This paper proposes an integrated optimization framework for electric bus systems, jointly addressing strategic charging facility planning and operational charging scheduling. The model accommodates heterogeneous fleets with varying energy consumption rates, explicitly models deadhead movements between routes and chargers, and enforces tight temporal tracking of charging start times and durations to avoid charger conflicts and minimize idle charger usage. To address computational challenges on large-scale transit networks, a decomposition-based heuristic algorithm (DHA) was developed. The framework is validated on a real-world transit instance in Kingston, Canada. Results show that the integrated approach yields cost-efficient combinations of fleet composition, charger deployment, and charging schedules; a heterogeneous fleet matched to route requirements outperforms homogeneous alternatives. DHA provides substantial reductions in computation time with only minor losses in solution quality, making it suitable for large-scale exploratory analysis and preliminary design, while CPLEX can be used to refine high-quality candidates. Sensitivity analyses reveal that restricting en-route or overnight depot charging notably increases fleet sizes and system costs, while total costs exhibit a U-shaped relationship with charging power and duration. These findings offer actionable guidance for transit planners prioritizing investments and designing resilient charging strategies during staged electrification.

Large-scale natural disasters result in significant human casualties and economic losses. Effective disaster management necessitates the strategic prepositioning of relief supplies before disasters and their swift distribution afterward. Different from prior studies that focused on single-period distribution with the objective of minimizing total costs, this paper presents a novel biobjective stochastic programming model that integrates prepositioning and multiperiod distribution of relief supplies in humanitarian logistics. The model concurrently aims to minimize overall costs and maximize transportation efficiency. We evaluate the model through a case study based on the Ya'an, China, earthquakes, demonstrating its effectiveness in optimizing relief supply strategies. Sensitivity analyses explore the impact of varying objective function weights, road capacity, and supply urgency, providing valuable managerial insights into humanitarian logistics decision-making. The insights gained from this study highlight the importance of proactive planning and strategic investment in infrastructure and logistics. By employing our biobjective stochastic programming model, decision-makers can develop robust strategies that balance cost and efficiency, ensuring a more effective and responsive disaster relief operation.

Battery electric buses (BEBs) have emerged as a promising solution for reducing transportation emissions; however, prior research has rarely addressed the combined environmental and economic impacts of BEB fleets and their charging facility. This study presents a comprehensive lifecycle assessment (LCA) framework that quantifies carbon emissions and total costs across the production, operation, and recycling phases of BEB systems, integrating heterogeneous bus fleets with diverse charger types and route-specific constraints. In a case study of a public transit network in Nanjing, China, seven scenarios with different BEB system configurations were evaluated. Results show that the capital cost of BEBs accounts for approximately 66% of the total lifecycle cost, with Scenario #1 costing $232.51 million and Scenario #3 reaching $377.07 million-a 62% increase. Sensitivity analyses further indicate that reducing the power grid carbon emission factor from 0.910 to 0.095 kgCO2e/kWh can lower lifecycle emissions by 81.3%, while a 5% increase in BEB energy consumption leads to a 4% rise in emissions, with only a 0.2% impact on overall costs. These numerical results substantiate the effectiveness of our coupled LCA framework by demonstrating that strategic interventions - such as grid decarbonization and improvements in BEB energy efficiency - can significantly mitigate both environmental impacts and lifecycle expenditures. Our findings provide critical quantitative insights for decision-makers, highlighting the potential for substantial reductions in lifecycle impacts as cleaner energy sources and advanced vehicle technologies become increasingly prevalent.

Background: Laryngeal and hypopharyngeal cancers are prominent within head and neck malignancies. The diagnosis of distant metastasis (DM) invariably signals poor prognosis, underscoring the need to optimize current treatment approaches. Methods: Patient data for metastatic laryngeal and hypopharyngeal cancer were extracted from the SEER database (2000-2020). Cox regression and propensity score matching (PSM) analyses identified independent prognostic factors and performed stratified survival analyses based on the receipt of primary tumor surgery and radiotherapy. A random survival forest (RSF) model was subsequently developed to predict patient survival. Results: A total of 1,626 patients were included. PSM-based stratified analysis revealed that primary tumor surgery significantly improved survival in patients under 70 years and those with primary laryngeal cancer. Radiotherapy enhanced survival across all age groups, with a benefit primarily for patients with primary laryngeal cancer and squamous-cell carcinoma (SCC). The RSF model demonstrated robust predictive performance, highlighting chemotherapy, primary tumor surgery, and radiotherapy as the top three factors influencing patient survival. Conclusion: The clinical and pathological features of metastatic laryngeal/hypopharyngeal cancer were systematically analyzed using an artificial intelligence (AI) model to predict survival. Subgroup analyses identified patients most likely to benefit from primary tumor surgery and radiotherapy. These findings may guide the development of personalized treatment strategies, potentially improving the prognosis of patients with DM.

Shelter site selection is a key topic in research on the preparedness and response phases of disaster management, significantly impacting the availability of safe, temporary residential refuges and rescue locations during disasters. The pre-expropriation system presents a novel direction for the government to reduce response time and improve efficiency. However, the question arises: how can a government establish a pre-expropriation-emergency-expropriation-compensation system targeting collective economic organizations (a kind of public ownership economic organization that the means of production are owned by part of the laborers), enterprises, and institutions by comprehensively considering the correlation between disaster risk and shelter location under a pre-expropriation system? This study explores a three-level networked distribution center-shelter-disaster point structure and establishes a model for selecting shelter sites that is based on a pre-expropriation system and flood risk assessment. Furthermore, we developed a multiobjective mixed-integer stochastic programming model, with the objectives of achieving the shortest distribution time and evacuation time, the least number of people not evacuated in time, the lowest disaster risk, and the smallest government compensation payment. The proportion of the evacuated population at each disaster point was considered as a random variable. Given the model's high-dimensional nature, a multiobjective artificial bee colony algorithm based on crossvariation was designed for model solving. By comprehensively balancing the interests of governmental agencies, expropriation locations, and disaster victims, this study considers multiple objective factors and constructs a regional relative flood risk assessment model and a shelter location model grounded in multiobjective mixed-integer stochastic programming. These provide scientific and effective theoretical support for optimal pre-expropriation shelter site selection. The case study offers recommendations for selecting optimal pre-expropriation shelters based on risk scenarios, demonstrating that the multiobjective framework constructed provides decision makers with better flexibility and applicability.

The rapid growth of electric vehicles is hindered by insufficient electric vehicle charging infrastructure (EVCI). Public-Private Partnerships (PPPs) present a viable solution to accelerate sustainable EVCI deployment by leveraging private investment and public resources. However, high initial costs and uncertain returns often deter private sector participation, while governments focus on public welfare and environmental objectives. This study explores the potential of PPPs to promote sustainable EVCI deployment, using evolutionary game theory to analyze the decision-making processes of key stakeholders - property owners, operators, and government regulators - involved in EVCI-PPP projects. Evolutionary stability strategies (ESS) are then identified by solving replicated dynamic equations and equilibrium point stability analysis. Lastly, a numerical illustration based on a real-world case supports the theoretical findings, revealing key insights: (1) the game model identifies eight equilibrium points and four potential ESSs, shaped by stakeholders' cost-revenue trade-offs; (2) the revenue-sharing coefficient critically impacts cooperation and project efficiency, with higher returns linked to greater risks; (3) both excessively high and low subsidies can destabilize the market, highlighting the need for a balanced approach; and (4) a strategically structured charging price can attract more EV users, improving station utilization and providing attractive investment returns for the private sector.

BACKGROUND: Topical intranasal medication is required following functional endoscopic sinus surgery (FESS). The optimal particle size of transnasal nebulization aimed at the sinonasal cavities is not conclusive. The current study aims to evaluate the effect of particle size and various surgery scope of middle turbinectomy (MT) on post-full FESS drug delivery to the sinonasal cavities. METHODS: Sinonasal reconstructions were performed from post-full FESS CT scans in 6 chronic rhinosinusitis with nasal polyps (CRSwNP) patients. Four additional models representing alternative surgery scopes of MT were established from each post-FESS reconstruction for simulation data comparison. Airflow and particle deposition of nebulized delivery were simulated via computational fluid dynamics (CFD) and validated through in vitro experiments. The optimal particle sizes reaching a deposition of at least 75% of the maximum in the targeted regions were identified. RESULTS: The drug deposition rate onto the targeted regions increased following MT, with the greatest deposition following posterior MT (P-MT). Droplets in the range of 18-26 μm reached a deposition of larger than 75% of the maximum onto the targeted regions. Drug delivery rate in the sinonasal cavities varied significantly among individuals and across different types of MT with varying surgical scopes. CONCLUSIONS: This study is the first to investigate the effect of various surgery scope on drug delivery by transnasal nebulization to the sinonasal cavities. The findings strongly affirm the vast potential of transnasal nebulization as an effective post-FESS treatment option. Moreover, it emphasizes that the drug delivery process via atomizers to the nasal cavity and paranasal sinuses is highly sensitive to the particle size.

Foreign direct investment (FDI) promotes economic growth of a country in multiple ways by setting up industries, infrastructure, and power plants, and such activities usually accelerate climate change by raising greenhouse gases (GHG) emissions. While the research on the role of FDI in promoting economic growth has gained widespread attention, it remains largely unclear how much global FDI research focuses on climate change activities. To fill this gap, a global scale bibliometric analysis was conducted to quantify published research on FDI, including the share dedicated to climate change. Global FDI research was heavily focused on non-climate change issues contributing 85% share in publications (13,835 publications) and 78% in citations (3,70,000 citations) and minimal attention was paid to FDI research relevant to climate change (2438 publications, 1,07,478 citations). Even with less publications, the research impact (RI; citations pre document) of FDI-climate change studies was 62% higher than non-climate change studies. Global FDI research was mainly concentrated in a few countries, with only 15 countries publishing approximately 75% of FDI research. China, USA, and UK combined produced approximately half (47%) of the global FDI research. The institutions and funding agencies from China, USA, and UK contributed greatest number of FDI publications and collaborated widely with rest of the world. However, China, Pakistan and Turkey produced 57% of global publications on FDI-climate change. Though developing countries are the major recipients of FDI and are most vulnerable to climate change, but their contributions towards climate change research were minimal. The most recent (2019–2023) topic trends for FDI research were green finance, renewable energy, economic growth, trade, and FDI. However, the recent topic trends for FDI-climate change research were green finance, clean energy, FDI, carbon emission, and carbon dioxide emission. FDI and climate change activities are generally positively correlated. Therefore, global research should upsurge its focus on FDI related climate change activities in order to find ways to improve economies sustainably without damaging environment.

Li, Chaofan MD; Wang, Yusheng MD; Liu, Mengjie MD; Qu, Jingkun; Zhang, Shuqun Author Information

Inhalation therapy is widely used in the treatment of acute epiglottitis (AE). However, few studies specifically consider the anatomical variations in pediatric AE treatment. This study aims to evaluate the deposition pattern of inhalation corticosteroids (ICS) in realistic upper airway models of pediatric AE patients. The epiglottic angle increases with the severity of AE, and five models of epiglottic angle were constructed. The computational fluid-particle dynamics (CFPD) was employed to simulate the airflow transport of pediatric AE and the particle deposition in the target area. The results show that as AE progressed, worsened airway narrowing was observed in the epiglottis region, accompanied by a decreasing trend in the optimal ICS particle size. The deposition pattern of ICS in pediatric AE is closely linked to the disease severity. Furthermore, the airway resistance increases significantly at 75°AE, providing an aerodynamic basis for assessing the severity of laryngeal obstruction.

Large-scale natural disasters result in significant human casualties and economic losses. Effective disaster management strategies require the strategic pre-positioning of relief supplies before a disaster and the rapid distribution of these supplies to affected regions afterward. In this paper, we propose a novel stochastic programming model that integrates the pre-positioning and multi-period distribution of relief supplies in humanitarian logistics. The model takes into account uncertainties related to relief supply demand, potential damage to pre-positioned supplies at relief facilities, and vulnerabilities in the road infrastructure. To assess the practical utility of our model, we conduct a real-world case study based on the earthquakes in Ya'an, China. The results demonstrate the model's effectiveness in aiding decision-makers to optimize both pre-positioning and distribution strategies for relief supplies.

To understand inhaled nanoparticle transport and deposition characteristics in pediatric nasal airways with adenoid hypertrophy (AH), with a specific emphasis on the olfactory region, virtual nanoparticle inhalation studies were conducted on anatomically accurate child nasal airway models. The computational fluid-particle dynamics (CFPD) method was employed, and numerical simulations were performed to compare the airflow and nanoparticle deposition patterns between nasal airways with nasopharyngeal obstruction before adenoidectomy and healthy nasal airways after virtual adenoidectomy. The influence of different inhalation rates and exhalation phase on olfactory regional nanoparticle deposition features was systematically analyzed. We found that nasopharyngeal obstruction resulted in significant uneven airflow distribution in the nasal cavity. The deposited nanoparticles were concentrated in the middle meatus, septum, inferior meatus and nasal vestibule. The deposition efficiency (DE) in the olfactory region decreases with increasing nanoparticle size (1-10 nm) during inhalation. After adenoidectomy, the pediatric olfactory region DE increased significantly while nasopharynx DE dramatically decreased. When the inhalation rate decreased, the deposition pattern in the olfactory region significantly altered, exhibiting an initial rise followed by a subsequent decline, reaching peak deposition at 2 nm. During exhalation, the pediatric olfactory region DE was substantially lower than during inhalation, and the olfactory region DE in the pre-operative models were found to be significantly higher than that of the post-operative models. In conclusions, ventilation and particle deposition in the olfactory region were significantly improved in post-operative models. Inhalation rate and exhalation process can significantly affect nanoparticle deposition in the olfactory region.

Background: Occult breast cancer (OBC) is an uncommon malignant tumor and the prognosis and treatment of OBC remain controversial. Currently, there exists no accurate prognostic clinical model for OBC, and the treatment outcomes of chemotherapy and surgery in its different molecular subtypes are still unknown. Methods: The SEER database provided the data used for this study’s analysis (2010–2019). To identify the prognostic variables for patients with ODC, we conducted Cox regression analysis and constructed prognostic models using six machine learning algorithms to predict overall survival (OS) of OBC patients. A series of validation methods, including calibration curve and area under the curve (AUC value) of receiver operating characteristic curve (ROC) were employed to validate the accuracy and reliability of the logistic regression (LR) models. The effectiveness of clinical application of the predictive models was validated using decision curve analysis (DCA). We also investigated the role of chemotherapy and surgery in OBC patients with different molecular subtypes, with the help of K-M survival analysis as well as propensity score matching, and these results were further validated by subgroup Cox analysis. Results: The LR models performed best, with high precision and applicability, and they were proved to predict the OS of OBC patients in the most accurate manner (test set: 1-year AUC = 0.851, 3-year AUC = 0.790 and 5-year survival AUC = 0.824). Interestingly, we found that the N1 and N2 stage OBC patients had more favorable prognosis than N0 stage patients, but the N3 stage was similar to the N0 stage (OS: N0 vs. N1, HR = 0.6602, 95%CI 0.4568–0.9542, p < 0.05; N0 vs. N2, HR = 0.4716, 95%CI 0.2351–0.9464, p < 0.05; N0 vs. N3, HR = 0.96, 95%CI 0.6176–1.5844, p = 0.96). Patients aged >80 and distant metastases were also independent prognostic factors for OBC. In terms of treatment, our multivariate Cox regression analysis discovered that surgery and radiotherapy were both independent protective variables for OBC patients, but chemotherapy was not. We also found that chemotherapy significantly improved both OS and breast cancer-specific survival (BCSS) only in the HR−/HER2+ molecular subtype (OS: HR = 0.15, 95%CI 0.037–0.57, p < 0.01; BCSS: HR = 0.027, 95%CI 0.027–0.81, p < 0.05). However, surgery could help only the HR−/HER2+ and HR+/HER2− subtypes improve prognosis. Conclusions: We analyzed the clinical features and prognostic factors of OBC patients; meanwhile, machine learning prognostic models with high precision and applicability were constructed to predict their overall survival. The treatment results in different molecular subtypes suggested that primary surgery might improve the survival of HR+/HER2− and HR−/HER2+ subtypes, however, only the HR−/HER2+ subtype could benefit from chemotherapy. The necessity of surgery and chemotherapy needs to be carefully considered for OBC patients with other subtypes.

Croup is the most frequent cause of pediatric upper airway obstruction characterized by spindle-shaped stenosis in the subglottis mucosa. Inhaled corticosteroids (ICSs) serve as the first-line therapy for croup. Traditional ICS particles (1-4 mu m in diameter) are primarily designed for trachea and lung diseases and show extremely low larynx deposition. Moreover, the specific correlation between airway minimal cross-sectional area (CSA) and airway resistance has not been fully understood. In this study, three healthy pediatric upper airway models with commercial nebulizer masks attached to their faces were reconstructed from computed tomography (CT) scans. Virtual mild, moderate, and severe croup were incorporated into these healthy models. To enhance the per-formance of conventional nebulizing drug delivery, the aerodynamic properties of croup with different degrees of stenosis were quantitatively analyzed, and the respiratory transit and deposition of ICS particles sized between 1 and 20 mu m in our target area (glottis + subglottis) were modeled utilizing the Computational Fluid Particle Dynamics (CFPD) method. Results showed that in all models, maximum deposition fractions (DF) can be reached when the ICS particle sizes are 7-8 mu m, and for particles sized at 8 and 9 mu m, all models can achieve effective target area delivery (>= 75% of the maximum DF), whereas the majority of traditional nebulizers produce smaller particles than what we recommended. Pediatric upper airway resistance is negatively correlated with the min-imum airspace CSA (R proportional to CSA-1), which is in good agreement with the Bernoulli Obstruction Theory. Further-more, when the constriction of the subglottis reaches a specific level (>= 70% obstruction), the upper airway pressure drop abruptly surged and the dyspneic respiration symptoms of patients develop instantly.

Background: To provide quantification of the postoperative Artemisia pollen deposition as well as potential contribution from allergen distribution in the sinonasal cavities for patients underwent FESS.Methods: Employing the largest cohort of post-FESS patients, Artemisia pollen deposition in 16 postoperative sinonasal cavity models were analyzed. The effects of maxillary sinus ostia diameter on allergen deposition were investigated.Results: Artemisia pollen had higher deposition in the ethmoid, maxillary and sphenoid sinuses following FESS, for example with (3.25 +/- 4.14)% vs.(0.79 +/- 1.34)% in the ethmoid sinus; allergen deposition increased significantly when the hydraulic diameters of maxillary sinus ostia >10 mm; post-FESS pollen deposition was seen to reduce consequently in the middle turbinate with (5.24 +/- 6.92)% vs.(13.59 +/- 8.98)%.Conclusions: Artemisia pollen could enter all sinuses following FESS and allergen deposition may contribute to the recurrence of postoperative nasosinusitis; size of the maxillary sinus ostia significantly affects the pollen deposition rate; Meanwhile, Artemisia pollen deposition in post-FESS patients is seen to reduce in the middle turbinate area when compared with healthy adults.

Abstract Background Breast cancer brain metastases (BCBM) are the most fatal, with limited survival in all breast cancer distant metastases. These patients are deemed to be incurable. Thus, survival time is their foremost concern. However, there is a lack of accurate prediction models in the clinic. What’s more, primary surgery for BCBM patients is still controversial. Methods The data used for analysis in this study was obtained from the SEER database (2010–2019). We made a COX regression analysis to identify prognostic factors of BCBM patients. Through cross-validation, we constructed XGBoost models to predict survival in patients with BCBM. Meanwhile, a BCBM cohort from our hospital was used to validate our models. We also investigated the prognosis of patients treated with surgery or not, using propensity score matching and K–M survival analysis. Our results were further validated by subgroup COX analysis in patients with different molecular subtypes. Results The XGBoost models we created had high precision and correctness, and they were the most accurate models to predict the survival of BCBM patients (6-month AUC = 0.824, 1-year AUC = 0.813, 2-year AUC = 0.800 and 3-year survival AUC = 0.803). Moreover, the models still exhibited good performance in an externally independent dataset (6-month: AUC = 0.820; 1-year: AUC = 0.732; 2-year: AUC = 0.795; 3-year: AUC = 0.936). Then we used Shiny-Web tool to make our models be easily used from website. Interestingly, we found that the BCBM patients with an annual income of over USD$70,000 had better BCSS (HR = 0.523, 95%CI 0.273–0.999, P < 0.05) than those with less than USD$40,000. The results showed that in all distant metastasis sites, only lung metastasis was an independent poor prognostic factor for patients with BCBM (OS: HR = 1.606, 95%CI 1.157–2.230, P < 0.01; BCSS: HR = 1.698, 95%CI 1.219–2.365, P < 0.01), while bone, liver, distant lymph nodes and other metastases were not. We also found that surgical treatment significantly improved both OS and BCSS in BCBM patients with the HER2 + molecular subtypes and was beneficial to OS of the HR−/HER2− subtype. In contrast, surgery could not help BCBM patients with HR + /HER2− subtype improve their prognosis (OS: HR = 0.887, 95%CI 0.608–1.293, P = 0.510; BCSS: HR = 0.909, 95%CI 0.604–1.368, P = 0.630). Conclusion We analyzed the clinical features of BCBM patients and constructed 4 machine-learning prognostic models to predict their survival. Our validation results indicate that these models should be highly reproducible in patients with BCBM. We also identified potential prognostic factors for BCBM patients and suggested that primary surgery might improve the survival of BCBM patients with HER2 + and triple-negative subtypes.

China is vigorously pursuing carbon neutrality targets to combat global warming. As a powerful tool for encouraging individuals to adjust their consumption patterns and fostering the advancement of green consumption, the overall planning of carbon-labeling policy is accelerating accordingly. To better comprehend and strengthen the sustainable implementation of carbon-labeling policy, this study constructs a tripartite mainstay game to explore the interactive behavior of carbon-labeled enterprises and customers, with the substantial involvement of governmental regulators. First, the evolutionary stability strategy (ESS) is determined by solving replicated dynamic equations and stability analysis of equilibrium points. Then, the practicability and rationality of the evolutionary game model are assessed ESSs corresponding to various scenarios in the carbon-labeling scheme. Finally, the first Chinese television manufacturer to acquire carbon-labeled certification, TCL Group, is utilized as evidence to validate the theoretical findings and support the subsequent arguments: There are eight equilibrium points and three potential ESSs in the game model, and the selection of each ESS is primarily determined by the trade-off between costs and revenues for each stakeholder; the level of carbon-labeled enterprises' green R&D effort has a beneficial effect on the implementation of carbon-labeling policy, while customers' identification of carbon-labeled products will potentially affect operational orientation; and governments' stringent oversight is a vital assurance that both the carbon-labeled enterprises and customers adhere to green initiatives. The results thus not only elaborate on the efficient approach and insights for the sustainable implementation of carbon-labeling policy with the participation of multiple stakeholders, but also offer suggestions for enhancing incentives to improve regulatory regimes and market outcomes.

The public-private partnership (PPP) has emerged as a promising financing approach to effectively alleviate the financial burden on governments involved in watershed ecological compensation (WEC) projects. This study specifically focuses on the WECPPP project in China's Chishui River Basin (CSRB) and employs a tripartite evolutionary game model to examine sustainable cooperative behavior among social capital, governmental regulatory entity, and the general public. To ascertain the evolutionary stability strategy (ESS), replicated dynamic equations and equilibrium point stability analysis are then solved. Lastly, a numerical illustration grounded in the WECPPP-CSRB project is utilized as empirical evidence to validate the theoretical findings and substantiate ensuing recognitions: the game model elucidates the presence of eight equilibrium points, while delineating four potential ESSs, each discerned through stakeholder-oriented cost-benefit assessments; stringent government regulations play a pivotal role in coordinating stakeholder interests in the WECPPP-CSRB project, thereby safeguarding both ecological integrity and economic gains; and the optimization of the income distribution mechanism coupled with the augmentation of performance-linked subsidies is acknowledged as imperative measures for ensuring the long-term viability of the WECPPP-CSRB project. The findings thus not only provide a comprehensive analysis of the effective approach and insights into fostering sustainable cooperation among multiple stakeholders in the WECPPP project, but also proffer invaluable recommendations for enhancing incentives, refining regulatory frameworks, and attaining favorable market outcomes.

The success or failure of executing the watershed ecological compensation (WEC) policy is primarily contingent on incentive designs. How do different contractual designs influence the actions of micro-individuals in WEC? How may individuals be enticed to engage in WEC project? Taking the first inter-provincial WEC-Xin'an River Basin (XRB) pilot in China as a case, this study investigates the impacts of government-oriented, market-oriented, and incentive-cooperation contracts on individuals' behavior based on the framework of Stackelberg games. Subsequently, differences in efforts and profits of diverse individuals are compared and analyzed for each contract. The case-specific numerical example is then utilized to validate theoretical outcomes and to support subsequent key insights. First, the government-oriented contract exhibits effectiveness in bolstering the efforts and interests of micro-individuals, whereas it also places the government under tremendous financial strain. Second, the market-oriented contract formed by the output contribution rate assists in overcoming deficiencies of excurrent government-oriented contract. But it remains controversial if, in the absence of government inspection, investors that devote more cooperative-efforts are not rewarded with further dividends, ultimately diminishing their enthusiasm for the WEC-XRB project. Lastly, the incentive-cooperation contract reinforced by market dominance is advantageous for improving the efficacy of water resource management under the existing government-oriented policy relying on command-and-control instruments.

Nowadays, frequent meteorological disasters that cause huge economic losses and ecological damages have swept the world. Thus, research investigates how to overcome the adverse impacts of storm debris flow by exploring sustainable interaction among disaster, economy, and ecology. To achieve this goal, the study analyzes coupling coordination for disaster–economy–ecology system through data-driven technology named Scrapy engine. To be specific, a comprehensive index system of disaster–economy–ecology is established. Accordingly, a projection pursuit method is used to reduce the dimensions of data involved in the system. Then, an integrated weighting method of interval-valued hesitant fuzzy entropy and maximum deviation of weight is utilized. For further analysis of the internal laws in disaster–economy–ecology system, a coupling coordination model based on order preference by similarity to ideal solution is proposed. Moreover, a back-propagation artificial neural network is designed to identify the key influencing factors in disaster–economy–ecology system. Finally, an empirical study is carried out using the panel data related to storm debris flow of 31 provincial areas in China within 11 years to illustrate the study. The study results show that the overall sustainable development of disaster, economy, and ecology in China does not achieve an ideal status. Various measures based on local conditions are required to improve the imbalanced development of disaster–economy–ecology system in different areas of China. At last, strategic suggestions for sustainable development of disaster–economy–ecology system are provided.