Bankruptcy risk prediction, a core issue in financial risk management, plays a critical role in assessing corporate financial health and supporting decision-making for financial institutions. Traditional machine learning models often struggle with parameter optimization when handling high-dimensional, nonlinear financial data. In contrast, metaheuristic algorithms, owing to their global search capabilities, have emerged as effective tools to enhance model performance. This paper proposes a novel bankruptcy prediction model that integrates a Kernel Extreme Learning Machine (KELM) with an improved Northern goshawk Optimizer (TIS_NGO), which incorporates a Thought-Inspired Strategy (TIS). The enhancements to TIS_NGO include a divergence-based thought innovation mechanism, a prey-attacking strategy inspired by differential evolution, and a centroid opposition-based boundary control mechanism. Experimental evaluations on the CEC2017 and CEC2022 benchmark suites demonstrate that TIS_NGO outperforms the standard NGO as well as other well-known algorithms such as Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) in terms of convergence speed and solution accuracy. The optimized KELM, with TIS_NGO-tuned penalty parameter [Formula: see text] and kernel parameter [Formula: see text], achieves high classification accuracy and robustness on the Wieslaw bankruptcy dataset. These results validate the effectiveness of combining improved metaheuristic algorithms with machine learning models for financial risk forecasting. Overall, the proposed method offers a promising approach to improving the accuracy and stability of bankruptcy prediction, thereby contributing a new technical pathway for early warning in the financial domain.
Introduction: China’s energy transition is confronted with intertwined challenges of unprecedented demographic shifts (persistent low fertility, population decline, rapid aging), the rapid expansion of the digital economy, and the imperative of green low-carbon transformation. The 2025 delayed retirement policy has reshaped the labor market structure of energy system operation and maintenance, while digitalization has become a core driver of improving energy operational efficiency. Against this background, exploring the synergistic effect of delayed retirement and digitalization on energy efficiency is critical for advancing China’s low-carbon transition and safeguarding energy security. This paper proposes a D3 (Demography–Digitalization–Decarbonization) framework to investigate how demographic change and digitalization jointly drive operational energy efficiency gains across key energy-intensive sectors.
The rapid growth of digital technology has fundamentally reshaped the entrepreneurial landscape, giving rise to complex digital entrepreneurial ecosystem. While digital entrepreneurship is often presented as a means of empowering women, its actual impact remains debated. By integrating social role theory (SRT), this study constructs and applies an integrated Gender–Technology–Entrepreneurship (GTE) framework. Through a grounded theory (GT) analysis of in-depth interviews with seven Chinese women digital entrepreneurs, this study examines the dual role of digital technology in simultaneously enabling and constraining their enterprises. Our findings reveal that digital technology functions as both an empowerment amplifier and a constraint mechanism. We further identify digital private domain entrepreneurship (DPDE) as a strategic adaptation. It involves constructing sustainable sub-ecosystems by leveraging privatized user relationships and emotional capital. This study contributes to a nuanced understanding of the dynamic interplay between gender, technology, and entrepreneurship. It also offers practical insights for entrepreneurs and policymakers aiming to foster sustainable digital ecosystems.
Resident-generated service requests are increasingly used in data-driven urban management, but complaint records may reflect uneven administrative visibility rather than objective service needs. This study develops a benchmark-based 311 visibility audit as a diagnostic tool for sustainability governance, examining whether local service conditions become sufficiently visible to support inclusive, anticipatory, and resilient urban management. Using New York City’s 2023 311 records for six complaint categories—Heat/Hot Water, Noise—Residential, Rodent, Water System, Sewer, and Air Quality—the analysis constructs a tract-by-month-by-complaint-type panel and compares tract-level complaint shares with population, housing-related exposure, and rodent inspection benchmarks. The final dataset contains 661,251 geocoded requests across 2243 positive-population census tracts. Results show substantial complaint-type differences in population-benchmark mismatch: Air Quality has the largest exact total variation distance and Noise—Residential the smallest, while under-visible coverage exceeds over-visible coverage in all six categories. Alternative housing benchmarks change selected Heat/Hot Water and Rodent interpretations, and the external rodent check identifies 46 primary blind-spot candidates, narrowed to 17 benchmark-confirmed candidates. The study concludes that 311 systems should be used as diagnostic infrastructures for identifying information blind spots, not as direct measures of urban need or sustainability performance.
