Smart city mobility faces mounting challenges as urban mobility systems grow increasingly complex. Large language models (LLMs) have promise in interpreting and processing multi-modal urban data, but issues like model instability, computational inefficiency, and concerns about reliability hinder their implementations. In this Comment, we outline feasible LLM application scenarios, critically evaluate existing challenges, and highlight avenues for advancing LLM-based mobility systems through multi-modal data integration and developing robust, lightweight models.

This paper focuses on the economic resilience aspects of the financialization of infrastructure projects concerning enhancing market dynamics and risk regulation. We examined 39 infrastructure REITs listed in China between June 2021 and June 2024. Utilizing an economic resilience evaluation model to assess the resistance and recovery capacities of these infrastructure REITs, we incorporate seven investor heterogeneity measures. A geographic detector model is employed to analyze divergence, identify key determinant factors, pinpoint risk zones, and investigate the interaction between these measures within the context of PPP-REITs and DI-REITs. The empirical results show that the investor ratio and expected investment tenure are critical to the construction of economic resilience indices for infrastructure REITs. Also, the interaction of factors holds significance toward influencing the divergence in economic resilience. Our findings reveal a “barrel effect” of investor heterogeneity in infrastructure project financial products, indicating a consistency between economic resilience and investor heterogeneity. By integrating the investor heterogeneity index into the resilience evaluation framework of infrastructure REITs, this study offers valuable insights into the risk-resistance capacity of infrastructure financial products and the enhancement of economic resilience in these projects.

Diabetes is a serious public health threat. Therefore, the need for the supply and dispensing of diabetic drugs cannot be neglected. This study explores the impacts of supply disruption risks on pricing strategies for two diabetic drugs under three power structures, i.e., supplier–Stackelberg (SS), drugstore–Stackelberg (DRS), and centralized setting (CS), in an attempt to track the optimum strategies. We show how changes in procurement costs and disruption likelihood alter the balance between consumer surplus, profit, and overall social welfare within the pharmaceutical supply chain. CS will be preferred in scenarios in which centralized control over procurement and distribution is highly valuable, particularly in the presence of high procurement costs and supply disruptions, such as those that occur with specialized medications such as insulin analogs and biologics. In addition, in scenarios of low to moderate procurement costs, especially for generic drugs, the DRS strategy dominates CS in the advocacy of social welfare since drugstores can buy at competitive prices. Overall, DRS and CS consistently outperform SS in terms of consumer surplus. However, SS becomes more effective in scenarios where supply disruptions occur and procurement costs drop to zero, such as when governments subsidize drugs during emergencies.

Participants in oil markets are increasingly aware of the climate risks posed by frequent extreme weather. This paper examines the role of extremely high-temperature weather information in predicting oil futures prices on the China International Energy Exchange (INE). An extreme high-temperature weather index (HTI) is developed on the basis of meteorological data at INE’s crude oil production and storage sites. The local interpretable model-agnostic explanations (LIME) and accumulated local effects (ALE) methods are used to compare the predictive contribution of the HTI with that of 15 common predictors. The results indicate that the HTI enhances the out-of-sample accuracy of five classical prediction models for INE oil prices. The recurrent neural network (RNN) model exhibits superior out-of-sample forecast performance, with an MAE of 14.379, an RMSE of 19.624, and a DS of 66.67%. The predictive importance of the HTI in the best RNN model ranks third in most test instances, surpassing conventional oil price predictors such as stock market indicators. The ALE analysis reveals a positive correlation between extremely high-temperature weather and INE oil prices. These findings can help investors and oil market regulators improve oil price forecast accuracy while also providing new evidence about the relationship between climate risk and oil prices.

Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques. Optimal control, as used in supply chain management, is the process of using mathematical optimisation techniques to identify the best course of action for controlling a given objective function over time. Modeling the supply chain’s dynamics, which include elements like production rates, inventory levels, demand trends, and transportation constraints, is the best control strategy when applied to a supply chain. In this study, we have considered that production rate is an unknown function of time, which is a controlling function. The demand for the product is taken as a function of price and time. The emission of carbon is taken as a linear function of the production rate of the system. To solve the suggested supply chain system, we have used an optimal control approach for determining the unknown production rate. To find the optimal values of the objective function as well as the decision variables, we have used different meta-heuristic algorithms and compared their results. It is observed that the equilibrium optimizer algorithm performed better than other algorithms used. Finally, a sensitivity analysis is performed, which is presented graphically in order to choose the best course of action.

This paper analyzes the relationship between the carbon, electricity and natural gas markets in Europe. To identify the origin and paths of price transmission among these markets, we employ the Diebold Yilmaz spillover approach. To investigate the multiscale response to price signals, we use the time-varying parameter stochastic volatility vector autoregression system. The results provide evidence that the market for natural gas plays a significant role in setting carbon prices, which are negative in the short-term and positive in the medium term. These effects can, however, be negated by the Russia–Ukraine conflict, and the resulting market for natural gas does not exert any such shocks on the electricity market. Since the conflict, the electricity market has become a major price transmitter and has produced short-term positive but medium-term negative effects on the carbon market. Our results suggest that short- and medium-term policies should focus on avoiding price distortions and stabilizing markets.

Unethical behaviors among contractors are prevalent in engineering management activities within the construction industry, significantly affecting project performance, public safety, and the industry’s reputation. Despite the urgent need to enhance the ethical performance of contractor managers, current research lacks a theoretical framework to systematically categorize and characterize these unethical behaviors. This study fills this gap by conducting semi-structured interviews with 20 experienced construction project managers in China, followed by a qualitative content analysis. The findings indicate that contractor mangers’ unethical behaviors can be organized into a framework comprising three levels, five dimensions, and 18 themes. The most common behaviors identified include “construction disturbance,” “qualification rental,” and “deception in settlement.” Additionally, the study explores the causes of these unethical behaviors, revealing power and responsibility imbalances within the supply chain and the lack of moral competencies among contractor managers in the construction industry. This study offers a theoretical taxonomy framework for contractor managers to identify and assess ethical performance in practice and provides a scientific basis for authorities to establish ethical guidelines and enhance ethical management practices in the construction industry.

Knowledge transfer among New Product Development (NPD) projects is beneficial for reducing project duration and promoting technological innovation. To support effective knowledge transfer, we propose a clustering method for NPD projects based on similarity, integrating both structural and attribute similarities. First, to measure project structural similarity, we analyze both direct and indirect knowledge transfer relationships among project activities using the dependency structure matrix (DSM). Second, we measure project attribute similarity by calculating knowledge increments derived from sequential and iterative development processes. Finally, we apply a hierarchical clustering method to group similar projects, forming different programs. An industrial example is provided to demonstrate the proposed model. The results show that clustering projects into programs can enhance multi-project management by reducing coordination time for knowledge transfer within each program. Additionally, this approach provides some new insights, including quantifying project similarity based on knowledge transfer and understanding the influence of structural and attribute similarities on multi-project management.