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Two-dimensional semiconductors, including transition metal dichalcogenides, are of interest in electronics and photonics but remain nonmagnetic in their intrinsic form. Previous efforts to form two-dimensional dilute magnetic semiconductors utilized extrinsic doping techniques or bulk crystal growth, detrimentally affecting uniformity, scalability, or Curie temperature. Here, we demonstrate an in situ substitutional doping of Fe atoms into MoS 2 monolayers in the chemical vapor deposition growth. The iron atoms substitute molybdenum sites in MoS 2 crystals, as confirmed by transmission electron microscopy and Raman signatures. We uncover an Fe-related spectral transition of Fe:MoS 2 monolayers that appears at 2.28 eV above the pristine bandgap and displays pronounced ferromagnetic hysteresis. The microscopic origin is further corroborated by density functional theory calculations of dipole-allowed transitions in Fe:MoS 2 . Using spatially integrating magnetization measurements and spatially resolving nitrogen-vacancy center magnetometry, we show that Fe:MoS 2 monolayers remain magnetized even at ambient conditions, manifesting ferromagnetism at room temperature. Ferromagnetism with a Curie temperature above room temperature in 2D materials is highly desirable for practical spintronics applications. Here, the authors demonstrate such phenomenon in monolayer MoS 2 via in situ iron-doping and measured local magnetic field strength up to 0.5 ± 0.1 mT.

Construction robots are increasingly becoming a significant force in the digital transformation and intelligent upgrading of the construction industry. Path planning is crucial for the advancement of building robot technology. Based on the understanding of construction site information, this paper categorizes path-planning algorithms into two types: global path-planning and local path-planning. Local path planning is further divided into classical algorithms, intelligent algorithms, and reinforcement learning algorithms. Using this classification framework, this paper summarizes the latest research developments in path-planning algorithms, analyzes the advantages and disadvantages of various algorithms, introduces several optimization strategies, and presents the results of these optimizations. Furthermore, common environmental modeling methods, path quality evaluation criteria, commonly used sensors for robots, and the future development of path-planning technologies in swarm-based construction robots are also discussed. Finally, this paper explores future development trends in the field. The aim is to provide references for related research, enhance the path-planning capabilities of construction robots, and promote the intelligent development of the construction industry.

Detecting the factors affecting drivers’ safe driving and taking early warning measures can effectively reduce the probability of automobile safety accidents and improve vehicle driving safety. Considering the two factors of driver fatigue and distraction state, their influences on driver behavior are elaborated from both experimental data and an accident library analysis. Starting from three modes and six types, intelligent detection methods for driver fatigue and distraction detection from the past five years are reviewed in detail. Considering its wide range of applications, the research on machine vision detection based on facial features in the past five years is analyzed, and the methods are carefully classified and compared according to their innovation points. Further, three safety warning and response schemes are proposed in light of the development of autonomous driving and intelligent cockpit technology. Finally, the paper summarizes the current state of research in the field, presents five conclusions, and discusses future trends.

This paper proposes a path correction scheduling strategy for the underground mining boom roadheader by ably combining a back propagation (BP) neural network and state estimation. First, a pose deviation-based tracking model is designed for the roadheader, and it is then further studied and optimized by incorporating the benefits of BP neural networks into the model adaptation. Considering the fact that there is skidding between tracks on the ground and errors during the instant pose detection of the roadheader underground, singular value decomposition (SVD)–Unscented Kalman filtering is applied to estimate the real pose deviation, based on the summarized distribution regularities of the track skidding ratios and the pose detection errors, instead of complicated analysis mechanisms. The BP neural network and states estimation are well combined in structure, enabling this scheduling strategy to update the control law and revise the control instruction simultaneously in the procedure. The proposed path tracking model for the roadheader is simple and clear, without adding extra devices or massive algorithms, which is attractive in terms of industrial use. The path tracking simulations show that this proposed strategy achieves path tracking well in different scenarios and is of high adaptability when facing complex trajectory while still giving stable control instructions for the roadheader.

The pitch angle of a roadheader is the key factor to ensure the quality during the formation of a roadway. Various control strategies have been employed to adjust the attitude angle of different cars or planes to achieve distinct goals for a roadheader. In this paper, we established a mathematical model of the pitch angle and adjustment actuator, as well as their transitive relation. The model was verified experimentally using a real roadheader. The accuracy in the adjustment of the pitch angle was analyzed using the model by designing and simulating the PID controller, fuzzy logic controller (FLC), and dynamic error elimination controller (DEEC). The performance of the controllers were tested by simulation and experiment using four types of input signal. The responses were compared with the expected results. The DEEC reduced the response time and overshoot but increased the robustness. Thus, this controller is better than the PID and FLC.

This study presents a dynamical model of the attitude angles of boom-type roadheader, to reveal the response characteristics of the attitude angles in steep coal seam. Based on the Lagrange equation, the dynamical model of the attitude angles of roadheader is established, then the simulation system is constructed in Simulink , to solve the dynamical model. Afterwards, a calculating method of the cutting load is proposed, to formulate the external loads of the roadheader during cutting process. Focused on the steep coal seam, of which the dip angle is 30°, the dynamical model is solved, and the solving results influenced by different factors are obtained. The results show that, the variations of roadheader’s attitude are affected greatly by the cutting load, while the influence of swing angle of the cutting arm is relatively slight. Among the three attitude angles, the pitch angle varies most greatly, which can reach up to 9.1° and 8.7° during horizontal and vertical cutting process respectively. Finally, the numerical simulation results are verified by experimental data. The dynamical model and response characteristics of roadheader’s attitude angle presented in this paper, can provide useful basis for prediction of roadheader’s operating status and rectification of roadheader’s attitude in steep coal seam. Article Highlights The dynamical model of roadheader’s attitude is established, to accurately describe dynamic behaviors of roadheader during cutting process. The response regularities of roadheader’s attitude in steep coal seam, under effects of different factors, are revealed comprehensively. A method to calculate the cutting load is proposed, upon which the three dimension forces on the cutting head can be obtained.

Calcium carbide sludge is a kind of dense paste; when transported by pipeline, the pressure loss is enormous. However, how to calculate the pressure loss accurately has not been solved until now. This article aims to present a new method to build a pressure loss calculation model based on the experimental data of pipeline transportation. To determine the relationship between the pressure loss and the properties of calcium carbide sludge, a new circulating pipeline testing apparatus was designed. The test studied pressure losses arising from changes in the mass concentration of the paste, flow velocity, pipe diameter, and pipe length. Analyzing the obtained data by means of nonlinear curve fitting, the adsorption coefficient and viscosity coefficient were defined and then the calculation formula was deduced. Finally, the calculation formula was verified for a project pipeline with a relatively large diameter equal to φ200 mm and the same parameters as those of industrial applications. The results show that under the appropriate conditions of applying the model, the relative error is less than 20%, so the model can be applied to engineering pressure loss estimation. This method provides a suitable pipe transportation calculation method for dense paste, combining experimental data and theoretical derivation.

System ASC amino acid transporter-2 (ASCT2) is abnormally highly expressed in tumor cells and closely associated with a poor prognosis, but the regulatory mechanism of abnormally high ASCT2 expression is scarcely investigated. MicroRNAs (miRNAs) that are abnormally expressed regulate gene expression to have either oncogenic or tumor-suppressive effects in pancreatic cancer (PC). MicroRNA-122-5p (miR-122-5p) dysregulation has been seen in various cancer entities, but the biological function of miR-122-5p in PC and its regulation mechanisms remain unknown. Western blot and quantitative RT-PCR were used to measure the expression of miR-122-5p, ASCT2, and apoptosis-related proteins. CCK-8 assays were used to elucidate the effect on cell proliferation. Flow cytometry (FCM) assays were utilized to evaluate cell apoptosis. A dual-luciferase reporter assay was utilized to determine if miR-122a-5p directly targeted ASCT2. Glutamine consumption and the α-ketoglutarate (α-KG) and adenosine triphosphate (ATP) contents were determined using respective assays. MiR-122-5p expression was low whereas ASCT2 expression was high in PC tissues and cells. Overexpressing miR-122-5p restrained pancreatic cancer cell proliferation, accelerated apoptosis, and decreased glutamine consumption, α-ketoglutarate (α-KG) production and ATP generation, whereas suppressing miR-122-5p had the opposite effect. Moreover, the reporter gene test established ASCT2 as a miR-122-5p target. Overexpression of miR-122-5p decreased ASCT2 expression, whereas miR-122-5p repression increased ASCT2 expression. In addition, miR-122-5p also regulated apoptosis-related pathways. MiR-122-5p may function as a tumor suppressor by inhibiting the proliferation, glutamine metabolism, and inducing apoptosis via altering the expression of ASCT2 in pancreatic cancer cells.

Based on the Lagrange equation in system dynamics, aiming at the horizontal cutting process, the dynamical coupling model of boom-type roadheader’s body pose was established. According to input problem of solving the model, a calculation method of the cutting head load was proposed, and the relationship between the cutting head load and pressure of the driving cylinders and swing angle of the cutting arm was obtained through simulating analysis. The simulation model was established to solve the dynamical coupling model. The cutting head load, horizontal swing angle of the cutting arm, and dip angle of coal seam were regarded as independent variables to perform changing parameter analysis in variations of the body pose. The field experiment was carried out, and the measured data is basically consistent with the simulation values. The results show that lateral displacement of the body can reach up to 6.5 cm, backward displacement can reach up to 5.2 cm, floor-based quantity can reach up to 11 cm, pitch angle of the body can reach up to 7.8°, and roll angle can reach up to 2.1°. Variations of the body pose parameters are influenced greatly by the cutting head load, while the influence from horizontal swing angle of the cutting arm and dip angle of coal seam is slighter. Among the pose parameters, floor-based quantity and pitch angle of the body vary relatively greatly, which tend to seriously influence forming quality of the roadway and should be mainly considered in deviation rectification of the roadheader’s body pose.

Condition monitoring and accurate fault diagnosis are always concerned for stable operating of mechanical equipment. The fault diagnosis based on supervised deep learning has been proved to be effective by their powerful capacities in feature extracting, but usually requiring large number of labeled data. Faced with the actual situation that labeled samples are often in short, data are imbalanced in category etc., accurate fault diagnosis based on deep learning is still challenging, so does to explore and explain the evolution of complex faults. A mechanical fault diagnosis model with Semi-Supervised Variational Autoencoder based on Long Short-Term Memory network (LSTM-SSVAE) is proposed in this paper. Through semi-supervised learning, LSTM-SSVAE uses unlabeled data to enhance the extraction of discriminant features of data, which make the model less dependent on only labeled data while giving improved fault diagnosis accuracy. The LSTM networks are applied as the encoder and decoder innovatively, and regularization constraints are added in loss function, to improve the clustering effect of the intermediate hidden variables, so that to achieve effective feature extraction and state detection. Based on open datasets, experimental results show that with the same number of labeled samples, the fault diagnosis accuracy obtained by using LSTM-SSVAE is higher than other typical semi-supervised learning models. Based on actual vibration data of working equipment in coal mining, the feasibility of clustering analysis of intermediate hidden variables also proves that the LSTM-SSVAE model is recommendable for fault evolution analysis and is potential for operating conditions prediction of mechanical equipment.