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An experimental investigation was conducted in order to understand the installation effects of inlet measurement probes on the vibration characteristics of the rotor blades in two axial compressors. The vibration signal of the rotor blades was analyzed for different layouts of the inlet measurement probes. For the three-stage axial compressor, the first-order resonance occurs on the first stage of the rotor blades at an engine order excitation condition, which is induced by the cylindrical probe support with a diameter of 10 mm. When the size of the probe support is decreased, the vibration level reduces evidently. In contrast, for the six-stage axial compressor, the first-order resonance occurs on the first stage of the rotor blades at an excitation source of 6th order, which is triggered by the inlet measurement probes and the upstream struts. When the number of the inlet measurement probes is changed, the resonance of the rotor blades vanishes.

Magnetotelluric (MT) inversion algorithms are an essential tool in exploration geophysics because they provide us with resistivity models of the subsurface. Inverse problems are ill-posed and non-unique. Resistivity models produced by geologically unconstrained MT inversions are typically smooth, which means that the geological interpretations obtained based on the resistivity models will be ambiguous and uncertain. To solve the above problems, geophysicists have made many efforts over the years to introduce any prior information (such as geological structure information and physical property data) into models, which could help to better constrain the inverted model with the inversion algorithms. In the last few years, there has been renewed interest in machine learning techniques. Various machine learning methods have been applied to inversions. Using the fuzzy c-means (FCM) clustering method, geophysicists proposed new inversion algorithms that are capable of building petrophysical information into the inversion. Although the FCM has been used and advanced in previous work, geophysicists concluded that a priori information of the correct number and value of cluster centers is very important for a successful FCM inversion. In fact, it is difficult to obtain the appropriate clustering information in some geophysical survey areas. In this study, we present an effective way to build the petrophysical information for the MT inversion based on FCM clustering algorithm. When the actual petrophysical information is insufficient, considering the characteristics of MT data, we perform a one-dimensional blocky inversion that can clearly identify the interface with distinct electrical property contrast; then, we obtain the number and value of cluster centers from 1D blocky inversion for the further MT inversion with FCM clustering. The algorithm uses guided FCM clustering to improve the model within the iterative minimization during MT inversion. In our MT inversion method, we integrate the geophysical inversion and geological differentiation into a unified scheme, which interact and enhance each other. The resistivity models obtained from the inversion respect the geophysical and petrophysical data and are easy to geologically interpret. We tested the algorithm using two synthetic examples and a field data example.

We have used the integrated interpretation of gravity, magnetotelluric (MT) data, and seismic data to improve the structural imaging of the Dayangshu Basin. The Dayangshu Basin is mainly composed of clastic and volcanic rocks. The logging data in the basin show different degrees of direct hydrocarbon indication, suggesting that the Dayangshu Basin has good potential for exploration. However, the widely distributed volcanic rocks attenuate seismic waves and lead to poor seismic imaging. Thus, the seismic signal is weak in the Ganhe Formation (K1g) and reliable seismic images cannot be obtained below that formation. MT data can accurately obtain images of deep structures because the resistivity of volcanic rocks is significantly higher than that of sedimentary rocks. Therefore, to obtain a more reliable geologic model, we combine the traditional 3D MT inversion result with logging and seismic data to establish an initial model. The 3D MT fuzzy constrained inversion (FCI) produces a more reliable geophysical model and geologically meaningful results. The resistivity model inverted from FCI shows that volcanic rocks are widely distributed in the Ganhe Formation, and the resistivity value of the lower section of the Longjiang Formation is greater than that of the upper section of the Longjiang Formation. Finally, the 3D gravity inversion with structural constraints from 3D MT FCI method was performed to improve the model resolution in depth and to highlight the density variations within the Jiufengshan Formation, which can further optimize the geologic model. We have determined how the effective integration of gravity, MT, and seismic data can improve the structural imaging of the Dayangshu Basin.

We carried out a multigeophysical data joint interpretation to image volcanic units in an area where seismic imaging is difficult due to complicated and variable volcanic lithology. The gravity and magnetic methods can be effective in imaging the volcanic units because volcanic rocks are often strongly magnetic and have large density contrasts. Gravity and magnetic data have good lateral resolution, but they are faced with challenges in defining the depth extent. Although seismic data make for poor imaging in volcanic rocks, they can provide a reliable stratigraphic structure above volcanic rocks to improve the vertical resolution of the gravity and magnetic method. We have developed an integrated interpretation method that combines the advantages of seismic, gravity, magnetic, and well data to generate a 3D quasigeology model to image volcanic units. We first use seismic data to obtain the stratigraphic boundaries, and then we apply an anomaly stripping method based on a seismic-derived structure to extract residual gravity and magnetic anomaly produced by volcanic rocks. We further perform the 3D gravity and magnetic amplitude inversion to recover the distribution of the density and effective susceptibility. We perform geology differentiation using the inverted density and effective magnetic susceptibility to identify the spatial distribution of four groups of volcanic units. The results show that the integrated interpretation of multigeophysical data can significantly decrease the uncertainty associated with any single data set and yield more reliable imaging of lateral and vertical distribution of volcanic rocks.

We present a case study on imaging volcanic units in gas exploration by constraining magnetic amplitude inversions using magnetotelluric (MT) sounding data at sparse locations. Magnetic data can be effective in mapping volcanic units because they have remanent magnetization and significant susceptibility contrast with surrounding rocks. Although magnetic data can identify the lateral distribution of volcanic units, they often have difficulties in defining the depth extent. For this reason, additional structural constraints from other geophysical methods can often help improve the vertical resolution. Among the independent geophysical methods, MT data can provide the needed structural information at a low cost. We have investigated an approach to combine a set of sparse MT soundings with magnetic amplitude data to image the distribution of volcanics in a basin environment. We first use a blocky 1D MT inversion based on Ekblom norm to obtain the structural constraint, and then we perform a constrained 3D magnetic amplitude inversion to recover the distribution of effective susceptibility by incorporating the structural information from MT soundings. We determine that even a small number of MT stations (e.g., 20) in a [Formula: see text] area is sufficient to drastically improve the magnetic amplitude inversion. Our results indicate that magnetic amplitude inversion with structural constraint from MT soundings form a practical and cost-effective means to map the lateral and vertical distribution of volcanics.

Air transportation systems are often subject to failures or attacks induced by unexpected abominable weather or temporal airspace occupation, while complex networks have been springing up as a convenient yet efficient tool to represent and analyze various realistic complex systems such as realistic airline system. In terms of Chinese airline network formed during the spring festival timespan, structural empirical research and invulnerability simulation analysis against various deliberate attack strategies were made using complex network theory, where nodes and edges denotes domestic airports and direct flights between them respectively. The analysis results indicate: The presented airline network is a small net-work with scale-free characteristics, and correlation shows remarkable hierarchical structure and obvious assortative characteristics; The network shows obvious invulnerability under deliberate node attack, while shows partly robustness under edge attack even with obvious attack effects against various attack strategies.

An ionic liquid, 1-allyl-3-methylimidazolium methyl phosphonate (Amim(MeO)PHO2), was synthesized and used to dissolve and regenerate cellulose under mild conditions. The dissolution mechanism of cellulose in Amim(MeO)PHO2 was investigated by in situ NMR spectroscopy under different conditions. Inorganic salt Na2PHO3 with an anion structure similar to that of Amim(MeO)PHO2 was added to form a Na2PHO3/Amim(MeO)PHO2 composite system. The effects of the composite system on the cellulose structure were investigated by Fourier transform infrared spectroscopy, thermogravimetric analysis, X-ray diffraction, and the measurement of the degree of polymerization. The results indicated that the regenerated cellulose was not derivatized; however, its thermal stability, crystallinity, and DP were lower than that of the original cellulose. Therefore, the dissolution mechanism of cellulose in Amim(MeO)PHO2, and its composite system with Na2PHO3 was investigated in this study, which might provide a simple guide for designing efficient and safe ionic liquids for the dissolution of cellulose.

A series of anion exchange membranes (AEMs) consisting of polyvinyl alcohol/branched polyethyleneimine (PVA/BPEI) were simply prepared by casting method using glutaraldehyde (GA) as crosslinking agent, then quaternized with benzyl chloride and alkalized in KOH solution. Subsequently, the comparative analysis was performed to select the optimal ratio of PVA to BPEI and crosslinking degree of AEMs. The structure and properties of AEMs were verified by FT-IR, XRD, TGA, mechanical analysis, water uptake test, swelling ratio test, ion exchange capacity (IEC) test, ionic conductivity measurement, and alkaline/oxidative stability test. The results showed that the maximum value of ionic conductivity was 86.0 mS/cm at 80 °C with IEC value of 2.35 meq/g. Moreover, the AEM had excellent chemical stability, as the ionic conductivity decreased by

One of the key issues in three-dimensional (3D) potential field data inversion is computational efficiency. We have developed an efficient 3D potential field data inversion method using fast proximal objective function (FPOF) optimization algorithm. A distinguishing feature of the proposed inversion method is to calculate each unknown element in the model space independently at each iteration. In addition, based on subsequent data misfits in the iterative process, we design a computationally efficient method to choose an appropriate regularization parameter. We take gravity data inversion as an example and apply the proposed inversion method to synthetic data contaminated with noise. The results demonstrate that the proposed method provides almost identical results as the conventional conjugate gradient (CG) method, however, with a significant reduction in the execution time. Lastly, an application on real field data acquired in the Western China is carried out, yielding a 3D density model which is in good agreement with the available geological knowledge.

BACKGROUND: Piloting an aircraft is a complex cognitive task. Human error represents a major contributing factor in aviation accidents. Emotion plays an important role in aviation safety. We performed a functional magnetic resonance imaging (fMRI) study to explore whether pilots and nonpilots may differ in the neural mechanisms responsible for the processing of conflict emotional information.METHODS: A total of 27 civil aviation pilots and 24 nonpilot controls performed the emotional Stroop task, in which participants were required to identify the facial expressions of the stimuli while ignoring the congruent or incongruent emotional words superimposed on the faces. Neural responses to the stimuli were compared between pilots and controls. Also, a psychophysiological interaction (PPI) analysis was performed to explore whether there were differences in effective connectivity between pilots and nonpilots.RESULTS: Behavioral data showed that pilots (21.23 ms) and nonpilots (26.78 ms) had equivalent congruency effects. Nevertheless, their neural activation patterns differed. Compared with pilots, nonpilots exhibited neural activity in the right supramarginal gyrus when processing incongruent stimuli, and more regions were activated in the process of conflict monitoring. The PPI analysis showed greater activity between the right supramarginal gyrus and the right lingual gyrus when nonpilots confronted incongruent vs. congruent stimuli. However, this effective connectivity was not found in pilots.CONCLUSION: These results suggest different mechanisms underlying emotional conflict control between pilots and the general population.Jiang H, Xu K, Chen X, Wang Q, Yang Y, Fu C, Guo X, Chen X, Yang J. The neural underpinnings of emotional conflict control in pilots. Aerosp Med Hum Perform. 2020; 91(10):798805.

Background Airlines occupy an increasingly important place in the economy of many countries. Because air disasters may cause substantial losses, comprehensive surveys of the psychophysiological mechanism of flying are needed; however, relatively few studies have focused on pilots. The default mode network (DMN) is an important intrinsic connectivity network involved in a range of functions related to flying. This study aimed to examine functional properties of the DMN in pilots. Method Resting-state functional magnetic resonance imaging data from 26 pilots and 24 controls were collected. Independent component analysis, a data-driven approach, was combined with functional connectivity analysis to investigate functional properties of the DMN in pilots. Results The pilot group exhibited increased functional integration in the precuneus/posterior cingulate cortex (PCC) and left middle occipital gyrus. Subsequent functional connectivity analysis identified enhanced functional connection between the precuneus/PCC and medial superior frontal gyrus. Conclusion The pilot group exhibited increased functional connections within the DMN. These findings highlight the importance of the DMN in the neurophysiological mechanism of flying.

To remove the funitional payload variation, the attitude oscillation and trajectory tracking error of civil UAV in complex environment, a combined inner and outer control strategy for the civil UAV is designed based on the interval system control theory and dynamic tracking error minimization principle, which can guarantee the attitude stabilization in inner loop and the heading tracking in outer loop, thus solving the control effect deterioration and trajectory error drawback of the existing UAV with large scale model variation. The simulation results show that, the proposed combined control strategy can keep flight state's stabilization and effective trajectory tracking with good closed-loop robustness. This study is useful to develop effective and automatic civil UAV system's flight control strategy.

Ibuprofen sodium is the sodium salts form of ibuprofen which exhibits better effects than standard ibuprofen as an analgesic by inhibiting the cyclooxygenase enzyme system. 3-[4-(2-Methylpropyl)phenyl] propanoic acid as one of potential impurities of ibuprofen was determined by a gas chromatography method after the methylation reaction according to the European Pharmacopoeia. Now, a new simple normal phase high-performance liquid chromatography (NP-HPLC) method has been established to separate 3-[4-(2-methylpropyl)phenyl]propanoic acid from ibuprofen sodium as well as from ibuprofen. Separation was performed on an Ultimate Silica column (250 mm × 4.6 mm, 5 μm) using hexane, ethyl acetate and trifluoroacetate in the ratio (97:3:0.95, v/v/v) as mobile phase. The detection wavelength was performed at 264 nm. The NP-HPLC method could successfully separate 3-[4-(2-methylpropyl)phenyl]propanoic acid from the main peak within an analysis time of less than 20 min. Complementary of this work, a forced degradation study was carried out under specified conditions to investigate the chemical stability of ibuprofen sodium. The method was found to be precise, linear, accurate, sensitive, specific and robust according to the ICH guidelines.

Identifying the most important hub airport of complex network formed during the long- time flight training possesses effective solution and obvious potential to alleviate resource tensionǃimprove training ability and strengthen safety management. The flight training network(FTN) was innovatively constructed employing the airports and training course via the complex network framework, and its fundamental scale-free network characteristic was analyzed. The FTN data was simulated based on the classic network topological node analysis and an improved method based on the key assessment index correlation. The main results are as follows: degree-value or betweenness-value can effectively identify the hub airport while clustering-value showing no obvious rules, moreover, the correction between node’s betweenness and degree can identify the complete list of hub airports. The simulation results show high consistence with actual situation, which can serve future guidance and direction in hub airport application in actual flight training.

With the fast development of the society economy in recent years, the Chinese aviation network is playing a more and more critical and irreplaceable developing role in today’s integrated transportation system. In this paper, an empirical structural analysis of Chinese airline network is made utilizing complex network theory. The results show that the presented network is a small network with two regime pow-law degree distribution. Meanwhile, the betweenness-degree relevance takes a positive correlated relationship, while its node average degree-degree relevance is obviously negative assortative reflecting high degree nodes preferring to relate to small degree nodes, and the clustering coefficient distribution between degree follows negative correlated relationship reflecting node with small degree tends to cluster into group.

An ionic liquid, 1-allyl-3-methylimidazolium methyl phosphonate (Amim(MeO)PHO

Gravity anomalies detected by different measurement platforms have different characteristics and advantages. There are different kinds of gravity data fusion methods for generating single gravity anomaly map with a rich and accurate spectral content. Former studies using wavelet based gravity fusion method which is a newly developed approach did not pay more attention to the fusion uncertainties. In this paper, we firstly introduce the wavelet based gravity fusion method, and then apply this method to one synthetic model and also to the northern margin of the South China Sea. Wavelet type and the decomposition level are two input parameters for this fusion method, and the uncertainty tests show that fusion results are more sensitive to wavelet type than the decomposition level. The optimal application result of the fusion methodology on the synthetic model is closer to the true anomaly field than either of the simulated shipborne anomaly and altimetry-based anomaly grid. The best fusion result on the northern margin of the South China Sea is based on the 'rbio1.3' wavelet and four-level decomposition. The fusion result contains more accurate short-wavelength anomalies than the altimetry-based gravity anomalies along ship tracks, and it also has more accurate long wavelength characteristics than the shipborne gravity anomalies between ship tracks. The real application case shows that the fusion result has better correspondences to the seafloor topography variations and sub-surface structures than each of the two input gravity anomaly maps (shipborne based gravity anomaly map and altimetry based gravity anomaly map). Therefore, it is possible to map and detect more precise seafloor topography and geologic structures by the new gravity anomaly map. (C) 2016 Elsevier B.V. All rights reserved.

Civil aviation is a distinctive career. Pilots need to monitor the entire system in real time. However, the psychophysiological mechanism of flying is largely unknown. The human brain is a large-scale interconnected organization, and many stable intrinsic large-scale brain networks have been identified. Among them are three core neurocognitive networks: default mode network (DMN), central executive network (CEN), and salience network (SN). These three networks play a critical role in human cognition. This study aims to examine the dynamic properties of the three large-scale brain networks in civil aviation pilots. We collected resting-state functional magnetic resonance imaging data from pilots. Independent component analysis, which is a data-driven approach, was combined with sliding window dynamic functional connectivity analysis to detect the dynamic properties of large-scale brain networks. Our results revealed that pilots exhibit an increased interaction of the CEN with the DMN and the SN along with a decreased interaction within the CEN. In addition, the temporal properties of functional dynamics (number of transitions) increased in pilots compared to healthy controls. In general, pilots exhibited increased between-network functional connectivity, decreased within-network functional connectivity, and a higher number of transitions. These findings suggest that pilots might have better functional dynamics and cognitive flexibility.

In order to analyze the characterizations of the domestic and integrated airway network of China comparatively, Chinese flight data is divided into two types, and the network models which are named as the domestic airway network of China (DANC) and the integrated airway network of China (IANC) are created separately. The topological features of two networks are analyzed based on complex network theory, and it is found that both networks are small-world networks with power-law decaying degree and have strong local aggregations. What's more, the average shortest path of IANC is larger and the average clustering coefficient is smaller than DANC's. Important airports identifications of two networks are analyzed by calculating the degree centrality, closeness centrality, and betweenness centrality of all airports in two networks.The results show that the important airports in the two networks are roughly same, while the Hong Kong airport has different status in the two networks.