Nanjing Institute of Railway Technology

The rapid growth of Online-to-Offline (O2O) food delivery has exacerbated food waste, creating a challenge at the intersection of economic and environmental concerns. This paper examines how pricing strategies can balance merchant profit, consumer demand, and waste reduction under anti-food waste regulations. Using a game-theoretic model with a restaurant and a platform, we compare uniform and differentiated pricing under consumer appetite and value heterogeneity. Results show that regulations raise prices, especially online, and that their effect depends critically on regulatory stringency and consumer structure: a higher share of small appetite consumers lowers prices under lenient regulation but raises them under strict rules. Furthermore, while differentiated pricing often aligns supply chain interests under moderate regulation or high degree of differentiation in perceived value between the two types of consumers, platforms may prefer uniform pricing under high penalties. This paper provides a quantitative framework for O2O food waste analysis and suggests that effective mitigation requires context-aware regulation and adaptive pricing strategies.

Global oilseed markets have become increasingly volatile due to climate, geopolitical, and trade disruptions, intensifying uncertainty in China's rapeseed supply chain. Using micro-level survey data from 863 rapeseed-producing households (2022-2023, National Rapeseed Industry Technology System), this study examines how market price expectations and agricultural subsidies jointly shape farmers' rapeseed acreage decisions. An ordered Probit model serves as the baseline, complemented by a Multivalued Treatment Effect (MTE) model and a Conditional Mixed Process (CMP) estimator to address selection and endogeneity. Expectations are proxied by the gap between the minimum acceptable price and the realized selling price, and robustness checks use alternative expectation proxies, subsidy-type disaggregation, continuous acreage changes, and regional subsamples. Results show that optimistic expectations significantly increase the likelihood of acreage expansion, while higher subsidy intensity weakens this marginal incentive; the negative interaction remains in alternative specifications. These findings imply that support policies should balance income stabilization with market responsiveness, and that improved price information and risk-management tools can strengthen farmers' adaptive capacity under uncertainty.

Ovarian endometriosis (OvE) is a gynecological disorder with endometrial tissue in the ovaries, for which effective non-invasive diagnosis and curative treatments are currently lacking. Serum samples were collected from both discovery and validation cohorts to examine the metabolomic signatures. Fifty-six differential metabolites between patients with OvE and healthy controls were identified using untargeted metabolomic profiling. Weighted gene co-expression network analysis was further conducted to validate the differential metabolites. Subsequently, twenty-one metabolites were selected for further validation using targeted metabolomic profiling. Five machine learning algorithms confirmed the efficacy and stability of these metabolites for diagnosing OvE. Least absolute shrinkage and selection operator -logit regression identified six serum metabolites and two clinicopathological features with high diagnostic accuracy. Three differential metabolites were found to exhibit therapeutic potential for OvE in an in vivo study. Diagnostic, predictive, and therapeutic potential of serum metabolomes for OvE are provided in this study.

Soil stress monitoring is critical for accurate soil state assessment in geotechnical engineering, yet conventional resistance-based stress sensors—typically encapsulated in rigid materials—often generate measured values deviating upward from analytical solution predictions, limiting their practical reliability. To address this key limitation, this study aims to propose a novel fiber Bragg grating (FBG)-based flexible stress sensor that integrates the high precision of FBG technology with the adaptive conformability of flexible sensing platforms. For validation, systematic physical modeling tests were conducted using multiple soil types (clay and sand) under different loading protocols, where the proposed sensor’s performance was comprehensively compared with that of a conventional resistance-based rigid sensor. Key findings reveal that the FBG-based flexible sensor delivers more accurate stress measurements in both clay and sand; notably, during cyclic loading within the elastic range, it reverts to its initial state with minimal hysteresis, outperforming the rigid counterpart in dynamic stability. The novelty of this work lies in the fusion of FBG sensing with flexible encapsulation, which mitigates the overestimation issue of rigid sensors and enables more reliable soil stress monitoring for geotechnical applications.