This study investigates whether and how firms' engagement in government-initiated targeted poverty alleviation (TPA) programs influences their corporate risk-taking behavior. Drawing on stakeholder theory and using a unique panel dataset of Chinese A-share listed firms from 2016 to 2022, we provide empirical evidence that TPA participation is significantly associated with lower levels of corporate risk-taking. Our results further reveal that corporate reputation strengthens this negative relationship, indicating that firms with stronger reputational capital are more responsive to stakeholder expectations. We also find that information asymmetry acts as a mediating channel through which TPA initiatives reduce managerial risk appetite. To address potential endogeneity concerns, we employ a battery of robustness checks, including alternative proxies, instrumental variable approaches, entropy balancing, and Heckman two-stage models. These findings extend prior research on corporate social responsibility (CSR) by highlighting the role of government-driven poverty alleviation in shaping firms' risk profiles. The study contributes to the growing literature on the economic consequences of CSR in emerging markets and offers practical insights for policymakers seeking to encourage socially responsible business practices without compromising firms' financial stability.
We review burnout risk factors in interventional radiology (IR) and explore how artificial intelligence (AI) would address burnout from a workplace aspect. We performed a literature search on PubMed on risk factors for burnout in interventional radiology and AI tools to address burnout challenges. IR specialists face burnout risk at personal, workplace and system levels. AI could identify burnout using demographic data and free text, alleviate administrative workload, and manage workflow. AI could also enhance procedural efficiency via automated navigation systems, reducing stress from radiation exposure. Future directions include enhanced burnout identification and medical coding for access to longitudinal data. AI may be a solution to addressing specific burnout risk factors in interventional radiology. No level of evidence. Review Article.
Purpose This study examines how expatriates' initial motivation to work abroad – namely, continuance commitment – influences their discretionary behaviors by taking a closer look at the relationship between perceived organizational justice and organizational citizenship behaviors among self-initiated expatriates. Design/methodology/approach The study uses a multi-wave, multi-source dataset of insights obtained from self-initiated expatriates in Saudi Arabia to examine how continuance commitment changes the relationship between perceived organizational justice and organizational citizenship behaviors. Findings The results showed that perceived organizational justice predicts the discretionary behaviors, only among self-initiated expatriots SIEs with high continuance commitment; the relationship disappears at lower commitment levels. This finding suggests that expatriates with strong personal, professional or financial investments in the host country are more likely to display citizenship behaviors when they perceive fairness to protect their roles and to avoid the costs of repatriation. Practical implications Recognizing that expatriates may strategically engage in organizational citizenship behaviors to safeguard their international roles can inform management practices focused on retention and performance. Originality/value This study contributes to the management literature by identifying continuance commitment as both a motivator for expatriation and a boundary condition in the relationship between perceived organizational justice and organizational citizenship behaviors.
ABSTRACT This study introduces a hybrid analytical–machine learning framework for solving the Schrödinger equation with complex potentials. The semi‐inverse variational method is first used to generate highly accurate eigenfunctions and eigenenergies for both 1D radial potentials (Yukawa and Cornell) and a 2D coupled anharmonic oscillator. Based on these rigorous, physics‐consistent results, we train supervised machine learning models; including Random Forest and Neural Network regressors; to predict energy eigenvalues across wide parameter ranges. Both models achieve near‐perfect predictive accuracy (R2 > 0.999) with errors of only a few millielectronvolts, while preserving fundamental quantum‐mechanical trends. Feature importance analysis confirms that the quantum number n and potential strength parameters dominate the energy scaling, in agreement with theoretical expectations. By integrating variational physics with data‐driven emulation, this hybrid framework reduces computational cost by orders of magnitude; enabling rapid, high‐throughput exploration of quantum systems across dimensions. The approach not only accelerates parameter screening but also serves as a discovery tool, uncovering emergent scaling laws and critical confinement behavior in mixed potentials. This synergy between analytical rigor and machine learning efficiency opens new pathways for quantum simulation, materials design, and the discovery of novel quantum phenomena.
Perovskite-type hydrides have emerged as promising candidates for next-generation solid-state hydrogen storage owing to their tunable structural, mechanical, and thermal properties. Here, we combine density functional theory (DFT) and machine learning (ML) to investigate cubic XMnH3 perovskites (X = Li, Na, K) and extend predictions to unexplored compositions and pressures. DFT calculations confirm that all compounds stabilize in a ferromagnetic cubic phase with negative formation energies, competitive hydrogen storage capacities (4.66 wt.% for LiMnH3, 3.73 wt.% for NaMnH3, and 3.12 wt.% for KMnH3), and hydrogen desorption temperatures between 517-886 K. Elastic constants establish mechanical stability up to 30 GPa, though with inherent brittleness, while thermal analysis via the quasi-harmonic Debye model highlights pressure-driven stiffening of the lattice. To circumvent the limitations of small DFT datasets, we trained Random Forest, XGBoost, and neural network models on direct and derived DFT descriptors, and we demonstrated that ensemble tree models yield the most accurate predictions. LiMnH3 continuously outperforms heavier analogues, and expected monotonic trends are reproduced when the Debye and melting temperatures are extended to 60 GPa. Moreover, screening of about 46 ABH(3) hydrides was made possible by composition-based descriptors created with Matminer, which showed systematic ionic-radius trends and identified BeMnH3 and MgMnH3 as promising candidates with superior vibrational stability and hardness. This integrated DFT-ML framework establishes a predictive strategy for accelerating the discovery of hydrides with optimized hydrogen storage performance.
