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Abstract The Belt and Road Initiative (BRI) makes headway in constructing railway projects invested by China, yet related environmental impacts are rarely discussed. Here we quantify the carbon footprint of railway project construction (RPC) under the BRI and identify the drivers behind their variations to find possible carbon reduction pathways. Our results show that China‐invested overseas RPC in BRI countries induced 2,095.2 Mt CO2 emissions through the global supply chains during 2008–2017. Specifically, 73% of the carbon footprint relies on supply chains in BRI countries, and 27% spills over to other regions serving as the suppliers of global resources. Upgrading key industries' technological levels in BRI countries to China's average level can mitigate the carbon footprint by 77%. Once all industries achieve China's average technological level, nearly 90% of the carbon footprint can be avoided. The findings potentially provide valuable insights into achieving sustainable project construction in cross‐regional cooperation and promoting green development of the BRI.

Abstract The accurate prediction of flight delays is of great significance to airports, airlines and passengers. This paper presents a causal flight delay prediction model developed for a single airport. A long short‐term memory network of delay prediction with an attention mechanism (LSTM‐AM) is established to predict flight delays and analyse their primary causes. In this model, the direct and indirect factors related to delays are comprehensively considered. LSTM‐AM can focus on input data combined with the attention vector to capture the critical time points, which can make the prediction more accurate. The model's performance is verified by actual operational data of Beijing International Airport, one of the busiest airports in the world. Experimental results show that LSTM‐AM has better prediction accuracy than baseline algorithms such as some machine learning methods and deep learning methods. The mean absolute error of LSTM‐AM is about 8.15 min on the test dataset. The study found that using the predicted results of this paper to release delayed information in advance can effectively alleviate the nervousness of passengers. The critical time point captured by LSTM‐AM combined with runway and apron flow control can reduce or eliminate delays of one flight.

Abstract Battery electric buses (BEBs) have been regarded as effective options to address the congestion and pollution problems in the field of urban transportation. However, since the limited driving range of BEBs brings challenges for their promotion, the accurate estimation of the driving range with limited available information has become a critical issue for public transport operators. The real‐world data collected from 50 BEBs operated in two different cities is used to develop the driving range estimation method by considering the battery degradation effects. Firstly, the incremental capacity analysis method is introduced to characterize the battery performance, and the battery degradation levels under different charging modes are recognized. Afterward, four types of ensemble machine learning (EML) methods are adopted to model the driving range estimation. The BEB driving data, weather condition data and battery degradation levels are used to train and test the models with consideration of 17 impact factors together with two different charging modes. The results indicate that the ensemble machine learning methods have good performance overall, of which the random forest has the highest accuracy. Furthermore, the importance of influencing factors is analysed, and the relevant insights are discussed.

Due to the large amount of waste generated by urban construction, the transportation of construction waste has a significant impact on urban traffic. Understanding the transportation trajectory of garbage trucks can improve the management of transportation routes and reduce traffic accidents. This study analyzes electric waybill and state data of garbage trucks to identify hot nodes of construction waste transportation, where the volume of garbage trucks is relatively high. Management should strengthen the hot nodes to reduce traffic accidents. First, several machine learning methods are used to improve the prediction accuracy of electric waybill generation, where the garbage truck recorded on the electric waybill is regarded as a working truck. Second, the transportation trajectory of working trucks is extracted, and its spatiotemporal characteristics are further analyzed. Hot nodes are found based on density clustering. Finally, a case study is conducted based on the Shenzhen construction waste transportation system. The results show that the XGBoost model can improve the accuracy of the generation of waybill to 90.5% compared with the decision tree model, random forest, and GBDT. Moreover, the density clustering model can discover the hot nodes of construction waste transportation. Considering the minimum number of samples and the neighborhood radius, the clustering number is determined as 100. The ratio of noise points is determined as 0.79. The results can provide decision support for the management of electronic waybill and garbage truck transportation.

Abstract China has pledged to cap its carbon emission by 2030 and achieve carbon neutrality by 2060, making knowledge about how the Chinese general public understands climate change crucial and timely. This article reports findings from surveys of climate change perceptions in six Chinese cities (∼40 million people). We identify 10 distinct mental images and 37 subcategories that represent a wide spectrum of perceptions of climate change among the Chinese public. The results reveal that people tend to conflate climate change with air pollution and seasonal weather changes. Although skepticism is not prominent, voices for action are also limited. Furthermore, climate change perceptions are heterogenous across regions and demographic groups. Respondents from developed cities are less likely to conflate climate change with local weather. People living in polluted regions tend to equate climate change with air pollution. Well‐educated, high‐income, and young residents are more aware of the scientific dimensions of climate change and its consequences. Females and the elderly think more about health implications and how to adapt. Compared to Western countries, opinions about climate change in China are less polarized and controversial, probably due to different political realities and media framings. This study provides an updated picture of climate change perceptions among the Chinese general public and recommends targeted and multi‐level communication strategies for policymakers.

Abstract Background Cervical cancer is a major public health concern in China. We report the end‐of‐study results of a phase II/III trial to assess the efficacy, immunogenicity, and safety of the AS04‐human papillomavirus (HPV)‐16/18 vaccine in Chinese women aged 18‐25 years followed for up to 72 months after first vaccination. Results of approximately 57 months following first vaccination have been previously reported. Methods Healthy 18‐25‐year‐old women (N = 6051) were randomized (1:1) to receive three doses of AS04‐HPV‐16/18 vaccine or Al(OH)3 (control) at Months 0‐1‐6. Vaccine efficacy against HPV‐16/18 infection and cervical intraepithelial neoplasia (CIN), cross‐protective vaccine efficacy against infections and lesions associated with nonvaccine oncogenic HPV types, immunogenicity, and safety were assessed. Efficacy was assessed in the according‐to‐protocol efficacy (ATP‐E) cohort (vaccine N = 2888; control N = 2892), total vaccinated cohort for efficacy (TVC‐E; vaccine N = 2987; control N = 2985) and TVC‐naïve (vaccine N = 1660; control N = 1587). Results In initially HPV‐16/18 seronegative/DNA‐negative women, vaccine efficacy against HPV‐16/18‐associated CIN grade 2 or worse was 87.3% (95% CI: 5.5, 99.7) in the ATP‐E, 88.7% (95% CI: 18.5, 99.7) in the TVC‐E, and 100% (95% CI: 17.9, 100) in the TVC‐naïve. Cross‐protective efficacy against incident infection with HPV‐31, HPV‐33 and HPV‐45 was 59.6% (95% CI: 39.4, 73.5), 42.7% (95% CI: 15.6, 61.6), and 54.8% (95% CI: 19.3, 75.6), respectively (ATP‐E). At Month 72, >95% of initially seronegative women who received HPV vaccine in the ATP cohort for immunogenicity (N = 664) remained seropositive for anti‐HPV‐16/18 antibodies; anti‐HPV‐16 and anti‐HPV‐18 geometric mean titers were 678.1 EU/mL (95% CI: 552.9, 831.5) and 343.7 EU/mL (95% CI: 291.9, 404.8), respectively. Serious adverse events were infrequent (1.9% vaccine group [N = 3026]; 2.7% control group [N = 3025]). Three and zero women died in the control group and the vaccine group respectively. New onset autoimmune disease was reported in two women in the vaccine group and two in the control group. Conclusions This is the first large‐scale randomized clinical trial of HPV vaccination in China. High and sustained vaccine efficacy against HPV‐16/18‐associated infection and cervical lesions was demonstrated up to Month 72. The vaccine had an acceptable safety profile. Combined with screening, prophylactic HPV vaccination could potentially reduce the high burden of HPV infection and cervical cancer in China. Trial registration NCT00779766.

This work explores the characteristics of the usage behaviour of station-based car-sharing users based on the actual operation data from a car-sharing company in Gansu, China. We analyse the characteristics of the users’ demands, such as usage frequency and order quantity, for a day with 24 1 h time intervals. Results show that most car-sharing users are young and middle-aged men with a low reuse rate. The distribution of users’ usage during weekdays shows noticeable morning and evening peaks. We define two attributes, namely, the latent ratio and persistence ratio, as classification indicators to understand the user diversity and heterogeneity thoroughly. We apply the k-means clustering algorithm to group the users into four categories, namely, lost, early loyal, late loyal, and motivated users. The usage characteristics of lost users, including maximum rental time and travel distance, minimum percentage of same pickup and return station, and low percentage of locals, have noticeable differences from those of the other users. Late loyal users have lower rental time and travel distance than those of the other users. This manifestation is in line with the short-term lease of shared cars to complete short- and medium-distance travel design concepts. We also propose a model that predicts the driver cluster based on the decision tree. Numerical tests indicate that the accuracy is 91.61% when the user category is predicted four months in advance using the observation-to-judgment period ratio of 3 : 1. The results in this study can support enterprises in user management.

Gate assignment problem (GAP) is the core issue of airport operation management. However, the limited resources of airport gates and the increase of flight scale result in serious problems for gate allocation. In this paper, to provide decision-making support for large-scale GAPs, a model based on gate assignment rules (e.g., flight type constraints, safe time interval constraints, and adjacency conflict constraints) is built to formulate the problem. An improved adaptive parallel genetic algorithm (APGA) is then designed to solve the model. The algorithm is effective because it introduces the idea of elite strategy and parallel design and can adaptively adjust the crossover probability. Moreover, different instances are presented to demonstrate the proposed algorithm. The calculation results of this algorithm are compared with those of standard genetic algorithm and CPLEX, which show that the proposed algorithm has better performance and takes a shorter computational time. In addition, we verify the stability and practicability of the algorithm by repeated experiments on large-scale flight data.

It is of great significance to improve the driving range prediction accuracy to provide battery electric vehicle users with reliable information. A model built by the conventional multiple linear regression method is feasible to predict the driving range, but the residual errors between -3.6975 km and 3.3865 km are relatively unfaithful for real-world driving. The study is innovative in its application of machine learning method, the gradient boosting decision tree algorithm, on the driving range prediction which includes a very large number of factors that cannot be considered by conventional regression methods. The result of the machine learning method shows that the maximum prediction error is 1.58 km, the minimum prediction error is -1.41 km, and the average prediction error is about 0.7 km. The predictive accuracy of the gradient boosting decision tree is compared against that of the conventional approaches.

The soil water characteristic curve (SWCC) describes the relationship between matric suction and moisture of soil, the testing process of which is time-consuming. The test time of particle size distribution (PSD), in contrast, is relatively short. Thus, it is quite important to establish a proper model for PSD to forecast SWCC. This paper analyzed PSD of 25 groups of loess by way of laser diffraction technique (LD) and sieve-settlement method. Works were carried out on fitting analysis on PSD with Logarithmic model, Fredlund model, Jaky model, and Gompertz model. Statistical method was used to explain the fitting performance. Meanwhile, an empirical model was put forward. Compared to the four models, the empirical model has fewer parameters, simple model form, and smaller fluctuations of parameters. Results of LD showed higher clay content but lower silt content. It is suggested that Fredlund model or the empirical model be adopted to forecast SWCC of Malan loess.

Abstract Gliomas, the most common type of primary intracranial tumor, arise from the brain's glial cells and constitute 30% of all brain and central nervous system tumors. MiR‐34b/c plays an important role in colorectal cancer and other cancers, but its role in glioma is unknown. In this study, we attempted to assess the effect of rs4938723 T > C in miR‐34b/c on the susceptibility of glioma Chinese children. Odds ratios and 95% confidence intervals (CIs) were calculated to evaluate the association between the selected polymorphism and glioma susceptibility. Our results showed that there is no significant association between this polymorphism and the risk of glioma in Chinese children. However, stratified analysis showed that the rs4938723 TC/CC genotype significantly reduced the risk of glioma in participants under 60 months of age (adjusted OR = 0.56, 95% CI = 0.35–0.88, p = .013). Overall, our study indicates that miR‐34b/c rs4938723 T > C polymorphism may have a weak influence on glioma susceptibility. Nevertheless, these findings need to be validated in well‐designed studies with larger sample sizes and different populations.

Senescent microenvironments play an important role in tumor progression. Here, we report that doxorubicin (DOX)‐pretreated or replicative senescent stromal cells (WI‐38 and HUVEC) promote colorectal cancer (CRC) cell growth and invasion in vitro and in vivo. These pro‐tumorigenic effects were attenuated by exogenous administration of Klotho, an anti‐aging factor. We subsequently identified several senescence‐associated secretory phenotype (SASP)‐associated genes, including CCL2, which were significantly upregulated in both types of senescent stromal cells during replication and DNA damage‐induced senescence. Importantly, we found that the secretion of CCL2 by senescent stromal cells was significantly higher than that seen in nonsenescent cells or in senescent cells pretreated with Klotho. Notably, CCL2 was found to accelerate CRC cell proliferation and invasion, while this effect could be blocked by administration of a specific CCR2 antagonist. We further show that Klotho can suppress NF‐κB activation during DOX‐induced senescence and thus block CCL2 transcription. Low expression of Klotho, or high expression of CCL2 in patient tumor tissues, correlated with poor overall survival of CRC patients. Collectively, our findings suggest that senescent stromal cells are linked to progression of CRC. Klotho can suppress the senescent stromal cell‐associated triggering of CRC progression by inhibiting the expression of SASP factors including CCL2. The identification of key SASP factors such as CCL2 may provide potential therapeutic targets for improving CRC therapy.

The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches—K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)—were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.

The paracrine effect is the major mechanism of stem cell therapy. However, the details of the effect’s mechanism remain unknown. The aim of this study is to investigate whether adipose tissue-derived stem cells (ADSCs) can ameliorate cavernous nerve injury-induced erectile dysfunction (CNIED) rats and to determine its mechanism. Twenty-eight days after intracavernous injection of 5-ethynyl-2-deoxyuridine- (EdU-) labeled ADSCs, the erectile function of all the rats was evaluated by intracavernosal pressure (ICP). The ADSCs steadily secreted detectable pigment epithelium-derived factor (PEDF) in vitro. The expression of PEDF increased in the penis of the bilateral cavernous nerve injury (BCNI) group for 14 days and then gradually decreased. On day 28 after the intracavernous injection, the ADSCs group exhibited a significantly increased ICP compared with the phosphate buffered saline- (PBS-) treated group. Moreover, the neuronal nitric oxide synthase (nNOS) and S100 expression in penile dorsal nerves and the smooth muscle content to collagen ratio in penile tissues significantly increased. Furthermore, elevated PEDF, p-Akt, and p-eNOS were identified in the ADSCs group. This study demonstrated that intracavernous injection of ADSCs improved erectile function, repaired the nerve, and corrected penile fibrosis. One potential mechanism is the PEDF secretion of ADSCs and subsequent PI3K/Akt pathway activation.

Purpose. The aim of the present study was to analyze the clinical characteristics of visual dysfunction in patients with carbon monoxide (CO) poisoning. Methods. A total of 436 patients with CO poisoning were enrolled in our hospital from October 2012 to December 2018, including 193 patients with moderate poisoning (MP group), 165 with severe poisoning (SP group), and 78 with delayed encephalopathy (DE group). The clinical characteristics of visual dysfunction in patients with CO poisoning were analyzed through the collection of medical history, regular physical examination, brain magnetic resonance imaging (MRI), ophthalmological examination, the National Eye Institute Visual Function Questionnaire (NEI-VFQ), and its influencing factors. Results. Some patients in the three groups had visual dysfunction. The main ocular symptoms were local pain, eye movement disorder, and visual field defect. The key pathological factors were keratopathy, retinal nerve cell damage, optic nerve damage, retinal vascular disease, macular disease, and occipital visual center damage. The clinical symptoms of visual dysfunction after CO poisoning lasted for a long time (>12 months) and were not completely consistent with the positive results of the ophthalmological examination. A few sequelae of ophthalmology were still left after the help of medicine. Conclusion. The incidence of visual dysfunction in patients with CO poisoning was high, the clinical symptoms were rich and diverse, the duration of disease was long, and the prognosis was poor. Thus, the relevant ophthalmological examination and intervention treatment should be perfected as soon as possible.