The AI-enabled intelligent learning system (AEILS) is able to provide personalised and intelligent tutoring and is more capable of meeting individuals’ need. Nevertheless, limited studies focused on the effect of AI-specific factors on user behaviour. To fill this research gap, we identified the AEILS-specific affordances (i.e. interactivity, personalisation, competition, convenience) and explored their effect on user engagement and foreign language speaking anxiety (FLSA). In this paper, we integrate quantitative and qualitative studies to explore user behaviour in AEILS. Survey data was collected from 457 respondents and analysed using structural equation modelling with the smart-PLS software. The results showed that AEILS-specific affordances significantly affect flow experience and self-expansion, thus facilitating user engagement and alleviating FLSA. Semi-structured interviews were conducted to corroborate the findings of the quantitative study. These findings highlighted the importance of technology affordance in AEILS. This study contributed to the literature on IS and education by incorporating context-specific factors into account.

While Digital Twins (DTs) bring convenience to city managers, they also generate new challenges to city network security. Currently, cyberspace security becomes increasingly complicated. Intrusion detection and Deep Learning (DL) are combined with shunning security threats in service computing systems and improving network defense capabilities. DTs can be applied to network security. People's understanding of cyberspace security can be improved using DTs to digitally define, model, and display the network environment and security status. The intrusion detection data are optimized based on DL technology, and a network intrusion detection algorithm integrated with Deep Neural Network (DNN) model is proposed. In the cloud service system, a trust model based on Keyed-Hashing-based Self-Synchronization (KHSS) is introduced. This model predicts the security state and detects attacks according to existing malicious attacks, ensuring the network security defense system's regular operation. Finally, simulation experiments verify the Deep Belief Networks (DBN) model's feasibility and the cloud trust model. The DBN algorithm proposed improves the correct detection rate of unknown samples by 4.05% compared with the Support Vector Machine (SVM) algorithm. From the 20,100 pieces of data in the test dataset, the number of correct attacks detected by the DBN algorithm exceeds those by the SVM algorithm by 818. DBN algorithm requires a short detection time while ensuring optimal detection accuracy. The KHSS+DBN model predicts cloud security states, and the results are the same as the actual states, with an error of only 1%∼2%.

With the rapid development of information technology, great changes have taken place in the way of managing, analyzing, and using data in all walks of life. Using deep learning algorithm for data analysis in the field of medicine can improve the accuracy of disease recognition. The purpose is to realize the intelligent medical service mode of sharing medical resources among many people under the dilemma of limited medical resources. Firstly, the Digital Twins module in the Deep Learning algorithm is used to establish the medical care and disease auxiliary diagnosis model. With the help of the digital visualization model of Internet of Things technology, data is collected at the client and server. Based on the improved Random Forest algorithm, the demand analysis and target function design of the medical and health care system are carried out. Based on data analysis, the medical and health care system is designed using the improved algorithm. The results show that the intelligent medical service platform can collect and analyze the clinical trial data of patients. The accuracy of improved ReliefF & Wrapper Random Forest (RW-RF) for sepsis disease recognition can reach about 98%, and the accuracy of algorithm for disease recognition is also more than 80%, which can provide better technical support for disease recognition and medical care services. It provides a solution and experimental reference for the practical problem of scarce medical resources.

In the trend of the accelerated progression of communication network technology, the emergence of virtual communities (VCs), virtual societies (VSs), metaverse, and other technologies not only makes data access and sharing easier but also leads to the proliferation of fake news (FN). To effectively monitor and identify FN in VC, VS, and metaverse, and to create a safer virtual space, this work takes FN in VC, VS, and metaverse as objects. First, the content and display methods of FN are reviewed and explained, and it is understood that FN is mainly displayed by single-modal and multimodal representations. Second, the application scenarios in many important fields such as transportation are reviewed and analyzed, so as to further understand the impact and detection effect of FN in different scenarios. Finally, an intelligent outlook and summary analysis are carried out on the detection and information security of FN, which provides theoretical reference and new opportunities for the detection and identification of FN in the virtual cyberspace.

This work aims to analyze malicious communication behaviors that pose a threat to the security of digital twins (DTs) and safeguard user privacy. A unified and integrated multidimensional DTs Network (DTN) architecture is constructed. On this basis, the propagation process model of malware in the network is built to analyze the malicious propagation behavior that threatens network security. This model ensures the protection of mobile distributed machine learning system security. Blockchain technology is a distributed data protection mechanism with broad prospects. It is characterized by decentralization, transparency, and anonymity, which can help ensure secure network data sharing and privacy protection. Based on this, this work designs a secure distributed data sharing (DDS) architecture based on blockchain to improve the security and reliability of data protection with the support of the Internet of Things (IoT). Then, digital resource allocation based on semi-distributed learning is examined to propose a broad learning federated continuous learning (BL-FCL) algorithm combining blockchain and DTs. This algorithm significantly speeds up the model training process. Broad learning technology supports incremental learning. In this way, each client does not need to retrain when learning the newly generated data. In the experimental part, the prediction accuracy of BL-FCL on the mixed national institute of standards and technology data set is similar to that of the FedAvg-50 and FedAvg-80 schemes. As the number of devices increases from 1 to 6, the detection probability exhibits a rapid decrease. However, as the number of devices further increases from 6 to 10, the detection probability gradually decreases at a slower rate until it reaches 0. Comparatively, the prediction accuracy of the BL-FCL outperforms the federated averaging algorithm-based scheme by 20%–60%. The BL-FCL reported here can deal with the problem of inaccurate training while ensuring the privacy and security of users. This work is of great significance for ensuring the security of the DTN and promoting the development of the digital economy. The results can provide references for applying blockchain and distributed learning in the DT field.

Applying artificial intelligence to Chinese language translation in computational linguistics is of practical significance for economic boosts and cultural exchanges. In the present work, the bi-directional long short-term memory (BiLSTM) network is employed to extract Chinese text features regarding the overlapping semantic roles in Chinese language translation and hard-to-converge training of high-dimensional text word vectors in text classification during translation. In addition, AlexNet is optimized to extract the local features of the text and meanwhile update and learn network parameters in the deep network. Then, the attention mechanism is introduced to build a forecasting algorithm of Chinese language translation based on BiLSTM and improved AlexNet. Last, the forecasting algorithm is simulated to validate its performance. Some state-of-the-art algorithms are selected for a comparative experiment, including long short-term memory, regions with convolutional neural network features, AlexNet, and support vector machine. Results demonstrate that the forecasting algorithm proposed here can achieve a feature identification accuracy of 90.55%, at least an improvement of 4.24% over other algorithms. In addition, it provides an area under the curve of above 90%, a training duration of about 54.21 seconds, and a test duration of about 19.07 seconds. Regarding the performance of Chinese language translation, the algorithm proposed here provides a bilingual evaluation understudy (BLEU) value of 28.21 on the training set, with a performance gain ratio reaching 111.55%; on the test set, its BLEU reaches 40.45, with a performance gain ratio of 129.80%. Hence, this forecasting algorithm is notably superior to other algorithms, which can enhance the machine translation performance. Through experiments, the Chinese language translation algorithm constructed here improves translation performance while ensuring a high correct identification rate, providing experimental references for the later intelligent development of Chinese language translation in computational linguistics.

OBJECTIVE: it aims to adopt deep transfer learning combined with Digital Twins (DTs) in Magnetic Resonance Imaging (MRI) medical image enhancement. METHODS: MRI image enhancement method based on metamaterial composite technology is proposed by analyzing the application status of DTs in medical direction and the principle of MRI imaging. On the basis of deep transfer learning, MRI super-resolution deep neural network structure is established. To address the problem that different medical imaging methods have advantages and disadvantages, a multi-mode medical image fusion algorithm based on adaptive decomposition is proposed and verified by experiments. RESULTS: the optimal Peak Signal to Noise Ratio (PSNR) of 34.11dB can be obtained by introducing modified linear element and loss function of deep transfer learning neural network structure. The Structural Similarity Coefficient (SSIM) is 85.24%. It indicates that the MRI truthfulness and sharpness obtained by adding composite metasurface are improved greatly. The proposed medical image fusion algorithm has the highest overall score in the subjective evaluation of the six groups of fusion image results. Group III had the highest score in Magnetic Resonance Imaging- Positron Emission Computed Tomography (MRI-PET) image fusion, with a score of 4.67, close to the full score of 5. As for the objective evaluation in group I of Magnetic Resonance Imaging- Single Photon Emission Computed Tomography (MRI-SPECT) images, the Root Mean Square Error (RMSE), Relative Average Spectral Error (RASE) and Spectral Angle Mapper (SAM) are the highest, which are 39.2075, 116.688, and 0.594, respectively. Mutual Information (MI) is 5.8822. CONCLUSION: the proposed algorithm has better performance than other algorithms in preserving spatial details of MRI images and color information direction of SPECT images, and the other five groups have achieved similar results.

In this article, the proposed work aims to further optimize the fault diagnosis effect of manufacturing equipment, explore the application of digital twins technology in intelligent manufacturing. The equipment failures in Poka Yoke technology are adopted, and a fault identification and chopping algorithm is designed based on the active learning—deep neural network (AL-DNN) and domain adversarial neural networks (DANN). In addition, a digital twins workshop management and control system is designed for intelligent manufacturing management. The experimental exploration reveals that the accuracy of the AL-DNN algorithm is as high as 99.248%, which is more in line with practical applications. The DANN algorithm can realize fault identification and diagnosis under different working conditions. Compared with other deep learning algorithms, the accuracy of the DANN can be increased by up to 20.256%, showing higher accuracy in contrast to the traditional algorithm, so the effect is more stable. In addition, the digital twins manufacturing management system designed shows good performances, which can intuitively display the specific conditions of workshop and realize basic operating functions. The concept of digital twins is innovatively introduced into equipment fault diagnosis and trend prediction, which can provide scientific and effective reference data for subsequent research on intelligent manufacturing.

The papers in this special section focus on embedded microprocessors under wireless transmission in medical imaging lesion detection systems and provides researchers in related fields with opportunities for discussions.

The purposes are to explore the safety performance of the Maritime Transportation System (MTS) based on Digital Twins (DTs) Internet of Things (IoT) and develop maritime transportation towards intelligence and digitalization. Because the comprehensive operational security of modern MTS is not yet mature, historical transportation data of the Maritime Silk Road are acquired and preprocessed. Afterward, DTs are introduced, and relay nodes are added to data transmission paths to construct a maritime transportation DTs model based on relay cooperation IoT. Eventually, this model's security performance is validated through simulation experiments. Relay security analysis suggests that interference information is a vital guarantee to assist in information non-disclosure, from which the constructed model can harvest energy to increase the data transmission power, thereby improving communication performance and secrecy rate. Outage probability analysis reveals that the simulated and the theoretical results are almost the same; moreover, given the system's multi-hop paths in the same environment, the more the relays and the greater the fading index, the better the system performance and the lower the outage probability. Once the iterations reach a particular number, the node secrecy rate becomes optimal and cannot cause excessive burden to the system; besides, the power distribution can establish a new equilibrium when the nodes are in different locations, so that system security performance gets improved. The simulated value is closest to the actual result under 100% successful transmission probability and 0.01~0.05 λ value. To sum up, the constructed maritime transportation DTs model presents extraordinary transmission and security performance, providing an experimental basis for intelligent and secure maritime transportation in the future.

The Internet of Things (IoT), as an important part of the new generation of information technology, connects any object to the Internet according to the agreed protocol through radio frequency identification, global positioning system, and other information sensing equipment for information exchange and communication. With the continuous development of the IoT technology, it has injected new power into its further development and improvement. The Internet of Vehicles (IoV) is the development focus of the IoT, and the improvement of its connection capability enables the application of IoV to be upgraded from vehicle entertainment to unmanned driving, fleet arrangement and management, and traffic intelligent service. With the release of the market potential of IoV, the transportation cost will also drop significantly, and more transformation opportunities will emerge in the traditional intelligent transportation industry. As an important part of intelligent transportation, intelligent city, intelligent village, and intelligent park, the IoT intelligent infrastructure plays an important role in providing high-quality public services, reducing costs, and achieving sustainable development. At present, the IoT intelligent infrastructure has a wide range of demand around the world, and has become an important innovation and industrial development force in this field.

With the continuous increase of the amount of information, people urgently need to identify the information in the image in more detail in order to obtain richer information from the image. This work explores the dynamic complex image segmentation of self-driving vehicle under Digital Twins (DTs) based on Memory-augmented Neural Networks (MANNs), so as to further improve the performance of self-driving in intelligent transportation. In view of the complexity of the environment and the dynamic changes of the scene in intelligent transportation, this work constructs a segmentation model for dynamic complex image of self-driving vehicle under DTs based on MANNs by optimizing the Deep Learning algorithm and further combining with the DTs technology, so as to recognize the information in the environment image during the self-driving. Finally, the performance of the constructed model is analyzed by experimenting with different image datasets (PASCALVOC 2012, NYUDv2, PASCAL CONTEXT, and real self-driving complex traffic image data). The results show that compared with other classical algorithms, the established MANN-based model has an accuracy of about 85.80%, the training time is shortened to 107.00 s, the test time is 0.70 s, and the speedup ratio is high. In addition, the average algorithm parameter of the given energy function α=0.06 reaches the maximum value. Therefore, it is found that the proposed model shows high accuracy and short training time, which can provide experimental reference for future image visual computing and intelligent information processing.

To accurately and effectively analyze electroencephalogram (EEG) with high complexity, large amount of data, and strong uncertainty, brain–computer interface (BCI) cognitive computing and its signal analysis algorithms are studied based on the digital twins (DTs) cognitive computing platform. To avoid the influence of noise on EEG analysis results, it is necessary to use filtering and defalsification methods to process EEG. Four methods, including Butterworth filter, finite impulse response (FIR) filter, elliptic filter, and wavelet decomposition, are summarized. Based on the Riemann manifold theory, a feature extraction algorithm under transfer learning based on tangent space selection (TL-TSS) is proposed. In the process of decoding EEG, an EEG decoding method combining entropy measure and singular spectrum analysis (SSA) is proposed. An algorithm performance is tested on the motor imagery dataset of the two International BCI Competitions. It is found that when the training sample size accounts for 5%, the TL-TSS algorithm proposed in this work is superior to other algorithms in classification accuracy. In particular, compared with common spatial pattern (CSP) algorithm, it has great advantages. The classification accuracy of A2, A4, A8, and A9 users is the best, and especially for A8 users, the classification accuracy reaches 97.88%. In summary, in the EEG interface technology of DT cognitive computing platform, the combination of cognitive computing and deep learning can improve the recognition and analysis effect of EEG, which is of great value for further optimization of DT cognitive computing system.

With the integration of the healthcare system, Internet of Things, and cloud storage service, more and more medical institutions upload their electronic medical records (EMRs) to the cloud to reduce the local storage burden and realize data sharing among external researchers. To secure the sensitive information, EMRs usually should be encrypted before being stored on the cloud. However, the existing searchable encryption schemes that encrypt the entire EMRs can hide the sensitive information, but this results in the shared EMRs being unable to be used by researchers. In addition, if the queried and extracted EMRs are incorrect, it will lead to misdiagnosis and even endanger the patient’s life. In order to solve the aforementioned problems, in this article, we propose a verifiable keyword search scheme supporting sensitive information hiding for the cloud-based healthcare sharing system. The sensitive information is encrypted, while other contents in EMR can be shared among users in this scheme. Doctors and researchers can quickly perform search operations based on keywords to extract the EMRs they require. This time complexity is$O(n)$, where$n$is the number of attribute values in the record. But the sensitive information is hidden for the researchers. Furthermore, the correctness of EMRs can be verified when they are extracted from the cloud. This time complexity is max$\lbrace O(n^{\prime }),O(N^{\prime })\rbrace$, where$n^{\prime }$is the number of query keywords and$N^{\prime }$is the number of the retrieved records. We expound the security and carry out experiments to estimate the efficiency of the proposed scheme.

Lignocellulose is an abundant xylose-containing biomass found in agricultural wastes, and has arisen as a suitable alternative to fossil fuels for the production of bioethanol. AlthoughSaccharomyces cerevisiaehas been thoroughly used for the production of bioethanol, its potential to utilize lignocellulose remains poorly understood. In this work, xylose-metabolic genes ofPichia stipitisandCandida tropicalis, under the control of different promoters, were introduced intoS. cerevisiae. RNA-seq analysis was use to examine the response ofS. cerevisiaemetabolism to the introduction of xylose-metabolic genes. The use of thePGK1promoter to drive xylitol dehydrogenase (XDH) expression, instead of theTEF1promoter, improved xylose utilization in “XR-pXDH” strain by overexpressing xylose reductase (XR) and XDH formC. tropicalis, enhancing the production of xylitol (13.66$\pm$0.54 g/L after 6 days fermentation). Overexpression of xylulokinase and XR/XDH fromP. stipitisremarkably decreased xylitol accumulation (1.13$\pm$0.06 g/L and 0.89$\pm$0.04 g/L xylitol, respectively) and increased ethanol production (196.14$\%$and 148.50$\%$increases during the xylose utilization stage, respectively), in comparison with the results of XR-pXDH. This result may be produced due to the enhanced xylose transport, Embden-Meyerhof and pentose phosphate pathways, as well as alleviated oxidative stress. The low xylose consumption rate in these recombinant as well as alleviated strains comparing withP. stipitisandC. tropicalismay be explained by the insufficient supplementation of NADPH and NAD$^+$. The results obtained in this work provide new insights on the potential utilization of xylose using bioengineeredS. cerevisiaestrains.

The problems of traditional traffic flow detection and calculation methods include limited traffic scenes, high system costs, and lower efficiency over detecting and calculating. Therefore, in this paper, we presented the updating Connected and Automated Vehicles (CAVs) model as the scanner of heterogeneous traffic flow, which uses various sensors to detect the characteristics of traffic flow in several traffic scenes on the roads. The model contains the hardware platform, software algorithm of CAV, and the analysis of traffic flow detection and simulation by Flow Project, where the driving of vehicles is mainly controlled by Reinforcement Learning (RL). Finally, the effectiveness of the proposed model and the corresponding swarm intelligence strategy is evaluated through simulation experiments. The results showed that the traffic flow scanning, tracking, and data recording performed continuously by CAVs are effective. The increase in the penetration rate of CAVs in the overall traffic flow has a significant effect on vehicle detection and identification. In addition, the vehicle occlusion rate is independent of the CAV lane position in all cases. The complete street scanner is a new technology that realizes the perception of the human settlement environment with the help of the Internet of Vehicles based on 5G communications and sensors. Although there are some shortcomings in the experiment, it still provides an experimental reference for the development of smart vehicles.

The present work aims to improve the communication security of Internet of Vehicles (IoV) nodes in intelligent transportation through studying the safety of IoV in smart transportation based on Blockchain (BC). An IoV DTs model is built by combining big data with Digital Twins (DTs). Then, regarding the current IoV communication security issues, a secure communication architecture for the IoV system is proposed based on the immutable and trackable BC data. Besides, Wasserstein Distance Based Generative Adversarial Network (WaGAN) model constructs the IoV node risk forecast model. Because the WaGAN model calculates the loss function through Wasserstein distance, the learning rate of the model accelerates remarkably. After ten iterations, the loss rate of the WaGAN model is close to zero. Massive in-vehicle devices in IoV are connected simultaneously to the base station, causing network channel congestion. Therefore, a Group Authentication and Privacy-preserving (GAP) scheme is put forward. As users increase during authentication, the GAP scheme performs better than other authentication access schemes. In summary, the Intelligent Transportation System driven by DTs can promote intelligent transportation management. Besides, introducing BC into IoV can improve access control’s accuracy and response efficiency. The research reported here has significant value for improving the security of the information sharing of the IoV.

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.

The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43%. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy’s effects become more apparent. When MPR ranges between 40% ~ 80% and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.

To realize the great potential of the intelligent transportation infrastructure, the investment in the transportation infrastructure in the intelligent transportation system should be rationally planned. Firstly, the application status of cutting-edge Data Envelopment Analysis (DEA) model in transportation infrastructure efficiency evaluation is analyzed, and based on this, a DEA model of transportation infrastructure efficiency evaluation under Digital Twins technology is established. Secondly, with the transportation infrastructure of 12 prefecture-level cities in Jiangsu Province from 2005 to 2020 as the research object, the Digital Twins DEA model and the traditional Stochastic Frontier Approach (SFA) model are used to estimate the efficiency of transportation infrastructure in 12 cities. Finally, the traffic flow data of a certain road section in Zhenjiang City (J11 City) is simulated and predicted by using the Long Short-term Memory (LSTM) traffic flow prediction model. The results show that the average efficiency of the 12 cities estimated by the DEA model based on the Digital Twins is 0.7083, the average efficiency of the 12 cities estimated by the SFA model is 0.6445, and there are significant differences in the efficiency rankings of the cities. Compared with the actual efficiency, the established Digital Twins DEA model is more reasonable for the calculation of transportation infrastructure efficiency. The results of the LSTM traffic flow prediction model show that the Mean Absolute Error (MAE) of the LSTM model is 24.29, the Root Mean Square Error (RSME) is 0.1186, and the Mean Absolute Perce (MAPE) is 17.78, which are all lower than other models. Compared with other models, the proposed LSTM-based traffic flow prediction model is more accurate in traffic flow prediction. Hence, the research content provides a reference for the investment planning of intelligent transportation system infrastructure.

To explore the airspace structure and safety performance of unmanned aerial vehicle (UAV) system based on spatial digital twins (DTs), the study introduces DTs technology, and combines convolutional neural network (CNN) algorithm with UAV autonomous network. The DTs system of UAV is constructed by using wireless communication technology, and its security performance is simulated. The results show that in the analysis of the system packet loss rate, it is found that with the increase of the acquisition points, the amount of transmitted data only increases slightly, but the packet loss rate does not change significantly. In the analysis of the network performance of the unmanned aircraft system, it is found that the node energy-based weighted clustering algorithm (EWCA) can be used to increase the life of the overall network and enhance its availability by rationally controlling the number of nodes and the number of switching between clusters. As the number of nodes increases, the minimum survival time of each clustering algorithm decreases linearly. When the number of nodes is less than 600, the growth rate of cluster head is higher; When the number of nodes is more than 600, the curve growth is relatively smooth. In the analysis of the probability of network safety interruption, it is found that using the model constructed, when the energy acquisition coefficient is close to 0.5, the energy conversion efficiency is higher, the signal-to-noise ratio is larger. Also, when the number of intermediate nodes is increased to 10, the UAV has the best network safety performance. Therefore, through the research, it is found that the UAV DTs system constructed can significantly improve the safety performance of the UAV during its airspace flight. It can provide experimental references for the widespread application of the UAV in the later period.

The purposes are to investigate the personalized situation adaptive Human-Computer Interaction (HCI) in the COVID-19 context, achieve accurate predictions for HCI in different urban transportation situations, and solve the urban intelligent transportation problems. Problems of Human-Vehicles-Interaction (HVI) in context awareness are analyzed. Historical traffic flow in three different situations, including novice user situation, mid user situation, and expert user situation, are taken as the data sources. The HVI data are preprocessed afterward. Next, Dilated Convolution (DC) and Long-Short Term Memory (LSTM) are integrated (DC-LSTM) to build an HVI model based on situation adaptive. The proposed model is simulated to analyze its performance. Simulation experiments suggest that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed model are 4.64%, 5.34%, and 7.82%, respectively. Although these three metrics increase under the mid user and expert user situations, the proposed model can still provide a higher accuracy than LTSM, Convolutional Neural Network (CNN), Simple Recurrent Network (SRN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Besides, the prediction velocity can maintain about 60 Frame-Per-Second (FPS) under all three user situations. Regarding the path guidance performance, the proposed model can suppress the traffic congestion and dredge the congested sections effectively. Hence, the HVI model based on situational adaptation constructed has high prediction accuracy and traffic congestion evacuation performance, which can provide an experimental basis for the later intelligent transportation field and improving situational self-adaptability.

With the rapid development of information technology and the spread of Corona Virus Disease 2019 (COVID-19), the government and urban managers are looking for ways to use technology to make the city smarter and safer. Intelligent transportation can play a very important role in the joint prevention. This work expects to explore thebuilding information modeling (BIM) big data (BD)processing method ofdigital twins (DTs)of Smart City, thus speeding up the construction of Smart City and improve the accuracy of data processing. During construction, DTs build the same digital copy of the smart city. On this basis, BIM designs the building's keel and structure, optimizing various resources and configurations of the building. Regarding the fast data growth in smart cities, a complex data fusion and efficient learning algorithm, namely Multi-Graphics Processing Unit (GPU), is proposed to process the multi-dimensional and complex BD based on the compositive rough set model. The Bayesian network solves the multi-label classification. Each label is regarded as a Bayesian network node. Then, the structural learning approach is adopted to learn the label Bayesian network's structure from data. On the P53-old and the P53-new datasets, the running time of Multi-GPU decreases as the number of GPUs increases, approaching the ideal linear speedup ratio. With the continuous increase of K value, the deterministic information input into the tag BN will be reduced, thus reducing the classification accuracy. When K = 3, MLBN can provide the best data analysis performance. On genbase dataset, the accuracy of MLBN is 0.982 ± 0.013. Through experiments, the BIM BD processing algorithm based onBayesian Network Structural Learning (BNSL)helps decision-makers use complex data in smart cities efficiently.

The particularity of the marine underwater environment has brought many challenges to the development of underwater sensor networks (UWSNs) . This research realized the effective monitoring of targets by UWSNs and achieved higher quality of service in various applications such as communication, monitoring, and data transmission in the marine environment. After analysis of the architecture, the marine integrated communication network system (MICN system) is constructed based on the maritime wireless Mesh network (MWMN) by combining with the UWSNs. A distributed hybrid fish swarm optimization algorithm (FSOA) based on mobility of underwater environment and artificial fish swarm (AFS) theory is proposed in response to the actual needs of UWSNs. The proposed FSOA algorithm makes full use of the perceptual communication of sensor nodes and lets the sensor nodes share the information covered by each other as much as possible, enhancing the global search ability. In addition, a reliable transmission protocol NC-HARQ is put forward based on the combination of network coding (NC) and hybrid automatic repeat request (HARQ) . In this work, three sets of experiments are performed in an area of 200 × 200 × 200 m. The simulation results show that the FSOA algorithm can fully cover the events, effectively avoid the blind movement of nodes, and ensure consistent distribution density of nodes and events. The NC-HARQ protocol proposed uses relay nodes for retransmission, and the probability of successful retransmission is much higher than that of the source node. At a distance of more than 2,000 m, the successful delivery rate of data packets is as high as 99.6%. Based on the MICN system, the intelligent ship constructed with the digital twins framework can provide effective ship operating state prediction information. In summary, this study is of great value for improving the overall performance of UWSNs and advancing the monitoring of marine data information.

The mobile satellite communication networks (MSCN) enable network expansion and supplement in remote areas. Users in these regions can obtain specific network services with low latency and high transmission rates utilizing the low-earth-orbit (LEO) satellite constellation. However, due to the frequent switching of MSCN topology, the challenge is how to ensure the quality and continuity of data transmission paths in a certain time period. In this paper, we build a user satisfaction (US) indicator to measure the performance of pathfinding. The MSCN pathfinding optimization problem for the maximum US is first formulated. To simplify the complex calculation, we utilize the special-temporal division to solve the problem in two stages. In each time slot, the modified heuristic algorithm is utilized to find paths for the maximum US. Then, an active time slot division scheme is proposed. The divided time slot sequences are disconnected and reorganized to seek the time-extended optimal solution. Simulation results show that the proposed scheme achieves superior performance in improving the total US and guarantees reliable service continuity for MSCN.

The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

This paper designs a smart urban environment monitoring system based on the wireless network of ZigBee to complete the real-time collection of urban environment information. The system consists of the basic monitoring network and the remote receiving terminal. The basic monitoring network connects the streetlights as routes and the taxis as nodes. After dynamically organizing the network, each node is assigned with an address as the only identity in the network. Then, the system designed conducts the simulation experiment to prove that it could meet the needs and send the collected information to the designated terminal in the form of message according to the setting. The sensor organized through the wireless network of ZigBee could inspire the infrastructure construction of the smart city. With the network, a smarter and more comfortable society could be well offered to people.

The distributed energy system is used as the prototype of the energy Internet, including a variety of forms of energy networks, plenty of distributed equipment and energy storage equipment composed of energy flow, and real-time communication and data volume of information systems. As an important energy system that is closely related to people's lives, its security and stability is one of the cores of its development. With the access of a large number of distributed devices, the structure of the power system has changed greatly. The addition of various forms of energy network, distributed equipment, and energy storage equipment has made it more difficult for the energy Internet to achieve the coordination among and control over these devices. Regardless of the fluctuation of the power load and the sudden change of the thermal load, problems such as energy network failure and demand will affect the security and stability of the energy Internet. Traditional energy systems are independent of one another, while integrated energy systems include subsystems, such as the power system, thermal system, and natural gas system, which can complement one another in planning and operation. To improve the utilization rate of all kinds of energy, reduce the waste of energy, and cut the emission of pollutants, it is crucial to realize the economic utilization of energy as well as the safe and stable operation of the energy Internet.

In the context of big data and the Internet of Things, with the advancement of geospatial data acquisition and retrieval, the volume of available geospatial data is increasing every minute. Thus, new data-management architecture is needed. We proposed a building information model (BIM) big data-storage-management solution with hybrid storage architecture based on web virtual reality geographical information system (WebVRGIS). BIM is associated with the integration of spatial and semantic information on the various stages of urban building. In this paper, based on the spatial distribution characteristics of BIM geospatial big data, a data storage and management model is proposed for BIM geospatial big data management. The architecture primarily includes Not only Structured Query Language (NoSQL) database and distributed peer-to-peer storage. The evaluation of the proposed storage method is conducted on the same software platform as our previous research about WebVR. The experimental results show that the hybrid storage architecture proposed in this research has a lower response time compared to the traditional relational database in geospatial big data searches. The integration and fusion of BIM big data in WebVRGIS realizes a revolutionary transformation of city information management during a full lifecycle. The system also has great promise for the storage of other geospatial big data, such as traffic data.

Mobile data demand of users is soaring with the increasing number of smartphones, which brings huge challenges to cellular network providers. To meet the user traffic demand, we study the problem that the user traffic data is served by cellular and WiFi network concurrently, i.e., mobile data offloading between cellular and WiFi operators. Different from the existing work where the users or network operators possess the complete information about each other, we model the mobile data offloading problem as a multi-user multi-operator non-cooperative game with the incomplete information. In the proposed model, the users and operators are assumed to be rational, and each of them pursues its own maximum benefit. To address this problem, in a distributed way, we first develop a Marginal Utility-Based Traffic Allocation (MUBTA) algorithm to arrange the users' traffic among different networks. Then, a bidding model is built to adjust the transaction price between users and operators. In addition, a Nash equilibrium is proven to be existed by theoretical analysis. Simulation results show that the proposed approach achieves a near-optimal solution.

Wireless communication via unmanned aerial vehicles (UAVs) is a promising way to provide transmission coverage and computation capacity for mobile user devices, especially in remote areas where the communication resources and infrastructures are extremely limited. In this paper, we study a UAV-assisted traffic and computation offloading (UTCO) problem, where one UAV can provide communication and computation capabilities for its covered user equipments (UEs). The UTCO problem is formulated as a problem of maximizing user satisfaction, in which the bandwidth allocation, transmit power including the UEs and UAV, and UAV trajectory are jointly considered. However, the UTCO problem is proven to be non-convex and some variables are nonlinear coupled, which cause it difficult to be solved optimally. To tackle this challenge, we propose a two-stage alternative optimization approach by leveraging successive convex approximation (SCA) method to obtain a near-optimal solution. The simulation results show that the proposed approach can achieve an outstanding performance in convergence speed and user satisfaction.

The rapid growth of user demand for mobile data traffic, especially video streaming, places a serious burden on base stations. Cache-enabled D2D communication has emerged as a promising paradigm to offload cellular traffic, in which contents are cached at user devices and then shared among neighbors via D2D communications. It is a key step to optimize the content caching policy in the cache-enabled D2D-assisted offloading. To maximize D2D offloading probability, we propose an optimal content caching policy to cache valuable contents by considering both the mode selection and user preference of neighbors. Then we use a dual simplex method to solve the maximum D2D offloading probability problem. Comparing with existing schemes, the simulation results prove the advantage of our proposed scheme in the improvement of D2D offloading performance.

Device-to-device (D2D) communication is a promising technique for traffic offloading in next-generation cellular systems. In this paper, we study the D2D-assisted cellular traffic offloading (DACTO) problem, where Wi-Fi Direct technology is employed in D2D communication in consideration of its wide communication coverage and high transmission rate. Taking into account the user traffic demands and population distributions, we formulate the DACTO problem as a "Min-Max" problem, in which the operator energy consumption is minimized and meanwhile the user satisfaction is maximized. The DACTO is proven to be a NP-complete problem and is difficult to tackle with the increasing number of population. To achieve a feasible solution, we convert the DACTO problem into an approximate combination optimization problem, and develop a backpack algorithm combined with an improved Hungarian algorithm to solve it. Simulation results show that the proposed method achieves the near-optimal solution for the DACTO problem.

The improved differential evolutionary algorithm (EA) discussed in this paper is used to solve high-dimensional big data. Specifically, the algorithm improves population diversity by expanding the searching scope of the population, prevents premature deaths of the population through wider and more specific searches, and aims to solve the high-dimensional issue. To achieve this improvement goal, the paper suggests a multilayer hierarchical architecture on the basis of the above-mentioned heuristic mechanism. In each layer of the hierarchical architecture in the dynamic subpopulation, individuals who are more suitable for isolated evolution can better coexist with the original main population. We propose a new multiobjective optimization algorithm based on nondominated sorting and bidirectional local search (NSBLS). The algorithm takes the local beam search as the main body. NSBLS outputs the nondominated solution set through a continuous iterative search when the iteration termination condition is satisfied. It is worthy to note that the iteration of NSBLS is similar to the generation of the EA; therefore, this paper uses generation to represent the iterations. An algorithm introduces a new distribution maintaining strategy based on the sampling theory to combine with the fast nondominated sorting algorithm in order to select a new population into the next iteration. NSBLS will compare with three classical algorithms: NSGA-II, MOEA/D-DE, and MODEA through a series of bi-objective test problems. The proposed nondominated sorting and local search is able to find a better spread of solutions and better convergence to the true Pareto-optimal front compared to the other four algorithms. The outstanding performance of the proposed technology was proven in well-known benchmark problems.

Shipboard X-Band radar images acquired on June 24th, 2009 are used to study internal solitary waves (ISWs) characteristics at northeast of the South China Sea (SCS). The studied images show one ISW in a packet. A methodology based on the Radon Transform (RT) technique is introduced to calculate internal wave parameters such as direction of propagation, internal wave velocity from backscatter image. The result shows that the ISW amplitude is more than 100 meters and it approximately propagates northwestward continent shelf at a speed of 3.04 m/s. Compared with the other researches, especially only with satellite remote sensing images, the ISW propagation speed we got seems higher than other results. A new explanation is presented among different remote sensing images. The periods of most internal waves at northeast of SCS acquired from SAR images aren't regular M2 tidal period (T = 12.4h), but less than 12.4h. This may be the reason that the ISW propagation speed we got from X-band radar images is higher than others from SAR images.