Despite the rapid proliferation of generative AI, empirical understanding of how hotel managers integrate ChatGPT into their professional workflows remains limited. Adopting a constructivist grounded theory approach, this study develops a hotel manager-centered framework of ChatGPT usage benefits and challenges, explaining hotel managers’ utilization levels and continued use of ChatGPT. We identify five benefit domains—content creation and support, efficiency and productivity, well-being, enhanced communication, and knowledge enhancement—and three challenge domains: dependency, drawbacks, and capability requirements. By examining the interplay between these drivers and barriers, this study provides one of the first qualitative characterizations of professional ChatGPT engagement in hospitality. Theoretically, it extends technology adoption models by proposing a grounded framework explaining how benefits and challenges influence continuance use and utilization level. Practically, it offers a roadmap for hotel organizations to develop policies—and training that mitigate dependency risks while maximizing productivity gains from large language models.
To address the growing public concerns regarding ethics, fairness, and the protective nature of digital transformation, hospitality organizations are incorporating corporate digital responsibility (CDR) practices, including enhanced data security measures. This study draws upon the deontic model of justice to examine the impact of CDR practices directed toward customers on employees’ perceived organizational justice and subsequent organizational outcomes. A total of 281 responses from U.S. hospitality employees were collected using a time-lagged design with a two-wave data collection approach. The proposed model is tested using PLS-SEM in SmartPLS 4.0. Findings indicate that CDR practices related to digital ethics and data privacy and protection positively affect employees’ perceived organizational justice. Similarly, perceived organizational justice has a positive influence on job satisfaction, perceived job performance, and organizational trust. The study highlights the critical role of CDR practices in enhancing employees' perceptions and influencing their attitudes and behaviors in the workplace.
The tumor-node-metastasis (TNM) anatomic system is the current clinical standard for breast cancer staging, yet it inadequately captures the molecular heterogeneity that drives disease progression, motivating the development of expression-based biomarker classifiers. We integrated microRNA (miRNA) and messenger RNA (mRNA) expression profiles from 1081 primary TCGA-BRCA tumors (early-stage, Stages I–II, n = 822; late-stage, Stages III–IV, n = 259) and benchmarked nine machine learning algorithms using a stratified 70/15/15 train–validation–test split with class-imbalance weighting. On the held-out test set, XGBoost, selected after Bayesian hyperparameter optimization on the validation set, achieved AUC = 0.687 (95% CI: 0.622–0.748), accuracy = 79.8%, and good model calibration (Hosmer-Lemeshow p = 0.31), significantly outperforming mRNA-only (AUC = 0.654; p = 0.028) and miRNA-only (AUC = 0.612; p = 0.007) models. Differential expression analysis identified 15 significant miRNAs and 194 significant mRNAs (FDR <0.05), and regulatory network analysis revealed three co-expression modules governing epithelial-mesenchymal transition, metabolic reprogramming, and immune evasion, with 15 hub miRNAs regulating 97–516 predicted targets. Multi-omics integration captures progression-associated molecular signatures beyond anatomic staging and could, pending external validation, complement conventional TNM staging by providing a molecularly informed risk-stratification layer; the framework provides a reproducible basis for exploratory translational biomarker discovery.
The incorporation of artificial intelligence (AI) and machine learning (ML) into microalgal research is transforming biomass generation, biofuel synthesis, and wastewater remediation strategies. Sophisticated ML techniques, such as artificial neural networks (ANN), support vector machines (SVM), and genetic algorithms (GA), facilitate precise simulation and forecasting of highly intricate microalgal systems. Although constraints related to data accessibility and model scalability persist, ML-based methodologies are increasingly demonstrating their value in enhancing the sustainability and operational efficiency of microalgal processes. Simultaneously, technoeconomic analysis (TEA) has become an indispensable framework for assessing biorefinery viability through systematic evaluation of life-cycle environmental burdens. Recent progress in TEA methodologies has strengthened iterative design optimization, uncertainty quantification, and user accessibility via open-source computational platforms. Broader systems boundaries now account for policy mechanisms, performance during end-use phase, and international market dynamics, thereby reinforcing TEA’s contribution to sustainable bioeconomic advancement. Collectively, these computational and analytical innovations are expediting the deployment of scalable and economically feasible microalgal technologies. Highlights
Air pollution remains critical global challenge, with sulfur oxides (SOx), nitrogen oxides (NOx), and volatile organic compounds (VOCs) contributing to environmental degradation and adverse health outcomes. Among mitigation technologies, biochar (BC) has gained attention as sustainable adsorbent for gas-phase pollutant control due to its hierarchical porosity, tunable surface chemistry, and production from renewable biomass. This review examines mechanistic foundations and design strategies of engineered biochar for removal of SOx, NOx, and VOCs, while comparing its performance with conventional technologies that are pollutant-specific, energy-intensive, or limited under industrial conditions. Key synthesis routes including pyrolysis, hydrothermal carbonization, and co-pyrolysis are discussed alongside modification strategies such as activation, heteroatom doping, and metal functionalization, which enhance pore structure, surface reactivity, and pollutant selectivity. Reported studies indicate that engineered biochars achieve adsorption capacities up to 200 mg g−1 for SO₂ and 245 mg g−1 for aromatic VOCs such as toluene, while demonstrating effective NOx removal under flue-gas conditions. These performances are governed by hierarchical porosity, defect-rich carbon structures, and oxygen-containing functional groups that promote acid–base interactions, π–π stacking, and redox-mediated adsorption pathways. Computational tools increasingly support adsorbent design: Density Functional Theory provides atomistic insight, while Machine Learning enables rapid prediction across datasets. Despite progress, challenges remain, including regeneration energy demand, reduced selectivity under humid conditions, and limited industrial scalability. By integrating experimental insights with computational approaches, this review outlines a predictive framework for developing efficient and durable advanced biochar adsorbents for next-generation air pollution control.
