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Accurate prediction results can provide an excellent reference value for the prevention of large-scale flight delays. Most of the currently available regression prediction algorithms use a single time series network to extract features, with less consideration of the spatial dimensional information contained in the data. Aiming at the above problem, a flight delay prediction method based on Att-Conv-LSTM is proposed. First, in order to fully extract both temporal and spatial information contained in the dataset, the long short-term memory network is used for getting time characteristics, and a convolutional neural network is adopted for obtaining spatial features. Then, the attention mechanism module is added to improve the iteration efficiency of the network. Experimental results show that the prediction error of the Conv-LSTM model is reduced by 11.41 percent compared with the single LSTM, and the prediction error of the Att-Conv-LSTM model is reduced by 10.83 percent compared with the Conv-LSTM. It is proven that considering spatio-temporal characteristics can obtain more accurate prediction results in the flight delay problem, and the attention mechanism module can also effectively improve the model performance. [ABSTRACT FROM AUTHOR]

In exploring the flight delay problem, traditional deep learning algorithms suffer from low accuracy and extreme computational complexity; therefore, the deep flight delay prediction algorithm is difficult to directly deploy to the mobile terminal. In this paper, a flight delay prediction model based on the lightweight network ECA-MobileNetV3 algorithm is proposed. The algorithm first preprocesses the data with real flight information and weather information. Then, in order to increase the accuracy of the model without increasing the computational complexity too much, feature extraction is performed using the lightweight ECA-MobileNetV3 algorithm with the addition of the Efficient Channel Attention mechanism. Finally, the flight delay classification prediction level is output via a Softmax classifier. In the experiments of single airport and airport cluster datasets, the optimal accuracy of the ECA-MobileNetV3 algorithm is 98.97% and 96.81%, the number of parameters is 0.33 million and 0.55 million, and the computational volume is 32.80 million and 60.44 million, respectively, which are better than the performance of the MobileNetV3 algorithm under the same conditions. The improved model can achieve a better balance between accuracy and computational complexity, which is more conducive mobility. [ABSTRACT FROM AUTHOR]

The current flight delay not only affects the normal operation of the current flight, but also spreads to the downstream flights through the flights schedule, resulting in a wide range of flight delays. The analysis and prediction of flight delay propagation in advance can help civil aviation departments control the flight delay rate and reduce the economic loss caused by flight delays. Due to the small number of data samples that can constitute flight chains, it is difficult to construct flight chain data. In recent years, the analysis of the flight delay propagation problem is generally based on traditional machine learning methods with a small sample size. After obtaining a large amount of raw data from the China Air Traffic Management Bureau, we have constructed 36,287 pieces of three-level flight chain data. Based on these data, we tried to use a deep learning method to analyze and forecast flight delays. In the field of deep learning, there are CNN models and RNN models that deal with classification problems well. Based on these two classes of models, we modify and innovate the study of the problem of flight delay propagation and prediction. Firstly, the CNN-based CondenseNet algorithm is used to predict the delay level of the three-level flight chain data. Based on this, the CondenseNet network is improved by inserting CBAM modules and named CBAM-CondenseNet. The experimental results show that the improved algorithm can effectively improve the network performance, and the prediction accuracy can reach 89.8%. Compared with the traditional machine learning method, the average prediction accuracy increased by 8.7 percentage points. On the basis of the CNN model, we also considered the superiority of the LSTM (Long Short-Term Memory network) considering the processing time sequence information, and then constructed the CNN-MLSTM network and injected the SimAM module to enhance the attention of flight chain data. In the experiment of flight delay propagation prediction, the accuracy rate is 91.36%, which is a significant improvement compared to using the CNN or LSTM alone. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) "black flight" incidents cause serious security risks and economic losses to airports. Additionally, existing UAV detection methods are mainly radar technology at airports. Whereas, it is unable to correctly identify the number of UAVs and visualise their size, which undoubtedly poses a serious security risk. Accordingly, an anchor‐free UAV detection method HollowBox is proposed to supplement radar detection equipment for the airport "black flight" problem. It is inspired by the FoveaBox object detection method, the object detection feature layers are reset and the allocation ratio of positive and negative samples in the training phase are redefined, the HollowBox UAV detection is proposed according to the multi‐size characteristics. Extensive experiments show that, the approach achieves 90.1% AP, 6% false detection rate and 17.2 FPS inference speed, which accomplished a satisfactory performance, as verified by the real‐shot data collected of an airport in Tianjin. This work is of great significance for the application in airport UAV detection. [ABSTRACT FROM AUTHOR]

The history of modern Chinese schools in Singapore may be traced back to the early 20th century, when efforts to provide vernacular education in the British colony were made by community leaders across Chinese dialect groups, with support of the Qing Empire. Only a handful of these were selected as elite schools for bilingual education under the Special Assistance Plan (SAP) introduced in 1979 in independent Singapore. This paper examines the historical trajectories of these early schools from early association with Chinese nationalism to becoming multi-ethnic schools or simply defunct. It will focus on the case of the former Yeung Ching School in 'Chinatown' catering to the Cantonese community, to explore how the legacy of a Chinese school may be impacted by state formation and urban development since the 1950s, and also to point out a gap in current heritagisation pertaining to the role of education in shaping cultural identities. [ABSTRACT FROM AUTHOR]

An echo model plays a pivotal role in the processing of synthetic aperture radar (SAR) data. It has been shown that the widely adopted echo models based on stop-and-go approximation (SAG) are unsuitable for use in many emerging radar systems with high resolution or high-speed platforms. More accurate echo models without SAG have been proposed for some special monostatic scenarios, such as low-Earth-orbit SAR and geosynchronous SAR. However, due to their inherent assumptions, such as linear and constant-speed/orbit-based motion, these conventional echo models cannot be applied to general cases, which involve bistatic configurations and maneuvers. Maneuvers or nonlinear/inconstant-speed motion are very common in cases, such as circular SAR and maneuvering-platform-borne SAR. In this letter, we propose a generalized echo model for bistatic SAR, which accounts for maneuvers of the platforms during pulse propagation. Moreover, we obtain a high-precision closed-form expression of the model by approximating high-order terms of the accurate range history of the receiver. Compared with the conventional models, the proposed echo model can be applied to a much greater number of cases, ranging from slow and constant-speed platforms to fast and maneuvering platforms. Theoretical analysis and backprojection-based image formation simulations confirm the validity of the proposed echo model. [ABSTRACT FROM AUTHOR]

In geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-BiSAR) system, the airborne platform achieves high-resolution imaging by passively receiving the signal from the interested scenario. In this paper, the Doppler characteristics of GEO-BiSAR and the individual contribution of the transmitter and the receiver are first analyzed. The airborne receiver is found to be the dominant contributor for the total Doppler bandwidth, which will lead to Doppler spectrum aliasing regarding the low pulse repetition frequency (PRF) adopted by the GEO-SAR. In order to suppress the Doppler ambiguity without adjusting the PRF of GEO-SAR, azimuth multichannel receiving technique is introduced to the airborne receiver. The multichannel transfer function is derived based on the method of series reversion and the spectrum reconstruction algorithm is then modified for multichannel GEO-BiSAR. Moreover, the reconstruction performance is closely related to the corresponding spacing between each channel (i.e., channel configuration). Therefore, the channel configuration design for GEO-BiSAR aims at optimizing the azimuth ambiguity-to-signal ratio with a satisfactory level of signal-to-noise ratio scaling factor by adjusting the channel configuration. The channel configuration design is modeled as a constrained single objective optimization problem (CSOP). Then, a channel configuration design method based on differential evolution and feasibility rule is proposed to solve the CSOP and obtain the channel configuration for the receiver with the optimal reconstruction performance. Finally, simulations results are presented to verify the effectiveness of the proposed method, and characteristics of channel configuration are analyzed in detail, which can be a practical guide for the implementation of multichannel GEO-BiSAR systems. [ABSTRACT FROM AUTHOR]

The fast development of wearable and portable smart electronics has immensely encouraged the demand for miniaturized integrated electronic devices. In this study, we utilized a duplex printing strategy to construct an all-in-one integrated electronic device by integrating asymmetric micro-supercapacitors (MSCs) with a tandem line-type temperature sensor (TLTS) based on reduced graphene oxide (rGO). In the integrated configuration, the 3D printing asymmetric MSCs based on MXenes/single-walled carbon nanotubes (SWCNTs) positive electrode and rGO/SWCNTs negative electrode delivered a large areal specific capacitance of 30.76 mF cm−2, high energy density of 8.37 μW h cm−2, and long lifetime (no significant capacitance decay of over 10 000 cycles). Moreover, the assembled device can offer the stable out power to TLTSs. Particularly, the temperature sensitivity of the duplex printed integrated electronic device can reach 1.2% per degree celsius. Collectively, the efficient 3D printing strategy can be used to build diverse all-in-one integrated electronic devices with state-of-the-art performance for next-generation wearable and portable electronics. [ABSTRACT FROM AUTHOR]

Extensive experiments on rats have shown that environmental cues play an important role in goal locating and navigation. Major studies about locating and navigation are carried out based only on place cells. Nevertheless, it is known that navigation may also rely on grid cells. Therefore, we model locating and navigation based on both, thus developing a novel grid-cell model, from which firing fields of grid cells can be obtained. We found a continuous-time dynamic system to describe learning and direction selection. In our simulation experiment, according to the results from physiology experiments, we successfully rebuild place fields of place cells and firing fields of grid cells. We analyzed the factors affecting the locating accuracy. Results show that the learning rate, firing threshold and cell number can influence the outcomes from various tasks. We used our system model to perform a goal navigation task and showed that paths that are changed for every run in one experiment converged to a stable one after several runs. [ABSTRACT FROM AUTHOR]

This paper examines the effects of current and conductance noises in a minimal Hodgkin-Huxley type model of a cold receptor neuron. Current noise enters the membrane equation directly while conductance noise is propagated through the activation variables. Compared with common used interspike interval method, ISI-distance is a simple complementary approach to measure the different effects of current and conductance noises. ISI-distance extracts information from the interspike intervals by evaluating the ratio of instantaneous firing rates, which is parameter-free, time scale-independent and easy to visualize. Simulation results show that the most significant differences between different noise implementations in a pacemaker-like tonic firing regime at the transition to chaotic burst discharges. [ABSTRACT FROM AUTHOR]

Soon after the rebellion by An Lushan and Shi Siming in the eighth century, there was an urgent desire among the intellectuals in China for a restoration of power to the Tang court and a pragmatic application of Confucian thought to state governance. Such an ideological inclination also found its parallel in the literary arena. Chang Gun (729-783) was an outstanding example of this new trend in prose writing. His imperial edicts addressed a variety of issues in politics and culture, both domestic and in relation to foreign affairs. And, under his influence, a new generation of wenru came into being, i.e. scholars who were Confucian by training, skillful officials in court, and accomplished writers of imperial edicts. [ABSTRACT FROM AUTHOR]

In the olfactory system, both the temporal spike structure and spatial distribution of neuronal activity are important for processing odor information. In this paper, a biophysically-detailed, spiking neuronal model is used to simulate the activity of olfactory bulb. It is shown that by varying some key parameters such as maximal conductances of Ks and Nap the spike train of single neuron can exhibit various firing patterns. Synchronization in coupled neurons is also investigated as the coupling strength varying in the situation of two neurons and network. It is illustrated that the coupled neurons can exhibit different types of pattern when the coupling strength varies. These results may be instructive to understand information transmission in olfactory system. [ABSTRACT FROM AUTHOR]

In order to explore the dynamic characteristics of neural coding in the transmission of neural information in the brain, a model of neural network consisting of three neuronal populations is proposed in this paper using the theory of stochastic phase dynamics. Based on the model established, the neural phase synchronization motion and neural coding under spontaneous activity and stimulation are examined, for the case of varying network structure. Our analysis shows that, under the condition of spontaneous activity, the characteristics of phase neural coding are unrelated to the number of neurons participated in neural firing within the neuronal populations. The result of numerical simulation supports the existence of sparse coding within the brain, and verifies the crucial importance of the magnitudes of the coupling coefficients in neural information processing as well as the completely different information processing capability of neural information transmission in both serial and parallel couplings. The result also testifies that under external stimulation, the bigger the number of neurons in a neuronal population, the more the stimulation influences the phase synchronization motion and neural coding evolution in other neuronal populations. We verify numerically the experimental result in neurobiology that the reduction of the coupling coefficient between neuronal populations implies the enhancement of lateral inhibition function in neural networks, with the enhancement equivalent to depressing neuronal excitability threshold. Thus, the neuronal populations tend to have a stronger reaction under the same stimulation, and more neurons get excited, leading to more neurons participating in neural coding and phase synchronization motion. [ABSTRACT FROM AUTHOR]