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The effects of sintering temperatures on the microstructures and mechanical properties of titanium carbide particles reinforced iron matrix composites (TiC/Fe MCs) fabricated by the spark plasma sintering (SPS) process with pure element powders have been systematically investigated. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), electron back scattering diffractometer (EBSD), and energy dispersive spectroscopy (EDS) have been conducted for microstructural analysis. The results show that with increasing sintering temperatures, the porosity of the composites initially decreases and then increases. Simultaneously, the grain size gradually diminishes while element diffusion becomes more uniform. Upon reaching a critical sintering temperature (1120 °C), the original grain size disappears and carbides undergo decomposition and reprecipitation to reach an equilibrium state, with which optimal comprehensive properties can be achieved (porosity decreases to a minimum of 3.85%, grain size of 2.69 μm, Vickers hardness reaches 595 HV0.5, bending strength is at 662 MPa, coefficient of friction is at 0.74, and wear loss to 0.21 mg). These property enhancements have been attributed to reduced porosity in the composites, decreased grain size, and improved anchoring effect of carbides within the matrix. Additionally, the primary fracture mechanisms and wear mechanisms of TiC/Fe MCs with different process parameters have been analyzed. When the temperature is below 1080 °C, intergranular fracture predominates, whereas transgranular and ductile fractures become predominant above this threshold. When the temperature is below 1120 °C, fatigue wear, oxidation wear, and abrasive wear are predominantly observed. Conversely, when the temperature exceeds 1120 °C, oxidation wear and abrasive wear become the primary mechanisms.

Aiming at the problem of coal seam roof water disaster in arid and semi-arid mining areas in northwest China, in order to find out the distribution range of roof water channel in mining face, based on the whole space transient electromagnetic theory and the finite difference time domain algorithm, combined with the electrical characteristics of the strata in the demonstration mining area, a forward geoelectric model was established. The magnetic field response characteristics of different magnetic field components, different time and different detection angles of roof water channel in mining face were studied by numerical simulation, and the application experiment was carried out in the demonstration mining area of Xinjiang. The results show that the amplitude of the Hy magnetic field component corresponding to the normal direction of the wire frame is the largest, the magnetic field distribution trend is the simplest. The magnetic field intensity and attenuation gradient gradually decrease with time delay, and a secondary induction will be generated at the water channel of the roof, which will be used as the secondary field source to diffuse outward and attenuate. The Hy magnetic field component can identify the roof water channel, and the maximum effective detection elevation angle is 75°. The application experiment and later drilling in the demonstration mining area verify the feasibility and accuracy of the transient electromagnetic method in the detection of the roof water channel in the mining face.

As the coal mining area shifts towards deeper depths, mining depth continues to increase, mining conditions become more complex, and ground pressure disasters become increasingly severe. There is an urgent need for accurate detection methods capable of spatially detecting high-stress areas in coal mining faces. Through uniaxial compression test, the variation of seismic longitudinal wave velocity of coal and rock samples under uniaxial compression was studied. It was found that during the elastic stage of uniaxial compression, the axial seismic longitudinal wave velocity of coal and rock samples increased with increasing stress until the coal and rock blocks were damaged, and the wave velocity rapidly decreased. The response curve of seismic wave velocity to stress showed that there was a positive correlation between seismic longitudinal wave and stress during the elastic stage of coal and rock samples; at the same time, the response curve characteristics of seismic slot wave velocity and stress under uniaxial compression conditions were also studied, and it was found that there was a positive correlation between seismic slot wave and stress, which could also be used for exploring high-stress areas in coal mining working face. It is proposed to use seismic slot wave and longitudinal wave together to detect stress concentration areas. Simple geological conditions of coal mining working face were selected for high-stress detection experiments, and the initial arrival speed of longitudinal wave and the speed at a specific frequency of slot wave were extracted from the test data. After CT tomography imaging, a contour map of wave velocity was formed. Then, combined with the relevant information of roadway, the high-speed area was circled respectively, and according to the positive correlation between seismic wave velocity and stress, the high-speed area was interpreted as a high-stress area. Finally, the overlapping area of the two was taken as a high-risk area of impact ground pressure in the working face. Through drilling and mining verification: when drilling in the stress overlap area, the amount of coal powder was obviously larger, easy to shrink holes; when mining passed through the stress overlap area, the deformation of roadway was relatively large, and “coal cannon” occasionally occurred. The joint exploration test shows that trough waves can be used to detect high-stress areas in coal mining working face, and combining longitudinal waves can overcome the limitations of single geophysical exploration means and more accurately detect high-stress areas in impact ground pressure mines.

In the field of petroleum extraction, the welding technology of the core wire (the hybrid structure of copper and the Ni-Cr alloy) in high-power oilfield heaters is a key process that determines the efficiency of the heater. Using the tungsten inert gas (TIG) welding method of filling pure copper wire, this work effectively joins the dissimilar metals of red copper and the Cr20Ni80 nickel–chromium alloy. The microstructure, mechanical properties, and conductivity of the joint were analyzed. The results showed that the surface of the welded dissimilar metal joint was smooth and uniform; radiographic nondestructive testing did not reveal any macroscopic forming defects such as pores or cracks. The microstructure of the joint fusion zone exhibits an equiaxed grain morphology. The interface between the copper and the fusion zone displays a columnar grain structure, growing perpendicular to the fusion line. An interdiffusion layer of elements was formed at the interface between the Ni-Cr alloy and the fusion zone. The microhardness of the joint shows a stepwise decreasing trend, with the highest hardness on the nickel–chromium alloy side, followed by the fusion zone, and the lowest on the copper side. The joint fractures at the copper base material, with a tensile strength greater than 220 MPa, indicating a ductile fracture mode. During the electrical heating process, the joint temperature does not significantly increase compared to the copper side, demonstrating good thermal stability.

In recent years, the safety issues of high-speed railways have remained severe. The intrusion of personnel or obstacles into the perimeter has often occurred in the past, causing derailment or parking, especially in the case of bad weather such as fog, haze, rain, etc. According to previous research, it is difficult for a single sensor to meet the application needs of all scenario, all weather, and all time domains. Due to the complementary advantages of multi-sensor data such as images and point clouds, multi-sensor fusion detection technology for high-speed railway perimeter intrusion is becoming a research hotspot. To the best of our knowledge, there has been no review of research on multi-sensor fusion detection technology for high-speed railway perimeter intrusion. To make up for this deficiency and stimulate future research, this article first analyzes the situation of high-speed railway technical defense measures and summarizes the research status of single sensor detection. Secondly, based on the analysis of typical intrusion scenarios in high-speed railways, we introduce the research status of multi-sensor data fusion detection algorithms and data. Then, we discuss risk assessment of railway safety. Finally, the trends and challenges of multi-sensor fusion detection algorithms in the railway field are discussed. This provides effective theoretical support and technical guidance for high-speed rail perimeter intrusion monitoring.

Aiming at the problems related to the spread and prevention and control of COVID-19, the COVID-19 epidemic was studied based on agent based modeling thought.Firstly, based on the COVID-19 transmission mechanism, a mathematical model was established on the NetLogo platform to simulate the real process of epidemic spread.Secondly, the impacts of different factors (such as epidemic prevention and control measures, population migration patterns, COVID-19 incubation period, cure period and mortality) were analyzed and evaluated on the spread of the epidemic, so as to understand the core factors affecting the epidemic situation.Then, the internal law of the epidemic spread was analyzed and summarized.

In addressing the challenge of tracking moving targets at sea, our focus has been directed towards the development of a reconstruction methodology founded upon satellite orbital manoeuvres. This endeavour has led us to devise a predictive model for manoeuvres within a geographic coordinate system, alongside the creation of a three-phase orbital manoeuvre model. A Non-dominant Sorting Adaptive Memetic (NSAM) algorithm is proposed in this paper, which is a two-layer multi-objective optimization algorithm that retains the advantages of evolutionary algorithms based on the population’s evolution and has an excellent local optimization ability of local search algorithms. The proposed algorithm can be used to solve multi-objective optimization problems. By comparing the target observation results before and after the satellite reconstruction simulation, it can be concluded that the orbital manoeuvring can effectively improve the observation probability and observation duration of the target at a certain speed. The orbital manoeuvre model created in this paper provides a certain methodical support for the tracking problem of moving targets at sea.

Rolling bearing fault diagnosis is one of crucial tasks in mechanical equipment fault diagnosis. Currently, artificial intelligence and machine learning-driven fault diagnosis methods are extensively utilized for rolling bearing. When compared to traditional techniques, the diagnostic accuracy has significantly improved. These methods, however, need a substantial amount of labelled training data, which is difficult to obtain in actual failures. In order to resolve this problem, Transfer Learning (TL) was created to learn in the target domain by accessing knowledge from the pertinent labelled source domain. Inspired by Maximum Mean Discrepancy, this paper puts forward a Convolutional Neural Network (CNN) based Two-layer Transfer Learning (CTTL) method for fault diagnosis. In the first layer, the fault features are automatically extracted by CNN and a term called Feature Weighted Maximum Mean Discrepancy (WMMD) is considered to minimise the difference between source and target domains. In the second layer, the Third Dataset, which is based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method was designed on the basis of the predicted labels from the first layer. The Calinski-Harabaz (CH) index of the Target Dataset controls the iteration times of CTTL. CTTL change the process of Transfer Learning method from learning the distribution of domains to learning the distribution of fault types in more detail, which will get higher accuracy. Proposed CTTL is tested by the bearing datasets of Case Western Reserve University (CWRU) and XJTU-SY. The experimental findings reveal that CTTL is capable of achieving a high diagnosis accuracy across different load domains. In the majority of experiments, CTTL outperformed other algorithms, including Deep Neural Network, Support Vector Machine, and several other methods.

Several different methods of opponent modeling were introduced, leading to the problem of intention recognition in behavior modeling.Then, the process, classification, main methods, research prospects and practical applications of intention recognition were analyzed inductively, the latest research in related fields were summarized.Finally, some shortcomings of the current intention recognition and design methods were pointed out and some new insights for the future research were presented.

Abstract Poly (ADP-ribose) polymerase (PARP) inhibitors offer a significant clinical benefit for triple-negative breast cancers (TNBCs) with BRCA1/2 mutation. However, the narrow clinical indication limits the development of PARP inhibitors. Phosphoinositide 3-kinase (PI3K) inhibition sensitizes BRCA-proficient TNBC to PARP inhibition, which broadens the indication of PARP inhibitors. Previously researches have reported that PI3K inhibition induced the defect of homologous recombination (HR) mediated repair by downregulating the expression of BRCA1/2 and Rad51. However, the mechanism for their synergistic effects in the treatment of TNBC is still unclear. Herein, we focused on DNA damage, DNA single-strand breaks (SSBs) repair and DNA double-strand breaks (DSBs) repair three aspects to investigate the mechanism of dual PI3K and PARP inhibition in DNA damage response. We found that dual PI3K and PARP inhibition with BKM120 and olaparib significantly reduced the proliferation of BRCA-proficient TNBC cell lines MDA-MB-231 and MDA231-LM2. BKM120 increased cellular ROS to cause DNA oxidative damage. Olaparib resulted in concomitant gain of PARP1, forkhead box M1 (FOXM1) and Exonuclease 1 (Exo1) while inhibited the activity of PARP. BKM120 downregulated the expression of PARP1 and PARP2 to assist olaparib in blocking PARP mediated repair of DNA SSBs. Meanwhile, BKM120 inhibited the expression of BRAC1/2 and Rad51/52 to block HR mediated repair through the PI3K/Akt/NFκB/c-Myc signaling pathway and PI3K/Akt/ FOXM1/Exo1 signaling pathway. BKM120 induced HR deficiency expanded the application of olaparib to HR proficient TNBCs. Our findings proved that PI3K inhibition impaired the repair of both DNA SSBs and DNA DSBs. FOXM1 and Exo1 are novel therapeutic targets that serves important roles in DNA damage response.

Unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been widely used in delivery. In the context of the COVID-2019, in order to control the development of the epidemic, many places have adopted measures to isolate and close the area once a confirmed case is found. While reducing the contact between people, it also blocks the normal driving of vehicles. Only by changing the traditional logistics and distribution methods can customers who have been in a closed and isolated area for a long time be served. Therefore, we use the Cooperative UGV-UAV to achieve it. In this article, when commanding cooperative UGV and UAV for emergency resource delivery, we mainly focus on two questions: how to accept the operation order (OPORD) from the commander, how to generate a nested vehicle routing planning. We first employ one intelligent task understanding module to drive the intelligent unmanned vehicles to accept and process the C-BML (Coalition Battle Management Language) formatted OPORD with 5W (what, who, where, why, when) elements. Then, we slove the nested vehicle routing planning problem as a mixed integer linear program (MILP) with the outputs of what is the UGV route, what is the UGV sortie, and how to control the customers' distribution between the UGV and the UAV. Experimental results of random instances and case study show that using the iterative improvement algorithm increase the speed rate of solving more than 10%.

Hexagonal grids use a hierarchical subdivision tessellation to cover the entire plane or sphere. Due to the 6-fold rotational symmetry, hexagonal grids have some advantages (e.g. isoperimetry, equidistant neighbors, and uniform connectivity) over quadrangular and triangular girds, which makes them suitable to tackle tasks of geospatial information processing and intelligent decision-making. In this paper, we first introduce some applications based on the hexagonal grids. Then, we introduce the planer and spherical hexagonal grids and analyze the group representations for them, we review geometric deep learning, some Convolutional Neural Networks (CNNs) for hexagonal grids, and group-based equivariant convolution. Next in importance, we propose the HexagonNet for hexagonal grids, and define a new convolution operator and pooling operator. Finally, in order to evaluate the effectiveness of the proposed HexagonNet, we perform experiments on two tasks: aerial scene classification on the aerial image dataset (AID), and 3D shape classification on the ModelNet40 dataset. The experimental results verify the practical applicability of the HexagonNet given some fixed parameter budgets.

The fiber optic gyroscope (FOG) is a high precision inertial navigation device, and it is necessary to ensure its reliability for effective use. However, the extracted fault features are easily distorted due to the interference of vibrations when the FOG is in operation. In order to minimize the influence of vibrations to the greatest extent, a fusion diagnosis method was proposed in this paper. It extracted features from fault data with Fast Fourier Transform (FFT) and wavelet packet decomposition (WPD), and built a strong diagnostic classifier with a sparse auto encoder (SAE) and a neural network (NN). Then, a fusion neural network model was established based on the diagnostic output probabilities of the two primary classifiers, which improved the diagnostic accuracy and the anti-vibration capability. Then, five fault types of the FOG under random vibration conditions were established. Fault data sets were collected and generated for experimental comparison with other methods. The results showed that the proposed fusion fault diagnosis method could perform effective and robust fault diagnosis for the FOG under vibration conditions with a high diagnostic accuracy.

To obtain the optimal uniform prestress of a tensegrity structure with geometric configuration given, a novel method is developed for prestress design of tensegrity structures by utilizing the artificial fish swarm algorithm (AFSA). In the beginning, the form-finding process is implemented by solving a linear homogeneous system concerning the self-equilibrium system. The issue is subsequently performed as a minimum problem by regulating the value of an objective function where the unilateral condition and the stress uniformity condition are entirely considered. The AFSA is adopted to search for the global minimum, leading to a set of initial prestresses that guarantee all the above conditions. Two illustrative examples have been fully studied to prove the accuracy and efficiency of the presented approach in prestress design of tensegrities according to the practical requirements. Furthermore, the numerical examples investigated in this paper confirm that the AFSA has explicit advantages of rapid convergence and overcoming the local minima.

The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios. Research on black-box attack methods and the generation of adversarial samples is helpful to discover the defects of machine learning models. It can strengthen the robustness of machine learning algorithms models. Such methods require queries frequently, which are less efficient. This paper has made improvements in the initial generation and the search for the most effective adversarial examples. Besides, it is found that some indicators can be used to detect attacks, which is a new foundation compared with our previous studies. Firstly, the paper proposed an algorithm to generate initial adversarial samples with a smaller L2 norm; secondly, a combination between particle swarm optimization (PSO) and biased boundary adversarial attack (BBA) is proposed. It is the PSO-BBA. Experiments are conducted on the ImageNet. The PSO-BBA is compared with the baseline method. Experimental comparison results certificate that: (1) A distributed framework for adversarial attack methods is proposed; (2) The proposed initial point selection method can reduces query numbers effectively; (3) Compared to the original BBA, the proposed PSO-BBA algorithm accelerates the convergence speed and improves the accuracy of attack accuracy; (4) The improved PSO-BBA algorithm has preferable performance on targeted and non-targeted attacks; (5) The mean structural similarity (MSSIM) can be used as the indicators of adversarial attack.

As one of the key components for active compliance control and human–robot collaboration, a six-axis force sensor is often used for a robot to obtain contact forces. However, a significant problem is the distortion between the contact forces and the data conveyed by the six-axis force sensor because of its zero drift, system error, and gravity of robot end-effector. To eliminate the above disturbances, an integrated compensation method is proposed, which uses a deep learning network and the least squares method to realize the zero-point prediction and tool load identification, respectively. After that, the proposed method can automatically complete compensation for the six-axis force sensor in complex manufacturing scenarios. Additionally, the experimental results demonstrate that the proposed method can provide effective and robust compensation for force disturbance and achieve high measurement accuracy.

The Monte Carlo Tree Search (MCTS) has demonstrated excellent performance in solving many planning problems. However, the state space and the branching factors are huge, and the planning horizon is long in many practical applications, especially in the adversarial environment. It is computationally expensive to cover a sufficient number of rewarded states that are far away from the root in the flat non-hierarchical MCTS. Therefore, the flat non-hierarchical MCTS is inefficient for dealing with planning problems with a long planning horizon, huge state space, and branching factors. In this work, we propose a novel hierarchical MCTS-based online planning method named the HMCTS-OP to tackle this issue. The HMCTS-OP integrates the MAXQ-based task hierarchies and the hierarchical MCTS algorithms into the online planning framework. Specifically, the MAXQ-based task hierarchies reduce the search space and guide the search process. Therefore, the computational complexity is significantly reduced. Moreover, the reduction in the computational complexity enables the MCTS to perform a deeper search to find better action in a limited time. We evaluate the performance of the HMCTS-OP in the domain of online planning in the asymmetric adversarial environment. The experiment results show that the HMCTS-OP outperforms other online planning methods in this domain.

Goal recognition (GR) is a method of inferring the goals of other agents, which enables humans or AI agents to proactively make response plans. Goal recognition design (GRD) has been proposed to deliberately redesign the underlying environment to accelerate goal recognition. Along with the GR and GRD problems, in this paper, we start by introducing the goal recognition control (GRC) problem under network interdiction, which focuses on controlling the goal recognition process. When the observer attempts to facilitate the explainability of the actor’s behavior and accelerate goal recognition by reducing the uncertainty, the actor wants to minimize the privacy information leakage by manipulating the asymmetric information and delay the goal recognition process. Then, the GRC under network interdiction is formulated as one static Stackelberg game, where the observer obtains asymmetric information about the actor’s intended goal and proactively interdicts the edges of the network with a bounded resource. The privacy leakage of the actor’s actions about the real goals is quantified by a min-entropy information metric and this privacy information metric is associated with the goal uncertainty. Next in importance, we define the privacy information metric based GRC under network interdiction (InfoGRC) and the information metric based GRC under threshold network interdiction (InfoGRCT). After dual reformulating, the InfoGRC and InfoGRCT as bi-level mixed-integer programming problems, one Benders decomposition-based approach is adopted to optimize the observer’s optimal interdiction resource allocation and the actor’s cost-optimal path-planning. Finally, some experimental evaluations are conducted to demonstrate the effectiveness of the InfoGRC and InfoGRCT models in the task of controlling the goal recognition process.