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In atomistic simulations, pre-existing dislocations have been reported to reduce the yield stress compared to the ideal crystals. However, the underlying physics behind yield stress reduction is still unrevealed, which hammers the design of advanced materials. Here, large-scale molecular dynamics simulations are carried out to investigate the influence of pre-existing dislocations on the mechanical properties of body-centered cubic Fe crystals with dislocation, twinning, and phase transformation-dominated deformation mechanisms. The results suggest that the overestimated yield stress of all the crystals is significantly reduced by increasing dislocation numbers and obtaining closer flow stress on the uniform plastic deformation stage. This reduction in yield stress can be attributed to the lower thermo-dynamical driving force required to activate existing dislocations in pre-existing dislocation crystals than that to nucleating new dislocations in ideal crystals. Furthermore, pre-existing dislocations inhibited the phase transformation-dominated deformation process, but the twinning/dislocation-dominated deformation process still exhibited its original deformation mechanism.

Tungsten coil is one of the important components of the magnetron, and its surface quality and geometry directly affect the service life of the magnetron. The focus of this article is to apply machine vision and deep learning algorithms to study a surface defect detection and geometric measurement system for tungsten coils. Firstly, in order to solve the problem of difficulty, high cost, and low efficiency in manual detection caused by small and random surface cracks on tungsten coils, this paper proposes a surface crack detection method based on deep learning technology. By constructing the dataset and training the semantic segmentation model, the surface cracks were effectively detected and identified. Secondly, a sub-pixel edge detection method based on image processing technology and the Canny algorithm is proposed to address the issue of difficulty in measuring the geometric dimensions of tungsten coils due to their high bending spiral structure. The method accurately measures the length, outer diameter, and the maximum error is only 0.023mm. The results of the verification experiment indicate that the developed system can detect an average of 70 tungsten coil samples in one minute, with sensitivity and accuracy of 99.66% and 98.52%, respectively. This system not only has high robustness and efficiency, but also reduces 95.23% of manual workload, meeting the requirements of production lines for surface defect detection and geometric dimension measurement of tungsten coils, and solving the limitations of traditional manual detection.

Wood diameter needs to be measured in the process of production, sales and import and export. In order to solve the problem that it is difficult to accurately measure the densely stacked and irregularly arranged vehicle wood manually, this paper proposes a timber segmentation methodology that leverages a Swin Transformer model mechanism to enhance the performance of the target detection model. The method automatically learns and calculates distinct regions in the input image, assigning varying weights to different sizes and shapes of wood. This approach achieves finer detection of densely stacked logs, thereby promoting intelligent inspection and enhancing inspection efficiency.This study optimizes the backbone network by refining its modules and incorporating the operation of the log-space bias module. Additionally, improvements are made to the feature fusion network and loss function to further enhance network performance. The instance segmentation model parameters are also modified, encompassing multi-scale training, an increased number of training samples, improved image input size, and effective data widening techniques, all of which enhance log measurement accuracy and resolve the issue of partially occluded logs.This study conducts multiple control experiments to evaluate various scale metrics, such as mean average precision (mAP), log true detection rate, false detection rate, as well as comparing the root count and volume of logs through prediction. The experiments demonstrate that the mAP of this methodology reaches 0.685, and the true detection rate reaches 0.96 when compared with mainstream neural networks of similar scale, highlighting the advantages of this paper’s approach in wood segmentation detection. The model exhibits a strong detection effect on dense wood, effectively overcoming occlusion challenges, leading to more accurate measurement data. Moreover, the algorithm demonstrates robustness and migration ability, rendering it highly applicable to the task of detecting and segmenting dense wood of all sizes.

Wood is one of the energy sources for sustainable development, which is closely related to our life. In the wood production process, it will be sorted according to different diameter grade is an essential link, at present the wood sorting work mainly rely on manual completion, not only low efficiency, high cost, and cannot meet the automated era of production needs. In this paper, a wood diameter level automatic sorting system is designed, first of all, using computer vision technology to measure the diameter of the wood section in real time, then the diameter data is transmitted to the PLC control system, and finally the PLC control system pushes out the wood in the specified position, realizing the automated on-line sorting of wood. In addition, a wood diameter measurement method based on deep learning combined with binocular vision is proposed, which can quickly and accurately recognize the diameter of each piece of wood, and provides a solid foundation for the efficient performance of the automatic wood diameter grade sorting system. The experimental results show that the measurement method proposed in this paper has high robustness and the maximum error is within 5mm, which meets the accuracy requirements needed for sorting, and the sorting system can sort 10 pieces of wood per minute on average, which greatly improves the efficiency of wood sorting and reduces the labor workload and cost, and the research design is of great significance to the wood production and manufacturing industry.

During strong earthquakes, pounding may occur on large-span bridges and their approach bridges. The effect and mitigation measures of such pounding have rarely been explored in previous studies. This paper primarily uses finite element models to investigate the pounding effects at the expansion joints between the main cable-stayed bridge and its approach bridge. Friction pendulum bearings (FPBs) and fluid viscous dampers (FVDs) are used to alleviate poundings. Furthermore, a detailed analysis is conducted on how the pounding effect of the isolated main bridge with FPBs and FVDs is affected by the wave passage effect, ground motion type, and soil type. This study reveals that FPBs and FVDs can effectively reduce pounding effects and the associated risks. Even with the installation of FPBs and FVDs, lower seismic wave velocities and near-fault seismic motions with pulse effects can significantly increase the pounding effects between the cable-stayed bridge and its approach bridge.

We propose an active learning guided density functional theory calculation framework for rapid screening of multi-principal element alloys (MPEAs) with superior mechanical properties. Using this framework, we fast construct the datasets of the bulk modulus (B), shear modulus (G), yield strength, and Pugh’s ratio of 12,698 noble metal MPEAs. These datasets were obtained with density functional theory prediction accuracy (R2 = 0.98 and 0.96 for B and G, respectively) based on active learning guided 120 DFT calculated data. Analysis of the dataset shows that Ni and Au would enhance the yield strength and the toughness of these noble MPEAs, respectively.

Background: Circular RNAs (circRNAs) act as crucial regulators in tumorigenesis. In this study, the working mechanism of circ_0091579 in hepatocellular carcinoma (HCC) progression was investigated. Methods: The expression of RNA and protein was measured via RT-qPCR and Western blot assay. Cell proliferation ability was analyzed via CCK8, EdU and colony formation assays. Cell migration and invasion abilities were detected via transwell assays. Flow cytometry was applied to assess cell cycle and apoptosis. The target relation between miR-136-5p and circ_0091579 or tripartite motif containing 27 (TRIM27) was certified using dual-luciferase reporter assay. Xenograft tumor model was utilized to assess the role of circ_0091579 in tumor growth in vivo. The protein level of Ki67 in tumor tissues was analyzed by immunohistochemistry (IHC) assay. Results: Circ_0091579 expression was elevated in HCC tissues and cell lines. HCC patients with high circ_0091579 expression displayed low survival rate. Circ_0091579 knockdown suppressed the proliferation, migration, invasion, cell cycle progression and epithelial–mesenchymal transition (EMT) and induced apoptosis of HCC cells. Circ_0091579 acted as a molecular sponge for miR-136-5p, and circ_0091579 silencing-mediated effects were largely overturned by the knockdown of miR-136-5p in HCC cells. MiR-136-5p interacted with the 3′ untranslated region (3′UTR) of TRIM27, and TRIM27 overexpression largely counteracted miR-136-5p overexpression-induced influences in HCC cells. Circ_0091579 sponged miR-136-5p to up-regulate TRIM27 expression in HCC cells. Circ_0091579 silencing suppressed xenograft tumor growth in vivo. Conclusion: Circ_0091579 exhibited an oncogenic role to enhance the malignant potential of HCC cells through mediating miR-136-5p/TRIM27 axis in vitro and in vivo.

Due to the insufficient slip systems, Mg and its alloys exhibit poor ductility during plastic deformation at room temperature. To solve this problem, alloying is considered as a most effective method to improve the ductility of Mg alloys, which attracts wide attentions of industries. However, it is still a challenge to understand the ductilization mechanism, because of the complicated alloying elements and their interactions with Mg matrix. In this work, pure Mg and Mg-Y alloys were comparatively studied to investigate the effect of Y addition on microstructure evolution and mechanical properties. A huge increase of uniform elongation, from 5.3% to 20.7%, was achieved via only 3 wt% addition of yttrium. TEM results revealed that the only activated slip system in pure Mg was basal slip, led to its poor ductility at room temperature. In contrast, a large number of stacking faults and non-basal dislocations with component were observed in the deformed Mg-Y alloy, which was proposed as the main reason for significant improvement of strain hardening and ductility. High resolution TEM indicated that most of the stacking faults were I1 and I2 intrinsic faults, which played a critical role in improving the ductility of Mg-Y alloy. Addition of Y into Mg alloy decreased the stacking fault energy, which induced high density stacking faults in the grain interior.

Operators collect user data by deploying DPI system at some network node,and combining with the security control system to achieve an operator-level security operation and management system.The new architecture,technologies and services of the 5G network meet the unprecedented demands of connection,but also bring new security risks.The evolution of the data collection scheme of the carrier network was mainly discussed under the background of 5G network,as well as the innovative ideas of content security and network security systems that follow.

The slope coefficient ω is defined based on the insufficiency of the area equivalent method, the slope of the equivalent M–C criterion obtained from the instantaneous equivalent, and the optimal first-order approximation to reduce the error between the simulated value and the measured value of the surrounding rock and ensure the safety of the project. Different ω conditions are set to obtain multiple equivalent M–C strength parameter combinations. The above combinations are input to the ubiquitous joint model of FLAC3D, and the surrounding rock strength of layered tunnels with different inclination angles (0°, 30°, 45°, 60°, and 90°) is corrected. The results show that (1) after the tunnel excavation is completed, the displacement of key points (e.g., the vault and waist) increases when the slope coefficient is increased and the deviator stress decreases when the slope coefficient is increased. (2) After the area equivalent method is revised, the displacement and the deviator stress are more significantly affected by the inclination of the rock strata than the uncorrected ones, suggesting that the equivalent area can more effectively highlight the anisotropy characteristics of the layered surrounding rock. (3) After the simulation results of the displacement and the deviator stress at the respective key point are comprehensively modified, the optimal slope coefficient corresponding to each rock layer inclination is obtained, and the area is optimized by ensuring reasonableness to reduce the error between the simulated value and the measured value. (4) The layered surrounding rock at a dip angle of 30° is studied. The development of the plastic zone is promoted when the slope coefficient is increased, and the rock shear failure and the joint shear failure occur simultaneously on both sides of its axis.

Mg-Y based alloys exhibit a promising combination of strength and deformability through tuning precipitation and solute strengthening mechanisms and tailoring the activity of non-basal dislocations. Understanding the precipitation sequence of Mg-Y based alloys and its dependence on Yttrium concentration in the matrix will provide a guideline for fine tuning structure, morphology and distribution of precipitates in Mg-Y based alloys. In this paper, we explore the precipitation behaviors of Mg-11Y (wt%) and Mg-11Y-1Al (wt%) alloys using aberration-corrected scanning transmission electron microscopy, and rationalize the experimental observations based on first-principles density functional theory calculations. The precipitation sequence during ageing at 225 °C is identified to be SSSS → clusters/G.P. Zones →β′ (Mg7Y) → β′′/βt′′ (Mg3Y). A novel βt′′ phase forms through in-situ transformation from the β′ phase, which shares the same Mg3Y composition with D019-β′′ phase and exhibits the same cbco-structure as β′ phase in Mg-Y based alloys.

Fundamental understanding of the dissociation mode of 〈c + a〉 dislocations on the 101¯1 plane is required before the goal of improving the ductility of Mg alloys attained. In this study, our density-functional theory calculations reveal that the atoms in the 101¯1 plane slip along a zigzag trace through a low-energy pathway. We thus propose a novel zigzag dissociation mode based on this slip trace. In particular, the shuffling motion of atoms is observed at the position of stable stacking fault, which is closely related to the c/a ratio of the hexagonal closed-packed lattices.

To study the stability of a transition section of a tunnel from soft to hard surrounding rock under seepage conditions, FLAC3D software was used to numerically simulate the transition section of the Laomushan Tunnel from the Shiqian to Yuping (Dalong) Expressways in Guizhou Province, China, and to explore different working conditions. The characterization of the tunnel seepage field, stress field, and displacement field and the analysis of the force of the primary lining support structure describe the influence of the seepage field on the stress field distribution and displacement field changes. The reliability of the calculation results is verified by comparison with displacement measurements collected during field monitoring. The design values of the primary support structure parameters of the transition section from soft to hard surrounding rock of the Laomushan Tunnel basically met the strength requirements. The research results provide references for the design and construction of similar projects.

The excavation unloading of deep foundation pits in soft soil areas often produces negative excess pore water pressure. The rebound deformation of soil on the excavation surface of the foundation pit can be predicted reliably through the accurate expression of relevant variation laws. In combination with the principle of effective stress and the general equation of unidirectional seepage consolidation, an equation for calculating the rebound deformation from the bottom in the process of foundation pit excavation unloading was obtained. Additionally, a triaxial unloading test was adopted to simulate the excavation unloading processes for actual foundation pit engineering. After studying the variation law of the excess pore water pressure generated by excavation unloading, it was found that the negative excess pore water pressure increased with increasing unloading rate, while the corresponding peak value decreased with increasing confining pressure. The equation for rebound calculation was verified through a comparison with relevant measured data from actual engineering. Therefore, it is considered that the equation can reliably describe the rebound deformation law of the base. This paper aims to guide the design and construction of deep foundation pits in soft soil areas.

In the background of new business requirements,the monitory point selection for new monitoring business,anti-spam system architecture,the function of the system need and the key points that should pay attention to system deployment were analyzed.Finally a set of two level monitoring system architecture and deployment guidelines for monitoring system were put forward.

The deformation of metals is known to be largely affected by their stacking fault energies (SFEs). In the review, we examine the theoretical background of three normally used models, supercell model, Ising model, and bond orientation model, for the calculation of SFE of hexagonal-close-packed (hcp) metals and their alloys. To predict the nature of slip in nanocrystalline metals, we further review the generalized stacking fault (GSF) energy curves in hcp metals and alloys. We conclude by discussing the outstanding challenges in the modeling of SFE and GSF energy for studying the mechanical properties of metals.

To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.