Search

Abstract Superalloy materials exhibit susceptibility to fracture failures stemming from the influence of thermomechanical factors. To comprehensively understand the fracture mechanisms, material properties, root causes of failure, and the subsequent optimization of alloys, a detailed analysis of the internal fracture process and the morphological traits of the fracture surface is imperative. Traditional analysis of fracture surfaces solely relies on 2D images, thus lacking crucial 3D information. Although in situ experiments can capture the fracture process, their effectiveness is confined to the specimen’s surface, precluding insight into internal changes. Here we introduce an integrated framework encompassing the process of 3D reconstruction of fracture surfaces, aiming to enhance the visual information obtained with micron-level accuracy, visual intuitiveness and sense of depth. Additionally, this framework also facilitates the scrutiny and inference of internal fracture processes. These results demonstrate that under specific service conditions, material deformation fracture probably stems from a combination of surface cracking and internal cracking rather than exclusively one or the other. Overall, our description and analysis of internally initiated cracking due to defects within the specimens can be beneficial in guiding future alloy design and optimization efforts.

In order to effectively adjust reservoir heterogeneity and further exploit the remaining oil, a new type of low-viscosity gel was prepared by adding a regulating agent, retarder, and reinforcing agent on the basis of a polymer + Cr3+ crosslinking system. The new gel has the advantages of low initial viscosity, a slow gel formation rate, and high strength after gel formation. The effectiveness of the gel was verified through three-layer core displacement experiments, and the injection scheme was optimized by changing the slug combination of the polymer and the gel. The results showed that the gel can effectively block the high-permeability layer and adjust reservoir heterogeneity. An injection of 0.1 pore volume (PV) low-initial-viscosity gel can improve oil recovery by 5.10%. By changing the slug combination of the gel and polymer, oil recovery was further increased by 3.12% when using an injection of 0.07 PV low-initial-viscosity gel +0.2 PV high-concentration polymer +0.05 PV low-initial-viscosity gel +0.5 PV high-concentration polymer.

Abstract Conventional direction-of-arrival (DOA) estimation methods for multiple-input multiple-output (MIMO) joint sensing and communication system normally pursue high estimation accuracy and resolution by imposing orthogonal waveforms. However, such operation results in a deterioration of communication performance. In this paper, we propose a nonorthogonal waveform assisted DOA estimation algorithm, where an augmented virtual array is derived by exploiting the nonorthogonal MIMO communication waveforms, while a high communication rate can still be maintained. To estimate the round-trip sensing channels of each subcarrier, we utilize the transmitted symbols as pilot symbols, and obtain all the channel coefficients with a minimum mean square error solver. A virtual channel matrix can be formulated with these channel coefficients, which can be regarded as the samples of an augmented virtual array. Based on that, the subspace processing can be conducted for DOA estimation with fully nonorthogonal waveforms. Furthermore, the rank deficiency property of the equivalent signal matrix of the virtual array is analyzed when the distance of targets are identical. To address the problem, a Toeplitz reconstruction method is proposed to restore the rank of the rank-deficient equivalent signal matrix for DOA estimation. Simulations show that the proposed nonorthogonal waveform assisted DOA estimation algorithm outperforms the conventional methods in terms of resolution and accuracy, while maintaining a satisfactory computational efficiency.

Abstract Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

In recent years, China has participated in the global climate governance with a positive stance.The accurate, rapid, and reliable monitoring is fundamental for the situation awareness and layout optimization of the carbon emission.However, the state-of-the-art review with systematic studies of this topic has hardly been identified.The development process and the existing standards of carbon emission monitoring were investigated.The terminal equipment-based methods and the internet of things (IoT)-based applications for carbon emission monitoring were explored, and the IoT-based methods were concluded which hold the advantages including clear structure, flexible design, and reliable transmission.Based on the IoT, an industrial carbon emission monitoring method using the “cloud-tube-edge-end” IoT structure was established.The enabling technologies that can implement the functions of the “cloud-tube-edge-end” were summarized, which are expected to provide guidance for future carbon emission monitoring.

Contact tracing using mobile phones has been a strategy used by many countries to limit the spread of COVID-19. Here, the authors use location-related phone data from Wuhan to construct a temporal contact graph and demonstrate a strong correlation between contacts level and epidemic size, thus justifying the effectiveness of the approach.

While the reconstruction of classic monolayer networks is a widely addressed problem, accurate reconstruction of multiplex networks is still an open challenge. Here the authors propose a statistical approach that allow to achieve optimal reconstruction of multiplex networks from partial observations and knowledge of the aggregated network.

The arrival interval at high-speed railway stations is one of the key factors that restrict the improvement of the train following intervals. In the process of practical railway operation, sudden conflicts occur sometimes. Especially when the conflict arises at the station, because the home signal cannot be opened in time, the emergency may affect the adjustment of the train operation under the scheduled timetable, resulting in a longer train following interval or even delay. With the development of artificial intelligence and the deep integration of big data, the architecture of train operation control and dispatch integration is gradually improving from the theoretical point. Based on this and inspired by the Green Wave policy, we propose an integrated operation method that reduces the arrival interval by avoiding unnecessary stops in front of the home signal and increasing the running speed of trains through the throat area. It is a two-step optimization method combining both intelligent optimization and mathematical–theoretical analysis algorithms. In the first step, the recommended approaching speed and position are obtained by analytical calculation. In the second step, the speed profile from the current position to the position corresponding to the recommended approaching speed is optimized by intelligent optimization algorithms. Finally, the integrated method is verified through the analysis of two distinct case studies. The first case study utilizes data from the Beijing–Shanghai high-speed railway line, while the second one is based on the field test. The numerical result shows that the proposed method could save the entry running time effectively, compared with the normal strategy given by the train driver. The method can mitigate controllable conflict events occurring at the station and provides theoretical support for practical operation.

Abstract Restoring the controllability of networked control systems is a fundamental issue that needs to be settled, especially when malicious attacks or malfunctions occur. Previous studies tried to address such an issue by adding extra driver nodes or rewiring edges, which will inevitably change the original network structure. In this paper, an algorithm to restore the controllability of networked systems while preserving the integrity of the original network structure is proposed. Specifically, a static uncontrollable network will be transformed into a controllable temporal network by means of the cactus‐based segmentation method. The original problem will be equivalent to the classical set cover problem, which is known to be NP‐Hard, if the least number of segmentations is considered. An approximation algorithm with polynomial time complexity is proposed and it is proved that the solution to the problem is two‐optimal. Finally, simulations are carried out to verify the effectiveness and feasibility of the proposed algorithm.

Prediction of drug-target interactions (DTI) plays a vital role in drug development through applications in various areas, such as virtual screening for lead discovery, drug repurposing and identification of potential drug side effects. Here, the authors develop a unified framework for DTI prediction by combining a knowledge graph and a recommendation system.

The proliferation of the Internet of Things (IoT) has paved the way for the future of smart cities. The large volume of data over the IoT can enable decision-making for various applications such as smart transportation, smart parking, and smart lighting. The key to the success of smart cities is data collection and aggregation over the IoT. Recently, crowdsensing has become a new data collection paradigm over the IoT, which can realize large-scale and fine-grained data collection with low cost for various applications. For example, we can leverage the power of the crowd to build a real-time noise map with microphones on smartphones. Despite the advantages of crowdsensing and the IoT, there are many challenges to utilize crowdsensing over the IoT for smart cities, such as how to allocate tasks to appropriate users to provide high-quality sensing data, how to incentivize users to participate in crowdsourcing, how to detect the reliability of the crowdsourced data, and how to protect the privacy of users.

Knowledge of the structural properties of biological neural networks can help in understanding how particular responses and actions are generated. Recently, Witvliet et al. published the connectomes of eight isogenic Caenorhabditis elegans hermaphrodites at different postembryonic ages, from birth to adulthood. We analyzed the basic structural properties of these biological neural networks. From birth to adulthood, the asymmetry between in-degrees and out-degrees over the C. elegans neuronal network increased with age, in addition to an increase in the number of nodes and edges. The degree distributions were neither Poisson distributions nor pure power-law distributions. We have proposed a model of network evolution with different initial attractiveness for in-degrees and out-degrees of nodes and preferential attachment, which reproduces the asymmetry between in-degrees and out-degrees and similar degree distributions via the tuning of the initial attractiveness values. In this study, we present the well-preserved structural properties of C. elegans neuronal networks across development, and provide some insight into understanding the evolutionary processes of biological neural networks through a simple network model.

Cloudlet is a new paradigm in mobile cloud computing to provide resources to nearby mobile users via one-hop wireless connections. In this paper, we leverage the social tie structure among mobile users to achieve mutually beneficial computation offloading decision making, and hence, enhance the system-wide performance. Drawing on a social group utility maximization (SGUM) framework, we cast users' decision making of whether to offload or not as a Socially aware computation offloading game (COG). We study the SGUM-based COG for both strong and weak information cases. For the strong information case, where each user has the knowledge of other users' actions and the perfect observation of its achieved social group utility, we show that there exists a socially aware Nash equilibrium (SNE). We then design a distributed algorithm to achieve the SNE and quantify its performance gap with respect to the social optimal solution. For the weak information case, where each user does not have the knowledge of other users' actions and observes a noise-corrupted social group utility, we develop a distributed reinforcement learning algorithm, which is shown to converge almost surely to an ϵ-SNE. The numerical results show that the computation offloading performance can be significantly enhanced by leveraging the social ties among the users.

With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one.

In wireless sensor networks (WSNs), there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm.