To simulate explosion fragments, it is necessary to predict many variables such as fragment velocity, size distribution and projection angle. For active protection systems these predictions need to be made very quickl...To simulate explosion fragments, it is necessary to predict many variables such as fragment velocity, size distribution and projection angle. For active protection systems these predictions need to be made very quickly, before the weapon hits the target. Fast predictions also need to be made in real time simulations when the impact of many different computer models need to be assessed. The research presented in this paper focuses on creating a fast and accurate estimate of one of these variables - the initial fragment velocity. The Gurney equation was the first equation to calculate initial fragment velocity. This equation, sometimes with modifications, is still used today where finite element analysis or complex mathematical approaches are considered too computationally expensive. This paper enhances and improves Breech’s two-dimensional Gurney equation using available empirical data and the principals of conservation of momentum and energy. The results are computationally quick, providing improved accuracy for estimating initial fragment velocity. This will allow the developed model to be available for real-time simulation and fast computation, with improved accuracy when compared to existing approaches.展开更多
The adsorptive denitrification performance of MIL-101(Cr)-0.5 toward pyridine,aniline or quinoline in simulated fuels with basic nitrogen content of 1732μg/g was evaluated separately.Furthermore,the effects of adsorp...The adsorptive denitrification performance of MIL-101(Cr)-0.5 toward pyridine,aniline or quinoline in simulated fuels with basic nitrogen content of 1732μg/g was evaluated separately.Furthermore,the effects of adsorption temperature,adsorption time and adsorbent dosage on their adsorptive denitrification performance were systematically investigated.The experimental results demonstrated that under a fixed adsorbent dosage of 0.05 g and a simulated fuel volume of 10 mL,the optimal removal efficiency for aniline was achieved at 30℃ within 30 min,whereas higher temperatures and longer times(40℃and 40 min)were required for effective removal of pyridine and quinoline.Density Functional Theory(DFT)calculations were conducted via Materials Studio(MS)software to study the adsorptive denitrification mechanism of MIL-101(Cr)toward these three basic nitrogen-containing compounds.The simulation calculation results revealed that the interaction between pyridine and MIL-101(Cr)primarily involved coordination adsorption.In contrast,the interaction between aniline or quinoline and MIL-101(Cr)proceeded mainly through coordination,with additional contributions fromπ-complexation and hydrogen bonding.The overall adsorption strength order is pyridine>aniline>quinoline.During the adsorption process,pyridine and quinoline transfer electrons to the MIL-101(Cr)surface through the H→C→N→Cr^(3+)pathway,while aniline transfers electrons to the MIL-101(Cr)surface through various pathways,including N→Cr^(3+),N→C→Cr^(3+)and N→H→O.Furthermore,adsorption kinetics studies indicated that the adsorption processes for all three basic nitrogen-containing compounds followed the quasi second order kinetic models.The experimental results on the effect of benzene on the adsorptive denitrification performance of MIL-101(Cr)-0.5 demonstrated that benzene exerted a more significant impact on the adsorption of aniline and quinoline.Finally,the adsorbent was regenerated using ethanol washing.It was found that MIL-101(Cr)-0.5 retained stable denitrification performance after two regeneration cycles.展开更多
Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dua...Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study invest...During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.展开更多
To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens...To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
The electric double layer(EDL)at the electrochemical interface is crucial for ion transport,charge transfer,and surface reactions in aqueous rechargeable zinc batteries(ARZBs).However,Zn anodes routinely encounter per...The electric double layer(EDL)at the electrochemical interface is crucial for ion transport,charge transfer,and surface reactions in aqueous rechargeable zinc batteries(ARZBs).However,Zn anodes routinely encounter persistent dendrite growth and parasitic reactions,driven by the inhomogeneous charge distribution and water-dominated environment within the EDL.Compounding this,classical EDL theory,rooted in meanfield approximations,further fails to resolve molecular-scale interfacial dynamics under battery-operating conditions,limiting mechanistic insights.Herein,we established a multiscale theoretical calculation framework from single molecular characteristics to interfacial ion distribution,revealing the EDL’s structure and interactions between different ions and molecules,which helps us understand the parasitic processes in depth.Simulations demonstrate that water dipole and sulfate ion adsorption at the inner Helmholtz plane drives severe hydrogen evolution and by-product formation.Guided by these insights,we engineered a“water-poor and anion-expelled”EDL using 4,1’,6’-trichlorogalactosucrose(TGS)as an electrolyte additive.As a result,Zn||Zn symmetric cells with TGS exhibited stable cycling for over 4700 h under a current density of 1 mA cm^(−2),while NaV_(3)O_(8)·1.5H_(2)O-based full cells kept 90.4%of the initial specific capacity after 800 cycles at 5 A g^(−1).This work highlights the power of multiscale theoretical frameworks to unravel EDL complexities and guide high-performance ARZB design through integrated theory-experiment approaches.展开更多
Existing numerical methods for complex composites, such as multiscale simulation and neural network algorithms, face significant limitations. Multiscale techniques are often prohibitively expensive for large models, w...Existing numerical methods for complex composites, such as multiscale simulation and neural network algorithms, face significant limitations. Multiscale techniques are often prohibitively expensive for large models, while neural networks struggle to represent underlying microscopic material properties. To overcome these challenges, a meso-micro scale numerical method using a virtual node approach is developed in this study. A Wbraid/Al/Epoxy functional structural material is fabricated, and a representative periodic unit cell is identified based on its architecture. The complex structure is then discretized into nodes, and mechanical interactions are governed by pre-defined computation rules. This virtual node method is systematically compared against both multiscale simulation and a neural network algorithm, with validation provided through mechanical experiments. The results demonstrate that the nodal operation strategy significantly reduces computational resource requirements. By quantifying microscopic bonding with coefficients, explicit interface treatment is avoided, granting the method strong adaptability to lattice materials. The method can simulate extremely complex structures using parameters from simple tests and is suited for large systems. Compared to three-point bending experiments, errors for multiscale, virtual node, and neural network methods were 12.4%, 6.9%, and 34.5%, respectively. Under dynamic compression, the errors were 2.7%, 9.3%, and 15.43%. The virtual node method demonstrated superior accuracy under static conditions, enabling efficient prediction and auxiliary development of complex structural materials.展开更多
Underwater gas-liquid two-phase propulsion technology is an emerging propulsion method that offers high efficiency and unrestricted navigation speed.The integration of this technology into water ramjet engines can sig...Underwater gas-liquid two-phase propulsion technology is an emerging propulsion method that offers high efficiency and unrestricted navigation speed.The integration of this technology into water ramjet engines can significantly enhance propulsion efficiency and holds substantial potential for broad applications.However,forming a gas-liquid two-phase flow within the nozzle requires introducing a large amount of rammed seawater.At this time,there is a complex phase transition problem of combustion products in the combustion chamber,which makes the thermodynamic calculation for gas-liquid two-phase water ramjet engines particularly challenging.This paper proposes a thermodynamic calculation method for gas-liquid two-phase water ramjet engines,based on the energy equation for gas-liquid two-phase flow and traditional thermodynamic principles,enabling thermodynamic calculations under conditions of ultra-high water-fuel ratios.Additionally,ground ignition tests of the gas-liquid two-phase engine were conducted,yielding critical engine test parameters.The results demonstrate that the gas-liquid two-phase water ramjet engine achieves a high specific impulse,with a theoretical maximum specific impulse of up to 7000(N s)/kg.The multiphase flow effects significantly impact engine performance,with specific impulse losses reaching up to 25.86%.The error between the thrust and specific impulse in the ground test and the theoretical values is within 10%,validating the proposed thermodynamic calculation method as a reliable reference for further research on gas-liquid two-phase water ramjet engines.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
The study of nuclear isomers can deepen our understanding of nuclear structure and astrophysics.In this work,we have performed the ab initio calculations of isomers in the N=49 isotones.With a chiral two-plus three-nu...The study of nuclear isomers can deepen our understanding of nuclear structure and astrophysics.In this work,we have performed the ab initio calculations of isomers in the N=49 isotones.With a chiral two-plus three-nucleon force,the valence-space effective Hamiltonian was derived using the ab initio many-body perturbation theory named Q-box folded diagrams.The effective operators of electromagnetic operators andβ-decay were obtained using ■-box folded diagrams.With the effective Hamiltonian and operators,we studied the properties of the isomers,gaining a microscopic understanding of the single-particle behaviour of the isomers which we are interested in,showing the reliability of the ab initio calculations.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
4-Bromo-3-methylphenol(BMP)is an important chemical intermediate with wide applications in the fields of medicine and pesticides.The synthesis of BMP from m-cresol via bromination is easy to carry out on an industrial...4-Bromo-3-methylphenol(BMP)is an important chemical intermediate with wide applications in the fields of medicine and pesticides.The synthesis of BMP from m-cresol via bromination is easy to carry out on an industrial scale.However,due to the formation of regioisomeric impurities during bromination and the low melting point of BMP,the separation process is prone to the formation of oily substances,resulting in low yield and purity.In this work,a new cocrystallization engineering approach was proposed to separate and purify BMP.Through design of experiments,the cocrystallization process of BMP and triethylenediamine(DABCO)was optimized using a minimum-run resolution IV screening design combined with response surface methodology.In addition,the obtained 2BMP-DABCO powder was characterized by thermal analysis,powder X-ray diffraction,infrared spectroscopy,and scanning electron microscopy.Single crystals of 2BMP-DABCO were grown from acetone by slow evaporation,and detailed structural information was obtained through single-crystal X-ray diffraction.The self-assembly mechanism was further clarified by density functional theory calculations.This study provides a simple,robust,and scalable method for the production of BMP and offers a reference for the separation and purification of phenolic substances.展开更多
Investigating the detonation reaction zone structures of high explosives is significant for understanding detonation reaction mechanism.This study employed an integrated approach combining machine learning prediction,...Investigating the detonation reaction zone structures of high explosives is significant for understanding detonation reaction mechanism.This study employed an integrated approach combining machine learning prediction,theoretical calculation,and experimental characterization to determine the detonation reaction zone width of CL-20-based aluminized explosive.In this study,the detonation reaction zone refers to the reaction zone between the von Neumann(VN)peak and sonic point,which usually means the so-called detonation driving zone(DDZ).For the machine learning prediction,an ensemble model integrating Random Forest and Support Vector Regression was developed to predict the reaction zone width using a dataset of 19 publicly available samples.For the theoretical calculation,the Wood-Kirkwood(W-K)detonation theory model was utilized to implement numerical calculation of the reaction zone structures,incorporating chemical reaction kinetics to describe the detonation reaction progress.In experimental characterization,the Photon Doppler Velocimetry(PDV)was applied with LiF as the optical window to measure the particle velocity profile of detonation products and derive the reaction zone width.The results indicate that the reaction zone width values are 0.25 mm,0.28 mm,and 0.26 mm obtained from machine learning prediction,theoretical calculation,and experimental characterization,respectively.The corresponding velocities at the Chapman-Jouguet(CJ)point are 1,938 m/s,2,047 m/s,and 1,982 m/s,respectively.The maximum relative deviation in reaction zone width among three methods is approximately 7.7%,while that for CJ particle velocity is approximately 3.3%.These results from all three methods agree well within engineering error.This validates the effectiveness of integrating machine learning prediction,theoretical calculation and advanced experimental techniques for studying the detonation reaction zone structures of high explosives.This research provides insights into the detonation reaction mechanism and reaction zone characteristics of CL-20-based aluminized explosive.展开更多
Deformations in high fill foundations comprising soil–stone mixtures must be accurately predicted to ensure construction quality and long-term operational safety.However,existing computational and analytical methods ...Deformations in high fill foundations comprising soil–stone mixtures must be accurately predicted to ensure construction quality and long-term operational safety.However,existing computational and analytical methods inadequately capture their complex mechanical behavior.We conducted a series of triaxial tests on unsaturated soil samples collected from a high fill project site in northwestern China under three stress paths.The incremental nonlinear and elastoplastic constitutive models for unsaturated soils were modified,and a calculation method was developed for the vertical and lateral deformations of high fill foundations using the layered summation approach.The results indicate that for soil samples with the same mixing ratio(m)and compaction coefficient(n),the strength of the sample and its tendency to exhibit shear dilation increase with the net confining pressure or matric suction.Additionally,the stress–strain curve of the soil sample gradually changes from the strain-hardening type to the ideal elastoplastic type as the compaction coefficient increases.Moreover,the compaction coefficient is an important factor influencing the magnitude of yield stress and yield suction in soil samples,and the yield points of both are similar in shape to the loadingcollapse(LC)and suction increase(SI)yield lines obtained using the Barcelona model in the net mean stress-generalized shear stress(p-s)plane,respectively.The modified incremental nonlinear instantaneous model simultaneously considers the effects of the compaction coefficient,suction and mixing ratio,and the model parameter can be simplified to the tangential modulus expression in the Duncan-Chang model when the suction is zero.Furthermore,the modified elastoplastic constitutive model,which considers the effects of the net mean stress,suction and partial stress,can be simplified to the elastoplastic constitutive relationship of saturated soil when the suction is zero.The proposed deformation calculation method,based on the layered summation theory,is applicable to both elastic and elastoplastic foundation states,as confirmed through numerical simulations.Our work can be used as a reference for the calculation of foundation deformation in similar mixed material high fill projects.展开更多
In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the fea...In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.展开更多
Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has b...Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.展开更多
Large portions of the tunnel boring machine(TBM)construction cost are attributed to disc cutter consumption,and assessing the disc cutter's wear level can help determine the optimal time to replace the disc cutter...Large portions of the tunnel boring machine(TBM)construction cost are attributed to disc cutter consumption,and assessing the disc cutter's wear level can help determine the optimal time to replace the disc cutter.Therefore,the need to monitor disc cutter wear in real-time has emerged as a technical challenge for TBMs.In this study,real-time disc cutter wear monitoring is developed based on sound and vibration sensors.For this purpose,the microphone and accelerometer were used to record the sound and vibration signals of cutting three different types of rocks with varying abrasions on a laboratory scale.The relationship between disc cutter wear and the sound and vibration signal was determined by comparing the measurements of disc cutter wear with the signal plots for each sample.The features extracted from the signals showed that the sound and vibration signals are impacted by the progression of disc wear during the rock-cutting process.The signal features obtained from the rock-cutting operation were utilized to verify the machine learning techniques.The results showed that the multilayer perceptron(MLP),random subspace-based decision tree(RS-DT),DT,and random forest(RF)methods could predict the wear level of the disc cutter with an accuracy of 0.89,0.951,0.951,and 0.927,respectively.Based on the accuracy of the models and the confusion matrix,it was found that the RS-DT model has the best estimate for predicting the level of disc wear.This research has developed a method that can potentially determine when to replace a tool and assess disc wear in real-time.展开更多
文摘To simulate explosion fragments, it is necessary to predict many variables such as fragment velocity, size distribution and projection angle. For active protection systems these predictions need to be made very quickly, before the weapon hits the target. Fast predictions also need to be made in real time simulations when the impact of many different computer models need to be assessed. The research presented in this paper focuses on creating a fast and accurate estimate of one of these variables - the initial fragment velocity. The Gurney equation was the first equation to calculate initial fragment velocity. This equation, sometimes with modifications, is still used today where finite element analysis or complex mathematical approaches are considered too computationally expensive. This paper enhances and improves Breech’s two-dimensional Gurney equation using available empirical data and the principals of conservation of momentum and energy. The results are computationally quick, providing improved accuracy for estimating initial fragment velocity. This will allow the developed model to be available for real-time simulation and fast computation, with improved accuracy when compared to existing approaches.
基金Supported by Basic Scientific Research Project of the Liaoning Provincial Department of Education Has Been Unveiled to Facilitate Local Project Funding (JYTMS20230835)Enhanced Scientific Research Project Funded by the Departmentof Higher Education in Liaoning Province (General program)(JYTMS20230852)。
文摘The adsorptive denitrification performance of MIL-101(Cr)-0.5 toward pyridine,aniline or quinoline in simulated fuels with basic nitrogen content of 1732μg/g was evaluated separately.Furthermore,the effects of adsorption temperature,adsorption time and adsorbent dosage on their adsorptive denitrification performance were systematically investigated.The experimental results demonstrated that under a fixed adsorbent dosage of 0.05 g and a simulated fuel volume of 10 mL,the optimal removal efficiency for aniline was achieved at 30℃ within 30 min,whereas higher temperatures and longer times(40℃and 40 min)were required for effective removal of pyridine and quinoline.Density Functional Theory(DFT)calculations were conducted via Materials Studio(MS)software to study the adsorptive denitrification mechanism of MIL-101(Cr)toward these three basic nitrogen-containing compounds.The simulation calculation results revealed that the interaction between pyridine and MIL-101(Cr)primarily involved coordination adsorption.In contrast,the interaction between aniline or quinoline and MIL-101(Cr)proceeded mainly through coordination,with additional contributions fromπ-complexation and hydrogen bonding.The overall adsorption strength order is pyridine>aniline>quinoline.During the adsorption process,pyridine and quinoline transfer electrons to the MIL-101(Cr)surface through the H→C→N→Cr^(3+)pathway,while aniline transfers electrons to the MIL-101(Cr)surface through various pathways,including N→Cr^(3+),N→C→Cr^(3+)and N→H→O.Furthermore,adsorption kinetics studies indicated that the adsorption processes for all three basic nitrogen-containing compounds followed the quasi second order kinetic models.The experimental results on the effect of benzene on the adsorptive denitrification performance of MIL-101(Cr)-0.5 demonstrated that benzene exerted a more significant impact on the adsorption of aniline and quinoline.Finally,the adsorbent was regenerated using ethanol washing.It was found that MIL-101(Cr)-0.5 retained stable denitrification performance after two regeneration cycles.
基金supported by the National Institute of Health(R37CA240806,U01CA288351,and R50CA283816)support from UCI Chao Family Comprehensive Cancer Center(P30CA062203).
文摘Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
基金National Natural Science Foundation of China,Grant/Award Number:52104120Hunan Provincial Key Laboratory of Key Technology on Hydropower Development,Grant/Award Number:PKLHD202303。
文摘During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.
基金supported by the National Natural Science Foundation of China(Grant No.42077244)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0434).
文摘To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
基金supported by the National Natural Science Foundation of China(52471240)the Natural Science Foundation of Zhejiang Province(LZ23B030003)+2 种基金the Fundamental Research Funds for the Central Universities(226-2024-00075)support from the Engineering and Physical Sciences Research Council(EPSRC,UK)RiR grant-RIR18221018-1EU COST CA23155。
文摘The electric double layer(EDL)at the electrochemical interface is crucial for ion transport,charge transfer,and surface reactions in aqueous rechargeable zinc batteries(ARZBs).However,Zn anodes routinely encounter persistent dendrite growth and parasitic reactions,driven by the inhomogeneous charge distribution and water-dominated environment within the EDL.Compounding this,classical EDL theory,rooted in meanfield approximations,further fails to resolve molecular-scale interfacial dynamics under battery-operating conditions,limiting mechanistic insights.Herein,we established a multiscale theoretical calculation framework from single molecular characteristics to interfacial ion distribution,revealing the EDL’s structure and interactions between different ions and molecules,which helps us understand the parasitic processes in depth.Simulations demonstrate that water dipole and sulfate ion adsorption at the inner Helmholtz plane drives severe hydrogen evolution and by-product formation.Guided by these insights,we engineered a“water-poor and anion-expelled”EDL using 4,1’,6’-trichlorogalactosucrose(TGS)as an electrolyte additive.As a result,Zn||Zn symmetric cells with TGS exhibited stable cycling for over 4700 h under a current density of 1 mA cm^(−2),while NaV_(3)O_(8)·1.5H_(2)O-based full cells kept 90.4%of the initial specific capacity after 800 cycles at 5 A g^(−1).This work highlights the power of multiscale theoretical frameworks to unravel EDL complexities and guide high-performance ARZB design through integrated theory-experiment approaches.
文摘Existing numerical methods for complex composites, such as multiscale simulation and neural network algorithms, face significant limitations. Multiscale techniques are often prohibitively expensive for large models, while neural networks struggle to represent underlying microscopic material properties. To overcome these challenges, a meso-micro scale numerical method using a virtual node approach is developed in this study. A Wbraid/Al/Epoxy functional structural material is fabricated, and a representative periodic unit cell is identified based on its architecture. The complex structure is then discretized into nodes, and mechanical interactions are governed by pre-defined computation rules. This virtual node method is systematically compared against both multiscale simulation and a neural network algorithm, with validation provided through mechanical experiments. The results demonstrate that the nodal operation strategy significantly reduces computational resource requirements. By quantifying microscopic bonding with coefficients, explicit interface treatment is avoided, granting the method strong adaptability to lattice materials. The method can simulate extremely complex structures using parameters from simple tests and is suited for large systems. Compared to three-point bending experiments, errors for multiscale, virtual node, and neural network methods were 12.4%, 6.9%, and 34.5%, respectively. Under dynamic compression, the errors were 2.7%, 9.3%, and 15.43%. The virtual node method demonstrated superior accuracy under static conditions, enabling efficient prediction and auxiliary development of complex structural materials.
基金supported by the Stable Support Fund forBasic Disciplines,China(No.3072024WD0201)。
文摘Underwater gas-liquid two-phase propulsion technology is an emerging propulsion method that offers high efficiency and unrestricted navigation speed.The integration of this technology into water ramjet engines can significantly enhance propulsion efficiency and holds substantial potential for broad applications.However,forming a gas-liquid two-phase flow within the nozzle requires introducing a large amount of rammed seawater.At this time,there is a complex phase transition problem of combustion products in the combustion chamber,which makes the thermodynamic calculation for gas-liquid two-phase water ramjet engines particularly challenging.This paper proposes a thermodynamic calculation method for gas-liquid two-phase water ramjet engines,based on the energy equation for gas-liquid two-phase flow and traditional thermodynamic principles,enabling thermodynamic calculations under conditions of ultra-high water-fuel ratios.Additionally,ground ignition tests of the gas-liquid two-phase engine were conducted,yielding critical engine test parameters.The results demonstrate that the gas-liquid two-phase water ramjet engine achieves a high specific impulse,with a theoretical maximum specific impulse of up to 7000(N s)/kg.The multiphase flow effects significantly impact engine performance,with specific impulse losses reaching up to 25.86%.The error between the thrust and specific impulse in the ground test and the theoretical values is within 10%,validating the proposed thermodynamic calculation method as a reliable reference for further research on gas-liquid two-phase water ramjet engines.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the National Key R&D Program of China under Grant Nos.2024YFA1610900 and 2023YFA1606401the National Natural Science Foundation of China under Grant Nos.12335007 and 12035001。
文摘The study of nuclear isomers can deepen our understanding of nuclear structure and astrophysics.In this work,we have performed the ab initio calculations of isomers in the N=49 isotones.With a chiral two-plus three-nucleon force,the valence-space effective Hamiltonian was derived using the ab initio many-body perturbation theory named Q-box folded diagrams.The effective operators of electromagnetic operators andβ-decay were obtained using ■-box folded diagrams.With the effective Hamiltonian and operators,we studied the properties of the isomers,gaining a microscopic understanding of the single-particle behaviour of the isomers which we are interested in,showing the reliability of the ab initio calculations.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金supported by the National Natural Science Foundation of China(22177011(R.Z.Qiao),21977012(R.Z.Qiao),and 21572018(C.Li))the National High-Level Hospital Clinical Research Funding(2023-NHLHCRF-YXHZ-ZRMS-02)the Joint Project of BRCBC(Biomedical Translational Engineering Research Center of BUCT-CJFH)(XK2020-06).
文摘4-Bromo-3-methylphenol(BMP)is an important chemical intermediate with wide applications in the fields of medicine and pesticides.The synthesis of BMP from m-cresol via bromination is easy to carry out on an industrial scale.However,due to the formation of regioisomeric impurities during bromination and the low melting point of BMP,the separation process is prone to the formation of oily substances,resulting in low yield and purity.In this work,a new cocrystallization engineering approach was proposed to separate and purify BMP.Through design of experiments,the cocrystallization process of BMP and triethylenediamine(DABCO)was optimized using a minimum-run resolution IV screening design combined with response surface methodology.In addition,the obtained 2BMP-DABCO powder was characterized by thermal analysis,powder X-ray diffraction,infrared spectroscopy,and scanning electron microscopy.Single crystals of 2BMP-DABCO were grown from acetone by slow evaporation,and detailed structural information was obtained through single-crystal X-ray diffraction.The self-assembly mechanism was further clarified by density functional theory calculations.This study provides a simple,robust,and scalable method for the production of BMP and offers a reference for the separation and purification of phenolic substances.
文摘Investigating the detonation reaction zone structures of high explosives is significant for understanding detonation reaction mechanism.This study employed an integrated approach combining machine learning prediction,theoretical calculation,and experimental characterization to determine the detonation reaction zone width of CL-20-based aluminized explosive.In this study,the detonation reaction zone refers to the reaction zone between the von Neumann(VN)peak and sonic point,which usually means the so-called detonation driving zone(DDZ).For the machine learning prediction,an ensemble model integrating Random Forest and Support Vector Regression was developed to predict the reaction zone width using a dataset of 19 publicly available samples.For the theoretical calculation,the Wood-Kirkwood(W-K)detonation theory model was utilized to implement numerical calculation of the reaction zone structures,incorporating chemical reaction kinetics to describe the detonation reaction progress.In experimental characterization,the Photon Doppler Velocimetry(PDV)was applied with LiF as the optical window to measure the particle velocity profile of detonation products and derive the reaction zone width.The results indicate that the reaction zone width values are 0.25 mm,0.28 mm,and 0.26 mm obtained from machine learning prediction,theoretical calculation,and experimental characterization,respectively.The corresponding velocities at the Chapman-Jouguet(CJ)point are 1,938 m/s,2,047 m/s,and 1,982 m/s,respectively.The maximum relative deviation in reaction zone width among three methods is approximately 7.7%,while that for CJ particle velocity is approximately 3.3%.These results from all three methods agree well within engineering error.This validates the effectiveness of integrating machine learning prediction,theoretical calculation and advanced experimental techniques for studying the detonation reaction zone structures of high explosives.This research provides insights into the detonation reaction mechanism and reaction zone characteristics of CL-20-based aluminized explosive.
基金funded by the National Natural Science Foundation of China(Grant Nos.52368049,52168051,and 42462028)Lanzhou Young Scientific and Technological Talents Innovation Project(Grant Nos.2023-QN-27 and 2023-QN-52)Major Project of the Joint Scientific Research Fund of Gansu Province(Grant No.25JRRL007)。
文摘Deformations in high fill foundations comprising soil–stone mixtures must be accurately predicted to ensure construction quality and long-term operational safety.However,existing computational and analytical methods inadequately capture their complex mechanical behavior.We conducted a series of triaxial tests on unsaturated soil samples collected from a high fill project site in northwestern China under three stress paths.The incremental nonlinear and elastoplastic constitutive models for unsaturated soils were modified,and a calculation method was developed for the vertical and lateral deformations of high fill foundations using the layered summation approach.The results indicate that for soil samples with the same mixing ratio(m)and compaction coefficient(n),the strength of the sample and its tendency to exhibit shear dilation increase with the net confining pressure or matric suction.Additionally,the stress–strain curve of the soil sample gradually changes from the strain-hardening type to the ideal elastoplastic type as the compaction coefficient increases.Moreover,the compaction coefficient is an important factor influencing the magnitude of yield stress and yield suction in soil samples,and the yield points of both are similar in shape to the loadingcollapse(LC)and suction increase(SI)yield lines obtained using the Barcelona model in the net mean stress-generalized shear stress(p-s)plane,respectively.The modified incremental nonlinear instantaneous model simultaneously considers the effects of the compaction coefficient,suction and mixing ratio,and the model parameter can be simplified to the tangential modulus expression in the Duncan-Chang model when the suction is zero.Furthermore,the modified elastoplastic constitutive model,which considers the effects of the net mean stress,suction and partial stress,can be simplified to the elastoplastic constitutive relationship of saturated soil when the suction is zero.The proposed deformation calculation method,based on the layered summation theory,is applicable to both elastic and elastoplastic foundation states,as confirmed through numerical simulations.Our work can be used as a reference for the calculation of foundation deformation in similar mixed material high fill projects.
基金the financial support of the Natural Science Foundation of Hubei Province,China (Grant No.2022CFB770)。
文摘In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.
基金support of the Natural Science Foundation of Jiangsu Province,China(BK20240977)the China Scholarship Council(201606850024)+1 种基金the National High Technology Research and Development Program of China(2016YFD0701003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1488)。
文摘Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.
文摘Large portions of the tunnel boring machine(TBM)construction cost are attributed to disc cutter consumption,and assessing the disc cutter's wear level can help determine the optimal time to replace the disc cutter.Therefore,the need to monitor disc cutter wear in real-time has emerged as a technical challenge for TBMs.In this study,real-time disc cutter wear monitoring is developed based on sound and vibration sensors.For this purpose,the microphone and accelerometer were used to record the sound and vibration signals of cutting three different types of rocks with varying abrasions on a laboratory scale.The relationship between disc cutter wear and the sound and vibration signal was determined by comparing the measurements of disc cutter wear with the signal plots for each sample.The features extracted from the signals showed that the sound and vibration signals are impacted by the progression of disc wear during the rock-cutting process.The signal features obtained from the rock-cutting operation were utilized to verify the machine learning techniques.The results showed that the multilayer perceptron(MLP),random subspace-based decision tree(RS-DT),DT,and random forest(RF)methods could predict the wear level of the disc cutter with an accuracy of 0.89,0.951,0.951,and 0.927,respectively.Based on the accuracy of the models and the confusion matrix,it was found that the RS-DT model has the best estimate for predicting the level of disc wear.This research has developed a method that can potentially determine when to replace a tool and assess disc wear in real-time.
