Explore the vast range of titles available.

China’s transport industry has made rapid progress, which has led to a great amount of carbon emissions. However, it is still unclear how carbon emissions from the transport sector are punctuated by shifts in underlying factors. This paper aims to examine the process of China’s carbon emissions from the transport sector as well as its major driving forces at the provincial level during the period of 2000 to 2015. We first estimate the carbon emissions from the transport sector at the provincial level based on the fuel and electricity consumption using a top-down method. We find that the carbon emission per capita is steadily increasing across the country, especially in the provinces of Chongqing and Inner Mongolia. However, the carbon emission intensity is decreasing in most provinces, except in Yunnan, Qinghai, Chongqing, Zhejiang, Heilongjiang, Jilin, Inner Mongolia, Henan and Anhui. We then quantify the effect of socioeconomic factors and their regional variations on carbon emissions using a panel model. The results show that the development of secondary industry is the most significant variable for carbon intensity at both the national and regional levels, while the effects of the other variables vary across regions. Among these factors, population density is the main factor of the increasing carbon emissions per capita from the transport sector for both the whole country and the western region, whereas the consumption level per capita of residents and the development of tertiary industry are the primary drivers of per capita carbon emissions for the eastern and central regions.Graphical abstract

Aircraft routing networks are complex systems vulnerable to cascading delays triggered by weather disruptions and airspace constraints. This paper proposes a Distributionally Robust Aircraft Routing (DRAR) model for systemic risk assessment. Conventional robust or stochastic optimization methods often rely on specific assumptions about delay distributions (e.g., fixed probability distributions or scenario sets). However, due to the suddenness and multi-source nature of flight delays, their true distribution is difficult to accurately characterize, limiting the effectiveness of these methods in real-world uncertain conditions. By constructing a Wasserstein-metric ambiguity set, the proposed model captures distributional uncertainty without assuming fixed probabilities, thereby handling delay risks more robustly. The study incorporated chance constraints to bound extreme delay probabilities and reformulated the model as a tractable mixed-integer program. Experiments on real airline data demonstrate that DRAR outperforms traditional benchmarks, reducing propagation delays by 4–6%, volatility by 7–9%, and extreme delay risks by up to 15.7%. Thus, the model provides a practical tool for aviation decision-makers: airlines can leverage it to optimize aircraft scheduling and routing, systematically mitigate delay propagation risk, control the probability of extreme delays, and consequently reduce indirect operational costs arising from crew overtime and airport scheduling conflicts, thereby enhancing overall resource efficiency and operational resilience. These results validate DRAR as an effective tool for controlling tail risks and ensuring sustainable operations in uncertain aviation environments.

Background Carotid body tumors (CBTs) are rare paragangliomas with an incidence of 1–2 per 100,000 individuals. While surgical resection remains the standard treatment, postoperative recurrence poses a clinical challenge. Case presentation We describe a 49-year-old female with recurrent CBT involving the jugular foramen, managed with IGRT (60 Gy/30 fractions) and 4 cycles of CVD chemotherapy. Post-treatment RECIST evaluation revealed stable disease, supporting chemoradiotherapy as a salvage option for anatomically complex recurrences. Conclusion This case underscores the necessity for standardized protocols and further research to validate multimodal therapy in recurrent CBTs.

Objective This study aimed to compare hospital and long-term clinical outcomes associated with various treatment methods for Stanford A type aortic intramural hematoma (IMH) to provide a reference for clinical decision-making. Methods In this single-center cohort study, we retrospectively analyzed 73 patients with Type A IMH treated at our center from August 1, 2018 to August 1, 2021. Among these patients, 26 were treated conservatively, and 47 underwent surgical intervention. We next compared this IMH cohort with 154 patients with acute type A aortic dissection (AD) who were treated surgically during the same study period. Results Computed tomography angiography revealed that the diameter of the ascending aorta of IMH patients treated with surgery was higher than IMH patients treated with conservative therapy (44.92 ± 7.58 mm vs. 51.22 ± 11.85 mm, P  < 0.05), while there was no significant difference in other clinical parameters. The in-hospital mortality of patients with IMH who underwent surgical treatment was lower than those undergoing conservative treatment (0% vs. 11.5%, P  < 0.05). The long-term mortality of the conservative IMH group was higher than the surgical IMH group (26.1% vs. 8.5%, P  < 0.05). There was no significant difference in the surgical parameters and postoperative complications between AD and IMH surgery patients. The proportion of circulatory arrest time in the lower body (19.98 ± 9.39 min vs. 17.51 ± 3.97 min) and arch involvement (98 (63.6%) vs. 22 (46.8%)) in the IMH surgery group was lower than in the AD surgery group ( P  < 0.05). Conclusions Compared with conservative treatment, surgical treatment of IMH significantly improves the survival rate of patients. Thus, surgical intervention should be considered the primary treatment option if feasible. Furthermore, The safety of IMH surgery can be guaranteed just like AD. But we still need in the future evidence on bigger samples. Graphical abstract

Cellulose nanocrystals (CNCs) have received a significant amount of attention due to their excellent physiochemical properties. Herein, based on bioinspired layered materials with excellent mechanical properties, a CNCs-graphene layered structure with covalent linkages (C-C bond) is constructed. The mechanical properties are systematically studied by molecular dynamics (MD) simulations in terms of the effects of temperature, strain rate and the covalent bond content. Compared to pristine CNCs, the mechanical performance of the CNCs-graphene layered structure has significantly improved. The elastic modulus of the layered structure decreases with the increase of temperature and increases with the increase of strain rate and covalent bond coverage. The results show that the covalent bonding and van der Waals force interactions at the interfaces play an important role in the interfacial adhesion and load transfer capacity of composite materials. These findings can be useful in further modeling of other graphene-based polymers at the atomic scale, which will be critical for their potential applications as functional materials.

In response to the problems of high energy consumption and difficulty in precise regulation of electric tracing anti-icing systems for polar marine engineering equipment in low-temperature, strong-wind, and high-humidity environments, this paper conducts experimental measurement and predictive modeling research on the convective heat transfer characteristics of electric heat-traced circular cylinders in cross-flow. First, a controllable environmental experimental system was set up to obtain 144 sets of steady-state convective heat transfer data under different combinations of wind speed, temperature, humidity, and heat flux density. Based on this, a Nusselt number (Nu) prediction model using a fully connected Deep Neural Network (DNN) was constructed, and its performance was evaluated through five-fold cross-validation. The results show that the DNN model can effectively capture nonlinear mapping relationships among multiple factors, and its prediction accuracy (R2 = 0.9828) is superior to that of traditional machine learning models. Furthermore, the Shapley Additive Explanations (SHAP) method was introduced to analyze the multi-factor coupling mechanisms, quantify the contribution of each input variable to the Nu prediction, and provide a data-driven reference for the optimization of engineering parameters under extreme polar conditions.

This paper presents an in-depth study and analysis of large datasets of mixed and attribute features under Spark using a large dataset clustering algorithm. The classical algorithm K-means based on division and the density-based clustering algorithm DPC, which has become more popular in recent years, are selected as the research objects of this paper. Secondly, the original K-means algorithm is improved by combining holdout validation and K-means++ method to address the shortcomings of the K-means algorithm that the number of class clusters K needs to be set in advance and the initial class cluster centers are chosen randomly, which leads to unstable iterations and slow convergence of clustering results. The similarity matrix will be continuously updated during the iterative process. It mainly refers to the process of dividing objects into multiple classes according to the degree of similarity between objects. After the division, the objects within the class are like each other, while the objects between the classes are different from each other. The comparison experiments of the improved algorithm before and after the MovieLens dataset are conducted to verify that the new algorithm has better performance in terms of clustering accuracy and efficiency. Again, to address the drawback that the clustering results in the DPC algorithm rely heavily on the subjective selection of the truncation distance parameter cd, and it is difficult to handle datasets with complex distribution and large density variation, the algorithm can generate the optimal cd adaptively by combining K-nearest neighbors and introducing the distance comparison quantity, which has a better performance by considering the overall and local distribution of the data. The feasibility of the improved method is verified by validating the algorithm with artificial datasets and UCI datasets as well as separation tests. Finally, the parallelized design and implementation of the improved K-means algorithm and CDPC-KNN algorithm are completed by building a Spark clustering environment, and the parallelized algorithm is verified to have much better data processing capability and be more adaptable to the clustering analysis of large-scale data by comparing algorithm string parallelism experiments.

The incidence of multiple lung cancer has been steadily increasing worldwide. Although cases of patients with lung cancers in the right upper and lower lobe have also become more frequently reported in clinical work, simultaneous right upper and lower lobectomy reports with the middle lobe preservation are still quite rare. A total of three patients with lung cancers in the right upper and lower lobe were included in the study. The patients underwent simultaneous right upper and lower lobectomy, whereas the remaining middle lobe was sutured and fixed to the intercostal muscle of the incision to prevent postoperative lobe torsion. There was no procedure to reduce residual space，such as phrenic nerve crush or thoracoplasty. All patients were discharged from the hospital 7 days after the operation. The chest tube was removed 5 days after the operation in two patients. One patient was discharged with the tube because of slight pulmonary leakage, and the tube was removed 2 weeks after the operation. Six months after the operation, the chest computer tomography showed that the middle lobe expanded well and no obvious cavity or pleural effusion was found. The suture of the remaining middle lobe and intercostal muscle of the incision is a simple and effective method that can be used to successfully avoid middle lobe torsion. This strategy is safe and can be used as the first choice for eligible patients. The patients with lung cancers in right upper and lower lobe have been found in clinical work. Right pneumonectomy has a higher risk of mortality than lobectomy. We found a new simple and effective method to fix the middle lobe after right upper and lower bilobectomy, and the method can be preferred as the first choice of surgical procedure for eligible patients in clinical work.

With the growth in demand for energy and the boom in energy internet (EI) technologies, comes the multi-energy complementary system. In this paper, we first model the components of the micro-energy-grid for a greenhouse, and then analyzed two types of protected agriculture load: time-shifting load and non-time-shifting load. Next, multi-scenario technology is directed against the uncertainty of photovoltaic (PV). Latin Hypercube Sampling (LHS) and the backward reduction algorithm are the two main methods we use to generate the representative scenarios and their probabilities, which are the basis for PV prediction in day-ahead scheduling. Third, besides the time of day (TOD) tariff, we present a model using real-time pricing of consumers’ electricity load, which is proposed to compare consumers’ demand response (DR). Finally, we establish a new optimization model of micro-energy-grid for greenhouses. By calculating the dispatch of electricity, heat, energy storage and time-shifting load under different conditions, the local consumption of PV and the comprehensive operational cost of micro-energy-grid can be analyzed. The results show that a storage device, time-shifting load and real-time pricing can bring more possibilities to the micro-energy-grid. By optimizing the time schedule of time-shifting load, the cost of the greenhouse is reduced.

As more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment (UC) problem involving the characteristics of both wind and nuclear generation are urgently needed. This paper first describes a model of nuclear power and wind power for the UC problem, and then establishes an objective function for the total cost of nuclear and thermal power units, including the cost of fuel, start-stop and peak-shaving. The operating constraints of multiple generation unit types, the security constraints of the transmission line, and the influence of non-gauss wind power uncertainty on the spinning reserve capacity of the system are considered. Meanwhile, a model of an energy storage system (ESS) is introduced to smooth the wind power uncertainty. Due to the prediction error of wind power, the spinning reserve capacity of the system will be affected by the uncertainty. Over-provisioning of spinning reserve capacity is avoided by introducing chance constraints. This is followed by the design of a UC model applied to different power sources, such as nuclear power, thermal power, uncertain wind power, and ESS. Finally, the feasibility of the UC model in the scheduling of a multi-type generation unit is verified by the modified IEEE RTS 24-bus system accommodating large scale green generation units.