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Aging is an important risk factor for tumorigenesis. Metabolic reprogramming is a hallmark of both aging and tumor initiation. However, the manner in which the crosstalk between aging and metabolic reprogramming affects the tumor microenvironment (TME) to promote tumorigenesis was poorly explored. We utilized a computational approach proposed by our previous work, MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to characterize aging-related metabolic plasticity events using pan-cancer bulk RNA-seq data. Our analysis revealed a high degree of metabolically organized heterogeneity across 17 aging-related cancer types. In particular, a higher degree of several energy generation pathways, i.e., glycolysis and impaired oxidative phosphorylation, was observed in older patients. Similar phenomena were also found via single-cell RNA-seq analysis. Furthermore, those energy generation pathways were found to be weakened in activated T cells and macrophages, whereas they increased in exhausted T cells, immunosuppressive macrophages, and Tregs in older patients. It was suggested that aging-induced metabolic switches alter glucose utilization, thereby influencing immune function and resulting in the remodeling of the TME. This work offers new insights into the associations between tumor metabolism and the TME mediated by aging, linking with novel strategies for cancer therapy. [ABSTRACT FROM AUTHOR]

Do U.S. households with battery electric vehicles (BEVs) drive less or more than U.S. households with internal combustion engine vehicles (ICEVs)? Answering this question is important to policymakers and transportation planners concerned with reducing vehicle miles traveled and the emissions of greenhouse gases from transportation. So far, this question has not been answered satisfactorily, possibly because of the relatively low number of EVs in the U.S. until recently, but also because of methodological issues. In this paper, we aim to fill this gap by analyzing data from the 2017 National Household Travel Survey (NHTS). We apply propensity score matching (PSM), a quasi-experimental method, to examine the differences in self-reported annual mileage and calculated daily mileage for various trip purposes among households with only BEVs (BEV-only), households with both BEVs and ICEVs (BEV+), and households without BEVs (non-BEV households). Our findings indicate that households with BEVs drive fewer annual miles than non-BEV households, but typically travel no less than they do for daily activities. This apparent discrepancy is likely due to taking fewer longer trips because the public charging infrastructure was still in its infancy in 2017, and its reliability was questionable. As technological progress is helping to overcome current battery limitations, policymakers may consider measures for fostering fast charging technologies while pondering new measures to fund both the charging infrastructure and the road network. [ABSTRACT FROM AUTHOR]

In order to solve the problems of many factors affecting spare parts consumption forecast, complex time correlation, and few actual consumption samples, in view of the difficulty of capturing the gradual dependence of small sample spare parts consumption data in time sequence with existing methods, a spare parts consumption prediction method based on the improved generation adversarial network generative model and the recurrent neural network prediction model is proposed. This method combines the supervised training of autoregressive learning with the unsupervised training of adversarial learning, and further explores the potential static distribution and temporal step-by-step dependence distribution of the spare parts consumption data. Samples that are closer to the real data are generated to achieve the purpose of expanding the sample volume and improving the forecast accuracy of spare parts consumption. Taking the consumption data of a certain type of domestic civil aircraft as an example, the generation performance and forecast accuracy of the model are evaluated by using the dimensionality reduction visualization method and the forecast score. Compared with other methods, the average absolute error of the predicted value presented by the proposed method is reduced by 3%, which verifies the effectiveness of the proposed method in solving the problem of spare parts prediction. [ABSTRACT FROM AUTHOR]

The reliability during aeroengine operation is significant in monitoring its time-varying operational status and providing valuable reference for both operation and design. To study aeroengine time-series operational reliability and parameter feature effects in whole flight stages, a tree model is developed to discuss the operational reliability of aeroengine through residual thrust and feature importance analysis. 24 parameter features from an aeroengine (CFM56) operation are applied to investigate the required thrust and residual thrust of aeroengine through quick access recorder (QAR) flight data. Characterizing aeroengine load case by the cosine similarity of aeroengine feature sets and operational reliability of aeroengine are conducted by combining cosine similarity and residual thrust of aeroengine. Further, XGBoost (extreme gradient boosting) model coupled with the Shapley additive ex-planations (SHAP) algorithm is constructed to enhance model interpretability analysis, in which the importance and influence direction of aeroengine features are analyzed. In this study, features similarity and importance were analyzed using aeroengine QAR data from a single flight. The results showed that (1) the operational reliability of aeroengine greatly varies under different flight stages, (2) the load case of aeroengine undulate greatly under take-off and descent stages, but is stable in the cruise stage, and (3) the feature importance and influence direction are indifferent among residual thrust and three aeroengine parameter sets, showing the complexity of aeroengine operation. Based on the operational and applicable restriction data, the efforts of this study provide a new analysis method for aeroengine to improve the design and operation by analyzing operational reliability. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an incurable neurodegenerative disorder. Early screening, particularly in blood plasma, has been demonstrated as a promising approach to the diagnosis and prevention of AD. In addition, metabolic dysfunction has been demonstrated to be closely related to AD, which might be reflected in the whole blood transcriptome. Hence, we hypothesized that the establishment of a diagnostic model based on the metabolic signatures of blood is a workable strategy. To that end, we initially constructed metabolic pathway pairwise (MPP) signatures to characterize the interplay among metabolic pathways. Then, a series of bioinformatic methodologies, e.g., differential expression analysis, functional enrichment analysis, network analysis, etc., were used to investigate the molecular mechanism behind AD. Moreover, an unsupervised clustering analysis based on the MPP signature profile via the Non-Negative Matrix Factorization (NMF) algorithm was utilized to stratify AD patients. Finally, aimed at distinguishing AD patients from non-AD groups, a metabolic pathway-pairwise scoring system (MPPSS) was established using multi-machine learning methods. As a result, many metabolic pathways correlated to AD were disclosed, including oxidative phosphorylation, fatty acid biosynthesis, etc. NMF clustering analysis divided AD patients into two subgroups (S1 and S2), which exhibit distinct activities of metabolism and immunity. Typically, oxidative phosphorylation in S2 exhibits a lower activity than that in S1 and non-AD group, suggesting the patients in S2 might possess a more compromised brain metabolism. Additionally, immune infiltration analysis showed that the patients in S2 might have phenomena of immune suppression compared with S1 and the non-AD group. These findings indicated that S2 probably has a more severe progression of AD. Finally, MPPSS could achieve an AUC of 0.73 (95%CI: 0.70, 0.77) in the training dataset, 0.71 (95%CI: 0.65, 0.77) in the testing dataset, and an AUC of 0.99 (95%CI: 0.96, 1.00) in one external validation dataset. Overall, our study successfully established a novel metabolism-based scoring system for AD diagnosis using the blood transcriptome and provided new insight into the molecular mechanism of metabolic dysfunction implicated in AD. [ABSTRACT FROM AUTHOR]

At alpine regional airports, aircraft are covered with frozen ice when they encounter extreme weather such as heavy snow or frost. The movement parts of aircraft cabin doors, flaps and landing gear may be affected due to the infiltration of freezing ice, and the movement stagnation may occur when the the accumulation of ice is more serious. This paper sets up a mechanical performance test of frozen ice for this engineering problem to provide data that is beneficial to the selection of the mechanism drive and the determination of ice-breaking loads. The test is conducted based on the standard tensile shear test. In order to overcome problems such as the poor icing effect of the traditional specimen or the easy damage of the specimen ice, we improved the structure of the specimen and the method of the test. According to the characteristics of growth of frozen ice, we introduced freezing time, type of water quality and adhesion materials as test variables. The results show that: the ice adhesion strength of frozen ice increases and then decreases (−15 ∘ C∼−55 ∘ C). At the ambient temperature of −15 ∘ C∼−55 ∘ C and freezing for 2 h∼6 h, the ice adhesion strength of aluminum alloy surface ranges from 0.009 MPa to 0.568 MPa, and that of frozen ice on a silicone rubber surface is 0.005 MPa∼0.147 MPa. The duration of freezing did not significantly affect the adhesion strength of frozen ice. Among the three water qualities, the frozen ice from distilled water has the greatest adhesion strength, the lake water is the most medium, and the sea water is the smallest. The results of this test can be widely used in the determination of the ice-breaking load of civil aircraft, amphibious aircraft, ships, and the design of anti-ice/de-icing systems. [ABSTRACT FROM AUTHOR]

To serve customized option selection for civil aircraft, a mathematical product option selection optimization model combined with an Improved Non-dominated Sorting Genetic Algorithm for decreasing aircraft fleet maintenance cost was investigated. For airlines, considering the economy and reliability in customized option selection is the most intuitive way to improve aircraft performance to generate the optimal formation configuration. Product option selection usually takes certain indicators as constraints (reliability and economy) to meet and maximize performance through equipment selection (the selected parameters include mean time between failures, price, etc.). To describe the customization needs of airlines by a mathematical model and find the optimal decision through an algorithm, a multi-objective, mathematical product option selection optimization model response with reliability parameters as a decision variable, maintainability as a link, and aircraft fleet maintenance and availability as an objective function is established to serve aircraft option selection in this paper. Next, the multi-objective genetic algorithm is used to solve the model, and the convergence, distribution and fitting accuracy of the objective functions are analyzed. Eventually, the landing gear system is used to verify the effectiveness of the model and method. After optimization, the aircraft fleet maintenance cost is reduced by 20.71%, and the availability is increased by 2.576%. Through the mathematical optimization model, the product configuration is provided for the development of the customization option selection project. [ABSTRACT FROM AUTHOR]

Spare parts support is essential to the safe and reliable operation of civil aircraft. Civil aircraft are operated in different regions, and spare parts support based on customer demand is also affected by the regional environment and transportation methods. In order to realize the agile support of civil aircraft spare parts, this paper comprehensively considers the influence of multiple factors, and proposes the nested analytic hierarchy process (NAHP) method based on the balance of the spare parts support network nodes, as well as the multi- region spare parts support network model for civil aircraft based on the genetic algorithm (GA). Combining the characteristics of civil aircraft operations in multiple regions, the factors affecting the spare parts support network are researched, and the NAHP is used to weigh and analyze the spare parts support network nodes, and the target nodes of the spare parts support network are obtained by ranking the importance of the candidate nodes. Then, taking timeliness as the optimization goal, a multi-regional spare parts support network node type positioning and multimodal transport route optimization model is constructed, which is solved by the GA that is designed to improve applicability. Finally, the effectiveness of the developed method and model is verified by comparing with the artificially planned spare parts support plans. The results show that the proposed method can reasonably determine the type and radiation range of the spare parts support network node, and at the same time complete the spare parts support task in the shortest time. It can provide feasible methods and ideas for the construction of the spare parts support network for civil aircraft operating in multiple regions. [ABSTRACT FROM AUTHOR]

The emergence extension system (a mechanical system) of nose landing gear (NLG) seriously influences the reliability, safety and airworthiness of civil aircrafts. To efficiently realize the NLG emergence extension, a promising driving plan of emergence extension is proposed in respect of the reliability sensitivity analyses with a mixture of models. The working principle, fault tree analysis and four reliability models are firstly discussed for NLG emergence extension. In respect of the mixture of models, the reliability sensitivity analyses of emergence extension are then performed under different flight speeds (270 Kts, 250 Kts, 220 Kts, and 180 Kts). We find dimpling torque and aerodynamic torques of forward and after doors are the top three failure factors and the start reliability is the most in emergence extension failures. Regarding the results, feasible driving plans of NLG emergence extension are developed by adjusting the aerodynamic torque of NLG forward door, and are validated by the aerodynamic torque experiment of forward door with regard to strut rotational angle under the flight speed 270 Kts. It is indicated that (1) the adverse torque generated by the new driving mechanism obviously reduces by about 24.8% from 1462.8 N·m to 1099.6 N·m, and the transmission ratio of aerodynamic torque (force) is greatly improved when the NLG strut is lowered near to 100°; (2) under different flight speeds (180 Kts, 220 Kts, 250 Kts, and 270 Kts), the new driving mechanism realizes the lower tasks of emergence extension which cannot be completed by the initial driving mechanism; and (3) the lowering time of the new driving mechanism shortens with the increasing flight speed. The proposed new driving mechanism is verified to be reliable for emergence extension of aircraft NLG besides normal extension and to be a promising feasible driving plan with high lowering reliability. The efforts of the paper provide an efficient driving mechanism for the design of NLG in civil and military aircrafts. [ABSTRACT FROM AUTHOR]

In order to solve the problems that traditional diagnostic method is heavily dependent on the signal processing techniques and expert experience, and the diagnostic accuracy is difficult to have big improvement anymore with the accumulation of operational data, which cannot meet the needs of fault diagnosis in the big data age, a multi-source signals feature fusion method by deep learning model is proposed in this paper. The stacked denoising autoencoders (SDAE) is used to extract the abstract and deep features from original features, and then locality preserving projections (LPP) is used for dimensionality reduction to complete the feature fusion. Finally, the fused low-dimensional features act as inputs to the support vector machine (SVM) to realize the failure detection and fault location of typical fault modes of the landing gear hydraulic retraction system. The inhibitory effect of the feedback control on the incipient fault is discussed as well. Moreover, a severity assessment method is presented considering the gradual degradation of leakage fault of the actuator. The diagnostic results show that the proposed method has a better feature fusion ability and higher diagnostic accuracy. The health assessment model can evaluate the health state of the actuator. The significance of this paper is to provide a feasible idea for the fault diagnosis of the landing gear hydraulic retraction system and health assessment of the actuator. [ABSTRACT FROM AUTHOR]