Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
This study investigates the effects of radiation force due to the rotational pitch motion of a wave energy device,which comprises a coaxial bottom-mounted cylindrical caisson in a two-layer fluid,along with a submerge...This study investigates the effects of radiation force due to the rotational pitch motion of a wave energy device,which comprises a coaxial bottom-mounted cylindrical caisson in a two-layer fluid,along with a submerged cylindrical buoy.The system is modeled as a two-layer fluid with infinite horizontal extent and finite depth.The radiation problem is analyzed in the context of linear water waves.The fluid domain is divided into outer and inner zones,and mathematical solutions for the pitch radiating potential are derived for the corresponding boundary valve problem in these zones using the separation of variables approach.Using the matching eigenfunction expansion method,the unknown coefficients in the analytical expression of the radiation potentials are evaluated.The resulting radiation potential is then used to compute the added mass and damping coefficients.Several numerical results for the added mass and damping coefficients are investigated for numerous parameters,particularly the effects of the cylinder radius,the draft of the submerged cylinder,and the density proportion between the two fluid layers across different frequency ranges.The major findings are presented and discussed.展开更多
Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class at...Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.展开更多
BACKGROUND The early diagnosis rate of pancreatic ductal adenocarcinoma(PDAC)is low and the prognosis is poor.It is important to develop an interpretable noninvasive early diagnostic model in clinical practice.AIM To ...BACKGROUND The early diagnosis rate of pancreatic ductal adenocarcinoma(PDAC)is low and the prognosis is poor.It is important to develop an interpretable noninvasive early diagnostic model in clinical practice.AIM To develop an interpretable noninvasive early diagnostic model for PDAC using plasma extracellular vesicle long RNA(EvlRNA).METHODS The diagnostic model was constructed based on plasma EvlRNA data.During the process of establishing the model,EvlRNA-index was introduced,and four algorithms were adopted to calculate EvlRNA-index.After the model was successfully constructed,performance evaluation was conducted.A series of bioinformatics methods were adopted to explore the potential mechanism of EvlRNA-index as the input feature of the model.And the relationship between key characteristics and PDAC were explored at the single-cell level.RESULTS A novel interpretable machine learning framework was developed based on plasma EvlRNA.In this framework,a two-layer classifier was established.A new concept was proposed:EvlRNA-index.Based on EvlRNA-index,a cancer diagnostic model was established,and a good diagnostic effect was achieved.The accuracy of PDACandCPvsHealth-Probabilistic PCA Index-SVM(PDAC and chronic pancreatitis vs health-probabilistic principal component analysis index-support vector machine)(1-18)was 91.51%,with Mathew’s correlation coefficient 0.7760 and area under the curve 0.9560.In the second layer of the model,the accuracy of PDACvsCP-Probabilistic PCA Index-RF(PDAC vs chronic pancreatitis-probabilistic principal component analysis index-random forest)(2-17)was 93.83%,with Mathew’s correlation coefficient 0.8422 and area under the curve 0.9698.Forty-nine PDAC-related genes were identified,among which 16 were known,inferring that the remaining ones were also PDAC-related genes.CONCLUSION An interpretable two-layer machine learning framework was proposed for early diagnosis and prediction of PDAC based on plasma EvlRNA,providing new insights into the clinical value of EvlRNA.展开更多
In the wake of major natural disasters or human-made disasters,the communication infrastruc-ture within disaster-stricken areas is frequently dam-aged.Unmanned aerial vehicles(UAVs),thanks to their merits such as rapi...In the wake of major natural disasters or human-made disasters,the communication infrastruc-ture within disaster-stricken areas is frequently dam-aged.Unmanned aerial vehicles(UAVs),thanks to their merits such as rapid deployment and high mobil-ity,are commonly regarded as an ideal option for con-structing temporary communication networks.Con-sidering the limited computing capability and battery power of UAVs,this paper proposes a two-layer UAV cooperative computing offloading strategy for emer-gency disaster relief scenarios.The multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithm integrated with prioritized experience replay(PER)is utilized to jointly optimize the scheduling strategies of UAVs,task offloading ratios,and their mobility,aiming to diminish the energy consumption and delay of the system to the minimum.In order to address the aforementioned non-convex optimiza-tion issue,a Markov decision process(MDP)has been established.The results of simulation experiments demonstrate that,compared with the other four base-line algorithms,the algorithm introduced in this paper exhibits better convergence performance,verifying its feasibility and efficacy.展开更多
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero....Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.展开更多
This work uses refined first-order shear theory to analyze the free vibration and transient responses of double-curved sandwich two-layer shells made of auxetic honeycomb core and laminated three-phase polymer/GNP/fib...This work uses refined first-order shear theory to analyze the free vibration and transient responses of double-curved sandwich two-layer shells made of auxetic honeycomb core and laminated three-phase polymer/GNP/fiber surface subjected to the blast load.Each of the two layers that make up the double-curved shell structure is made up of an auxetic honeycomb core and two laminated sheets of three-phase polymer/GNP/fiber.The exterior is supported by a Kerr elastic foundation with three characteristics.The key innovation of the proposed theory is that the transverse shear stresses are zero at two free surfaces of each layer.In contrast to previous first-order shear deformation theories,no shear correction factor is required.Navier's exact solution was used to treat the double-curved shell problem with a single title boundary,while the finite element technique and an eight-node quadrilateral were used to address the other boundary requirements.To ensure the accuracy of these results,a thorough comparison technique is employed in conjunction with credible statements.The problem model's edge cases allow for this kind of analysis.The study's findings may be used in the post-construction evaluation of military and civil works structures for their ability to sustain explosive loads.In addition,this is also an important basis for the calculation and design of shell structures made of smart materials when subjected to shock waves or explosive loads.展开更多
Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain les...Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.展开更多
In the context of China’s“double carbon”goals and rural revitalization strategy,the energy transition promotes the large-scale integration of distributed renewable energy into rural power grids.Considering the oper...In the context of China’s“double carbon”goals and rural revitalization strategy,the energy transition promotes the large-scale integration of distributed renewable energy into rural power grids.Considering the operational characteristics of rural microgrids and their impact on users,this paper establishes a two-layer scheduling model incorporating flexible loads.The upper-layer aims to minimize the comprehensive operating cost of the rural microgrid,while the lower-layer aims to minimize the total electricity cost for rural users.An Improved Adaptive Genetic Algorithm(IAGA)is proposed to solve the model.Results show that the two-layer scheduling model with flexible loads can effectively smooth load fluctuations,enhance microgrid stability,increase clean energy consumption,and balance microgrid operating costs with user benefits.展开更多
Microneedles(MNs)have been extensively investigated for transdermal delivery of large-sized drugs,including proteins,nucleic acids,and even extracellular vesicles(EVs).However,for their sufficient skin penetration,con...Microneedles(MNs)have been extensively investigated for transdermal delivery of large-sized drugs,including proteins,nucleic acids,and even extracellular vesicles(EVs).However,for their sufficient skin penetration,conventional MNs employ long needles(≥600μm),leading to pain and skin irritation.Moreover,it is critical to stably apply MNs against complex skin surfaces for uniform nanoscale drug delivery.Herein,a dually amplified transdermal patch(MN@EV/SC)is developed as the stem cell-derived EV delivery platform by hierarchically integrating an octopusinspired suction cup(SC)with short MNs(≤300μm).While leveraging the suction effect to induce nanoscale deformation of the stratum corneum,MN@EV/SC minimizes skin damage and enhances the adhesion of MNs,allowing EV to penetrate deeper into the dermis.When MNs of various lengths are applied to mouse skin,the short MNs can elicit comparable corticosterone release to chemical adhesives,whereas long MNs induce a prompt stress response.MN@EV/SC can achieve a remarkable penetration depth(290μm)for EV,compared to that of MN alone(111μm).Consequently,MN@EV/SC facilitates the revitalization of fibroblasts and enhances collagen synthesis in middle-aged mice.Overall,MN@EV/SC exhibits the potential for skin regeneration by modulating the dermal microenvironment and ensuring patient comfort.展开更多
Due to the limited regeneration capacity of myocardial tissue after infarction,designing tissue engineering scaffolds are in demand.In the present study,electrospun nanofibrous scaffolds were made out of polyurethane,...Due to the limited regeneration capacity of myocardial tissue after infarction,designing tissue engineering scaffolds are in demand.In the present study,electrospun nanofibrous scaffolds were made out of polyurethane,collagen and gold nanoparticles with random and aligned nanofiber morphologies.The nanoparticles were green-synthesized using saffron extract.Nanoparticle characterizations with UV-Vis.spectroscopy and DLS illustrated theoretical and hydrodynamic diameters of around 7 and 13 nm,respectively,having zeta potential of−37 mV.SEM and TEM micrographs showed the morphology and diameters of obtained nanofibers.Also,further characterization were done by ATR-FTIR,XRD and TGA investigations and degradation studies.Contact angle measurements showed hydrophilic nature of the scaffolds(59±0.6°for aligned PU/Col/Au50 nanofibers compared to 120±2.6°for random PU nanofibers).Mechanical testing demonstrated appropriate tensile properties of the scaffolds for cardiac tissue engineering(Young’s modulus:1.53±0.07 MPa for aligned PU/Col/Au50 nanofibers compared to 0.4±0.05 MPa for random PU nanofibers).Finally,alamar blue assay revealed proper survival of the cells of HUVEC cell line on the prepared scaffolds,where the highest percentages were observed for random and aligned PU/Col/Au50 nanofibers.According to the findings,the fabricated PU/Col/AuNPs nanofibrous scaffolds could be considered as potential cardiac patches.展开更多
Since the introduction of vision Transformers into the computer vision field,many vision tasks such as semantic segmentation tasks,have undergone radical changes.Although Transformer enhances the correlation of each l...Since the introduction of vision Transformers into the computer vision field,many vision tasks such as semantic segmentation tasks,have undergone radical changes.Although Transformer enhances the correlation of each local feature of an image object in the hidden space through the attention mechanism,it is difficult for a segmentation head to accomplish the mask prediction for dense embedding of multi-category and multi-local features.We present patch prototype vision Transformer(PPFormer),a Transformer architecture for semantic segmentation based on knowledge-embedded patch prototypes.1)The hierarchical Transformer encoder can generate multi-scale and multi-layered patch features including seamless patch projection to obtain information of multiscale patches,and feature-clustered self-attention to enhance the interplay of multi-layered visual information with implicit position encodes.2)PPFormer utilizes a non-parametric prototype decoder to extract region observations which represent significant parts of the objects by unlearnable patch prototypes and then calculate similarity between patch prototypes and pixel embeddings.The proposed contrasting patch prototype alignment module,which uses new patch prototypes to update prototype bank,effectively maintains class boundaries for prototypes.For different application scenarios,we have launched PPFormer-S,PPFormer-M and PPFormer-L by expanding the scale.Experimental results demonstrate that PPFormer can outperform fully convolutional networks(FCN)-and attention-based semantic segmentation models on the PASCAL VOC 2012,ADE20k,and Cityscapes datasets.展开更多
Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implemen...Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.展开更多
Adhesive patches offer an effective approach for wound closure,making them highly suitable for biomedical applications.However,conventional patches often face limitations such as dual-sided adhesion,lack of shape adap...Adhesive patches offer an effective approach for wound closure,making them highly suitable for biomedical applications.However,conventional patches often face limitations such as dual-sided adhesion,lack of shape adaptability,and limited maneuverability,which restrict their applications in deeper tissues.In this paper,we develop a magnetic patch robot(PatchBot),for targeted Janus adhesion with tissues.The PatchBot features a unique triple-layer structure,with adhesive,shape-morphing,and anti-adhesive layers,each fulfilling roles to support targeted attachment,enable shape transformation,and prevent unwanted adhesion to surrounding tissues.The Janus adhesion of the PatchBot was extensively demonstrated across a variety of tissues.A localized near-infrared(NIR)laser irradiation was used to induce programmable shape transformations.Magnetic actuation of the PatchBot for targeted adhesion was successfully demonstrated in ex vivo porcine stomach tissue.NIR light-activated shape-morphing and multimodal magnetic actuation significantly enhance its maneuverability and adaptability in confined in vivo environments while ensuring the structural integrity of the adhesive surface during deployment.This proof-of-concept study demonstrates the feasibility of using PatchBot for targeted wound adhesion,showing its potential for minimally invasive,precision therapies in complex in vivo environments.展开更多
This paper is a statistical survey of Southern Hemisphere cold and hot polar cap patches,in relation to the interplanetary magnetic field(IMF)and ionospheric convection geometry.A total of 11,946 patch events were ide...This paper is a statistical survey of Southern Hemisphere cold and hot polar cap patches,in relation to the interplanetary magnetic field(IMF)and ionospheric convection geometry.A total of 11,946 patch events were identified by Defense Meteorological Satellite Program(DMSP)F16 during the years 2011 to 2022.A temperature ratio of ion/electron temperature(T_(i)/T_(e))<0.68 is recommended to define a hot patch in the Southern Hemisphere,otherwise it is defined as a cold patch.The cold and hot patches have different dependencies on IMF clock angle,while their dependencies on IMF cone angle are similar.Both cold and hot patches appear most often on the duskside,and the distribution of cold patches gradually decreases from the dayside to the nightside,while hot patches have a higher occurrence rate near 14 and 21 magnetic local time(MLT).Moreover,we compared the key plasma characteristics of polar cap cold and hot patches in the Southern and Northern Hemispheres.The intensity of the duskside upward field-aligned current of patches in the Southern Hemisphere(SH)is stronger than that in the Northern Hemisphere(SH),which may be due to the discrepancy in conductivities between the two hemispheres,caused by the tilted dipole.In both hemispheres,the downward soft-electron energy flux of the dawnside patches is significantly greater than that of the duskside patches.展开更多
Objective:To evaluate the intervention effect of childlike nursing combined with Chinese herbal patching on pediatric bronchopneumonia.Methods:1036 children with bronchopneumonia(one family member included for each ch...Objective:To evaluate the intervention effect of childlike nursing combined with Chinese herbal patching on pediatric bronchopneumonia.Methods:1036 children with bronchopneumonia(one family member included for each child)who were admitted to the hospital between January 2024 and June 2024 were selected and randomly divided into two groups using a random number table.The combined group received childlike nursing combined with Chinese herbal patching,while the control group received routine nursing.Symptom recovery time,treatment compliance,inflammatory factor levels,quality of life of the children,and family satisfaction were compared between the two groups.Results:The symptom recovery time in the combined group was shorter than that in the control group,treatment compliance was higher,inflammatory factor levels after intervention were lower,quality of life scores of the children were lower,and family satisfaction was higher(P<0.05).Conclusion:The implementation of childlike nursing combined with Chinese herbal patching for children with bronchopneumonia can shorten their symptom recovery time,significantly improve treatment compliance and quality of life,reduce inflammatory reactions,and achieve high satisfaction among family members.展开更多
Microneedle(MN)patches could be a promising treatment for diabetic foot ulcers that plague thousands of people worldwide.While reducing skin resistance or increasing driving force can accelerate the efficiency of tran...Microneedle(MN)patches could be a promising treatment for diabetic foot ulcers that plague thousands of people worldwide.While reducing skin resistance or increasing driving force can accelerate the efficiency of transdermal drug delivery with conventional MN patches,it can create toxic chemical residues or require the help of additional devices.Herein,a thermo-responsive microneedles patch(TMN)with high biocompatibility without additional equipment is proposed.The TMN consisted of a bilayer microneedles composed of sodium alginate(SA)-g-poly(N-isopropylacrylamide)layer(SA-g-PNIPAM)loaded with sucrose octasulfate sodium salt(SOS)and hyaluronic acid layer and a polycaprolactone/chitosan nanofiber membrane loading with tetracycline hydrochloride(TH)and SOS.PNIPAM accelerates drug release by extruding the drug through a volumetric phase transition in response to temperature changes,and TH and SOS promote wound healing by inhibiting bacterial growth and promoting vascular regeneration and epithelial formation.The results showed that the drug release of TMN was significantly faster,with the drug release rate of more than 80% in the 10th h,and the antibacterial rate of TMN could reach 800%.In addition,TMN had good biocompatibility and good healing effects in vivo,which may be helpful for the design of multifunctional dressings in the future.展开更多
To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from patches.RGB crack images and their corresponding masks o...To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from patches.RGB crack images and their corresponding masks obtained from a public dataset are cropped into patches of 256 square pixels that are classified with a pre-trained deep convolution neural network,the true positives are segmented,and crack properties are extracted using two different methods.The first method is primarily based on active contour models and level-set segmentation and the second method consists of the domain adaptation of a mathematical morphology-based method known as FIL-FINDER.A statistical test has been performed for the comparison of the stated methods and a database prepared with the more suitable method.An advanced convolution neural network-based multi-output regression model has been proposed which was trained with the prepared database and validated with the held-out dataset for the prediction of crack-length,crack-width,and width-uncertainty directly from input image patches.The pro-posed model has been tested on crack patches collected from different locations.Huber loss has been used to ensure the robustness of the proposed model selected from a set of 288 different variations of it.Additionally,an ablation study has been conducted on the top 3 models that demonstrated the influence of each network component on the pre-diction results.Finally,the best performing model HHc-X among the top 3 has been proposed that predicted crack properties which are in close agreement to the ground truths in the test data.展开更多
Based on the synchronous joint gravity and magnetic inversion of single interface by Pilkington and the need of revealing Cenozoic and crystalline basement thickness in the new round of oil-gas exploration, we propose...Based on the synchronous joint gravity and magnetic inversion of single interface by Pilkington and the need of revealing Cenozoic and crystalline basement thickness in the new round of oil-gas exploration, we propose a joint gravity and magnetic inversion methodfor two-layer models by concentrating on the relationship between the change of thicknessI and position of the middle layer and anomaly and discuss the effects of the key parameters. Model tests and application to field data show the validity of this method.展开更多
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
基金supported by MHRD as researcher C.K.Neog received the MHRD Institute GATE scholarship from Govt.of India.
文摘This study investigates the effects of radiation force due to the rotational pitch motion of a wave energy device,which comprises a coaxial bottom-mounted cylindrical caisson in a two-layer fluid,along with a submerged cylindrical buoy.The system is modeled as a two-layer fluid with infinite horizontal extent and finite depth.The radiation problem is analyzed in the context of linear water waves.The fluid domain is divided into outer and inner zones,and mathematical solutions for the pitch radiating potential are derived for the corresponding boundary valve problem in these zones using the separation of variables approach.Using the matching eigenfunction expansion method,the unknown coefficients in the analytical expression of the radiation potentials are evaluated.The resulting radiation potential is then used to compute the added mass and damping coefficients.Several numerical results for the added mass and damping coefficients are investigated for numerous parameters,particularly the effects of the cylinder radius,the draft of the submerged cylinder,and the density proportion between the two fluid layers across different frequency ranges.The major findings are presented and discussed.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)—Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156334)in part by Liaoning Province Nature Fund Project(2024-BSLH-214).
文摘Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.
基金Supported by Talent Scientific Research Start-up Foundation of Wannan Medical College,No.WYRCQD2023045.
文摘BACKGROUND The early diagnosis rate of pancreatic ductal adenocarcinoma(PDAC)is low and the prognosis is poor.It is important to develop an interpretable noninvasive early diagnostic model in clinical practice.AIM To develop an interpretable noninvasive early diagnostic model for PDAC using plasma extracellular vesicle long RNA(EvlRNA).METHODS The diagnostic model was constructed based on plasma EvlRNA data.During the process of establishing the model,EvlRNA-index was introduced,and four algorithms were adopted to calculate EvlRNA-index.After the model was successfully constructed,performance evaluation was conducted.A series of bioinformatics methods were adopted to explore the potential mechanism of EvlRNA-index as the input feature of the model.And the relationship between key characteristics and PDAC were explored at the single-cell level.RESULTS A novel interpretable machine learning framework was developed based on plasma EvlRNA.In this framework,a two-layer classifier was established.A new concept was proposed:EvlRNA-index.Based on EvlRNA-index,a cancer diagnostic model was established,and a good diagnostic effect was achieved.The accuracy of PDACandCPvsHealth-Probabilistic PCA Index-SVM(PDAC and chronic pancreatitis vs health-probabilistic principal component analysis index-support vector machine)(1-18)was 91.51%,with Mathew’s correlation coefficient 0.7760 and area under the curve 0.9560.In the second layer of the model,the accuracy of PDACvsCP-Probabilistic PCA Index-RF(PDAC vs chronic pancreatitis-probabilistic principal component analysis index-random forest)(2-17)was 93.83%,with Mathew’s correlation coefficient 0.8422 and area under the curve 0.9698.Forty-nine PDAC-related genes were identified,among which 16 were known,inferring that the remaining ones were also PDAC-related genes.CONCLUSION An interpretable two-layer machine learning framework was proposed for early diagnosis and prediction of PDAC based on plasma EvlRNA,providing new insights into the clinical value of EvlRNA.
基金supported by the Basic Scientific Research Business Fund Project of Higher Education Institutions in Heilongjiang Province(145409601)the First Batch of Experimental Teaching and Teaching Laboratory Construction Research Projects in Heilongjiang Province(SJGZ20240038).
文摘In the wake of major natural disasters or human-made disasters,the communication infrastruc-ture within disaster-stricken areas is frequently dam-aged.Unmanned aerial vehicles(UAVs),thanks to their merits such as rapid deployment and high mobil-ity,are commonly regarded as an ideal option for con-structing temporary communication networks.Con-sidering the limited computing capability and battery power of UAVs,this paper proposes a two-layer UAV cooperative computing offloading strategy for emer-gency disaster relief scenarios.The multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithm integrated with prioritized experience replay(PER)is utilized to jointly optimize the scheduling strategies of UAVs,task offloading ratios,and their mobility,aiming to diminish the energy consumption and delay of the system to the minimum.In order to address the aforementioned non-convex optimiza-tion issue,a Markov decision process(MDP)has been established.The results of simulation experiments demonstrate that,compared with the other four base-line algorithms,the algorithm introduced in this paper exhibits better convergence performance,verifying its feasibility and efficacy.
基金supported by the Scientific Research Project of Xiang Jiang Lab(22XJ02003)the University Fundamental Research Fund(23-ZZCX-JDZ-28)+5 种基金the National Science Fund for Outstanding Young Scholars(62122093)the National Natural Science Foundation of China(72071205)the Hunan Graduate Research Innovation Project(ZC23112101-10)the Hunan Natural Science Foundation Regional Joint Project(2023JJ50490)the Science and Technology Project for Young and Middle-aged Talents of Hunan(2023TJ-Z03)the Science and Technology Innovation Program of Humnan Province(2023RC1002)。
文摘Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
文摘This work uses refined first-order shear theory to analyze the free vibration and transient responses of double-curved sandwich two-layer shells made of auxetic honeycomb core and laminated three-phase polymer/GNP/fiber surface subjected to the blast load.Each of the two layers that make up the double-curved shell structure is made up of an auxetic honeycomb core and two laminated sheets of three-phase polymer/GNP/fiber.The exterior is supported by a Kerr elastic foundation with three characteristics.The key innovation of the proposed theory is that the transverse shear stresses are zero at two free surfaces of each layer.In contrast to previous first-order shear deformation theories,no shear correction factor is required.Navier's exact solution was used to treat the double-curved shell problem with a single title boundary,while the finite element technique and an eight-node quadrilateral were used to address the other boundary requirements.To ensure the accuracy of these results,a thorough comparison technique is employed in conjunction with credible statements.The problem model's edge cases allow for this kind of analysis.The study's findings may be used in the post-construction evaluation of military and civil works structures for their ability to sustain explosive loads.In addition,this is also an important basis for the calculation and design of shell structures made of smart materials when subjected to shock waves or explosive loads.
文摘Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.
文摘In the context of China’s“double carbon”goals and rural revitalization strategy,the energy transition promotes the large-scale integration of distributed renewable energy into rural power grids.Considering the operational characteristics of rural microgrids and their impact on users,this paper establishes a two-layer scheduling model incorporating flexible loads.The upper-layer aims to minimize the comprehensive operating cost of the rural microgrid,while the lower-layer aims to minimize the total electricity cost for rural users.An Improved Adaptive Genetic Algorithm(IAGA)is proposed to solve the model.Results show that the two-layer scheduling model with flexible loads can effectively smooth load fluctuations,enhance microgrid stability,increase clean energy consumption,and balance microgrid operating costs with user benefits.
基金supported by National Research Foundation of Korea(NRF)grants funded by the Korean government(MSIT)(No.RS-2023-00256265,RS-2024-00352352,RS-2024-00405818)the Korean Fund for Regenerative Medicine(KFRM)grant funded by the Korea government(the Ministry of Science and ICT,the Ministry of Health&Welfare).(No.25A0102L1)support from the Market-led K-sensor technology program(RS-2022-00154781,Development of large-area wafer-level flexible/stretchable hybrid sensor platform technology for form factor-free highly integrated convergence sensor),funded By the Ministry of Trade,Industry&Energy(MOTIE,Korea).
文摘Microneedles(MNs)have been extensively investigated for transdermal delivery of large-sized drugs,including proteins,nucleic acids,and even extracellular vesicles(EVs).However,for their sufficient skin penetration,conventional MNs employ long needles(≥600μm),leading to pain and skin irritation.Moreover,it is critical to stably apply MNs against complex skin surfaces for uniform nanoscale drug delivery.Herein,a dually amplified transdermal patch(MN@EV/SC)is developed as the stem cell-derived EV delivery platform by hierarchically integrating an octopusinspired suction cup(SC)with short MNs(≤300μm).While leveraging the suction effect to induce nanoscale deformation of the stratum corneum,MN@EV/SC minimizes skin damage and enhances the adhesion of MNs,allowing EV to penetrate deeper into the dermis.When MNs of various lengths are applied to mouse skin,the short MNs can elicit comparable corticosterone release to chemical adhesives,whereas long MNs induce a prompt stress response.MN@EV/SC can achieve a remarkable penetration depth(290μm)for EV,compared to that of MN alone(111μm).Consequently,MN@EV/SC facilitates the revitalization of fibroblasts and enhances collagen synthesis in middle-aged mice.Overall,MN@EV/SC exhibits the potential for skin regeneration by modulating the dermal microenvironment and ensuring patient comfort.
基金supported by Shiraz University of Medical Sciences,Shiraz,Iran(grant No.:17780).
文摘Due to the limited regeneration capacity of myocardial tissue after infarction,designing tissue engineering scaffolds are in demand.In the present study,electrospun nanofibrous scaffolds were made out of polyurethane,collagen and gold nanoparticles with random and aligned nanofiber morphologies.The nanoparticles were green-synthesized using saffron extract.Nanoparticle characterizations with UV-Vis.spectroscopy and DLS illustrated theoretical and hydrodynamic diameters of around 7 and 13 nm,respectively,having zeta potential of−37 mV.SEM and TEM micrographs showed the morphology and diameters of obtained nanofibers.Also,further characterization were done by ATR-FTIR,XRD and TGA investigations and degradation studies.Contact angle measurements showed hydrophilic nature of the scaffolds(59±0.6°for aligned PU/Col/Au50 nanofibers compared to 120±2.6°for random PU nanofibers).Mechanical testing demonstrated appropriate tensile properties of the scaffolds for cardiac tissue engineering(Young’s modulus:1.53±0.07 MPa for aligned PU/Col/Au50 nanofibers compared to 0.4±0.05 MPa for random PU nanofibers).Finally,alamar blue assay revealed proper survival of the cells of HUVEC cell line on the prepared scaffolds,where the highest percentages were observed for random and aligned PU/Col/Au50 nanofibers.According to the findings,the fabricated PU/Col/AuNPs nanofibrous scaffolds could be considered as potential cardiac patches.
基金supported in part by the Gansu Haizhi Characteristic Demonstration Project(No.GSHZTS2022-2).
文摘Since the introduction of vision Transformers into the computer vision field,many vision tasks such as semantic segmentation tasks,have undergone radical changes.Although Transformer enhances the correlation of each local feature of an image object in the hidden space through the attention mechanism,it is difficult for a segmentation head to accomplish the mask prediction for dense embedding of multi-category and multi-local features.We present patch prototype vision Transformer(PPFormer),a Transformer architecture for semantic segmentation based on knowledge-embedded patch prototypes.1)The hierarchical Transformer encoder can generate multi-scale and multi-layered patch features including seamless patch projection to obtain information of multiscale patches,and feature-clustered self-attention to enhance the interplay of multi-layered visual information with implicit position encodes.2)PPFormer utilizes a non-parametric prototype decoder to extract region observations which represent significant parts of the objects by unlearnable patch prototypes and then calculate similarity between patch prototypes and pixel embeddings.The proposed contrasting patch prototype alignment module,which uses new patch prototypes to update prototype bank,effectively maintains class boundaries for prototypes.For different application scenarios,we have launched PPFormer-S,PPFormer-M and PPFormer-L by expanding the scale.Experimental results demonstrate that PPFormer can outperform fully convolutional networks(FCN)-and attention-based semantic segmentation models on the PASCAL VOC 2012,ADE20k,and Cityscapes datasets.
基金supported by grants received by the first author and third author from the Institute of Eminence,Delhi University,Delhi,India,as part of the Faculty Research Program via Ref.No./IoE/2024-25/12/FRP.
文摘Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.
基金supported by the National Key Technologies R&D Program of China(Grant No.2023YFC2415900)the National Natural Science Foundation of China(Grant Nos.62373182 and 52405619)+2 种基金the China Postdoctoral Science Foundation(Grant No.2024M751300)supported by the Shenzhen Science and Technology Program(Grant No.JCYJ20241202125417024)Guangdong Basic and Applied Basic Research Foundation(Grant No.2024A1515011915).
文摘Adhesive patches offer an effective approach for wound closure,making them highly suitable for biomedical applications.However,conventional patches often face limitations such as dual-sided adhesion,lack of shape adaptability,and limited maneuverability,which restrict their applications in deeper tissues.In this paper,we develop a magnetic patch robot(PatchBot),for targeted Janus adhesion with tissues.The PatchBot features a unique triple-layer structure,with adhesive,shape-morphing,and anti-adhesive layers,each fulfilling roles to support targeted attachment,enable shape transformation,and prevent unwanted adhesion to surrounding tissues.The Janus adhesion of the PatchBot was extensively demonstrated across a variety of tissues.A localized near-infrared(NIR)laser irradiation was used to induce programmable shape transformations.Magnetic actuation of the PatchBot for targeted adhesion was successfully demonstrated in ex vivo porcine stomach tissue.NIR light-activated shape-morphing and multimodal magnetic actuation significantly enhance its maneuverability and adaptability in confined in vivo environments while ensuring the structural integrity of the adhesive surface during deployment.This proof-of-concept study demonstrates the feasibility of using PatchBot for targeted wound adhesion,showing its potential for minimally invasive,precision therapies in complex in vivo environments.
基金supported by the National Natural Science Foundation of China(Grants 42325404,42120104003,42204164,42474219 and U22A2006)the Chinese Meridian Project,the International Partnership Program of Chinese Academy of Sciences(Grant 183311KYSB20200003)+7 种基金Shandong Provincial Natural Science Foundation(Grants ZR2022QD077,ZR2022MD034)the Stable-Support Scientific Project of China Research Institute of Radiowave Propagation(Grant A132312191)the foundation of the National Key Laboratory of Electromagnetic Environment(Grant 6142403180204)the Chongqing Natural Science Foundation(Grants cstc2021ycjh-bgzxm0072,CSTB2023NSCQ-LZX0082)National Program on Key Basic Research Project(Grant 2022173-SD-1)The work in Norway is supported by the Research Council of Norway Grant 326039Work at UCLA has been supported by NSF grant AGS-2055192This research was supported by the International Space Science Institute(ISSI)in Bern and Beijing,through ISSI International Team project#511(Multi-Scale Magnetosphere-Ionosphere-Thermosphere Interaction).
文摘This paper is a statistical survey of Southern Hemisphere cold and hot polar cap patches,in relation to the interplanetary magnetic field(IMF)and ionospheric convection geometry.A total of 11,946 patch events were identified by Defense Meteorological Satellite Program(DMSP)F16 during the years 2011 to 2022.A temperature ratio of ion/electron temperature(T_(i)/T_(e))<0.68 is recommended to define a hot patch in the Southern Hemisphere,otherwise it is defined as a cold patch.The cold and hot patches have different dependencies on IMF clock angle,while their dependencies on IMF cone angle are similar.Both cold and hot patches appear most often on the duskside,and the distribution of cold patches gradually decreases from the dayside to the nightside,while hot patches have a higher occurrence rate near 14 and 21 magnetic local time(MLT).Moreover,we compared the key plasma characteristics of polar cap cold and hot patches in the Southern and Northern Hemispheres.The intensity of the duskside upward field-aligned current of patches in the Southern Hemisphere(SH)is stronger than that in the Northern Hemisphere(SH),which may be due to the discrepancy in conductivities between the two hemispheres,caused by the tilted dipole.In both hemispheres,the downward soft-electron energy flux of the dawnside patches is significantly greater than that of the duskside patches.
文摘Objective:To evaluate the intervention effect of childlike nursing combined with Chinese herbal patching on pediatric bronchopneumonia.Methods:1036 children with bronchopneumonia(one family member included for each child)who were admitted to the hospital between January 2024 and June 2024 were selected and randomly divided into two groups using a random number table.The combined group received childlike nursing combined with Chinese herbal patching,while the control group received routine nursing.Symptom recovery time,treatment compliance,inflammatory factor levels,quality of life of the children,and family satisfaction were compared between the two groups.Results:The symptom recovery time in the combined group was shorter than that in the control group,treatment compliance was higher,inflammatory factor levels after intervention were lower,quality of life scores of the children were lower,and family satisfaction was higher(P<0.05).Conclusion:The implementation of childlike nursing combined with Chinese herbal patching for children with bronchopneumonia can shorten their symptom recovery time,significantly improve treatment compliance and quality of life,reduce inflammatory reactions,and achieve high satisfaction among family members.
基金supported by the Joint Funds of National Natural Science Foundation of China(No.U22A20162)the Natural Science Foundation of Hebei Province of China(No.C2021202002)+1 种基金the National Natural Science Foundation of China(No.52271245),the Natural Science Foundation of Tianjin(No.21JCQNJC01280)the financial support from the Danish Council for Independent Research(9040-00219B),European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement ENSIGN(Project ID:101086226),L4DNANO(Project ID:101086227).
文摘Microneedle(MN)patches could be a promising treatment for diabetic foot ulcers that plague thousands of people worldwide.While reducing skin resistance or increasing driving force can accelerate the efficiency of transdermal drug delivery with conventional MN patches,it can create toxic chemical residues or require the help of additional devices.Herein,a thermo-responsive microneedles patch(TMN)with high biocompatibility without additional equipment is proposed.The TMN consisted of a bilayer microneedles composed of sodium alginate(SA)-g-poly(N-isopropylacrylamide)layer(SA-g-PNIPAM)loaded with sucrose octasulfate sodium salt(SOS)and hyaluronic acid layer and a polycaprolactone/chitosan nanofiber membrane loading with tetracycline hydrochloride(TH)and SOS.PNIPAM accelerates drug release by extruding the drug through a volumetric phase transition in response to temperature changes,and TH and SOS promote wound healing by inhibiting bacterial growth and promoting vascular regeneration and epithelial formation.The results showed that the drug release of TMN was significantly faster,with the drug release rate of more than 80% in the 10th h,and the antibacterial rate of TMN could reach 800%.In addition,TMN had good biocompatibility and good healing effects in vivo,which may be helpful for the design of multifunctional dressings in the future.
文摘To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from patches.RGB crack images and their corresponding masks obtained from a public dataset are cropped into patches of 256 square pixels that are classified with a pre-trained deep convolution neural network,the true positives are segmented,and crack properties are extracted using two different methods.The first method is primarily based on active contour models and level-set segmentation and the second method consists of the domain adaptation of a mathematical morphology-based method known as FIL-FINDER.A statistical test has been performed for the comparison of the stated methods and a database prepared with the more suitable method.An advanced convolution neural network-based multi-output regression model has been proposed which was trained with the prepared database and validated with the held-out dataset for the prediction of crack-length,crack-width,and width-uncertainty directly from input image patches.The pro-posed model has been tested on crack patches collected from different locations.Huber loss has been used to ensure the robustness of the proposed model selected from a set of 288 different variations of it.Additionally,an ablation study has been conducted on the top 3 models that demonstrated the influence of each network component on the pre-diction results.Finally,the best performing model HHc-X among the top 3 has been proposed that predicted crack properties which are in close agreement to the ground truths in the test data.
基金Supported by the National Natural Science Foundation of China(Grant No.40674063)National Hi-tech Research and Development Program of China(863Program)(Grant No.2006AA09Z311)
文摘Based on the synchronous joint gravity and magnetic inversion of single interface by Pilkington and the need of revealing Cenozoic and crystalline basement thickness in the new round of oil-gas exploration, we propose a joint gravity and magnetic inversion methodfor two-layer models by concentrating on the relationship between the change of thicknessI and position of the middle layer and anomaly and discuss the effects of the key parameters. Model tests and application to field data show the validity of this method.
