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Salt stress is one of the main factors affecting mungbean production, and the assessment of salt tolerance in mungbean typically requires long-term morphological observation or time-consuming physiological and biochemical experiments, which are time-consuming and labor-intensive in field conditions. This study introduced a salt stress level identification model for mungbeans, named LMS-Net, which used early-stage chlorophyll fluorescence imaging and a lightweight convolutional neural network (CNN) model to identify the salt tolerance of seedlings. To facilitate deployment on mobile and portable devices, we incorporated depth-wise separable convolutions and reparameterization techniques into LMS-Net to reduce latency during inference. Moreover, to better capture the key features and regions of stress, we proposed a Multi-scale Collaborative Attention Mechanism (MSCA) into LMS-Net. Using the FluorCam Open Chlorophyll Fluorescence Imaging System, we collected 1,879 chlorophyll fluorescence images of mungbean natural populations under salt stress to create a mungbean salt stress chlorophyll fluorescence dataset. Through comprehensive comparisons with mainstream lightweight models based on CNN and Transformer architectures, the LMS-Net model achieved an average identification accuracy of 97.64%, an average precision of 97.78%, an average recall of 97.74%, and an average F1-score of 97.75% on the test set, which benefit from model architecture and MSCA. These classification performance metrics surpass those of the comparative models. Using this method, we identified two highly salt-tolerant mungbean materials, ‘Suhuang 1’ and ‘Wei 9002-341’, from a natural population of 230 mungbean accessions. In summary, this method is promising and valuable for early-stage salt tolerance assessment of mungbean seedlings, offering new insights for screening salt-tolerant germplasm resources in mungbeans and other crops.

The vehicle routing problem with time windows (VRPTW) is a well-known NP-Hard combinatorial optimization problem, which is frequently encountered in transportation and logistics scenarios. When traditional algorithms solve this problem, there are some problems, such as slow convergence speed. In this paper, a Self-competitive Particle Swarm Optimization (ScPSO) algorithm is proposed, which regulates the learning direction of the next generation of particles based on their degree of self-competition. When the particle’s self-competition degree is low, it is compelled to learn from the individual optimal solution using the self-competition selection probability, thereby achieving rapid convergence. ScPSO uses the nonlinear inertia weight adaptive strategy to adjust the search preference in the search process, and it can balance the relationship between exploration and exploitation. Meanwhile, Random greedy heuristic selection and variable neighborhood search strategies are used in initial solution construction and constraint restriction. Finally, ScPSO is tested on 56 Solomon 100-customers benchmark problems and compared with four state-of-the-art VRPTW algorithms and best-known solutions reported on Solomon’s webpage.The running results of the ScPSO algorithm are better than four state-of-the-art VRPTW algorithms and best-known solutions reported on Solomon’s webpage. The experimental results show that ScPSO can solve VRPTW problems efficiently.

Global regional conflicts are on the rise, threatening both local and global stability and development. This article used monthly and daily nighttime light (NTL) imagery from December 2021 to April 2022 to analyze NTL dynamics during the initial period of the Russia–Ukraine conflict at the national and subnational (oblast and city) levels. In addition, a novel method was proposed to quantify the direct impact of the conflict at the pixel level. In this study, a new conflict effect index (CEI) was constructed to distinguish conflict-damaged area (CDA) and conflict-recovered area (CRA) and to quantify the direct damage and subsequent recovery caused by the conflict. The results indicate that the outbreak of the conflict led to a significant decrease in NTL intensity of about 56% across Ukraine. The pixel-level CEI indicated that the onset of the conflict had a broad impact across Ukraine, with the affected area shrinking as the conflict progressed. Damaged areas accounted for 12% of Ukraine at the start of the conflict, but decreased at an average rate of 2963.25 km2/day over the following week, while the size of recovered areas peaked at 9258.75 km2 during the conflict, but decreased to a low of 3750 km2. The maximum rate of change in the area of the CDA and CRA was −67% and 135%, respectively. The study demonstrates that NTL data can capture the direct destruction of conflict and help to support humanitarian relief and postdisaster reconstruction efforts.

The presence of numerous space objects poses significant challenges to spacecraft launch, operation, and space security. To address this issue, a novel algorithm based on improved YOLOv5 joint Fuzzy C-Means (FCM) has been proposed for discerning and localizing closely-spaced objects (CSOs) in space. The algorithm employs the lightweight neural network YOLOv5 to estimate the quantity of the targets and employs the improved FCM localizing. In the first stage, it enhances the precision of CSOs quantity estimation by integrating small target layers and the Convolutional Block Attention Module (CBAM) into the YOLOv5 network. In the second stage, leveraging the estimated target quantity as input, the FCM algorithm based on Lanczos3 interpolation and particle distribution was used for the sub-pixel localization of each target cluster. The experimental results show that the algorithm's quantity estimation accuracy is more than 90%, and the average localization error is within 0.32 pixels. Moreover, the average running time of the algorithm is less than 0.034 s. Compared with other methods, the algorithm shows excellent performance, and its effectiveness is important for target tracking, 3D localization, and detailed analysis.

Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-to-stand phase using two inertial sensors. First, we defined the sit-to-stand transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum transfer phase, the extension phase, and the stable standing phase based on the preprocessed acceleration and angular velocity data. We then employed a threshold method to recognize the initial sitting and the stable standing phases. Finally, we designed a novel CNN-BiLSTM-Attention algorithm to identify the three transition phases, namely, the flexion momentum phase, the momentum transfer phase, and the extension phase. Fifteen subjects were recruited to perform sit-to-stand transition experiments under a specific paradigm. A combination of the acceleration and angular velocity data features for the sit-to-stand transition phase identification were validated for the model performance improvements. The integration of the CNN, Bi-LSTM, and Attention modules demonstrated the reasonableness of the proposed algorithms. The experimental results showed that the proposed CNN-BiLSTM-Attention algorithm achieved the highest average classification accuracy of 99.5% for all five phases when compared to both traditional machine learning algorithms and deep learning algorithms on our customized dataset (STS-PD). The proposed sit-to-stand phase recognition algorithm could serve as a foundation for the control of wearable exoskeletons and is important for the further development of intelligent wearable exoskeleton rehabilitation robots.

Aiming at the objective uncertainty, subjective uncertainty, and extreme events may be in a dynamic system simultaneously. This paper focuses on the differential game problem of a linear quadratic jump uncertain stochastic system. The system is described by both a jump uncertain differential equation and a stochastic differential equation. The principle of optimality and equation of optimality are established. Then, a differential game model based on linear quadratic jump uncertain stochastic system is constructed. Furthermore, Nash equilibrium are discussed by using the obtained equation. Finally, its application in the dynamic investment decision of enterprises is given, and numerical simulations are performed. This approach offers a new method for quantitative analysis in future studies.

This article proposes a novel hyperspectral image (HSI) denoising and destriping method based on graph signal processing that fully exploits the HSI properties. Double graph Laplacian regularizers (DGLR) are established to illustrate the spectral and spatial connections of an HSI. An HSI is of low-rank property, and its spectral differences exhibit a lower rank than the HSI itself. To characterize this property, we construct our first graph by using the difference between every two adjacent bands as the graph nodes. In addition, to preserve the spatial connection of the HSIs and avoid oversmoothing, the second graph takes the full-band HSI blocks as graph nodes. We use the Euclidean distance between nodes and the $\ell _{2}$ norm between graph signals to compute the edge weights of these two graphs. Besides, we also consider removing the sparse/impulse noise and stripe noise by using an $\ell _{1}$ norm and a nuclear norm as constraints. An augmented Lagrange multiplier method is developed to solve the proposed optimization problem. To testify the competitive performance of our method, we conduct numerous experiments on both simulated noisy HSIs and real noisy HSIs. The quantitative evaluation indices and visual performance show that DGLR surpasses many mainstream denoising methods within less or comparable time.

As university-industry collaborative innovation becomes an important driving force for technological development, the role of individuals in promoting knowledge production and innovation performance is becoming increasingly prominent. Research on individuals in this field has attracted a wide range of attention from scholars, however, scientometric analysis and visualization are inadequate. This study is based on scientific publications from 2000 to 2022 obtained from the Web of Science database. The Bibliometrix-R package and VOSviewer software were used to conduct quantitative analysis and visualization of bibliometric indicators, and to explore the current progress and leading trends of research on university-industry collaborative innovation of individuals. The results show growing academic interest in this topic, with the United States, the United Kingdom, the Netherlands, and Italy being the most productive countries, and the geographical scope of research expanding to emerging economies. The current research focuses on the channels, attitudes, and influencing factors of different individuals in collaborative innovation as well as their relationship with scientific productivity. Through scientometric analysis, it is possible to intuitively understand scientific performance, core journals, author clusters, collaborative networks, research hotspots, and thematic evolution, which helps to systematically recognize and focus research in this field, and provides a holistic view and potential directions for future research.

Thangka murals hold significant cultural value in Tibet, but unfortunately, many of these precious murals have sustained damage over time. Therefore, the restoration of Thangka murals is crucial in preserving Tibet’s cultural heritage. The potential of Gated Convolution for Thangka mural restoration is significant due to its impressive performance. However, existing Gated Convolution-based approaches for Thangka restoration face two challenges: 1) restoration results using Gated Convolution still show blurring at the pixel level, and 2) Gated Convolution remains dependent on the perceptual field of the convolution during image inpainting. To address two challenges, we propose a novel Thangka mural inpainting method that incorporates two key components: 1) an Edge-assisted Feature Fusion (EFF) method that leverages edge information to assist in inpainting the texture details of the image and enhance the consistency of textures and edges; and 2) a Self Attention based Local Refine (SALR) network that effectively enhances the perceptual field of the convolution and captures long-distance dependencies. The experimental results demonstrate that our proposed method is effective in repairing broken areas of Thangka murals, even on a small dataset (N = 5605). To validate the effectiveness of our proposed architecture and its components, we conduct a series of ablation experiments. The results demonstrate the necessity of our approach and highlight the contributions of each component. Moreover, the proposed end-to-end approach exhibits broad applicability and can be extended to other profiling tasks based on small datasets.

When using machine learning classifiers to classify data in cloud computing, it is crucial to maintain data privacy and ensure the correctness of classification results. To address these security concerns, we propose a new verifiable homomorphic secret sharing (VHSS) scheme. Our approach involves distributing the task of executing a polynomial form of the machine learning classifier among two servers who produce partial results on encrypted data. Each server cannot obtain any data information, and the classification result can be reconstructed and verified using a verification key in conjunction with the two partial results. Compared to previous VHSS schemes, our scheme can compute the degree of polynomials as high as the polynomials in the system security parameters while performing comparably to homomorphic secret sharing (HSS) schemes. We implement our proposed scheme and demonstrate its application to decision trees (a type of machine learning classifier). Our experiments show that our scheme is twice as fast as previous VHSS schemes when evaluating decision trees with depths ranging from 2–14.

Chance theory is a mathematical methodology for modelling complex systems including both uncertainty and randomness. Based on chance theory, this paper introduces the optimal control model for a multistage uncertain random system. For solving such a model, recurrence equations are presented via Bellman’s principle. With the aid of recurrence equations, the bang-bang problem of optimal control model and indefinite linear quadratic (LQ) problem of optimal control model are studied. Meanwhile, the exact solutions of such problems are presented. Furthermore, numerical examples are provided on the proposed theorems to show the effectiveness of our results.

Brain-controlled wheelchairs are one of the most promising applications that can help people gain mobility after their normal interaction pathways have been compromised by neuromuscular diseases. The feasibility of using brain signals to control wheelchairs has been well demonstrated by healthy people in previous studies. However, most potential users of brain-controlled wheelchairs are people suffering from severe physical disabilities or who are in a “locked-in” state. To further validate the clinical practicability of our previously proposed P300-based brain-controlled wheelchair, in this study, 10 subjects with severe spinal cord injuries participated in three experiments and completed ten predefined tasks in each experiment. The average accuracy and information transfer rate (ITR) were 94.8% and 4.2 bits/min, respectively. Moreover, we evaluated the physiological and cognitive burdens experienced by these individuals before and after the experiments. There were no significant changes in vital signs during the experiment, indicating minimal physiological and cognitive burden. The patients’ average systolic blood pressure before and after the experiment was 113±13.7 mmHg and 114±11.9 mmHg, respectively (P = 0.122). The patients’ average heart rates before and after the experiment were 79±8.4/min and 79±8.2/min, respectively (P = 0.147). The average task load, measured by the National Aeronautics and Space Administration task load index, ranged from 10.0 to 25.5. The results suggest that the proposed P300-based brain-controlled wheelchair is safe and reliable; additionally, it does not significantly increase the patient’s physical and mental task burden, demonstrating its potential value in clinical applications. Our study promotes the development of a more practical brain-controlled wheelchair system.

Motor disorder of upper limbs has seriously affected the daily life of the patients with hemiplegia after stroke. We developed a wearable supernumerary robotic limb (SRL) system using a hybrid control approach based on motor imagery (MI) and object detection for upper-limb motion assistance. SRL system included an SRL hardware subsystem and a hybrid control software subsystem. The system obtained the patient’s motion intention through MI electroencephalogram (EEG) recognition method based on graph convolutional network (GCN) and gated recurrent unit network (GRU) to control the left and right movements of SRL, and the object detection technology was used together for a quick grasp of target objects to compensate for the disadvantages when using MI EEG alone like fewer control instructions and lower control efficiency. Offline training experiment was designed to obtain subjects’ MI recognition models and evaluate the feasibility of the MI EEG recognition method; online control experiment was designed to verify the effectiveness of our wearable SRL system. The results showed that the proposed MI EEG recognition method (GCN+GRU) could effectively improve the MI classification accuracy (90.04% $\pm ~2.36$ %) compared with traditional methods; all subjects were able to complete the target object grasping tasks within 23 seconds by controlling the SRL, and the highest average grasping success rate achieved 90.67% in bag grasping task. The SRL system can effectively assist people with upper-limb motor disorder to perform upper-limb tasks in daily life by natural human-robot interaction, and improve their ability of self-help and enhance their confidence of life.

In this paper, we investigate the aggregated model quality maximization problem in hierarchical federated learning, the decision problem of which is proved NP-complete. We develop the mechanism MaxQ to maximize the sum of local model quality, which consists of two stages. In the first stage, an algorithm based on matching game theory is proposed to associate mobile devices with edge servers, which is proved able to achieve the stability and $\frac {1}{2}$ -approximation ratio. In the second stage, we design an incentive mechanism based on contract theory to maximize the quality of models submitted by mobile devices to edge servers. Through thorough experiments, we analyse the performance of MaxQ and compare it with the existing mechanisms FAIR and EHFL, under different deep learning models ResNet18, ResNet50 and AlexNet, individually. It is found that the model quality can be improved by 8.20% and 7.81%, 10.47% and 11.87%, 10.98% and 11.97% under different models, respectively.

Extracting buildings from remote sensing (RS) images is an important task with a variety of applications. Considerable attention has focused on achieving new state-of-the-art (SOTA) accuracy with more and more advanced deep learning (DL) models. However, the developed models still hardly generalize across geographical areas, hindering the practical use of SOTA approaches. To attack this problem, we established a baseline for model cross-area generalization ability using available datasets for building extraction (BE). In addition to two popular fully convolutional neural network (FCN) based models, we first adapted two novel transformer-based models, shifted windows (Swin) transformer and SegFormer, which are all able to output SOTA accuracy with no big difference when tested within one area. However, experimental results show that all models fail to generalize to a different area. We then propose to fine-tune pretrained models from one area on a small subset of an unseen area, the effectiveness of which depends on the model choice and the data size for tuning. By jointly taking advantage of the transfer learning idea and the multiscale feature learning ability of SegFormer, a distinct improvement has been achieved compared to results from Swin transformer and FCN-based models trained on the same amount of data. Commonly used metric, Intersection over Union, can be increased from 38.97% to 70.86%, and from 48.36% to 74.51%, when using 10% and 30% subset of the targeting area, respectively. The influence of model choice and data size for tuning has also been investigated. Our work contributes to complementing the algorithm development and within-area model evaluation in the hot field of BE from RS images.

The insulator cleaning robot is essential to grid maintenance and personal safety, where the precise positioning of insulator sheds is the basis. However, due to the great differences in shape and color of the insulator, as well as the cluttered background in the substation, the traditional methods based on color and texture features can not achieve accurate positioning of the insulator shed. Aiming at the problem that the traditional features of insulator are easily submerged in the background, we propose a new depth horizontal histogram (DHH) feature and use it to achieve the accurate and fast positioning of insulator shed. In order to reduce the influence of complex background on insulator positioning, we directly bandpass filter the depth image of the insulator from the RGB-D (RGB and Depth) camera. To obtain the effective DHH feature, the filtered image is processed by rotation, horizontal projection, and morphology. Finally, we perform gradient detection on the DHH and achieve the precise positioning of the insulator sheds. The experiments show that, compared with traditional methods, our method can achieve precise positioning of the insulator shed and performs better in speed, accuracy, and adaptability.

To achieve the efficient application of impedance analysis in the stability assessment and enhancement of multi-terminal DC distribution systems, this paper proposes the DC-side reduced-order impedance models with power control and AC voltage control, respectively, by taking the load converter station as the object. By using the DC-side current as the feed-forward state, the active compensator applied to the load converter station with two control modes is also derived as well as the corresponding reduced-order impedance models. Combined with the reduced-order impedance models, a method based on damping factor sensitivity is further proposed to design the parameters of the derived active compensators. The verification results in the frequency domain and time domain demonstrate the accuracy of the reduced-order impedance and the effectiveness of the proposed compensator parameter design method.

Hypersonic glide vehicle (HGV) has brought severe challenges to the existing defense system due to its characteristics of high maneuverability, high speed and high precision. Simultaneously, these characteristics also bring great difficulties to trajectory prediction. In this paper, a method for HGV motion state recognition and trajectory prediction based on deep learning is proposed. The proposed method consists of two modules, namely the motion state recognition module and the trajectory prediction module. The motion state recognition module can identify the HGV’s motion state according to state information, and divide it into eight categories. The softmax function is added to the state recognition module to calculate the probability of each motion state. The trajectory prediction module comprises a nonlinear prediction part and a linear prediction part. According to the result of motion state recognition, the appropriate prediction scheme is adopted to better extract the linear and nonlinear characteristics of HGV trajectory, which improves the robustness and prediction accuracy of the proposed method. The experimental results of HGV trajectory prediction show that the proposed method can maintain good stability when the HGV maneuver state changes, and has higher accuracy than the four benchmark methods.

A dual-mode (DM) vertical-cavity surface-emitting laser (VCSEL) is investigated for carrying high-speed data transmission with either the non-return-zero on-off keying (NRZ-OOK) or the quadrature amplitude modulation-orthogonal frequency division multiplexing (QAM-OFDM) format. In both the back-to-back (BtB) and the 100-m transmission cases with using either the OM4 multimode fiber (OM4-MMF) or the graded-index single-mode fiber (GI-SMF), the mismatch between the core diameter of the lensed MMF pick-up head and the mode field of the DM-VCSEL output controls the coupled transverse mode number and decrease the modal dispersion effect. Using pre-emphasized NRZ-OOK data can compensate the SNR degradation to improve the BER to 5.6 × 10−12 under BtB and 6.9 × 10−10 after 100-m GI-SMF for the DM-VCSEL delivered NRZ-OOK data at 53 Gbit/s. For 16-QAM OFDM transmission, the pre-leveling algorithm shows better data compensation than the pre-emphasis algorithm in both BtB and the 100-m GI-SMF cases. With the pre-leveling compensation of the 16-QAM OFDM data encoded to the DM-VCSEL with a 0.4-dB/GHz slope, the DM-VCSEL allows 136 Gbit/s for BtB, 112 Gbit/s for 100-m OM4-MMF, and 92 Gbit/s for 100-m GI-SMF transmissions. The pre-emphasis NRZ-OOK and the pre-leveling 16-QAM OFDM data carried by the DM-VCSEL can be used in ultrahigh-speed intra-data-center links in the future.

As a typical maneuver model, the second-order Markov model, which is based on acceleration periodic autocorrelation, is an effective way used for tracking near space hypersonic vehicle(NSHV) target tracking. It is found that, however, the model responds slowly to the target maneuvering and has weak maneuverability. To solve this problem, the adaptive non-zero mean damped oscillation model (ANM-DO), which based on the idea of mean compensation, is proposed. Then the difference of the mean compensation method between the first order Markov model and the two order Markov model is analyzed, and the physical essence of adaptive nonzero mean is discussed from time domain and frequency domain. Furthermore, to further investigate the performance of the ANM-DO model, we deduced the systematic dynamic errors of ANM-DO taking Kalman Filter (KF) as filtering algorithm. On this basis, the superior performance of ANM-DO model is verified in terms of maneuverability. Finally, simulation experiments in different scenarios show that the ANM-DO model shows lower filtering errors tracking near space hypersonic jump gliding targets, and verified the adaptability of the model proposed in this paper.

In crowdsourced testing, crowd workers from different places help developers conduct testing and submit test reports for the observed abnormal behaviors. Developers manually inspect each test report and make an initial decision for the potential bug. However, due to the poor quality, test reports are handled extremely slowly. Meanwhile, due to the limitation of resources, some test reports are not handled at all. Therefore, some researchers attempt to resolve the problem of test report prioritization and have proposed many methods. However, these methods do not consider the impact of duplicate test reports. In this paper, we focus on the problem of test report prioritization and present a new method named DivClass by combining a diversity strategy and a classification strategy. First, we leverage Natural Language Processing (NLP) techniques to preprocess crowdsourced test reports. Then, we build a similarity matrix by introducing an asymmetric similarity computation strategy. Finally, we combine the diversity strategy and the classification strategy to determine the inspection order of test reports. To validate the effectiveness of DivClass, experiments are conducted on five crowdsourced test report datasets. Experimental results show that DivClass achieves 0.8887 in terms of APFD (Average Percentage of Fault Detected) and improves the state-of-the-art technique DivRisk by 14.12% on average. The asymmetric similarity computation strategy can improve DivClass by 4.82% in terms of APFD on average. In addition, empirical results show that DivClass can greatly reduce the number of inspected test reports.

A gas refractive index (RI) sensor based on an inornate antiresonant hollow-core fiber (HCF) was theoretically analyzed and experimentally demonstrated. The transmission spectra evolution of the proposed sensor and the resonance intensity variations to the surrounding gas RI were simulated. Theoretical analysis found that the intensity sensitivity of inornate HCF is higher than that of coated HCF. In the experiment, a sensor with a 5 mm-long HCF provided exceptional sensitivity—as high as 2236.1 dB/RIU. The sensitivity was enhanced by improving the resonance intensity of the inornate HCF, which could be further improved by increasing the HCF length. In addition, temperature sensing was performed to resolve the temperature cross-sensitivity issue using a wavelength interrogation method. The proposed RI sensor exhibited the advantages of easily manufactured and with ultrahigh sensitivity.

As a critical part of prognostics and health management (PHM), remaining useful life (RUL) prediction can provide manufacturers and users with system lifetime information and improve the reliability of maintainable systems. Particle filters (PFs) are powerful tools for RUL prediction because they can represent the uncertainty of results well. However, due to the lack of measurement data, the parameters of the measurement model cannot be updated during the long-term prediction process. Additionally, for complex systems, the measurement model of a system often cannot be obtained in an analytical form. In this paper, a fusion prognostic method based on an extended belief rule base (EBRB) and a PF is designed to solve these problems. In the proposed framework, a double-layer maximum mean discrepancy-extended belief rule base (DMMD-EBRB) model with time delay is adopted to estimate and predict the hidden behavior of a degrading system. The unknown parameters of the degradation model are identified by the PF using the output of the EBRB. Afterwards, the system state is further predicted by the PF. The effectiveness of the proposed method is validated with the NASA-PCoE and CALCE lithium-ion battery degradation experiment datasets. In addition, several other related fusion methods are investigated for comparison with the proposed method. The experiments show that the proposed method yields better performance than the existing methods.

The assessment of liver fibrosis is usually required in the diagnostic and treatment procedures for chronic liver disease. In this paper, we propose a deep convolutional neural network (DCNN) with multi-feature fusion (DCNN-MFF) to extract and integrate features from ultrasound (US) B-mode image and Nakagami parametric map for significant liver fibrosis recognition. The DCNN-MFF model mainly consists of three branches, among which the first two branches are used for extracting deep features respectively from the US B-mode image and the Nakagami parametric map, while the remaining branch is designed for extracting quantitative US features from the Nakagami parametric map. At the backend of the DCNN model, the extracted deep and quantitative features are fused together for final decision making. The performance of the DCNN-MFF model was evaluated on an animal dataset collected from 84 rats with 168 liver lobes under various fibrosis stages. Across five-fold cross-validation, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) achieved by the proposed DCNN-MFF model for significant liver fibrosis recognition were respectively 0.827 (95% confidence interval [CI] 0.762–0.881), 0.821 (95% CI 0.729–0.892), 0.836 (95% CI 0.731–0.912), 0.869 (95% CI 0.809–0.916), which are significantly better than those provided by the comparative methods.

Application description analysis is applied for various purposes in software engineering domains. Besides the inherent challenges from the ambiguity of natural language, sparse permission semantics raise the difficulties of predicting functionalities and permission usages from app descriptions. More specifically, the functionalities common to the app’s category are intentionally abbreviated by developers due to the limited number of characters, and the permissions are often over-claimed. These are the main reasons that cause false positives in predicting permissions from app descriptions. Such unmentioned permissions can only be detected as suspicious in previous studies where effective assistance for developers in refining app descriptions and preventing potential security risks is not provided. In this paper, we propose the FideDroid, a framework to identify category-based common permissions to offset those essential functionalities while assessing the fidelity of app descriptions. Our framework augments the labeled dataset of app descriptions to improve the prediction of permissions. FideDroid compares inferred permissions with used ones to reveal the suspicious and unnecessary permissions based on the prediction. It helps developers to refine app descriptions and maintain permission usages. In our experiments on large real-world apps, we analyzed and revealed that the category-based common permissions may cover more unmentioned functionalities without considering all possible permissions during app description analysis. In addition, we discovered three factors causing the inconsistency between descriptions and permission usages to be: 1) human interventions in writing description; 2) bad practices on permission usages; and 3) prolific developers. These findings will facilitate developers to refine app descriptions and optimize permission usages in the apps.

The underdetermined blind source separation (UBSS) has been considered to be a novel signal processing technique, which can separate the fault source signals from their mixtures. The mixing matrix estimation is a major step in the UBSS, this paper focuses on boosting the accuracy level of the estimated mixing matrix in the underdetermined case. Since the traditional clustering algorithms may not capture the signal characteristics well and secure a satisfactory estimation of the mixing matrix, an effective two-stage clustering algorithm is proposed to estimate the mixing matrix through a combination of hierarchical clustering and K-means. More specifically, first, the sum of frequency points energy in the time-frequency (TF) domain is calculated to estimate the number of source signals before clustering, and the initial clustering centers are obtained with a hierarchical clustering algorithm. Second, after eliminating outliers deviating from the initial clustering centers with the cosine distance, the new clustering centers are obtained by recalculating the mean value of each sub-cluster. Finally, the new clustering centers are set as the initial clustering centers of the K-means algorithm to estimate the mixing matrix. Extensive simulations and experiments show that the proposed method can effectively separate the source signals and ensure an estimate of the mixing matrix that is substantially more accurate than the K-means algorithm alone.

With the increasing penetration of wind power, using wind turbines to participate in the frequency regulation to support power system has become a clear consensus. To accurately quantify the inertia provided by the doubly-fed induction generator (DFIG) based wind farm, the frequency response model of DFIG with additional frequency control is established, and then by using Routh approximation, the explicit expression of the virtual moment of inertia is derived for the DFIG grid-connected system. To further enhance the availability of the expression, an estimation method is proposed based on the matrix pencil method and the least squares algorithm for estimating the virtual moment of inertia provided by the wind farm. Finally, numerical results tested by a DFIG grid-connected system and a modified IEEE 30-bus system verify the derived expression of the virtual moment of inertia and the proposed estimation method.

Vehicular edge computing (VEC) has emerged as a promising paradigm to ensure the real-time task processing caused by the emerging 5G or high level intelligent assisted driving applications. The computing tasks can be processed via the edge services deployed as the roadside units (RSUs) or moving vehicles. However, the high dynamic topology of the vehicular communication system and the time-varying available computing resources in RSUs make a challenge of the efficient task offloading of vehicles. In this paper, we consider an efficient task offloading scheme for VEC networks based on trajectory prediction, we focus on the serving handover between the adjacent RSUs. The moving vehicles can cooperate with RSUs or the surrounding vehicles for task processing. To reduce the latency of task transmission between vehicles, we present a cooperative vehicle selection method based on trajectory prediction. Then, we propose an efficient task offloading scheme based on deep reinforcement learning (DRL), while the dynamically available computing and communication resources are considered jointly. The simulation results show that the proposed task offloading scheme has great advantages in improving the utility of vehicles.

Mobile app descriptions have been widely used in app markets to deliver various types of information to end-users. Even though this information may implicitly expose the dangerous permissions that allow access to sensitive data, most users cannot correctly identify and interpret the corresponding textual sentences owing to insufficient knowledge regarding Android permissions and the semantics of app descriptions. It is therefore important to assist users in understanding whether an app description accurately reflects whether the app may request dangerous permissions. To this end, we propose an approach named Fidelity Calculation for Description-to-Permissions (FCDP). It is aimed at assisting app-market auditors in assessing whether an app description indicates all information related to dangerous permissions using a quantified fidelity for providing a high-quality description to mobile users. Furthermore, we experimentally investigate the effect of different factors on FCDP, and we demonstrate that FCDP outperforms the state-of-the-art method by over 3.65% in predicting description-to-permissions. By using 64,265 Android descriptions crawled from Google Play, our in-depth analysis further indicates that most app descriptions do not entirely disclose the semantics of dangerous permissions for mobile users in the wild. It is therefore quite urgent to assist app-market auditors in regulating description writing in this regard.

An analysis model for an optical waveguide microcantilever sensor is developed combining the optical and mechanical models. An improved optical waveguide microcantilever sensor with a buffer is provided and taken as an example to explore using the analysis model. A systematic and detailed discussion about the couplers for the input waveguide to optical waveguide cantilever and the optical waveguide cantilever to the output waveguide of the improved waveguide cantilever sensor is presented. The sensitivity of an improved optical waveguide cantilever sensor is evaluated by analyzing the input/output waveguide, buffer, microcantilever, and gap. An improved optical waveguide microcantilever sensor by adding a buffer shows a sensitivity of 5.7 × 10-4nm-1, which is improved by 51.3%, compared with a conventional optical waveguide microcantilever sensor. The design of an optical waveguide cantilever sensor is a trade-off of different design parameters. These will be helpful for the study of an optical waveguide cantilever sensor.

The problem of coordinating a two-echelon green product supply chain with environmentally conscious consumers under demand uncertainty is studied in this paper. In the green product supply chain, a manufacturer produces green product through developing green technology and a retailer promotes the green product through green marketing. The demand of the green product is uncertain with the mean and the variance as the only known information. After analyzing the utility that a consumer gained from the green product, the problem is then formulated as a distribution-free-based Stackelberg game for a decentralized system under three contracts, i.e., wholesale price-only (WPO), revenue and green marketing cost-sharing (RGMS), and two-part tariff (TPTF) contracts. The analytical results are also proposed to show the effects of the system parameters on supply chain optimal decisions. By comparing with the centralized system, the coordination level for each contract is investigated. Numerical examples and sensitivity analysis with respect to several system parameters are presented to illustrate the effectiveness of the contracts under demand uncertainty. The results demonstrate that both RGMS and TPTF contracts are superior to WPO contract. Especially, TPTF contract can always coordinate the supply chain, while RGMS contract can improve the supply chain coordination level only if the revenue and cost sharing coefficients satisfy certain conditions.

The development of deep neural networks has driven the development of computer vision. Deep neural networks play an important role in object detection. To improve network performance, before using neural networks for object detection, they are commonly pre-trained on the data set and fine-tuned to their object detection tasks. Pre-training is not always helpful in object detection tasks, so studies have been performed on training neural networks from scratch. By consulting many relevant studies,we performed a systematic analysis of training networks from scratch for object detection. Our article is divided into the following three parts: (i) the reasons for which target detection requires training from scratch, (ii) mainstream networks that can be trained from scratch, and (iii) the criteria for training from scratch. Finally, we summarize some research directions relevant to this topic.

Semantic segmentation is an important approach in remote sensing image analysis. However, when segmenting multiobject from remote sensing images with insufficient labeled data and imbalanced data classes, the performances of the current semantic segmentation models were often unsatisfactory. In this paper, we try to solve this problem with transfer learning and a novel deep convolutional neural network with dense connection. We designed a UNet-based deep convolutional neural network, which is called TL-DenseUNet, for the semantic segmentation of remote sensing images. The proposed TL-DenseUNet contains two subnetworks. Among them, the encoder subnetwork uses a transferring DenseNet pretrained on three-band ImageNet images to extract multilevel semantic features, and the decoder subnetwork adopts dense connection to fuse the multiscale information in each layer, which can strengthen the expressive capability of the features. We carried out comprehensive experiments on remote sensing image datasets with 11 classes of ground objects. The experimental results demonstrate that both transfer learning and dense connection are effective for the multiobject semantic segmentation of remote sensing images with insufficient labeled data and imbalanced data classes. Compared with several other state-of-the-art models, the kappa coefficient of TL-DenseUNet is improved by more than 0.0752. TL-DenseUNet achieves better performance and more accurate segmentation results than the state-of-the-art models.

Deep learning based methods have made great progress in hyperspectral image classification. However, training a deep learning model often requires a large number of labeled samples, which are not always available in practical applications. In this paper, a simple but innovative classification paradigm to exploit morphological attribute profile cube is proposed to improve the small sample classification performance of hyperspectral image. First, morphological attribute profiles are constructed by applying different morphological filters to hyperspectral image. Morphological attribute profile cubes are then extracted as the feature of a sample. Second, the obtained morphological attribute profile cubes are scanned with multiple scale sliding windows to make full use of the rich spatial-spectral information. Finally, the features after multi-grained scanning are input into a deep forest classifier to complete the classification task. In this way, the proposed method could use a deep network structure to improve the classification accuracy. To demonstrate the effectiveness of the proposed method, the classification experiments are carried on three widely used hyperspectral data sets. The experimental results demonstrate that the proposed method can outperform the conventional semi-supervised methods and the state-of-the-art deep learning based methods. The demo code on the Salinas dataset is released on the page: https://github.com/liubing220524/ MAPC-DRF-HSI.

To minimize the sensing cost in MCS while preserving the participants' privacy, in this paper we propose a Data Sensing mechanism with User Privacy Preserved (DS-UPP). We introduce edge computing into MCS to support task allocation and user privacy protection. In DS-UPP, based on compressive sensing theory we minimize the amount of data needed to be submitted. We also design an algorithm based on local differential privacy theory. Selected participants only need to submit their real data along with the reconstructed data generated by the algorithm. It is proved that DS-UPP satisfies ε-differential privacy. We give the mathematical lower bound and upper bound of the number of participants needed for task accomplishment with the constraints that privacy budget is ε and recovery error of task data is 0, as well as the average amount of data that should be submitted by a participant. We also evaluate the performance of DS-UPP through simulations. Compared with the existing method PrivKV, DS-UPP can reduce the needed data amount by about 90% on the average while guarantee users' privacy preserved.

In recent years, blockchain has received increasing attention and numerous applications have emerged from this technology. A renowned Blockchain application is the cryptocurrency Bitcoin, that has not only been effectively solving the double-spending problem but also it can confirm the legitimacy of transactional records without relying on a centralized system to do so. Therefore, any application using Blockchain technology as the base architecture ensures that the contents of its data are tamper-proof. This paper uses the decentralized Blockchain technology approach to ensure that consumers do not fully rely on the merchants to determine if products are genuine. We describe a decentralized Blockchain system with products anti-counterfeiting, in that way manufacturers can use this system to provide genuine products without having to manage direct-operated stores, which can significantly reduce the cost of product quality assurance.

The increasing miniaturization of functional devices drives the microelectronic industry to pursuit large-span-ratio and high-density wire bonding packaging technology as it can facilely interconnect various chips at a low cost. The design method of wire bonder head trace is urgently needed in the Post Moore Era. Herein, a rational design strategy of wire bonder head trace was proposed. By solving the geometric mapping equations derived from the geometrical evolution of the wire loop, the wire bonder head trace can be easily designed to fit the desired loop profile. The explicit finite element model was also developed to verify the designed wire bonder head trace and disclose the dynamic evolution of strain/stress on wire loops simultaneously. Furthermore, by using orthogonal experimental design, the trace height was found to be the most critical geometric factor influencing loop profiles. The proposed strategy was illustrated to design wire bonder head traces with which large span-ratio loops (>5) with the finest reported gold wire (sub-10 μm) were successfully formed. The mechanical strength tests showed the wire loops were superior to loops with other larger wire. The proposed strategy significantly improves the efficiency of design reliable wire bonder head trace for high-density packaging and will have broad application prospects in microelectronic industry.

Polypropylene (PP) material as a DC cable insulation material has great prospects as a recyclable alternative to cross-linked polyethylene. This paper aims at investigate the effect of PP blends crystallinity morphology and trap energy level distribution on DC breakdown strength. Five different ratios iPP/sPP/aPP blends samples are prepared by melt-blending method. Polarizing optical microscope (POM) experiments are conducted to characterize the crystallinity morphology of five iPP/sPP/aPP blends and isothermal surface potential decay (ISPD) methods are employed to measure the trap levels distribution in composite dielectric. The DC breakdown strength is tested by ball-plate electrodes equipment, and the result of DC breakdown experiment indicates that sPP30 exhibits the highest breakdown field strength, which is 29.2% higher than sPP0. The addition of sPP can act as a nucleus to help the crystallization behavior of iPP, and the distribution of spherulites in sPP30 is the most uniform and dense among the five iPP/sPP/aPP blends samples, which can improve the trap characteristics of polymers. The ISPD results show that the decay rate of the sPP30 sample is the slowest at 50 °C, 70 °C and 90 °C, indicating that sPP30 is more conducive to suppressing the dissipation process of carriers during the depolarization process. Moreover, sPP30 has higher deep trap density and lower shallow trap density and exhibits superior breakdown performance. It is deduced that the addition of sPP can effectively regulate the crystallinity morphology of iPP, and then effectively improve the trap energy level distribution of iPP/sPP/aPP blends, and it shows the improvement of DC breakdown performance macroscopically.

When the source code is copied and pasted or modified, there will be a lot of identical or similar code snippets in the software system, which are called code clones. Because code clones are believed to result in undesirable maintainability of software, numerous approaches and techniques have been proposed for code clone detection. However, most of them are based on the source code, while only a few employ the bytecode to detect code clones. In this paper, we introduce an approach based on Java bytecode, which mainly contains the steps of bytecode sequence alignment and similarity score comparison. In particular, we apply the Smith-Waterman algorithm to align bytecode sequences for precise matching. Moreover, we separately consider the similarities between instruction sequences and method call sequences, thus improving its effectiveness in detecting code clones. We conducted an extensive experiment on five open-source software to evaluate the proposed approach. The results show that our approach outperforms other state-of-the-art techniques.

In the recent years, the vulnerabilities of conventional public key infrastructure are exposed by the real-world attacks, such as the certificate authority’s single-point-of-failure or clients’ private information leakage. Aimed at the first issue, one type of approach is that multiple entities are introduced to assist the certificate operations, including registration, update, and revocation. However, it is inefficient in computation. Another type is to make the certificate information publicly visible by bringing in the log servers. Nevertheless, the data synchronization among log servers may lead to network latency. Based on the second approach, the blockchain-based public key infrastructure schemes are proposed. Through these type of schemes, all the certificate operations are stored in the blockchain for public audit. However, the issue of revoked certificates’ status storage is worth paying attention, especially in the setting with massive certificates. In addition, the target web server that a client wants to access is exposed in the process of certificate status validation. In this paper, we propose a privacy-preserving blockchain-based certificate status validation scheme called PBCert to solve these two issues. First, we separate the revoked certificates control and storage plane. Only the minimal control information (namely, certificate hashes and related operation block height) is stored in the blockchain and it uses external data stores for the detailed information about all revoked certificates. Second, we design an obscure response to the clients’ certificate status query for the purpose of privacy preserving. Through the security analysis and experiment evaluation, our scheme is significant in practice.

Multiple sources’ localization using the time-of-arrival (TOA) measurements in the presence of sensor position uncertainty is studied in this paper. The non-cooperative scenario where the clock between the source and sensors is not synchronized is also considered. First, we theoretically prove that the Cramér–Rao lower bound of multi-source joint localization is lower than that of the single source case when the TOA measurements from different sources possess the same sensor position errors. Moreover, different from the conventional numerical algorithms, we propose to employ a neural circuit named Lagrange programming neural network (LPNN) to fulfill this non-trivial task of jointly locating multiple sources. The maximum likelihood problem of multi-source localization is reformulated by utilizing the LPNN framework, and then we build up a neural model, which is proved to be asymptotically stable through both mathematical analysis and numerical experiments. The simulation results show that the proposed method is superior to other positioning algorithms, and it has excellent localization performance and robustness even in the case of large measurement noise, synchronization error, and sensor position displacements.

Low-power wide-area (LPWA) communication has gained increasing attention in recent years with the rapid growth of fifth generation evolution (5G), the Internet of Things (IoT), and mobile computing. Narrowband Internet of Things (NB-IoT) is one kind of LPWA technology based on cellular IoT, which supports massive connections, wide area coverage, ultra-low power consumption, and ultra-low cost. Research on NB-IoT communication is increasingly attractive. Network calculus theory facilitates the performance analysis and network optimization of the NB-IoT system. We aim to analyze and optimize the NB-IoT networks. In this paper, we construct a random access traffic model including the NB-IoT user equipment (UE) arrival process and eNB service process. Then, we utilize the stochastic network calculus (SNC) to analyze the network delay in NB-IoT traffic model. Random latency bounds in different arrival processes are derived. Simulations show that SNC can evaluate the system delay under different distributions effectively. For the condition that numerous UEs access simultaneously following the Beta distribution, we first propose an improved K-means algorithm to cluster the NB-IoT terminals. Then, we raise the scheduling strategy on the basis of priority. It consists of the priority generation algorithm IPGNTQ and the NB-IoT task scheduling algorithm SANTQ. The extensive experiment results verify that our proposed optimized strategy can alleviate the network congestion effectually. Moreover, we compare our proposed optimized scheme with four existing uplink traffic scheduling schemes, showing that ours outperforms all of them.

Histogram shifting (HS) as a typical reversible data hiding (RDH) scheme is widely researched due to its high quality of stego-image. During HS process, the selected side information, i.e., peak and zero bins, usually greatly affects the performance of stego-image. Due to the massive solution space and burden in distortion computation, conventional HS-based schemes commonly utilize some empirical criterions associated with many artificial-designed constraints to determine side information, which could not lead to a global optimal performance for HS-based RDH. Later, our previous work proposed an adaptive information selection scheme for “multiple embedding” method by removing lots of constraints employed in conventional schemes. However, those constraints could not be completely avoided. Those chosen peak and zero bins in “multiple embedding” process are required to be different with each other, since they are commonly determined at one time as side information. In this paper, we employ “multilevel embedding” method and contrive to get rid of these above-mentioned constraints, which are called “unnecessary constraints” in this paper, so as to search essential global optimal side information around the entire solution space for HS-based RDH. Apparently, the process will dramatically increase the solution space and computation complexity. To effectively control the time cost, two novel approaches are proposed: 1) A pattern-based rapid performance evaluation method is designed to compute the rate and distortion; 2) Spirited by our previous work, a problem-oriented designed evolutionary algorithm, i.e., transfer learning-based genetic algorithm, is proposed to perform high-efficiency search around the entire huge solution space. For a given data payload, the proposed scheme could adaptively determine the optimal combination of peak and zero bins without any unnecessary constraint. The experimental results demonstrate the superiority of the proposed scheme compared with other state-of-the-art methods.

Vehicular networks are facing the challenges to support ubiquitous connections and high quality of service for numerous vehicles. To address these issues, mobile edge computing (MEC) is explored as a promising technology in vehicular networks by employing computing resources at the edge of vehicular wireless access networks. In this paper, we study the efficient task offloading schemes in vehicular edge computing networks. The vehicles perform the offloading time selection, communication, and computing resource allocations optimally, the mobility of vehicles and the maximum latency of tasks are considered. To minimize the system costs, including the costs of the required communication and computing resources, we first analyze the offloading schemes in the independent MEC servers scenario. The offloading tasks are processed by the MEC servers deployed at the access point (AP) independently. A mobility-aware task offloading scheme is proposed. Then, in the cooperative MEC servers scenario, the MEC servers can further offload the collected overloading tasks to the adjacent servers at the next AP on the vehicles' moving direction. A location-based offloading scheme is proposed. In both scenarios, the tradeoffs between the task completed latency and the required communication and computation resources are mainly considered. Numerical results show that our proposed schemes can reduce the system costs efficiently, while the latency constraints are satisfied.

Based on the Hall effect, a magneto-acousto-electrical tomography (MAET) has been indicated to have a good ability for distinguishing the conductivity variations along the acoustic propagation direction, and B-scan imaging of the MAET is expected to obtain pathological information of the tissue. For achieving a clear B-scan image, in this paper, we designed and implemented a MAET system with a planar transducer and conducted a series of experiments to explore the characteristics of the magneto-acoustic-electrical (MAE) signal and electromagnetic interference (EMI) signal. The influence of the EMI signal on the MAE voltage signal was demonstrated experimentally, and the generation mechanism of the EMI signal was explained. Concurrently, several effective methods were proposed for reducing the EMI signal and improving the imaging resolution of the B-scan image. Additionally, for obtaining a B-scan image with high resolution, the detection front end was redesigned and algorithms applying the characteristics of the MAE signal were proposed. The accuracy, feasibility, and effectiveness of the improved methods were verified. Finally, a B-scan image was reconstructed with the relative amplitude of the conductivity. The results showed that: 1) the MAE signal obtained by the redesigned platform could be well separated from the EMI signal and had a higher SNR than that obtained by the previous detection system; 2) the proposed imaging algorithms had a high detection accuracy and achieved an axis resolution of 1 mm on the z-axis; and 3) the interfaces of the conductivity changes of homogeneous phantoms with 0.5% salinity were clearly presented by measuring the MAE wave packets, and the measured thicknesses of the phantoms were highly consistent with the actual thicknesses. This paper provides a theoretical and experimental basis for detecting the interface positions of conductivity variation, and the presented MAET technology is expected to become an alternative medical imaging modality for the early diagnosis and detection of biological cancerous tissues.

Microstructure functional surface is widely used in an optical system because of its special micro-topological structure and particular physical properties. This paper presents a context-aware autonomously controlling method of micro-dots punching machine tool via establishing the digital twin-driven cyber-physical system. Key enabling techniques on twinning of cyberspace and physical equipment are discussed. A dynamic adjustment model of piezoelectric ceramics for micro-dots punching is presented based on the high-precision online detection and control system. A novel staggered punching approach is proposed for improving the punching speed. A joint optimization model is proposed for coordinating micro-punching system and staggered process. Context-aware autonomous adjusting of the system with errors analysis and compensations in the punching process is realized. Finally, a positioning accuracy of $2~\mu \text{m}$ and a high punching speed of 20-65 dots/s are achieved. This paper is expected to provide a new approach for incorporating smart-enabling techniques in the ultra-precision machining of microstructure arrays.

Iterative closest point algorithms suffer from non-convergence and local minima when dealing with cloud points with a different sampling density. Alternative global or semi-global registration algorithms may suffer from efficiency problem. This paper proposes a new registration algorithm through the differential topological singularity points (DTSP) based on the Helmholtz-Hodge decomposition (HHD), which is called DTSP-ICP method. The DTSP-ICP method contains two algorithms. First, the curvature gradient fields on surfaces are decomposed by the HHD into three orthogonal parts: divergence-free vector field, curl-free vector field, and a harmonic vector field, and then the DTSP algorithm is used to extract the differential topological singularity points in the curl-free vector field. Second, the ICP algorithm is utilized to register the singularity points into one aligned model. The singularity points represent the feature of the whole model, and the DTSP algorithm is designed to capture the nature of the differential topological structure of a mesh model. Through the singularity alignment, the DTSP-ICP method, therefore, possesses better performance in triangular model registration. The experimental results show that independent of sampling schemes, the proposed DTSP-ICP method can maintain convergence and robustness in cases where other alignment algorithms including the ICP alone are unstable. Moreover, this DTSP-ICP method can avoid the local errors of model registration based on Euclidean distance and overcome the computation insufficiencies observed in other global or semi-global registration publications. Finally, we demonstrate the significance of the DTSP-ICP algorithm's advantages on a variety of challenging models through result comparison with that of two other typical methods.

Tailings were used to prepare cemented paste backfill (CPB) reinforced with polypropylene (PP) fiber, and fiber content and fiber length were 0-20 % and 3-12 mm, respectively. The Brazilian indirect tensile strength tests, macrostructural and microstructural (SEM) failure mode analyses and nuclear magnetic resonance (NMR) tests were performed. The results demonstrate that PP fiber with high tensile strength can substantially enhance the tensile strength and ductility of CPB specimens. The fiber-reinforced CPB exhibits superior performance during the pre-peak and post-peak stages and can retain its residual strength after being broken. The fiber-reinforced CPB provided a higher secant Young's modulus and strain at peak stress than that of the unreinforced CPB. From the failure mode analysis, fiber was used to bridge the CPB microelements and combine these effectively with calcium silicate hydrate gelling. The governing failure mode of fiber is pulled out or pull off, through which fiber absorbs the tensile stress and energy. However, fiber is also believed to have confusion distribution in CPB because fiber cannot reach their full strain capacity, especially at the later curing stage. And the weak structural layers in the CPB can also be formed, and the higher tensile strength is not achieved with the additional fiber content or longer fiber length. The optimal fiber content in this study is recommended to be 0.15 %, and it's better to choose 6 or 9 mm fiber. Moreover, fiber can reduce the original porosity by 10.72 %, but only has a slight influence on the pore size. The decrease in porosity helps increase the tensile strength of the fiber-reinforced CPB.

An interconnected multi-microgrids (IMMGs) system takes advantage of various complementary power sources and effectively coordinates the energy sharing/trading among the MGs and the main grid to improve the stability, reliability, and energy efficiency of the system. The core of this structure is to achieve the optimal distribution of energy sharing through proper strategies. However, the volatility and intermittent characteristics of renewable resources, time-varying loads in the MGs, their correlated power generations, and the coupled energy among the MGs during energy trading, all bring about new challenges to achieving a stable operation and optimal scheduling in the power system. Many solutions have been proposed to solve these problems. In this paper, we provide an overview of the current energy management systems (EMS) in IMMGs, focusing on the IMMG structure, EMS objectives, timescales, and scheduling optimization structure. We then provide a review of the distributed optimization algorithms in IMMGs. We conclude this survey with a discussion of future directions.

Driven by the vision of 5G communication, the demand for mobile communication services has increased explosively. Ultra-dense networks (UDN) is a key technology in 5G. The combination of mobile edge computing (MEC) and UDN can not only cope with access from mass communication devices, but also provide powerful computing capacity for users at the edge of wireless networks. The UDN based on MEC can effectively process computation-intensive and data-intensive tasks. However, when a large number of users offload tasks to the edge server, both the network load and transmission interference would increase. In this paper, the problem of task offloading and channel resource allocation based on MEC in 5G UDN is studied. Specifically, we formulate task offloading as an integer nonlinear programming problem. Due to the coupling of decision variables, we propose an efficient task offloading and channel resource allocation scheme based on differential evolution algorithm. Simulation results show that the proposed scheme can obviously reduce energy consumption and has good convergence.