Effective tacit knowledge sharing (TKS) helps firms gain a competitive advantage in a knowledge economy. Drawing on social identity theory, the study posits that meaning-making through work (MMW) leads to TKS. Knowledge hoarding mediates, and formalization moderates the MMW-TKS relationship. As possession of knowledge is necessary for knowledge sharing, MMW promotes knowledge hoarding; therefore, knowledge hoarding is posited as a mediator between MMW and TKS. Formalization is hypothesized to negatively moderate the MMW-knowledge hoarding, and knowledge hoarding-knowledge sharing relationships as it promotes systemic organizational actions rather than individual-level behaviors. The study hypotheses were tested using a cross-sectional survey of 250 employees, with all hypotheses finding support. The study improves our understanding of TKS antecedents and calls for debate on the dominant conceptualization and operationalization of knowledge hoarding as a knowledge-withholding behaviour. It also sheds light on the substituting effects of institutional endeavours on individual efforts.
The Hubble constant, H0, quantifies the present-day expansion rate of the universe and anchors the cosmic distance scale, the inferred age of the universe, and the dark-energy equation of state. Two independent families of measurement now disagree at a formal significance exceeding 5σ: early-universe inferences from the cosmic microwave background (CMB) and baryon acoustic oscillations (BAO), analyzed within the standard ΛCDM model, yield H0 ≈ 67–68 km s⁻¹ Mpc⁻¹, whereas a broad ensemble of late-universe, largely distance-ladder-based measurements cluster around H0 ≈ 70–75 km s⁻¹ Mpc⁻¹. This review traces the history of H0 measurement, summarizes the theoretical framework relating H0 to the sound horizon and the expansion history, and synthesizes more than a dozen independent early- and late-universe determinations, including distance-ladder, geometric, gravitational-lensing, gravitational-wave, cosmic-chronometer, and quasar-based methods. It tabulates their central values and uncertainties, traces the systematic-error investigations — including recent James Webb Space Telescope Cepheid photometry and revised strong-lensing mass-profile modeling — that have narrowed but not eliminated plausible mundane explanations, and quantifies the statistical robustness of the tension across the full measurement ensemble. It then evaluates the principal theoretical proposals for reconciling the two regimes, spanning early dark energy, evolving dark-energy equations of state motivated by recent DESI results, modified gravity, interacting dark-sector models, and ladder-wide systematics, assessing each against current observational constraints in a summary comparison table. We conclude that the tension is unlikely to be resolved by any single known systematic and outline the observational programs — the Vera C. Rubin Observatory, Euclid, the Nancy Grace Roman Space Telescope, and next-generation gravitational-wave detectors — most likely to discriminate among the competing explanations over the coming decade.
The paper identifies and addresses a significant research gap by identifying factors contributing to the sustainability of family controlled MSMEs in India which is crucial sector for India’s growth and GDP. The paper performed bibliometric analysis to identify key factors that are responsible for sustainability. The bibliometric analysis was performed using 1343 papers identified from SCOPUS database covering publications from 2010 to 2025. Co-occurrence analysis, co-citation analysis and thematic clustering were performed to identify research trends and themes in this domain. Four important and crucial themes were identified for the sustainability of family controlled MSMEs in India. They are Family goals, succession planning, family culture and entrepreneurial orientation. The findings highlight the need for further qualitative research to validate these identified factors. The paper at the end a comprehensive roadmap, providing critical insights for researchers and practitioners to develop effective sustainability strategies for Indian family-owned MSMEs
Breast cancer remains one of the most commonly diagnosed malignancies worldwide, and timely, accurate discrimination between benign and malignant breast masses is critical to reducing unnecessary biopsies while ensuring early treatment of true malignancies. This study presents a comprehensive, methodologically rigorous machine learning (ML) analysis of six supervised classifiers — Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, k-Nearest Neighbors (k-NN), and a shallow Artificial Neural Network (ANN) — for binary classification of breast masses using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, a well-established, publicly available benchmark comprising 569 cases and 30 real-valued nuclear morphometric features computed from digitized fine-needle aspirate (FNA) images. Beyond standard accuracy benchmarking, this study contributes four analyses that are comparatively underreported in the WDBC literature: a feature-domain ablation quantifying the diagnostic contribution of mean-value, standard-error, and worst-value feature subsets; a class-imbalance handling comparison; a clinically motivated decision-threshold optimization using the Youden index; and a dual global-interpretability analysis combining Random Forest Gini importance with SHapley Additive exPlanations (SHAP). Each classifier was tuned via 5-fold cross-validated grid search and evaluated on a stratified 75/25 held-out split. Logistic Regression and k-Nearest Neighbors jointly achieved the highest held-out test accuracy (97.90%), with all six classifiers exceeding 95.8% accuracy; pairwise paired t-tests over cross-validation folds found only one statistically significant difference among fifteen pairwise comparisons, indicating that classifier choice for this task is largely accuracy-neutral. The best-performing model achieved a receiver operating characteristic area under the curve (ROC-AUC) of 0.997, an average precision of 0.998, and a Matthews correlation coefficient of 0.955. Feature-domain ablation showed that worst-value features alone recovered 96.50% accuracy versus 97.90% for the full feature set, while standard-error features alone achieved only 86.71%, and SHAP analysis corroborated Random Forest's Gini-based ranking, jointly identifying worst area, worst perimeter, and worst/mean concave points as the dominant diagnostic drivers. Youden-index threshold optimization recovered two additional correctly identified malignant cases relative to the default 0.5 probability threshold, at a small, quantified cost to specificity. These results, obtained on real diagnostic data rather than simulated signals, corroborate and extend a substantial body of prior work, and reinforce the broader case for feature-based, interpretable, computationally lightweight ML as a viable decision-support tool for cytological breast mass classification, while underscoring that any such tool requires prospective, multi-institutional clinical validation before informing real diagnostic decisions. Keywords: machine learning; explainable AI; SHAP; breast cancer; Computer-Aided Diagnosis.; diagnostic classification; Wisconsin Diagnostic Breast Cancer dataset; fine-needle aspiration; decision threshold optimization; feature ablation
Cosmic ray modulation is one of the most fundamental processes in heliophysics, describing the temporal, spatial, and energy-dependent variation of galactic cosmic ray (GCR) intensities as they propagate through the turbulent heliosphere before reaching Earth. The heliosphere, formed by the continuous expansion of the solar wind and permeated by the heliospheric magnetic field (HMF), acts as a dynamic magnetic shield that modifies the transport of energetic charged particles through diffusion, convection, gradient and curvature drifts, and adiabatic energy changes. These transport mechanisms are strongly influenced by the approximately 11-year solar activity cycle and the 22-year Hale magnetic polarity cycle, resulting in long-term modulation of cosmic ray fluxes as well as short-term transient phenomena associated with coronal mass ejections (CMEs), interplanetary shocks, magnetic clouds, and high-speed solar wind streams. Understanding cosmic ray modulation is essential not only for advancing heliospheric physics but also for improving space weather forecasting, assessing radiation hazards to astronauts and spacecraft, protecting satellite electronics, ensuring aviation safety on polar routes, and investigating atmospheric ionization and cosmogenic isotope production. This review presents a comprehensive synthesis of the current understanding of galactic cosmic ray modulation by integrating classical transport theory, heliospheric plasma physics, long-term observational datasets, numerical simulations, and emerging artificial intelligence (AI) techniques. The review begins by discussing the origin, acceleration mechanisms, energy spectrum, and chemical composition of cosmic rays, followed by an examination of heliospheric structure, including the solar wind, Parker spiral magnetic field, heliospheric current sheet, termination shock, heliosheath, heliopause, and the local interstellar medium. Particular emphasis is placed on the Parker Transport Equation, which forms the theoretical foundation for describing cosmic ray propagation through the combined effects of anisotropic spatial diffusion, solar wind convection, gradient and curvature drifts, current-sheet drift, and adiabatic energy losses. The review further examines the modulation of cosmic rays during Solar Cycles 20–25, highlighting the influence of solar magnetic polarity reversals, heliospheric magnetic field evolution, magnetic turbulence, recurrent high-speed streams, and transient solar eruptions on cosmic ray intensity. A critical assessment is presented of observational evidence obtained from ground-based neutron monitor networks and major space missions, including Voyager 1 and 2, Ulysses, ACE, SOHO, STEREO, PAMELA, AMS-02, Parker Solar Probe, Solar Orbiter, and Aditya-L1, which together provide continuous measurements of energetic particles, solar wind plasma, and heliospheric magnetic fields across multiple spatial scales. Modern numerical approaches—including finite-difference methods, stochastic differential equation (SDE) models, Monte Carlo simulations, and three-dimensional magnetohydrodynamic (MHD) heliospheric models such as HelMod, WSA–ENLIL, and EUHFORIA—are critically compared with respect to their capabilities and limitations in reproducing observed modulation patterns. The review also highlights the rapidly expanding role of artificial intelligence and machine learning in cosmic ray research. Recent developments involving recurrent neural networks (RNNs), long short-term memory (LSTM) networks, convolutional neural networks (CNNs), transformer architectures, random forests, gradient boosting algorithms, physics-informed neural networks (PINNs), explainable artificial intelligence (XAI), and digital-twin frameworks are examined for their ability to improve prediction of neutron monitor counts, Forbush decreases, solar energetic particle events, and heliospheric transport parameters. These data-driven methods, when integrated with first-principles transport theory, offer significant potential for real-time forecasting and operational space weather services. Finally, the review identifies major scientific challenges related to turbulence modeling, time-dependent heliospheric structure, multi-scale particle transport, uncertainty quantification, and physics–AI integration. Future research directions are proposed that emphasize high-performance computing, data assimilation, digital heliosphere modeling, multi-spacecraft observations, and next-generation AI-assisted forecasting systems. By combining theoretical developments, observational advances, computational methodologies, and intelligent data-driven techniques, this review provides a comprehensive and up-to-date perspective on cosmic ray modulation and its broad implications for heliophysics, astrophysics, and space weather science. Keywords: artificial intelligence; space weather; Cosmic rays; Heliosphere; Parker Transport Equation; Solar wind
