodology/approachSurvey data from students and faculty across multiple higher education institutions were used to test a structural model with latent constructs. These constructs are institutional support (IS), AI integration capability (AIC), digital literacy (DL), ethical concerns (EC), perceived educational value (PEV), psychological safety in AI use (PSAI), trust in AI systems (TAIS), academic flourishing (AF), PWB and INC. Partial least squares structural equation modeling was used to estimate relationships and explanatory power.FindingsIS, AIC and DL significantly enhance students' and faculty perceptions of the educational value of AI, psychological safety and TAIS. These perceptual mediators, in turn, positively influence perceived inclusion, AF and PWB. The model explains substantial variance in key outcomes (R & sup2;: PEV = 0.62, TAIS = 0.67, PSAI = 0.59, AF = 0.61, PWB = 0.64, INC = 0.60).Research limitations/implicationsThis study is based on a cross-sectional survey, which limits the ability to draw causal conclusions. The sample, while diverse, was confined to US-based higher education institutions, which may affect the generalizability of findings across global contexts. In addition, reliance on self-reported data introduces potential for response bias. Future research should use longitudinal and cross-cultural designs to explore how perceptions of AI evolve over time and in varied settings. Despite these limitations, the study offers a replicable ethical framework and empirical model that can inform responsible AI adoption and evaluation practices in higher education.Practical implicationsThe findings offer actionable guidance for higher education institutions implementing AI. Investing in DL and ethical AI integration enhances student trust, well-being and inclusion. Institutions should prioritize transparent, explainable systems that align with human values and provide user-centered experiences. EC must be addressed proactively through governance frameworks, data privacy protections and inclusive design practices. Trust in AI is not automatic; it must be cultivated through participatory implementation, clear communication and attention to student agency. Embedding ethics-by-design into AI deployment supports student flourishing and ensures equitable access to the benefits of educational technologies.Social implicationsThis study highlights the broader social consequences of AI adoption in education, particularly regarding equity, inclusion and mental well-being. AI systems that lack transparency or fairness can exacerbate existing digital divides and disproportionately disadvantage marginalized students. Conversely, ethically aligned AI - designed with justice, autonomy and psychological safety in mind - can foster more inclusive and supportive learning environments. Institutions have a social responsibility to ensure that AI tools do not replicate systemic biases but instead promote dignity, accessibility and human flourishing. The findings advocate for participatory, justice-oriented approaches to educational technology governance that prioritize collective well-being over efficiency alone.Originality/valueThe study extends AI adoption research in higher education by integrating psychological safety, trust, ethics and inclusivity into a socio-technical model of AI readiness. It shows that IS aligned with values and DL that positions students as critical agents is as important as technical proficiency for the responsible use of AI. The findings provide actionable implications for institutional strategy, AI policy and curriculum design.

ABSTRACT Microbes play a vital role in plant development, health, and resilience, yet relatively little is known about the specific metabolic mechanisms driving interactions in these host-associated communities. Systems biology models enable a computational approach to understanding metabolic interactions, which can be difficult to pinpoint experimentally; however, these methods cannot yet accommodate the large number of species in natural communities. Synthetic communities (SynComs) provide a more tractable alternative to explore targeted interactions. Here, we investigated metabolite exchange in a seven-member maize root-associated SynCom, specifically accounting for plant host context by designing a customized exudate medium. We constructed metabolic models for each bacterial species and curated them with in vitro phenotyping data to reflect experimentally based carbon uptake potential. Flux balance analysis of individual species demonstrated that integrating phenotype data and changing medium type had substantial impacts on predicted growth rates, which in turn shaped potential interspecies interactions. In silico community growth optimization of the seven-member community model showed that the exudate medium supported a more diverse community composition compared to minimal medium, with predictions of community member abundance closely aligned to literature-derived experimental results. Predicted metabolite exchange in the root exudate environment showed Enterobacter ludwigii as a community hub, and cross-feeding of indole suggested a potential effect of bacterial community interactions on the plant host. Our in silico findings indicate the host plays an important role in structuring microbial interactions and cross-feeding at the metabolic level, underscoring the importance of considering environmental context from both theoretical and experimental perspectives. IMPORTANCE True understanding of a system is marked by the ability to predict its behavior. The complexity of natural host-microbe systems represents a frontier of knowledge that scientists are working to understand, and elucidating principles of interactions within multi-partite microbial communities remains a challenge in microbial ecology. Synthetic communities provide a tractable starting point for investigating interaction mechanisms, and computational approaches complement laboratory experiments by systematically evaluating multiple possibilities for metabolic pathway processing, thereby allowing us to comprehensively study the interconnected metabolic networks of host-associated microbiota. The model we developed for the seven-member maize root-associated bacterial community presents a step toward predicting plant-microbe behavior, providing hypotheses for future experimental testing and serving as a template for expanding model complexity to more members and other systems.