In public health emergencies, misaligned authority-enterprise coordination amplifies supply shortfalls and response inefficiency. Addressing multi-objective and tail-risk conflicts, this study develops a mixed-incentive calibration mechanism within an authority-enterprise equilibrium framework, with joint commitments of physical reserves and production capacity reserves as a unified decision core. Formulating a globally parameterisable bi-level multi-objective model, we calibrate tax incentives and cash subsidies under fiscal constraints and tail-risk control, capturing the equilibrium trade-off between priority-weighted service performance and enterprise risk-adjusted viability with CSR-oriented preference under contracted prices. Priority performance is quantified via learning-to-rank (LTR)-based urgency order, and implementable equilibria are delineated as policy windows on the tax-cash policy plane. Parameterised to China's institutional constraints and calibrated with a Wuhan case, the model yields stable equilibrium windows that support priority-weighted service performance, reduce fiscal tail exposure, and sustain enterprise participation. Sensitivity analyses, propositions, and managerial implications further support robustness and policy relevance.

Enhancing energy resilience is an effective way to guarantee energy security and stability, and the rapid development of AI presents an unprecedented opportunity to do so. Investigating how China's level of AI affects energy resilience will provide an extremely valuable reference for the rest of the world as well. Thus, this study explores the impact of AI on energy resilience using data from 236 cities in China. The results show that AI makes a significant contribution to improving energy resilience, and the results remain robust after robustness check and endogeneity treatment. Further analysis indicates that this effect is heterogeneous across cities with different geographic locations, resource types, human capital levels, and manufacturing types. Additionally, mechanism analysis reveals that AI contributes to energy resilience by increasing energy efficiency, fostering technological innovation and upgrading industrial structure. Overall, this study confirms the contribution of AI to energy resilience and provide empirical references for paradigm shift in world energy governance.

By leveraging internal resources and incorporating external technologies, industrial sectors have the potential to become energy prosumers, that is, both energy consumers and producers. This study developed a framework that incorporated the technological paradigm and open innovation to understand the evolution of technologies over time and the structural innovations in transitional systems needed to facilitate industrial sector prosumption. The framework tracked the development and diffusion trajectories of emerging technologies and identified three innovation modes: utilizing internal system resources (internal resource-driven innovation), absorbing external knowledge (external technology-driven innovation), and integrating technologies from both internal and external sources (integrated innovation). As a prime example of industrial prosumption, the framework’s value was demonstrated in an investigation of the wastewater sector, for which bibliometric analysis of 2335 publications was employed to identify the wastewater sector’s energy transition technologies. The analysis revealed three distinct technological development and diffusion periods over the past three decades focused on sustainable development, greenhouse gas emission reductions, and eventual carbon neutrality. In the early stages, the internal resource-driven innovation resources were found to have higher competitive advantages; however, as the transition progressed, external technology-driven innovation influences played a more prominent role, which led to greater diversity in the integration of transition technologies. It is also concluded that integrated innovation is going to play a more important role in the future industrial prosumption. Overall, this review can enrich the knowledge of technological development and diffusion trends for industrial prosumers and provide openness strategies for wastewater sector energy transitions.

Urban metro network is highly vulnerable to flooding, yet most existing risk assessments rely on deterministic weighting methods that inadequately address uncertainty. To address this limitation, this study develops a probabilistic GIS-based flood risk assessment framework that integrates the Analytic Hierarchy Process (AHP) with its Monte Carlo extension (MC-AHP). Unlike conventional studies, the framework (i) explicitly quantifies uncertainty in expert judgments through β-PERT simulations, (ii) incorporates a comprehensive indicator system covering hazard, exposure, and vulnerability—including passenger flow and under-construction lines often overlooked in prior work, and (iii) provides spatially explicit risk mapping for both operational and planned infrastructure. Application to the Chengdu metro network reveals a concentric flood risk distribution, with the central urban core exhibiting the highest vulnerability despite moderate hazard levels. Compared to AHP, MC-AHP identifies a greater proportion of high-risk areas and provides probability distributions of indicator weights, thereby reducing the influence of subjective bias. Validation against historical flood events confirms the framework’s predictive reliability. By explicitly incorporating uncertainty and extending analysis to planned infrastructure, this study advances the methodological rigor of urban flood risk assessment and provides generalizable insights for strengthening metro network resilience worldwide in the face of accelerating climate change and rapid urbanization.

In this paper, nonlinear regression models with an increasing number of unknown parameters are considered. The specificity of these models is that the variances of random errors are unknown and different. At each point of observation there is no more than one response which does not allow to estimate variances. Using Gauss-Newton approach, the iterative process for finding the least square estimates has been created. The conditions for convergence of the iterative process are found. It is shown that under some conditions the deviation vector of unknown parameters has Gaussian distribution, which allows to create a confidence band for unknown functions in nonlinear regression models.

Promoting a 100% renewable energy system requires intelligent scheduling strategies, yet the challenge remains on the prediction and optimisation of variable renewable energy supply and demand. This study proposes a Predict-then-optimise paradigm to explore day-ahead scheduling strategies for high renewable energy systems and demonstrates its application in a grid-connected biogas-solar-wind-storage system with load shifting for wastewater treatment plants. The scheduling strategy aims to maximise energy prosumption and minimise operation costs. Demand response is enabled by the wastewater pre-treatment reservoir, battery storage, and biogas storage, all mathematically modelled in this study. The Temporal Convolutional Network- based Transformer model is applied to forecast uncertain variable renewable energy generation and wastewater flow for the upcoming day. Then budget uncertainty sets are constructed based on forecast errors for robust optimisation. A case from Sichuan, China is analysed to explore the practicality and effectiveness of the proposed framework. The results indicate that the robustness of the model increases the day-head scheduling operational cost and decreases the self-sufficiency ratio. Wastewater pre-treatment reservoir scheduling can effectively shift the demand load, promoting cost reduction and system prosumption; besides, pre-treatment reservoir, battery storage and biogas storage have substitution and combination effects on demand response, can reduce daily operating costs by 20%-50%. The influence of a defined allowable sale ratio, seasons, and weather conditions are also discussed. Overall, the proposed predict-then-optimise framework is an effective solution for the upcoming day's decision-making.

Management Science and Engineering Management (MSEM) is an interdisciplinary field that combines quantitative analysis, systems optimization, and engineering practice. It aims to address complex organizational decision-making and operational challenges through scientific methods and technological innovation. At its core, MSEM leverages tools such as mathematical modeling, data-driven methods, and intelligent algorithms to optimize resource allocation, improve operational efficiency, and support sustainable development goals. This study uses the proceedings of the 19th International Conference on Management Science and Engineering Management (ICMSEM) as the basis for analysis and systematically reviews emerging research trends in the field. First, by analyzing the themes of all accepted papers, five high-frequency research keywords are identified: Data Analytics, Information Technology, Operations Research, Supply Chain Management, and Sustainable Development. Second, the paper examines key thematic concerns and focal areas highlighted in the 19th ICMSEM proceedings. Finally, using CiteSpace for keyword clustering and burst analysis, the study explores emerging trends and future directions in the field. As a significant academic platform in the MSEM field, the 19th ICMSEM showcases diverse theoretical advancements and practical applications. Through interdisciplinary and international dialogue, it contributes fresh perspectives and methodologies for advancing future research.

The comprehensive benefit evaluation of LID based on multi-criteria decision-making methods faces technical issues such as the uncertainties and vagueness in hybrid information sources, which can affect the overall evaluation results and ranking of alternatives. This study introduces a multi-indicator fuzzy comprehensive benefit evaluation approach for the selection of LID measures, aiming to provide a robust and holistic framework for evaluating their benefits at the community level. The proposed methodology integrates quantitative environmental and economic indicators with qualitative social benefit indicators, combining the use of the Storm Water Management Model (SWMM) and ArcGIS for scenario-based analysis, and the use of hesitant fuzzy language sets and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for decision-making. The framework’s novelty lies in the integration of the hesitant fuzzy weighted average algorithm to handle subjective uncertainties in expert judgment and the incorporation of multi-return period scenarios to enhance the robustness of the evaluation. The comprehensive benefits of 26 LID configurations were conducted in Chenglong Road Subdistrict under five rainfall return period scenarios of 5, 10, 20, 50, and 100 years. The results show that LID measures, particularly combinations of sunken green spaces and permeable paving, offer significant reductions in runoff and peak flow, along with improved flood mitigation across multiple return periods. Additionally, this study identifies practical LID implementation priorities for local decision-makers. The relative closeness is influenced by the indicators and non-calibrated parameters. However, it overall does not affect the main trends and key insights derived. The robustness of the proposed approach is reinforced by four key aspects: the impact of the Thiessen polygon method in ArcGIS, the influence of composite runoff coefficient and iterative optimization in SWMM, the effect of hesitant fuzzy linguistic sets and TOPSIS on weight calculation, and the contribution of simulations under different return periods to stability analysis.

Multi-packer tubing systems play a critical role in multi-stage hydraulic fracturing, but repeated operations pose significant risks of seal failure and buckling deformation due to stress surges. This study proposes a system reliability model that integrates equivalent axial force to enhance stability and safety under complex operational conditions. By incorporating dynamic mechanical coupling, residual stress propagation, and multi-physics interactions, the model ensures systematic reliability. The equivalent axial force quantifies residual stresses after packer constraint removal, effectively reducing coupling errors caused by oversimplified rigid boundary assumptions in traditional models. Numerical simulations of a real-world case from a Sichuan well revealed severe buckling in the lower tubing sections and significant axial forces on Packer 1 and Packer 2 during sequential fracturing. The introduction of the equivalent axial force reduced tubing length prediction errors by 77.25% and accurately predicted a 3.34-meter tubing shortening below 4,400 meters, consistent with field logging data. Key findings indicate that shifting fracturing positions upward increases packer forces, contrary to conventional horizontal well models. Practical recommendations include the selection of high-yield-strength tubing, controlling annular pressure differentials through nitrogen injection, and adding devices such as expansion joints or anti-buckling joints to mitigate tubing deformation.

Background: More than half the domestic population in China were infected with COVID-19 in two months after ending “zero-infection policy”, which severely overwhelmed frontline healthcare providers with stress and fear. However, there is no study to date investigating the associations between nurses' fear of pandemic and cyberchondria. This study aimed to 1) investigate the correlations between fear pandemic and cyberchondria among frontline nurses, and 2) discover its potential mechanism. Methods: A cross-sectional sample of frontline nurses (N = 8161) was recruited from 98 hospitals across China in February 2023. Participants were invited to complete an online, self-rated standardized questionnaire focused on pandemic fear, alexithymia, psychological distress, and cyberchondria. Environmental, clinical and socioeconomic information were collected for adjustment while conducting chain mediation analysis. Results: When other covariates were controlled, it was found that fear of the pandemic significantly contributed to cyberchondria (b = 0.58, 95%CI [0.56, 0.60], p < .001). The chain mediation model suggested that both alexithymia and psychological distress were mediating factors between pandemic fear and cyberchondria. Conclusions: The higher the perceived fear, the greater the cyberchondria, which suggests that reducing fear about the pandemic and providing adequate support could reduce the incidence of cyberchondria. As alexithymia and psychological distress may be transdiagnostic mechanisms between fear and cyberchondria, targeted interventions focused on expression dysregulation and emotional identification could be useful.

Wastewater heat, a promising alternative to traditional district heating and cooling sources, can be recovered using heat pumps and transmitted to surrounding buildings for winter heating and summer cooling. Despite its potential benefits, the policies to support wastewater heat recovery remain limited. This study provides support policies for local governments to encourage energy recovery in urban wastewater treatment plants, incorporating subsidies and renewable energy certificates. The local government aims to maximize the recovered energy, minimize the subsidy expenditure, and minimize the certificate price. Urban wastewater treatment plants seek to optimize producer profits under realistic planning constraints. This model was applied in Chengdu, China, which indicates that plants with a daily treatment scale exceeding 30,000 m3 can invest in heat recovery without support policies, achieving recovery ratios of only 27%–29%. As the calculated electricity charge accounts for about 90% of operating cost, the electricity price subsidy proves the most significant in improving the installed capacities, followed by renewable energy certificates, capital refunds, and tax reductions. Electricity prices and renewable energy certificates have combination and substitution effects. Further analysis shows that heat pump efficiencies have low sensitivity in project feasibility. Fluctuations in infrastructure costs have a minimal impact, while charges for heating and cooling services significantly influence feasibility. Lowering the available thermal energy ratio also affects project returns. The government can reduce risks in sensitive parameters by electricity price subsidies or renewable energy certificates.

In this study, we propose an innovative approach to regulate municipal solid waste (MSW) resource utilization and renewable energy conversion. Under the concept of circular economy, clean anaerobic digestion (AD) and gasification are combined to form integrated technology to achieve sustainable waste-to-energy. A matched multi-objective optimization model is developed to balance the economic and environmental benefits during the waste-to-energy process under uncertainty. Robust optimization is used to produce optimal solutions that are feasible even in the worst-case scenario in terms of uncertain parameters. The practicality of the proposed methodology is tested in a real-world case, and it is found that utilizing integrated technology can effectively carry out MSW resource utilization while also obtaining ample renewable energy. Robust optimization is used to conduct scenario analyses on the fluctuation interval of the uncertain electricity subsidy in order to assess its impact on the model solutions. It was found that the cost and carbon emission objectives were highly sensitive to changes in electricity subsidy. Although AD has been the primary technology used for waste-to-energy, the use of gasification in integrated technology has been increasing. While the treatment plans of various MSW types were different, there were still some commonalities.

PurposeThis study examines the association between financial education and budgeting behavior among college students. Under the guidance of the extended theory of planned behavior, we use a comprehensive measure of budgeting behavior and explore mediating factors between financial education and budgeting behavior.Design/methodology/approachFinancial education was measured by both frequency and intensity of taking courses in finance and economics in college. Data from a sample of college students across China were analyzed using structural equation modeling and serial mediation analysis to explore the mediating roles of attitudes, subjective norms, perceived control and budgeting intentions in this relationship between financial education and budgeting behavior.FindingsBudgeting intentions alone did not mediate the relationship between financial education and budgeting behavior. However, the serial mediation involving attitudes, subjective norms and budgeting intentions was significant.Practical implicationsThe findings of this study have significant implications for financial educators, universities, governments and families. Financial educators should prioritize budgeting in curricula and aim to enhance students’ budgeting attitudes and intentions. Universities should enhance their financial education offerings, while governments and families should foster supportive environments and positive norms and attitudes around budgeting.Originality/valueThis research contributes a nuanced measurement of budgeting, analyzes the link between financial education and budgeting behavior among college students and highlights the roles of various components of the theory of planned behavior. It extends the theory by identifying how financial attitudes, subjective norms and budgeting intentions mediate the relationship between financial education and budgeting behavior.

INTRODUCTION An online ride-hailing driver(ORHD)refers to a driver who takes orders and provides rental car services to passengers via an online service platform.1 ORHD plays a significant role in the urban trans-port system worldwide,operating through many platforms.According to official data from the Chinese Ministry of Transport,a total of 1.6 million vehicle transport permits were issued by the end of March 2022.More-over,by the end of 2021,the number of Chinese online taxi service users has reached 453 million.